From 58f478edf0b593203d1d487bc034e698551c399a Mon Sep 17 00:00:00 2001
From: krlmlr <krlmlr@users.noreply.github.com>
Date: Sun, 17 Nov 2024 06:10:33 +0000
Subject: [PATCH] Built site for tibble@3.2.1.9034: 78d02ac

---
 dev/CODE_OF_CONDUCT.html                  |  2 +-
 dev/CONTRIBUTING.html                     |  2 +-
 dev/LICENSE-text.html                     |  2 +-
 dev/LICENSE.html                          |  2 +-
 dev/SUPPORT.html                          |  2 +-
 dev/articles/digits.html                  |  2 +-
 dev/articles/extending.html               |  2 +-
 dev/articles/formats.html                 |  2 +-
 dev/articles/index.html                   |  2 +-
 dev/articles/invariants.html              |  2 +-
 dev/articles/numbers.html                 |  2 +-
 dev/articles/tibble.html                  |  2 +-
 dev/articles/types.html                   |  8 ++++----
 dev/articles/wip/subassign.html           |  2 +-
 dev/articles/wip/subset.html              |  2 +-
 dev/authors.html                          |  6 +++---
 dev/index.html                            |  2 +-
 dev/news/index.html                       | 13 ++++++++++++-
 dev/pkgdown.yml                           |  4 ++--
 dev/reference/add_column.html             |  2 +-
 dev/reference/add_row.html                |  2 +-
 dev/reference/as_tibble.html              |  2 +-
 dev/reference/char.html                   |  2 +-
 dev/reference/deprecated.html             |  2 +-
 dev/reference/enframe.html                |  2 +-
 dev/reference/formatting.html             |  2 +-
 dev/reference/frame_matrix.html           |  2 +-
 dev/reference/index.html                  |  2 +-
 dev/reference/is.tibble.html              |  2 +-
 dev/reference/is_tibble.html              |  2 +-
 dev/reference/lst.html                    |  2 +-
 dev/reference/name-repair-superseded.html |  2 +-
 dev/reference/name-repair.html            |  2 +-
 dev/reference/new_tibble.html             |  2 +-
 dev/reference/num.html                    |  2 +-
 dev/reference/reexports.html              |  2 +-
 dev/reference/rownames.html               |  2 +-
 dev/reference/subsetting.html             |  2 +-
 dev/reference/tbl_df-class.html           |  2 +-
 dev/reference/tibble-package.html         |  2 +-
 dev/reference/tibble.html                 |  2 +-
 dev/reference/tibble_options.html         |  2 +-
 dev/reference/tribble.html                |  2 +-
 dev/reference/trunc_mat.html              |  2 +-
 dev/reference/view.html                   |  2 +-
 dev/search.json                           |  2 +-
 46 files changed, 63 insertions(+), 52 deletions(-)

diff --git a/dev/CODE_OF_CONDUCT.html b/dev/CODE_OF_CONDUCT.html
index 8eebc6c12..d5122dab0 100644
--- a/dev/CODE_OF_CONDUCT.html
+++ b/dev/CODE_OF_CONDUCT.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/CONTRIBUTING.html b/dev/CONTRIBUTING.html
index 13112f9e9..0f06afd4e 100644
--- a/dev/CONTRIBUTING.html
+++ b/dev/CONTRIBUTING.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/LICENSE-text.html b/dev/LICENSE-text.html
index 90e8a6067..83de16225 100644
--- a/dev/LICENSE-text.html
+++ b/dev/LICENSE-text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/LICENSE.html b/dev/LICENSE.html
index d7084d6de..613ccdca9 100644
--- a/dev/LICENSE.html
+++ b/dev/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/SUPPORT.html b/dev/SUPPORT.html
index 1d2786aff..623ff3d86 100644
--- a/dev/SUPPORT.html
+++ b/dev/SUPPORT.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/digits.html b/dev/articles/digits.html
index 27a0fcc33..0d1e42522 100644
--- a/dev/articles/digits.html
+++ b/dev/articles/digits.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/extending.html b/dev/articles/extending.html
index 43668ddab..8a646a3b0 100644
--- a/dev/articles/extending.html
+++ b/dev/articles/extending.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/formats.html b/dev/articles/formats.html
index 7114d90ef..56e62eb35 100644
--- a/dev/articles/formats.html
+++ b/dev/articles/formats.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/index.html b/dev/articles/index.html
index a8d79f645..6ccbbdd1c 100644
--- a/dev/articles/index.html
+++ b/dev/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/invariants.html b/dev/articles/invariants.html
index 2a9930f9f..f7a7a736f 100644
--- a/dev/articles/invariants.html
+++ b/dev/articles/invariants.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/numbers.html b/dev/articles/numbers.html
index 23ec3ba7e..855ab80fb 100644
--- a/dev/articles/numbers.html
+++ b/dev/articles/numbers.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/tibble.html b/dev/articles/tibble.html
index a74923594..6f41a6596 100644
--- a/dev/articles/tibble.html
+++ b/dev/articles/tibble.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/types.html b/dev/articles/types.html
index e34a68ef0..70e547bd0 100644
--- a/dev/articles/types.html
+++ b/dev/articles/types.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -309,8 +309,8 @@ <h2 id="example-values">Example values<a class="anchor" aria-label="anchor" href
 <span><span class="co">#&gt; $ `named list`  <span style="color: #949494; font-style: italic;">&lt;named list&gt;</span> 1</span></span>
 <span><span class="co">#&gt; $ factor        <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span> A</span></span>
 <span><span class="co">#&gt; $ ordered       <span style="color: #949494; font-style: italic;">&lt;ord&gt;</span> a</span></span>
-<span><span class="co">#&gt; $ Date          <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2024-11-10</span></span>
-<span><span class="co">#&gt; $ POSIXt        <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> 2024-11-10 03:36:11</span></span>
+<span><span class="co">#&gt; $ Date          <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2024-11-17</span></span>
+<span><span class="co">#&gt; $ POSIXt        <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> 2024-11-17 06:10:28</span></span>
 <span><span class="co">#&gt; $ difftime      <span style="color: #949494; font-style: italic;">&lt;drtn&gt;</span> 1 secs</span></span>
 <span><span class="co">#&gt; $ hms           <span style="color: #949494; font-style: italic;">&lt;time&gt;</span> 00:00:01</span></span>
 <span><span class="co">#&gt; $ integer64     <span style="color: #949494; font-style: italic;">&lt;int64&gt;</span> 10000000000</span></span>
@@ -349,7 +349,7 @@ <h2 id="implementation">Implementation<a class="anchor" aria-label="anchor" href
 <span><span class="co">#&gt;             "sym"</span></span>
 <span><span class="co">#&gt;         }, typeof(x))</span></span>
 <span><span class="co">#&gt; }</span></span>
-<span><span class="co">#&gt; &lt;bytecode: 0x564dfe87e5e0&gt;</span></span>
+<span><span class="co">#&gt; &lt;bytecode: 0x55c4303a0070&gt;</span></span>
 <span><span class="co">#&gt; &lt;environment: namespace:pillar&gt;</span></span></code></pre></div>
 </div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
diff --git a/dev/articles/wip/subassign.html b/dev/articles/wip/subassign.html
index 145133234..dea35b7bc 100644
--- a/dev/articles/wip/subassign.html
+++ b/dev/articles/wip/subassign.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/articles/wip/subset.html b/dev/articles/wip/subset.html
index 5a8334049..b8ae1f6fb 100644
--- a/dev/articles/wip/subset.html
+++ b/dev/articles/wip/subset.html
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="../../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/authors.html b/dev/authors.html
index 5aa59388b..025bb2a5b 100644
--- a/dev/authors.html
+++ b/dev/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -86,14 +86,14 @@ <h2 id="citation">Citation</h2>
 
       <p>Müller K, Wickham H (2024).
 <em>tibble: Simple Data Frames</em>.
-R package version 3.2.1.9033,
+R package version 3.2.1.9034,
 https://github.com/tidyverse/tibble, <a href="https://tibble.tidyverse.org/">https://tibble.tidyverse.org/</a>.
 </p>
       <pre>@Manual{,
   title = {tibble: Simple Data Frames},
   author = {Kirill Müller and Hadley Wickham},
   year = {2024},
-  note = {R package version 3.2.1.9033,
+  note = {R package version 3.2.1.9034,
     https://github.com/tidyverse/tibble},
   url = {https://tibble.tidyverse.org/},
 }</pre>
diff --git a/dev/index.html b/dev/index.html
index 3196902c1..a700d3dcd 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -33,7 +33,7 @@
 
     <a class="navbar-brand me-2" href="index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/news/index.html b/dev/news/index.html
index 6d6bc2ff0..f3bd97822 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -56,6 +56,17 @@
       <small>Source: <a href="https://github.com/tidyverse/tibble/blob/main/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="3.2.1.9034" id="tibble-3219034">tibble 3.2.1.9034<a class="anchor" aria-label="anchor" href="#tibble-3219034"></a></h2>
+<div class="section level3">
+<h3 id="continuous-integration-3-2-1-9034">Continuous integration<a class="anchor" aria-label="anchor" href="#continuous-integration-3-2-1-9034"></a></h3>
+<ul><li>
+<p>Trigger run (<a href="https://github.com/tidyverse/tibble/issues/1606" class="external-link">#1606</a>).</p>
+<ul><li><p>ci: Trigger run</p></li>
+<li><p>ci: Latest changes</p></li>
+</ul></li>
+</ul></div>
+</div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="3.2.1.9033" id="tibble-3219033">tibble 3.2.1.9033<a class="anchor" aria-label="anchor" href="#tibble-3219033"></a></h2>
 <div class="section level3">
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index ba0ee0a8e..f1ee9398d 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -1,6 +1,6 @@
 pandoc: 3.1.11
 pkgdown: 2.1.1.9000
-pkgdown_sha: c40ba2c989e786bbb2829b431114d26375bbe19a
+pkgdown_sha: 7645a47b08827c8ff6029c8afd09d9b13674ade5
 articles:
   digits: digits.html
   extending: extending.html
@@ -11,7 +11,7 @@ articles:
   wip/subset: wip/subset.html
   tibble: tibble.html
   types: types.html
-last_built: 2024-11-10T03:35Z
+last_built: 2024-11-17T06:09Z
 urls:
   reference: https://tibble.tidyverse.org/reference
   article: https://tibble.tidyverse.org/articles
diff --git a/dev/reference/add_column.html b/dev/reference/add_column.html
index b2fe7d2ac..45090bb9a 100644
--- a/dev/reference/add_column.html
+++ b/dev/reference/add_column.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/add_row.html b/dev/reference/add_row.html
index 3cc038aae..789114c33 100644
--- a/dev/reference/add_row.html
+++ b/dev/reference/add_row.html
@@ -15,7 +15,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
-    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9033</small>
+    <small class="nav-text text-danger me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="In-development version">3.2.1.9034</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/dev/reference/as_tibble.html b/dev/reference/as_tibble.html
index 33f6cf0f5..1673dced7 100644
--- a/dev/reference/as_tibble.html
+++ b/dev/reference/as_tibble.html
@@ -37,7 +37,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">tibble</a>
 
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SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with tibble","title":"Getting help with tibble","text":"Thanks using tibble! filing issue, places explore pieces put together make process smooth possible.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":"make-a-reprex","dir":"","previous_headings":"","what":"Make a reprex","title":"Getting help with tibble","text":"Start making minimal reproducible example using reprex package. haven’t heard used reprex , ’re treat! Seriously, reprex make R-question-asking endeavors easier (pretty insane ROI five ten minutes ’ll take learn ’s ). additional reprex pointers, check Get help! section tidyverse site.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":"where-to-ask","dir":"","previous_headings":"","what":"Where to ask?","title":"Getting help with tibble","text":"Armed reprex, next step figure ask. ’s question: start community.rstudio.com, /StackOverflow. people answer questions. ’s bug: ’re right place, file issue. ’re sure: let community help figure ! problem bug feature request, can easily return report . opening new issue, sure search issues pull requests make sure bug hasn’t reported /already fixed development version. default, search pre-populated :issue :open. can edit qualifiers (e.g. :pr, :closed) needed. example, ’d simply remove :open search issues repo, open closed.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":"what-happens-next","dir":"","previous_headings":"","what":"What happens next?","title":"Getting help with tibble","text":"efficient possible, development tidyverse packages tends bursty, shouldn’t worry don’t get immediate response. Typically don’t look repo sufficient quantity issues accumulates, ’s burst intense activity focus efforts. makes development efficient avoids expensive context switching problems, cost taking longer get back . process makes good reprex particularly important might multiple months initial report start working . can’t reproduce bug, can’t fix !","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"basic-differences","dir":"Articles","previous_headings":"Digits","what":"Basic differences","title":"Comparing display with data frames","text":"default getOption(\"digits\") 7, whereas \"pillar.sigfig\" option defaults 3. default setting, pillar prints first three digits (.e. digits represent > 99.9% value number). Another difference pillar show specified number significant digits, even space available. rationale allow quick glance significant digits number, without spending much horizontal space without distraction insignificant digits.","code":"options(digits = 3) c(1.2345, 12.345, 123.45, 1234.5, 12345) #> [1]     1.23    12.35   123.45  1234.50 12345.00 tibble(x = c(1.2345, 12.345, 123.45, 1234.5, 12345)) #> # A tibble: 5 × 1 #>          x #>      <dbl> #> 1     1.23 #> 2    12.3  #> 3   123.   #> 4  1234.   #> 5 12345"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"terminal-zeros","dir":"Articles","previous_headings":"Digits","what":"Terminal zeros","title":"Comparing display with data frames","text":"Terminal zeros shown pillar nonzero value past significant digits shown. contrast base R terminal zeros always shown space, hidden value insignificant:","code":"c(1, 1.00001) #> [1] 1 1 tibble(x = c(1, 1.00001)) #> # A tibble: 2 × 1 #>       x #>   <dbl> #> 1  1    #> 2  1.00"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"trailing-dot","dir":"Articles","previous_headings":"Digits","what":"Trailing dot","title":"Comparing display with data frames","text":"trailing decimal separator shown fractional part integer part already exceeds significant digits. presence decimal separator indicate number larger, exists nonzero fractional part:","code":"c(123, 123.45, 567.89) #> [1] 123 123 568 tibble(x = c(123, 123.45, 567.89)) #> # A tibble: 3 × 1 #>       x #>   <dbl> #> 1  123  #> 2  123. #> 3  568."},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"showing-more-digits","dir":"Articles","previous_headings":"Digits","what":"Showing more digits","title":"Comparing display with data frames","text":"show significant digits, set \"pillar.sigfig\" option larger value: Setting \"pillar.sigfig\" larger value enhance display digits deemed insignificant:","code":"options(digits = 7) options(pillar.sigfig = 7) c(1.2345, 12.345, 123.45, 1234.5, 12345) #> [1]     1.2345    12.3450   123.4500  1234.5000 12345.0000 tibble(x = c(1.2345, 12.345, 123.45, 1234.5, 12345)) #> # A tibble: 5 × 1 #>            x #>        <dbl> #> 1     1.2345 #> 2    12.345  #> 3   123.45   #> 4  1234.5    #> 5 12345 options(digits = 7) options(pillar.sigfig = 7) c(1.2345, 12.3456, 123.4567, 1234.5678, 12345.6789) #> [1]     1.2345    12.3456   123.4567  1234.5678 12345.6789 tibble(x = c(1.2345, 12.3456, 123.4567, 1234.5678, 12345.6789)) #> # A tibble: 5 × 1 #>            x #>        <dbl> #> 1     1.2345 #> 2    12.3456 #> 3   123.4567 #> 4  1234.568  #> 5 12345.68"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"fixed-number-of-digits","dir":"Articles","previous_headings":"Digits","what":"Fixed number of digits","title":"Comparing display with data frames","text":"show fixed number decimal digits, use num() digits argument: See vignette(\"numbers\") details.","code":"num(c(1.2345, 12.345, 123.45, 1234.5, 12345), digits = 2) #> <pillar_num:.2![5]> #> [1]     1.23    12.35   123.45  1234.50 12345.00"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"when-is-it-used","dir":"Articles","previous_headings":"Scientific notation","what":"When is it used?","title":"Comparing display with data frames","text":"base R pillar switch scientific notation decimal representation becomes wide. larger getOption(\"scipen\"), stronger resistance switching scientific notation. default 0 seems anchored 13 digits integer part. \"pillar.max_dec_width\" option similar, indicates width must exceeded switch scientific notation happen. width includes decimal separator.","code":"123456789012 #> [1] 123456789012 123456789012.3 #> [1] 123456789012 1234567890123 #> [1] 1.234568e+12 1234567890123.4 #> [1] 1.234568e+12 options(scipen = 1) 1234567890123 #> [1] 1234567890123 12345678901234 #> [1] 1.234568e+13 12345678901234.5 #> [1] 1.234568e+13 tibble(x = 123456789012) #> # A tibble: 1 × 1 #>              x #>          <dbl> #> 1 123456789012 tibble(x = 123456789012.3) #> # A tibble: 1 × 1 #>               x #>           <dbl> #> 1 123456789012. tibble(x = 1234567890123) #> # A tibble: 1 × 1 #>               x #>           <dbl> #> 1 1234567890123 tibble(x = 1234567890123.4) #> # A tibble: 1 × 1 #>             x #>         <dbl> #> 1 1.234568e12 options(pillar.max_dec_width = 14) tibble(x = 1234567890123) #> # A tibble: 1 × 1 #>               x #>           <dbl> #> 1 1234567890123 tibble(x = 12345678901234) #> # A tibble: 1 × 1 #>                x #>            <dbl> #> 1 12345678901234 tibble(x = 12345678901234.5) #> # A tibble: 1 × 1 #>             x #>         <dbl> #> 1 1.234568e13"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"enforce-notation","dir":"Articles","previous_headings":"Scientific notation","what":"Enforce notation","title":"Comparing display with data frames","text":"avoid switching scientific notation, set \"pillar.max_dec_width\" option large value. Note required width available show column, shown case. notation argument num() offers options:","code":"num(12345678901234567, notation = \"dec\") #> <pillar_num(dec)[1]> #> [1] 12345678901234568 num(12345678901234567, notation = \"sci\") #> <pillar_num(sci)[1]> #> [1] 1.234568e16 num(12345678901234567, notation = \"eng\") #> <pillar_num(eng)[1]> #> [1] 12.34568e15 num(12345678901234567, notation = \"si\") #> <pillar_num(si)[1]> #> [1] 12.34568P"},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"topics-documented-elsewhere","dir":"Articles","previous_headings":"","what":"Topics documented elsewhere","title":"Extending tibble","text":"Change tweak way tibble prints: vignette(\"extending\", package = \"pillar\") Change tweak way column type printed tibble: vignette(\"pillar\", package = \"vctrs\") Implement new column data type: vignette(\"s3-vector\", package = \"vctrs\") Making tibble subclass work well dplyr: ?dplyr::dplyr_extending","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"data-frame-subclasses","dir":"Articles","previous_headings":"","what":"Data frame subclasses","title":"Extending tibble","text":"tibble >= 3.0.0, \"tbl\" class responsible printing, \"tbl_df\" class adds tibble’s sturdy subsetting subset assignment behavior (see vignette(\"invariants\") details). means data frame class like (mostly) print like tibble keep behavior base R’s data frames can inherit c(\"tbl\", \"data.frame\") just \"tbl\". example \"tbl_sql\" class dbplyr package used implement lazy database tables.","code":"library(tibble)"},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"tibble-example","dir":"Articles","previous_headings":"Data frame subclasses","what":"Tibble example","title":"Extending tibble","text":"","code":"my_tbl_df <- new_tibble(   list(a = 1:3, b = 2:4),   class = \"my_tbl_df\" )  tbl_sum.my_tbl_df <- function(x, ...) {   c(     \"My custom tibble\" = \"Some info about it\",     NextMethod()   ) }  my_tbl_df ## # My custom tibble: Some info about it ## # A tibble:         3 × 2 ##       a     b ##   <int> <int> ## 1     1     2 ## 2     2     3 ## 3     3     4 my_tbl_df[, \"a\"] ## # My custom tibble: Some info about it ## # A tibble:         3 × 1 ##       a ##   <int> ## 1     1 ## 2     2 ## 3     3"},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"data-frame-example","dir":"Articles","previous_headings":"Data frame subclasses","what":"Data frame example","title":"Extending tibble","text":"","code":"my_tbl <- vctrs::new_data_frame(   list(a = 1:3, b = 2:4),   class = c(\"my_tbl\", \"tbl\") )  tbl_sum.my_tbl <- function(x, ...) {   c(     \"My custom data frame\" = \"Some info about it\",     NextMethod()   ) }  my_tbl ## # My custom data frame: Some info about it ## # A data frame:         3 × 2 ##       a     b ##   <int> <int> ## 1     1     2 ## 2     2     3 ## 3     3     4 my_tbl[, \"a\"] ## [1] 1 2 3"},{"path":"https://tibble.tidyverse.org/dev/articles/formats.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Column formats","text":"vignette shows decorate columns custom formatting. use formattable package demonstration already contains useful vector classes apply custom formatting numbers. x column tibble regular number formatting method. always shown three digits decimal point. also applies columns derived x. Summaries also maintain formatting. pivoting operations. ggplot2 need work show apply formatting scales.  pays specify formatting early process. diagram shows principal stages data analysis exploration “R data science”. subsequent diagram adds data formats, communication options, explicit data formatting. original r4ds transitions highlighted bold. two principal options apply formatting results: right communicating , right importing. Applying formatting early process gives added benefit showing data useful format “Tidy”, “Transform”, “Visualize” stages. useful, need ensure formatting options applied early: easy set keep sticky process data analysis exploration support analyst asking right questions data convey critical information glance, support go greater detail easier applied various communication options support everything necessary present data desired way Ensuring stickiness difficult, insufficient dbplyr workflow parts “Tidy”, “Transform” even “Visualize” stages run database. Often ’s possible derive rule-based approach formatting. rules can stored quos(): poses drawbacks: syntax repetitive intuitive Rules match multiple columns must given reverse order due way mutate() works, executed multiple times good API rule-based formatting look like?","code":"library(formattable)  tbl <- tibble(x = digits(9:11, 3)) tbl #> # A tibble: 3 × 1 #>   x          #>   <formttbl> #> 1 9.000      #> 2 10.000     #> 3 11.000 library(dplyr) tbl2 <-   tbl %>%   mutate(     y = x + 1,     z = x * x,     v = y + z,     lag = lag(x, default = x[[1]]),     sin = sin(x),     mean = mean(v),     var = var(x)   )  tbl2 #> # A tibble: 3 × 8 #>           x         y         z         v       lag      sin    mean   var #>   <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fm> <dbl:f> <dbl> #> 1     9.000    10.000    81.000    91.000     9.000    0.412 111.667     1 #> 2    10.000    11.000   100.000   111.000     9.000   -0.544 111.667     1 #> 3    11.000    12.000   121.000   133.000    10.000   -1.000 111.667     1 tbl2 %>%   group_by(lag) %>%   summarize(z = mean(z)) %>%   ungroup() #> # A tibble: 2 × 2 #>         lag         z #>   <dbl:fmt> <dbl:fmt> #> 1     9.000    90.500 #> 2    10.000   121.000 library(tidyr)  stocks <-   expand_grid(id = factor(1:4), year = 2018:2022) %>%   mutate(stock = currency(runif(20) * 10000))  stocks %>%   pivot_wider(id_cols = id, names_from = year, values_from = stock) #> # A tibble: 4 × 6 #>   id       `2018`    `2019`    `2020`    `2021`    `2022` #>   <fct> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> #> 1 1       $807.50 $8,343.33 $6,007.61 $1,572.08    $73.99 #> 2 2     $4,663.93 $4,977.77 $2,897.67 $7,328.82 $7,725.22 #> 3 3     $8,746.01 $1,749.41   $342.41 $3,203.86 $4,023.28 #> 4 4     $1,956.70 $4,035.38   $636.61 $3,887.01 $9,755.48 library(ggplot2)  # Needs https://github.com/tidyverse/ggplot2/pull/4065 or similar stocks %>%   ggplot(aes(x = year, y = stock, color = id)) +   geom_line() tbl3 <-   tibble(id = letters[1:3], x = 9:11) %>%   mutate(     y = x + 1,     z = x * x,     v = y + z,     lag = lag(x, default = x[[1]]),     sin = sin(x),     mean = mean(v),     var = var(x)   )  tbl3 #> # A tibble: 3 × 9 #>   id        x     y     z     v   lag    sin  mean   var #>   <chr> <int> <dbl> <int> <dbl> <int>  <dbl> <dbl> <dbl> #> 1 a         9    10    81    91     9  0.412  112.     1 #> 2 b        10    11   100   111     9 -0.544  112.     1 #> 3 c        11    12   121   133    10 -1.00   112.     1  tbl3 %>%   mutate(     across(where(is.numeric), ~ digits(.x, 3)),     across(where(~ is.numeric(.x) && mean(.x) > 50), ~ digits(.x, 1))   ) #> # A tibble: 3 × 9 #>   id            x         y         z         v     lag    sin  mean   var #>   <chr> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:f> <dbl:> <dbl> <dbl> #> 1 a         9.000    10.000      81.0      91.0   9.000  0.412 111.7 1.000 #> 2 b        10.000    11.000     100.0     111.0   9.000 -0.544 111.7 1.000 #> 3 c        11.000    12.000     121.0     133.0  10.000 -1.000 111.7 1.000 rules <- quos(   across(where(is.numeric), ~ digits(.x, 3)),   across(where(~ is.numeric(.x) && mean(.x) > 50), ~ digits(.x, 1)) )  tbl3 %>%   mutate(!!!rules) #> # A tibble: 3 × 9 #>   id            x         y         z         v     lag    sin  mean   var #>   <chr> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:f> <dbl:> <dbl> <dbl> #> 1 a         9.000    10.000      81.0      91.0   9.000  0.412 111.7 1.000 #> 2 b        10.000    11.000     100.0     111.0   9.000 -0.544 111.7 1.000 #> 3 c        11.000    12.000     121.0     133.0  10.000 -1.000 111.7 1.000"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"conventions","dir":"Articles","previous_headings":"","what":"Conventions","title":"Invariants: Comparing behavior with data frames","text":"article, behaviors demonstrated using one example data frame tibble equivalent: Results code data frames tibbles presented side side: results identical (converting data frame necessary), tibble result shown. Subsetting operations read-. objects reused examples: needed, also show examples hierarchical columns containing data frame matrix: subset assignment (subassignment, short), need fresh copy data test. with_*() functions (omitted brevity) allow concise notation. functions take assignment expression, execute fresh copy data, return data printing. first example prints ’s really executed, examples omit output.","code":"library(tibble) library(vctrs) new_df <- function() {   df <- data.frame(n = c(1L, NA, 3L, NA))   df$c <- letters[5:8]   df$li <- list(9, 10:11, 12:14, \"text\")   df } new_tbl <- function() {   as_tibble(new_df()) } new_df() #>    n c         li #> 1  1 e          9 #> 2 NA f     10, 11 #> 3  3 g 12, 13, 14 #> 4 NA h       text new_tbl() #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> df <- new_df() tbl <- new_tbl() new_tbl2 <- function() {   tibble(     tb = tbl,     m = diag(4)   ) } new_df2 <- function() {   df2 <- new_tbl2()   class(df2) <- \"data.frame\"   class(df2$tb) <- \"data.frame\"   df2 } df2 <- new_df2() tbl2 <- new_tbl2() new_tbl() #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> with_df(df$n <- rev(df$n), verbose = TRUE) #> { #>   df <- new_df() #>   df$n <- rev(df$n) #>   df #> } #>    n c         li #> 1 NA e          9 #> 2  3 f     10, 11 #> 3 NA g 12, 13, 14 #> 4  1 h       text with_tbl(tbl$n <- rev(tbl$n), verbose = TRUE) #> { #>   tbl <- new_tbl() #>   tbl$n <- rev(tbl$n) #>   tbl #> } #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1    NA e     <dbl [1]> #> 2     3 f     <int [2]> #> 3    NA g     <int [3]> #> 4     1 h     <chr [1]>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xj","dir":"Articles","previous_headings":"Column extraction","what":"Definition of x[[j]]","title":"Invariants: Comparing behavior with data frames","text":"x[[j]] equal .subset2(x, j). NB: x[[j]] always returns object size nrow(x) column exists. j must single number string, enforced .subset2(x, j). NA indexes, numeric --bounds (OOB) values, non-integers throw error: Character OOB access silent common package idiom check absence column .null(df[[var]]).","code":"tbl[[1]] #> [1]  1 NA  3 NA .subset2(tbl, 1) #> [1]  1 NA  3 NA df[[1:2]] #> [1] NA tbl[[1:2]] #> Error: #> ! The `j` argument of #>   `[[.tbl_df()` can't be a vector #>   of length 2 as of tibble 3.0.0. #> ℹ Recursive subsetting is #>   deprecated for tibbles. df[[c(\"n\", \"c\")]] #> Error in .subset2(x, i, exact = exact): subscript out of bounds tbl[[c(\"n\", \"c\")]] #> Error in `tbl[[c(\"n\", \"c\")]]`: #> ! Can't extract column with `c(\"n\", \"c\")`. #> ✖ Subscript `c(\"n\", \"c\")` must be size 1, not 2. df[[TRUE]] #> [1]  1 NA  3 NA tbl[[TRUE]] #> Error in `tbl[[TRUE]]`: #> ! Can't extract column with `TRUE`. #> ✖ `TRUE` must be numeric or character, not `TRUE`. df[[mean]] #> Error in .subset2(x, i, exact = exact): invalid subscript type 'closure' tbl[[mean]] #> Error in `tbl[[mean]]`: #> ! Can't extract column with `mean`. #> ✖ `mean` must be numeric or character, not a function. df[[NA]] #> NULL tbl[[NA]] #> Error in `tbl[[NA]]`: #> ! Can't extract column with `NA`. #> ✖ Subscript `NA` must be a location, not an integer `NA`. df[[NA_character_]] #> NULL tbl[[NA_character_]] #> Error in `tbl[[NA_character_]]`: #> ! Can't extract column with `NA_character_`. #> ✖ Subscript `NA_character_` must be a location, not a character `NA`. df[[NA_integer_]] #> NULL tbl[[NA_integer_]] #> Error in `tbl[[NA_integer_]]`: #> ! Can't extract column with `NA_integer_`. #> ✖ Subscript `NA_integer_` must be a location, not an integer `NA`. df[[-1]] #> Error in .subset2(x, i, exact = exact): invalid negative subscript in get1index <real> tbl[[-1]] #> Error in `tbl[[-1]]`: #> ! Can't extract column with `-1`. #> ✖ Subscript `-1` must be a positive location, not -1. df[[4]] #> Error in .subset2(x, i, exact = exact): subscript out of bounds tbl[[4]] #> Error in `tbl[[4]]`: #> ! Can't extract columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. df[[1.5]] #> [1]  1 NA  3 NA tbl[[1.5]] #> Error in `tbl[[1.5]]`: #> ! Can't extract column with `1.5`. #> ✖ Can't convert from `j` <double> to <integer> due to loss of precision. df[[Inf]] #> NULL tbl[[Inf]] #> Error in `tbl[[Inf]]`: #> ! Can't extract column with `Inf`. #> ✖ Can't convert from `j` <double> to <integer> due to loss of precision. tbl[[\"x\"]] #> NULL"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xname","dir":"Articles","previous_headings":"Column extraction","what":"Definition of x$name","title":"Invariants: Comparing behavior with data frames","text":"x$name x$\"name\" equal x[[\"name\"]]. Unlike data frames, tibbles partially match names. df$x rarely used packages, can raise warning:","code":"tbl$n #> [1]  1 NA  3 NA tbl$\"n\" #> [1]  1 NA  3 NA tbl[[\"n\"]] #> [1]  1 NA  3 NA df$l #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl$l #> Warning: Unknown or uninitialised #> column: `l`. #> NULL df$not_present #> NULL tbl$not_present #> Warning: Unknown or uninitialised #> column: `not_present`. #> NULL"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xj-1","dir":"Articles","previous_headings":"Column subsetting","what":"Definition of x[j]","title":"Invariants: Comparing behavior with data frames","text":"j converted integer vector vec_as_index(j, ncol(x), names = names(x)). x[c(j_1, j_2, ..., j_n)] equivalent tibble(x[[j_1]], x[[j_2]], ..., x[[j_n]]), keeping corresponding column names. implies j must numeric character vector, logical vector length 1 ncol(x).1 subsetting repeated indexes, resulting column names undefined, rely . tibbles repeated column names, subsetting name uses first matching column. nrow(df[j]) equals nrow(df). Tibbles support indexing logical matrix, values returned vector compatible.","code":"tbl[1:2] #> # A tibble: 4 × 2 #>       n c     #>   <int> <chr> #> 1     1 e     #> 2    NA f     #> 3     3 g     #> 4    NA h df[c(1, 1)] #>    n n.1 #> 1  1   1 #> 2 NA  NA #> 3  3   3 #> 4 NA  NA tbl[c(1, 1)] #> # A tibble: 4 × 2 #>       n     n #>   <int> <int> #> 1     1     1 #> 2    NA    NA #> 3     3     3 #> 4    NA    NA tbl[integer()] #> # A tibble: 4 × 0 df[is.na(df)] #> [[1]] #> [1] NA #>  #> [[2]] #> [1] NA tbl[is.na(tbl)] #> [1] NA NA df[!is.na(df)] #> [[1]] #> [1] 1 #>  #> [[2]] #> [1] 3 #>  #> [[3]] #> [1] \"e\" #>  #> [[4]] #> [1] \"f\" #>  #> [[5]] #> [1] \"g\" #>  #> [[6]] #> [1] \"h\" #>  #> [[7]] #> [1] 9 #>  #> [[8]] #> [1] 10 11 #>  #> [[9]] #> [1] 12 13 14 #>  #> [[10]] #> [1] \"text\" tbl[!is.na(tbl)] #> Error in `vec_c()` at tibble/R/subsetting-matrix.R:12:3: #> ! Can't combine `n` <integer> and `c` <character>."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-x-j","dir":"Articles","previous_headings":"Column subsetting","what":"Definition of x[, j]","title":"Invariants: Comparing behavior with data frames","text":"x[, j] equal x[j]. Tibbles perform column extraction x[j] yield single column.","code":"df[, 1] #> [1]  1 NA  3 NA tbl[, 1] #> # A tibble: 4 × 1 #>       n #>   <int> #> 1     1 #> 2    NA #> 3     3 #> 4    NA tbl[, 1:2] #> # A tibble: 4 × 2 #>       n c     #>   <int> <chr> #> 1     1 e     #> 2    NA f     #> 3     3 g     #> 4    NA h"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-x-j-drop-true","dir":"Articles","previous_headings":"Column subsetting","what":"Definition of x[, j, drop = TRUE]","title":"Invariants: Comparing behavior with data frames","text":"backward compatiblity, x[, j, drop = TRUE] performs column extraction, returning x[j][[1]] ncol(x[j]) 1.","code":"tbl[, 1, drop = TRUE] #> [1]  1 NA  3 NA"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi","dir":"Articles","previous_headings":"Row subsetting","what":"Definition of x[i, ]","title":"Invariants: Comparing behavior with data frames","text":"x[, ] equal tibble(vec_slice(x[[1]], ), vec_slice(x[[2]], ), ...).2 means must numeric vector, logical vector length nrow(x) 1. compatibility, can also character vector containing positive numbers. Exception: OOB values generate warnings instead errors: Unlike data frames, logical vectors length 1 recycled. NB: scalar logicals recycled, scalar numerics . makes x[NA, ] x[NA_integer_, ] return different results.","code":"tbl[3, ] #> # A tibble: 1 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     3 g     <int [3]> df[mean, ] #> Error in xj[i]: invalid subscript type 'closure' tbl[mean, ] #> Error in `tbl[mean, ]`: #> ! Can't subset rows with `mean`. #> ✖ `mean` must be logical, numeric, or character, not a function. df[list(1), ] #> Error in xj[i]: invalid subscript type 'list' tbl[list(1), ] #> Error in `tbl[list(1), ]`: #> ! Can't subset rows with `list(1)`. #> ✖ `list(1)` must be logical, numeric, or character, not a list. tbl[\"1\", ] #> # A tibble: 1 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> tbl[10, ] #> # A tibble: 1 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL> tbl[\"x\", ] #> # A tibble: 1 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL> df[c(TRUE, FALSE), ] #>   n c         li #> 1 1 e          9 #> 3 3 g 12, 13, 14 tbl[c(TRUE, FALSE), ] #> Error in `tbl[c(TRUE, FALSE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE)`. #> ✖ Logical subscript `c(TRUE, FALSE)` must be size 1 or 4, not 2. tbl[NA, ] #> # A tibble: 4 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL> #> 2    NA NA    <NULL> #> 3    NA NA    <NULL> #> 4    NA NA    <NULL> tbl[NA_integer_, ] #> # A tibble: 1 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL>"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-drop-true","dir":"Articles","previous_headings":"Row subsetting","what":"Definition of x[i, , drop = TRUE]","title":"Invariants: Comparing behavior with data frames","text":"drop = TRUE effect selecting single row:","code":"df[1, , drop = TRUE] #> $n #> [1] 1 #>  #> $c #> [1] \"e\" #>  #> $li #> $li[[1]] #> [1] 9 tbl[1, , drop = TRUE] #> # A tibble: 1 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-x-and-x","dir":"Articles","previous_headings":"Row and column subsetting","what":"Definition of x[] and x[,]","title":"Invariants: Comparing behavior with data frames","text":"x[] x[,] equivalent x.3","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j","dir":"Articles","previous_headings":"Row and column subsetting","what":"Definition of x[i, j]","title":"Invariants: Comparing behavior with data frames","text":"x[, j] equal x[, ][j].4","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j-1","dir":"Articles","previous_headings":"Row and column subsetting","what":"Definition of x[[i, j]]","title":"Invariants: Comparing behavior with data frames","text":"must numeric vector length 1. x[[, j]] equal x[, ][[j]], vctrs::vec_slice(x[[j]], ).5 implies j must numeric character vector length 1. NB: vec_size(x[[, j]]) always equals 1. Unlike x[, ], x[[, ]] valid.","code":"df[[1, 1]] #> [1] 1 df[[1, 3]] #> [1] 9"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xj---a","dir":"Articles","previous_headings":"Column update","what":"Definition of x[[j]] <- a","title":"Invariants: Comparing behavior with data frames","text":"vector x[[j]] <- replaces jth column value . recycled size x must size nrow(x) 1. (exception NULL, described .) Recycling also works list, data frame, matrix columns. j must scalar numeric string, NA. j OOB, new column added right hand side, name repair needed. df[[j]] <- replaces complete column can change type. [[<- supports removing column assigning NULL . Removing nonexistent column -op.","code":"with_tbl(tbl[[\"li\"]] <- list(0)) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <dbl [1]> #> 3     3 g     <dbl [1]> #> 4    NA h     <dbl [1]> with_df2(df2[[\"tb\"]] <- df[1, ]) #> Error in `[[<-.data.frame`(`*tmp*`, \"tb\", value = structure(list(n = 1L, : replacement has 1 row, data has 4 with_tbl2(tbl2[[\"tb\"]] <- tbl[1, ]) #> # A tibble: 4 × 2 #>    tb$n $c    $li       m[,1]  [,2] #>   <int> <chr> <list>    <dbl> <dbl> #> 1     1 e     <dbl [1]>     1     0 #> 2     1 e     <dbl [1]>     0     1 #> 3     1 e     <dbl [1]>     0     0 #> 4     1 e     <dbl [1]>     0     0 #> # ℹ 1 more variable: m[3:4] <dbl> with_df2(df2[[\"m\"]] <- df2[[\"m\"]][1, , drop = FALSE]) #> Error in `[[<-.data.frame`(`*tmp*`, \"m\", value = structure(c(1, 0, 0, : replacement has 1 row, data has 4 with_tbl2(tbl2[[\"m\"]] <- tbl2[[\"m\"]][1, , drop = FALSE]) #> # A tibble: 4 × 2 #>    tb$n $c    $li       m[,1]  [,2] #>   <int> <chr> <list>    <dbl> <dbl> #> 1     1 e     <dbl [1]>     1     0 #> 2    NA f     <int [2]>     1     0 #> 3     3 g     <int [3]>     1     0 #> 4    NA h     <chr [1]>     1     0 #> # ℹ 1 more variable: m[3:4] <dbl> with_tbl(tbl[[\"x\"]] <- 0) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0 with_df(df[[4]] <- 0) #>    n c         li V4 #> 1  1 e          9  0 #> 2 NA f     10, 11  0 #> 3  3 g 12, 13, 14  0 #> 4 NA h       text  0 with_tbl(tbl[[4]] <- 0) #> # A tibble: 4 × 4 #>       n c     li         ...4 #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0 with_df(df[[5]] <- 0) #> Warning in format.data.frame(if #> (omit) x[seq_len(n0), , drop = #> FALSE] else x, : corrupt data #> frame: columns will be truncated or #> padded with NAs #>    n c         li      V5 #> 1  1 e          9 NULL  0 #> 2 NA f     10, 11 <NA>  0 #> 3  3 g 12, 13, 14 <NA>  0 #> 4 NA h       text <NA>  0 with_tbl(tbl[[5]] <- 0) #> Error in `[[<-`: #> ! Can't assign to columns beyond the end with non-consecutive locations. #> ℹ Input has size 3. #> ✖ Subscript `5` contains non-consecutive location 5."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xname---a","dir":"Articles","previous_headings":"Column update","what":"Definition of x$name <- a","title":"Invariants: Comparing behavior with data frames","text":"x$name <- x$\"name\" <- equivalent x[[\"name\"]] <- .6 $<- perform partial matching.","code":"with_tbl(tbl$n <- 0) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl[[\"n\"]] <- 0) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl$l <- 0) #> # A tibble: 4 × 4 #>       n c     li            l #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0 with_tbl(tbl[[\"l\"]] <- 0) #> # A tibble: 4 × 4 #>       n c     li            l #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"column-subassignment-xj---a","dir":"Articles","previous_headings":"","what":"Column subassignment: x[j] <- a","title":"Invariants: Comparing behavior with data frames","text":"j missing, ’s replaced seq_along(x) j logical vector, ’s converted numeric seq_along(x)[j].","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-a-list-or-data-frame","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is a list or data frame","title":"Invariants: Comparing behavior with data frames","text":"inherits(, \"list\") inherits(, \"data.frame\") TRUE, x[j] <- equivalent x[[j[[1]]] <- [[1]], x[[j[[2]]]] <- [[2]], … length() equals 1, recycled length j. attempt update column twice gives error. contains NULL values, corresponding columns removed updating (.e. position indexes refer columns modifications). NA indexes supported. Just like column updates, [<- supports changing type existing column. Appending columns end (without gaps) supported. name new columns determined LHS, RHS, name repair (order precedence). Tibbles support indexing logical matrix, scalar RHS, columns updated compatible value assigned.","code":"with_tbl(tbl[1:2] <- list(\"x\", 4:1)) #> # A tibble: 4 × 3 #>   n         c li        #>   <chr> <int> <list>    #> 1 x         4 <dbl [1]> #> 2 x         3 <int [2]> #> 3 x         2 <int [3]> #> 4 x         1 <chr [1]> with_tbl(tbl[c(\"li\", \"x\", \"c\")] <- list(\"x\", 4:1, NULL)) #> # A tibble: 4 × 3 #>       n li        x #>   <int> <chr> <int> #> 1     1 x         4 #> 2    NA x         3 #> 3     3 x         2 #> 4    NA x         1 with_tbl(tbl[1:2] <- list(1)) #> # A tibble: 4 × 3 #>       n     c li        #>   <dbl> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     1     1 <int [2]> #> 3     1     1 <int [3]> #> 4     1     1 <chr [1]> with_df(df[1:2] <- list(0, 0, 0)) #> Warning in #> `[<-.data.frame`(`*tmp*`, 1:2, #> value = list(0, 0, 0)): provided 3 #> variables to replace 2 variables #>   n c         li #> 1 0 0          9 #> 2 0 0     10, 11 #> 3 0 0 12, 13, 14 #> 4 0 0       text with_tbl(tbl[1:2] <- list(0, 0, 0)) #> Error in `[<-`: #> ! Can't recycle `list(0, 0, 0)` (size 3) to size 2. with_df(df[1:3] <- list(0, 0)) #>   n c li #> 1 0 0  0 #> 2 0 0  0 #> 3 0 0  0 #> 4 0 0  0 with_tbl(tbl[1:3] <- list(0, 0)) #> Error in `[<-`: #> ! Can't recycle `list(0, 0)` (size 2) to size 3. with_df(df[c(1, 1)] <- list(1, 2)) #> Error in `[<-.data.frame`(`*tmp*`, c(1, 1), value = list(1, 2)): duplicate subscripts for columns with_tbl(tbl[c(1, 1)] <- list(1, 2)) #> Error in `[<-`: #> ! Column index 1 is used #>   more than once for assignment. with_tbl(tbl[1:2] <- list(NULL, 4:1)) #> # A tibble: 4 × 2 #>       c li        #>   <int> <list>    #> 1     4 <dbl [1]> #> 2     3 <int [2]> #> 3     2 <int [3]> #> 4     1 <chr [1]> with_tbl(tbl[c(\"x\", \"y\")] <- tibble(\"x\", x = 4:1)) #> # A tibble: 4 × 5 #>       n c     li        x         y #>   <int> <chr> <list>    <chr> <int> #> 1     1 e     <dbl [1]> x         4 #> 2    NA f     <int [2]> x         3 #> 3     3 g     <int [3]> x         2 #> 4    NA h     <chr [1]> x         1 with_tbl(tbl[3:4] <- list(\"x\", x = 4:1)) #> # A tibble: 4 × 4 #>       n c     li        x #>   <int> <chr> <chr> <int> #> 1     1 e     x         4 #> 2    NA f     x         3 #> 3     3 g     x         2 #> 4    NA h     x         1 with_df(df[4] <- list(4:1)) #>    n c         li V4 #> 1  1 e          9  4 #> 2 NA f     10, 11  3 #> 3  3 g 12, 13, 14  2 #> 4 NA h       text  1 with_tbl(tbl[4] <- list(4:1)) #> # A tibble: 4 × 4 #>       n c     li         ...4 #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2    NA f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4    NA h     <chr [1]>     1 with_df(df[5] <- list(4:1)) #> Error in `[<-.data.frame`(`*tmp*`, 5, value = list(4:1)): new columns would leave holes after existing columns with_tbl(tbl[5] <- list(4:1)) #> Error in `[<-`: #> ! Can't assign to columns beyond the end with non-consecutive locations. #> ℹ Input has size 3. #> ✖ Subscript `5` contains non-consecutive location 5. with_df(df[is.na(df)] <- 4) #>   n c         li #> 1 1 e          9 #> 2 4 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[is.na(tbl)] <- 4) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     4 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[is.na(df)] <- 1:2) #>   n c         li #> 1 1 e          9 #> 2 1 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 2 h       text with_tbl(tbl[is.na(tbl)] <- 1:2) #> Error in `[<-`: #> ! Subscript `is.na(tbl)` is #>   a matrix, the data `1:2` must #>   have size 1. with_df(df[matrix(c(rep(TRUE, 5), rep(FALSE, 7)), ncol = 3)] <- 4) #>   n c         li #> 1 4 4          9 #> 2 4 f     10, 11 #> 3 4 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[matrix(c(rep(TRUE, 5), rep(FALSE, 7)), ncol = 3)] <- 4) #> Error in `[<-`: #> ! Assigned data `4` must be #>   compatible with existing data. #> ℹ Error occurred for column `c`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double> to <character>."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-a-matrix-or-array","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is a matrix or array","title":"Invariants: Comparing behavior with data frames","text":".matrix(), coerced data frame .data.frame() assigning. rows assigned, matrix type must compatible columns. .array() (dim()[-1:-2] != 1), error thrown.","code":"with_tbl(tbl[1:2] <- matrix(8:1, ncol = 2)) #> # A tibble: 4 × 3 #>       n     c li        #>   <int> <int> <list>    #> 1     8     4 <dbl [1]> #> 2     7     3 <int [2]> #> 3     6     2 <int [3]> #> 4     5     1 <chr [1]> with_df(df[1:3, 1:2] <- matrix(6:1, ncol = 2)) #>    n c         li #> 1  6 3          9 #> 2  5 2     10, 11 #> 3  4 1 12, 13, 14 #> 4 NA h       text with_tbl(tbl[1:3, 1:2] <- matrix(6:1, ncol = 2)) #> Error in `[<-`: #> ! Assigned data `matrix(6:1, #>   ncol = 2)` must be compatible #>   with existing data. #> ℹ Error occurred for column `c`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <integer> to <character>. with_tbl(tbl[1:2] <- array(4:1, dim = c(4, 1, 1))) #> # A tibble: 4 × 3 #>       n     c li        #>   <int> <int> <list>    #> 1     4     4 <dbl [1]> #> 2     3     3 <int [2]> #> 3     2     2 <int [3]> #> 4     1     1 <chr [1]> with_tbl(tbl[1:2] <- array(8:1, dim = c(4, 2, 1))) #> # A tibble: 4 × 3 #>       n     c li        #>   <int> <int> <list>    #> 1     8     4 <dbl [1]> #> 2     7     3 <int [2]> #> 3     6     2 <int [3]> #> 4     5     1 <chr [1]> with_df(df[1:2] <- array(8:1, dim = c(2, 1, 4))) #>   n c         li #> 1 8 4          9 #> 2 7 3     10, 11 #> 3 6 2 12, 13, 14 #> 4 5 1       text with_tbl(tbl[1:2] <- array(8:1, dim = c(2, 1, 4))) #> Error in `[<-`: #> ! `array(8:1, dim = c(2, 1, #>   4))` must be a vector, a bare #>   list, a data frame, a matrix, or #>   NULL. with_df(df[1:2] <- array(8:1, dim = c(4, 1, 2))) #>   n c         li #> 1 8 4          9 #> 2 7 3     10, 11 #> 3 6 2 12, 13, 14 #> 4 5 1       text with_tbl(tbl[1:2] <- array(8:1, dim = c(4, 1, 2))) #> Error in `[<-`: #> ! `array(8:1, dim = c(4, 1, #>   2))` must be a vector, a bare #>   list, a data frame, a matrix, or #>   NULL."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-another-type-of-vector","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is another type of vector","title":"Invariants: Comparing behavior with data frames","text":"vec_is(), x[j] <- equivalent x[j] <- list(). primarily provided backward compatibility. Matrices must wrapped list() assignment create matrix column.","code":"with_tbl(tbl[1] <- 0) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl[1] <- list(0)) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl[1] <- list(matrix(1:8, ncol = 2))) #> # A tibble: 4 × 3 #>   n[,1]  [,2] c     li        #>   <int> <int> <chr> <list>    #> 1     1     5 e     <dbl [1]> #> 2     2     6 f     <int [2]> #> 3     3     7 g     <int [3]> #> 4     4     8 h     <chr [1]> with_tbl(tbl[1:2] <- list(matrix(1:8, ncol = 2))) #> # A tibble: 4 × 3 #>   n[,1]  [,2] c[,1]  [,2] li        #>   <int> <int> <int> <int> <list>    #> 1     1     5     1     5 <dbl [1]> #> 2     2     6     2     6 <int [2]> #> 3     3     7     3     7 <int [3]> #> 4     4     8     4     8 <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-null","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is NULL","title":"Invariants: Comparing behavior with data frames","text":"Entire columns can removed. Specifying error.","code":"with_tbl(tbl[1] <- NULL) #> # A tibble: 4 × 2 #>   c     li        #>   <chr> <list>    #> 1 e     <dbl [1]> #> 2 f     <int [2]> #> 3 g     <int [3]> #> 4 h     <chr [1]> with_tbl(tbl[, 2:3] <- NULL) #> # A tibble: 4 × 1 #>       n #>   <int> #> 1     1 #> 2    NA #> 3     3 #> 4    NA with_df(df[1, 2:3] <- NULL) #> Error in x[[jj]][iseq] <- vjj: replacement has length zero with_tbl(tbl[1, 2:3] <- NULL) #> Error in `[<-`: #> ! `NULL` must be a vector, a #>   bare list, a data frame or a #>   matrix."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-not-a-vector","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is not a vector","title":"Invariants: Comparing behavior with data frames","text":"type error. Note .list() TRUE, inherits(, \"list\") FALSE, considered scalar. See ?vec_is ?vec_proxy details.","code":"with_df(df[1] <- mean) #> Error in rep(value, length.out = n): attempt to replicate an object of type 'closure' with_tbl(tbl[1] <- mean) #> Error in `[<-`: #> ! `mean` must be a vector, a #>   bare list, a data frame, a #>   matrix, or NULL. with_df(df[1] <- lm(mpg ~ wt, data = mtcars)) #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 2 has 32 rows #> to replace 4 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 3 has 32 rows #> to replace 4 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 5 has 32 rows #> to replace 4 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 7 has 5 rows #> to replace 4 rows #> Error in `[<-.data.frame`(`*tmp*`, 1, value = structure(list(coefficients = c(`(Intercept)` = 37.285126167342, : replacement element 10 has 3 rows, need 4 with_tbl(tbl[1] <- lm(mpg ~ wt, data = mtcars)) #> Error in `[<-`: #> ! `lm(mpg ~ wt, data = #>   mtcars)` must be a vector, a bare #>   list, a data frame, a matrix, or #>   NULL."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"row-subassignment-xi---list---","dir":"Articles","previous_headings":"","what":"Row subassignment: x[i, ] <- list(...)","title":"Invariants: Comparing behavior with data frames","text":"x[, ] <- vec_slice(x[[j_1]], ) <- [[1]], vec_slice(x[[j_2]], ) <- [[2]], … .7 values size one can recycled. compatibility, warning issued indexing beyond number rows. Appending rows right end existing data supported, without warning. compatibility, can also character vector containing positive numbers.","code":"with_tbl(tbl[2:3, ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]> with_tbl(tbl[c(FALSE, TRUE, TRUE, FALSE), ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]> with_tbl(tbl[2:3, ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]> with_tbl(tbl[2:3, ] <- list(tbl$n[1], tbl$c[1:2], tbl$li[1])) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 f     <dbl [1]> #> 4    NA h     <chr [1]> with_df(df[2:4, ] <- df[1:2, ]) #> Error in `[<-.data.frame`(`*tmp*`, 2:4, , value = structure(list(n = c(1L, : replacement element 1 has 2 rows, need 3 with_tbl(tbl[2:4, ] <- tbl[1:2, ]) #> Error in `[<-`: #> ! Assigned data `tbl[1:2, ]` #>   must be compatible with row #>   subscript `2:4`. #> ✖ 3 rows must be assigned. #> ✖ Element 1 of assigned data has 2 #>   rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:169:7: #> ! Can't recycle input of size 2 to size 3. with_tbl(tbl[5, ] <- tbl[1, ]) #> # A tibble: 5 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> #> 5     1 e     <dbl [1]> with_tbl(tbl[5:7, ] <- tbl[1, ]) #> # A tibble: 7 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> #> 5     1 e     <dbl [1]> #> 6     1 e     <dbl [1]> #> 7     1 e     <dbl [1]> with_df(df[6, ] <- df[1, ]) #>    n    c         li #> 1  1    e          9 #> 2 NA    f     10, 11 #> 3  3    g 12, 13, 14 #> 4 NA    h       text #> 5 NA <NA>       NULL #> 6  1    e          9 with_tbl(tbl[6, ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't assign to rows beyond the end with non-consecutive locations. #> ℹ Input has size 4. #> ✖ Subscript `6` contains non-consecutive location 6. with_df(df[-5, ] <- df[1, ]) #>   n c li #> 1 1 e  9 #> 2 1 e  9 #> 3 1 e  9 #> 4 1 e  9 with_tbl(tbl[-5, ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't negate rows past the end. #> ℹ Location 5 doesn't exist. #> ℹ There are only 4 rows. with_df(df[-(5:7), ] <- df[1, ]) #>   n c li #> 1 1 e  9 #> 2 1 e  9 #> 3 1 e  9 #> 4 1 e  9 with_tbl(tbl[-(5:7), ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't negate rows past the end. #> ℹ Locations 5, 6, and 7 #>   don't exist. #> ℹ There are only 4 rows. with_df(df[-6, ] <- df[1, ]) #>   n c li #> 1 1 e  9 #> 2 1 e  9 #> 3 1 e  9 #> 4 1 e  9 with_tbl(tbl[-6, ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't negate rows past the end. #> ℹ Location 6 doesn't exist. #> ℹ There are only 4 rows. with_tbl(tbl[as.character(1:3), ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j---a","dir":"Articles","previous_headings":"Row and column subassignment","what":"Definition of x[i, j] <- a","title":"Invariants: Comparing behavior with data frames","text":"x[, j] <- equivalent x[, ][j] <- .8 Subassignment x[, j] stricter tibbles data frames. x[, j] <- can’t change data type existing columns. notable exception population column full NA (type logical), use NA right-hand side assignment. programming, always safer (faster) use correct type NA initialize columns. new columns, x[, j] <- fills unassigned rows NA. Likewise, new rows, x[, j] <- fills unassigned columns NA.","code":"with_df(df[2:3, 1] <- df[1:2, 2]) #>      n c         li #> 1    1 e          9 #> 2    e f     10, 11 #> 3    f g 12, 13, 14 #> 4 <NA> h       text with_tbl(tbl[2:3, 1] <- tbl[1:2, 2]) #> Error in `[<-`: #> ! Assigned data `tbl[1:2, #>   2]` must be compatible with #>   existing data. #> ℹ Error occurred for column `n`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_df(df[2:3, 2] <- df[1:2, 3]) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2:3, 2, #> value = list(9, 10:11)): provided 2 #> variables to replace 1 variables #>    n c         li #> 1  1 e          9 #> 2 NA 9     10, 11 #> 3  3 9 12, 13, 14 #> 4 NA h       text with_tbl(tbl[2:3, 2] <- tbl[1:2, 3]) #> Error in `[<-`: #> ! Assigned data `tbl[1:2, #>   3]` must be compatible with #>   existing data. #> ℹ Error occurred for column `c`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <list> to <character>. with_df(df[2:3, 3] <- df2[1:2, 1]) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2:3, 3, #> value = structure(list(n = c(1L, : #> provided 3 variables to replace 1 #> variables #>    n c   li #> 1  1 e    9 #> 2 NA f    1 #> 3  3 g   NA #> 4 NA h text with_tbl(tbl[2:3, 3] <- tbl2[1:2, 1]) #> Error in `[<-`: #> ! Assigned data `tbl2[1:2, #>   1]` must be compatible with #>   existing data. #> ℹ Error occurred for column `li`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <tbl_df> to <list>. with_df2(df2[2:3, 1] <- df2[1:2, 2]) #> Warning in matrix(value, n, p): #> data length [8] is not a #> sub-multiple or multiple of the #> number of columns [3] #>   tb.n tb.c tb.li m.1 m.2 m.3 m.4 #> 1    1    e     9   1   0   0   0 #> 2    1    0     0   0   1   0   0 #> 3    0    1     0   0   0   1   0 #> 4   NA    h  text   0   0   0   1 with_tbl2(tbl2[2:3, 1] <- tbl2[1:2, 2]) #> Error in `[<-`: #> ! Assigned data `tbl2[1:2, #>   2]` must be compatible with #>   existing data. #> ℹ Error occurred for column `tb`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double[,4]> to <tbl_df>. with_tbl2(tbl2[2:3, 2] <- tbl[1:2, 1]) #> # A tibble: 4 × 2 #>    tb$n $c    $li       m[,1]  [,2] #>   <int> <chr> <list>    <dbl> <dbl> #> 1     1 e     <dbl [1]>     1     0 #> 2    NA f     <int [2]>     1     1 #> 3     3 g     <int [3]>    NA    NA #> 4    NA h     <chr [1]>     0     0 #> # ℹ 1 more variable: m[3:4] <dbl> with_tbl({tbl$x <- NA; tbl[2:3, \"x\"] <- 3:2}) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>    NA #> 2    NA f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4    NA h     <chr [1]>    NA with_df({df[2:3, 2:3] <- NA}) #>    n    c   li #> 1  1    e    9 #> 2 NA <NA>   NA #> 3  3 <NA>   NA #> 4 NA    h text with_tbl({tbl[2:3, 2:3] <- NA}) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA NA    <NULL>    #> 3     3 NA    <NULL>    #> 4    NA h     <chr [1]> with_tbl({tbl$x <- NA_integer_; tbl[2:3, \"x\"] <- 3:2}) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>    NA #> 2    NA f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4    NA h     <chr [1]>    NA with_df(df[2:3, \"n\"] <- 1) #>    n c         li #> 1  1 e          9 #> 2  1 f     10, 11 #> 3  1 g 12, 13, 14 #> 4 NA h       text with_tbl(tbl[2:3, \"n\"] <- 1) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4    NA h     <chr [1]> with_tbl(tbl[2:3, \"x\"] <- 1) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>    NA #> 2    NA f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4    NA h     <chr [1]>    NA with_df(df[2:3, \"n\"] <- NULL) #> Error in x[[jj]][iseq] <- vjj: replacement has length zero with_tbl(tbl[2:3, \"n\"] <- NULL) #> Error in `[<-`: #> ! `NULL` must be a vector, a #>   bare list, a data frame or a #>   matrix. with_tbl(tbl[5, \"n\"] <- list(0L)) #> # A tibble: 5 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> #> 5     0 NA    <NULL>"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j---a-1","dir":"Articles","previous_headings":"Row and column subassignment","what":"Definition of x[[i, j]] <- a","title":"Invariants: Comparing behavior with data frames","text":"must numeric vector length 1. x[[, j]] <- equivalent x[, ][[j]] <- .9 NB: vec_size() must equal 1. Unlike x[, ] <-, x[[, ]] <- valid.","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"options","dir":"Articles","previous_headings":"","what":"Options","title":"Controlling display of numbers","text":"easiest way customize display numbers data tibble define options. See ?pillar::pillar_options comprehensive overview. changes display columns. Read see specify display options column--column basis.","code":"tibble(x = 123.4567) #> # A tibble: 1 × 1 #>       x #>   <dbl> #> 1  123. old <- options(pillar.sigfig = 7) tibble(x = 123.4567) #> # A tibble: 1 × 1 #>          x #>      <dbl> #> 1 123.4567 # Restore old options, see also rlang::local_options() for a more elegant way options(old)"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"per-column-number-formatting","dir":"Articles","previous_headings":"","what":"Per-column number formatting","title":"Controlling display of numbers","text":"new num() constructor allows creating vectors behave like numbers allow customizing display. examples shown, see ?num comprehensive overview. Similarly, char() allows customizing display character columns. pillar package responsible display tibbles tries hard get number display right, however impossible accommodate use cases. Whenever default formatting suit application, num() char() allow redefining formatting individual columns. formatting survives data transformations.","code":"num(-1:3, notation = \"sci\") #> <pillar_num(sci)[5]> #> [1] -1e0  0    1e0  2e0  3e0 tibble(   x4 = num(8:12 * 100 + 0.5, digits = 4),   x1 = num(8:12 * 100 + 0.5, digits = -1),   usd = num(8:12 * 100 + 0.5, digits = 2, label = \"USD\"),   percent = num(8:12 / 100 + 0.0005, label = \"%\", scale = 100),   eng = num(10^(-3:1), notation = \"eng\", fixed_exponent = -Inf),   si = num(10^(-3:1) * 123, notation = \"si\"),   char = char(paste(LETTERS, collapse = \" \"), shorten = \"mid\") ) #> # A tibble: 5 × 7 #>          x4       x1     usd percent   eng      si char                    #>   <num:.4!> <num:.1>     USD       % [e-3]    <si> <char>                  #> 1  800.5000    800.5  800.50    8.05     1 123   m A B C D E F…U V W X Y Z #> 2  900.5000    900.5  900.50    9.05    10   1.23  A B C D E F…U V W X Y Z #> 3 1000.5000   1000.5 1000.50   10.0    100  12.3   A B C D E F…U V W X Y Z #> 4 1100.5000   1100.5 1100.50   11.0   1000 123     A B C D E F…U V W X Y Z #> 5 1200.5000   1200.5 1200.50   12.0  10000   1.23k A B C D E F…U V W X Y Z"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"rule-based-number-formatting","dir":"Articles","previous_headings":"","what":"Rule-based number formatting","title":"Controlling display of numbers","text":"Currently, formatting must applied manually column. following pattern may help consistently columns tibble, columns based name.","code":"library(dplyr, warn.conflicts = FALSE)  markets <-   as_tibble(EuStockMarkets) %>%   mutate(time = time(EuStockMarkets), .before = 1)  markets #> # A tibble: 1,860 × 5 #>     time   DAX   SMI   CAC  FTSE #>    <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 1991. 1629. 1678. 1773. 2444. #>  2 1992. 1614. 1688. 1750. 2460. #>  3 1992. 1607. 1679. 1718  2448. #>  4 1992. 1621. 1684. 1708. 2470. #>  5 1992. 1618. 1687. 1723. 2485. #>  6 1992. 1611. 1672. 1714. 2467. #>  7 1992. 1631. 1683. 1734. 2488. #>  8 1992. 1640. 1704. 1757. 2508. #>  9 1992. 1635. 1698. 1754  2510. #> 10 1992. 1646. 1716. 1754. 2497. #> # ℹ 1,850 more rows markets %>%   mutate(across(-time, ~ num(.x, digits = 3))) #> # A tibble: 1,860 × 5 #>     time       DAX       SMI       CAC      FTSE #>    <dbl> <num:.3!> <num:.3!> <num:.3!> <num:.3!> #>  1 1991.  1628.750  1678.100  1772.800  2443.600 #>  2 1992.  1613.630  1688.500  1750.500  2460.200 #>  3 1992.  1606.510  1678.600  1718.000  2448.200 #>  4 1992.  1621.040  1684.100  1708.100  2470.400 #>  5 1992.  1618.160  1686.600  1723.100  2484.700 #>  6 1992.  1610.610  1671.600  1714.300  2466.800 #>  7 1992.  1630.750  1682.900  1734.500  2487.900 #>  8 1992.  1640.170  1703.600  1757.400  2508.400 #>  9 1992.  1635.470  1697.500  1754.000  2510.500 #> 10 1992.  1645.890  1716.300  1754.300  2497.400 #> # ℹ 1,850 more rows"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"computing-on-num","dir":"Articles","previous_headings":"","what":"Computing on num","title":"Controlling display of numbers","text":"Formatting numbers useful presentation results. defined early analysis, formatting options survive operations. worth defining output options suit data early process, benefit formatting throughout analysis. working seamlessly applying formatting final presentation (plots, tables, …).","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"arithmetics","dir":"Articles","previous_headings":"Computing on num","what":"Arithmetics","title":"Controlling display of numbers","text":"applying arithmetic operations numbers created num(), result inherits formatting first num object.","code":"num(1) + 2 #> <pillar_num[1]> #> [1] 3 1 + num(2) #> <pillar_num[1]> #> [1] 3 1L + num(2) #> <pillar_num[1]> #> [1] 3 num(3.23456, sigfig = 4) - num(2) #> <pillar_num:4[1]> #> [1] 1.235 num(4, sigfig = 2) * num(3, digits = 2) #> <pillar_num:2[1]> #> [1] 12 num(3, digits = 2) * num(4, sigfig = 2) #> <pillar_num:.2![1]> #> [1] 12.00 -num(2) #> <pillar_num[1]> #> [1] -2"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"mathematics","dir":"Articles","previous_headings":"Computing on num","what":"Mathematics","title":"Controlling display of numbers","text":"Similarly, mathematical operations, formatting inherited.","code":"min(num(1:3, label = \"$\")) #> <pillar_num{$}[1]> #> [1] 1 mean(num(1:3, notation = \"eng\")) #> <pillar_num(eng)[1]> #> [1] 2e0 sin(num(1:3, label = \"%\", scale = 100)) #> <pillar_num{%}*100[3]> #> [1] 84.1 90.9 14.1"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"override","dir":"Articles","previous_headings":"Computing on num","what":"Override","title":"Controlling display of numbers","text":"cases, ideal formatting changes transformation. num() can applied repeatedly, last setting wins.","code":"num(1:3 + 0.125, digits = 4) #> <pillar_num:.4![3]> #> [1] 1.1250 2.1250 3.1250 transf <- 10^num(1:3 + 0.125, digits = 4) transf #> <pillar_num:.4![3]> #> [1]   13.3352  133.3521 1333.5214 num(transf, sigfig = 3) #> <pillar_num:3[3]> #> [1]   13.3  133.  1334."},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"recovery","dir":"Articles","previous_headings":"Computing on num","what":"Recovery","title":"Controlling display of numbers","text":"var() function one examples formatting lost: median() function worse, breaks num() objects: One way recover apply num() result: automatic recovery, can also define version var(), even overwrite base implementation. Note pattern still experimental may subject change: pattern can applied functions lose formatting. make_restore() function defined function factory consumes function returns derived function:","code":"x <- num(c(1, 2, 4), notation = \"eng\") var(x) #> [1] 2.333333 median(x) #> Error in `median()`: #> ! `median.pillar_num()` not implemented. num(var(x), notation = \"eng\") #> <pillar_num(eng)[1]> #> [1] 2.33e0 num(median(as.numeric(x)), notation = \"eng\") #> <pillar_num(eng)[1]> #> [1] 2e0 var_ <- function(x, ...) {   out <- var(vctrs::vec_proxy(x), ...)   vctrs::vec_restore(out, x) } var_(x) #> <pillar_num(eng)[1]> #> [1] 2.33e0 make_restore <- function(fun) {   force(fun)   function(x, ...) {     out <- fun(vctrs::vec_proxy(x), ...)     vctrs::vec_restore(out, x)   } }  var_ <- make_restore(var) sd_ <- make_restore(sd)  var_(x) #> <pillar_num(eng)[1]> #> [1] 2.33e0 sd_(x) #> <pillar_num(eng)[1]> #> [1] 1.53e0"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"creating","dir":"Articles","previous_headings":"","what":"Creating","title":"Tibbles","text":"tibble() nice way create data frames. encapsulates best practices data frames: never changes input’s type (.e., stringsAsFactors = FALSE!). makes easier use list-columns: List-columns often created tidyr::nest(), can useful create hand. never adjusts names variables: evaluates arguments lazily sequentially: never uses row.names(). whole point tidy data store variables consistent way. never stores variable special attribute. recycles vectors length 1. recycling vectors greater lengths frequent source bugs.","code":"tibble(x = letters) #> # A tibble: 26 × 1 #>    x     #>    <chr> #>  1 a     #>  2 b     #>  3 c     #>  4 d     #>  5 e     #>  6 f     #>  7 g     #>  8 h     #>  9 i     #> 10 j     #> # ℹ 16 more rows tibble(x = 1:3, y = list(1:5, 1:10, 1:20)) #> # A tibble: 3 × 2 #>       x y          #>   <int> <list>     #> 1     1 <int [5]>  #> 2     2 <int [10]> #> 3     3 <int [20]> names(data.frame(`crazy name` = 1)) #> [1] \"crazy.name\" names(tibble(`crazy name` = 1)) #> [1] \"crazy name\" tibble(x = 1:5, y = x ^ 2) #> # A tibble: 5 × 2 #>       x     y #>   <int> <dbl> #> 1     1     1 #> 2     2     4 #> 3     3     9 #> 4     4    16 #> 5     5    25"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"coercion","dir":"Articles","previous_headings":"","what":"Coercion","title":"Tibbles","text":"complement tibble(), tibble provides as_tibble() coerce objects tibbles. Generally, as_tibble() methods much simpler .data.frame() methods. method lists written eye performance: speed .data.frame() usually bottleneck used interactively, can problem combining thousands messy inputs one tidy data frame.","code":"l <- replicate(26, sample(100), simplify = FALSE) names(l) <- letters  timing <- bench::mark(   as_tibble(l),   as.data.frame(l),   check = FALSE )  timing #> # A tibble: 2 × 14 #>   expression    min         mean         median      max         `itr/sec` #>   <chr>         <bench_tm>  <bench_tm>   <bench_tm>  <bench_tm>      <dbl> #> 1 as_tibble(l)  0.000287696 0.0006251376 0.000327178 0.004508219     1600. #> 2 as.data.fram… 0.000791522 0.0016640039 0.001098172 0.007652914      601. #> # ℹ 8 more variables: mem_alloc <bnch_byt>, n_gc <dbl>, n_itr <int>, #> #   total_time <bench_tm>, result <list>, memory <list>, time <list>, #> #   gc <list>"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"tibbles-vs-data-frames","dir":"Articles","previous_headings":"","what":"Tibbles vs data frames","title":"Tibbles","text":"three key differences tibbles data frames: printing, subsetting, recycling rules.","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"printing","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Printing","title":"Tibbles","text":"print tibble, shows first ten rows columns fit one screen. also prints abbreviated description column type, uses font styles color highlighting: Numbers displayed three significant figures default, trailing dot indicates existence fractional component. can control default appearance options: options(pillar.print_max = n, pillar.print_min = m): n rows, print first m rows. Use options(pillar.print_max = Inf) always show rows. options(pillar.width = n): use n character slots horizontally show data. n > getOption(\"width\"), result multiple tiers. Use options(pillar.width = Inf) always print columns, regardless width screen. See ?pillar::pillar_options ?tibble_options available options, vignette(\"types\") overview type abbreviations, vignette(\"numbers\") details formatting numbers, vignette(\"digits\") comparison data frame printing.","code":"tibble(x = -5:100, y = 123.456 * (3^x)) #> # A tibble: 106 × 2 #>        x         y #>    <int>     <dbl> #>  1    -5     0.508 #>  2    -4     1.52  #>  3    -3     4.57  #>  4    -2    13.7   #>  5    -1    41.2   #>  6     0   123.    #>  7     1   370.    #>  8     2  1111.    #>  9     3  3333.    #> 10     4 10000.    #> # ℹ 96 more rows"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"subsetting","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Subsetting","title":"Tibbles","text":"Tibbles quite strict subsetting. [ always returns another tibble. Contrast data frame: sometimes [ returns data frame sometimes just returns vector: extract single column use [[ $: Tibbles also stricter $. Tibbles never partial matching, throw warning return NULL column exist: However, tibbles respect drop argument provided: Tibbles support row names. removed converting tibble subsetting: See vignette(\"invariants\") detailed comparison tibbles data frames.","code":"df1 <- data.frame(x = 1:3, y = 3:1) class(df1[, 1:2]) #> [1] \"data.frame\" class(df1[, 1]) #> [1] \"integer\"  df2 <- tibble(x = 1:3, y = 3:1) class(df2[, 1:2]) #> [1] \"tbl_df\"     \"tbl\"        \"data.frame\" class(df2[, 1]) #> [1] \"tbl_df\"     \"tbl\"        \"data.frame\" class(df2[[1]]) #> [1] \"integer\" class(df2$x) #> [1] \"integer\" df <- data.frame(abc = 1) df$a #> [1] 1  df2 <- tibble(abc = 1) df2$a #> Warning: Unknown or uninitialised column: `a`. #> NULL data.frame(a = 1:3)[, \"a\", drop = TRUE] #> [1] 1 2 3 tibble(a = 1:3)[, \"a\", drop = TRUE] #> [1] 1 2 3 df <- data.frame(a = 1:3, row.names = letters[1:3]) rownames(df) #> [1] \"a\" \"b\" \"c\" rownames(as_tibble(df)) #> [1] \"1\" \"2\" \"3\"  tbl <- tibble(a = 1:3) rownames(tbl) <- letters[1:3] #> Warning: Setting row names on a tibble is deprecated. rownames(tbl) #> [1] \"a\" \"b\" \"c\" rownames(tbl[1, ]) #> [1] \"1\""},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"recycling","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Recycling","title":"Tibbles","text":"constructing tibble, values length 1 recycled. first column length different one determines number rows tibble, conflicts lead error: also extends tibbles zero rows, sometimes important programming:","code":"tibble(a = 1, b = 1:3) #> # A tibble: 3 × 2 #>       a     b #>   <dbl> <int> #> 1     1     1 #> 2     1     2 #> 3     1     3 tibble(a = 1:3, b = 1) #> # A tibble: 3 × 2 #>       a     b #>   <int> <dbl> #> 1     1     1 #> 2     2     1 #> 3     3     1 tibble(a = 1:3, c = 1:2) #> Error in `tibble()`: #> ! Tibble columns must have compatible sizes. #> • Size 3: Existing data. #> • Size 2: Column `c`. #> ℹ Only values of size one are recycled. tibble(a = 1, b = integer()) #> # A tibble: 0 × 2 #> # ℹ 2 variables: a <dbl>, b <int> tibble(a = integer(), b = 1) #> # A tibble: 0 × 2 #> # ℹ 2 variables: a <int>, b <dbl>"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"arithmetic-operations","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Arithmetic operations","title":"Tibbles","text":"Unlike data frames, tibbles don’t support arithmetic operations columns. result silently coerced data frame. rely behavior, may become error forthcoming version.","code":"tbl <- tibble(a = 1:3, b = 4:6) tbl * 2 #>   a  b #> 1 2  8 #> 2 4 10 #> 3 6 12"},{"path":"https://tibble.tidyverse.org/dev/articles/types.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Column types","text":"vignette shows overview known data types abbreviations, origin. example, <int> header column indicates integer column, <chr> denotes character column.","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/types.html","id":"example-values","dir":"Articles","previous_headings":"","what":"Example values","title":"Column types","text":"following overview contains example values type:","code":"#> Rows: 1 #> Columns: 23 #> $ logical       <lgl> TRUE #> $ integer       <int> 1 #> $ double        <dbl> 1.5 #> $ character     <chr> \"A\" #> $ complex       <cpl> 0+1i #> $ raw           <raw> 01 #> $ list          <list> 1 #> $ `named list`  <named list> 1 #> $ factor        <fct> A #> $ ordered       <ord> a #> $ Date          <date> 2024-11-10 #> $ POSIXt        <dttm> 2024-11-10 03:36:11 #> $ difftime      <drtn> 1 secs #> $ hms           <time> 00:00:01 #> $ integer64     <int64> 10000000000 #> $ blob          <blob> 00 #> $ data.frame    <df[,1]> <data.frame[1 x 1]> #> $ tbl_df        <tibble[,1]> <tbl_df[1 x 1]> #> $ AsIs          <I<int>> 1 #> $ unspecified   <???> NA #> $ vctrs_list_of <list<int>> 1 #> $ vctrs_vctr    <vctrs_vc> 1 #> $ quosures      <quos> <~1>"},{"path":"https://tibble.tidyverse.org/dev/articles/types.html","id":"implementation","dir":"Articles","previous_headings":"","what":"Implementation","title":"Column types","text":"column header obtained calling pillar::type_sum() column. S3 method can overridden, time useful override vctrs::vec_ptype_abbr():","code":"pillar:::type_sum.default #> function (x)  #> { #>     pillar_attr <- attr(x, \"pillar\", exact = TRUE) #>     label <- pillar_attr$label #>     if (!is.null(label)) { #>         return(I(label)) #>     } #>     if (is.object(x) || vec_is(x)) { #>         return(vec_ptype_abbr(x)) #>     } #>     switch(typeof(x), builtin = , special = , closure = \"fn\",  #>         environment = \"env\", symbol = if (is_missing(x)) { #>             \"missing\" #>         } else { #>             \"sym\" #>         }, typeof(x)) #> } #> <bytecode: 0x564dfe87e5e0> #> <environment: namespace:pillar>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"scalars-and-full-length","dir":"Articles > Wip","previous_headings":"$<-","what":"Scalars and full length","title":"Subassignment","text":"Assigning scalar full-length vector column consistently overwrites existing data appends new column end. Partial matching doesn’t happen:","code":"with_tbl(tbl$a <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl$b <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     2     1 <int [2]> #> 3     3     1 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl$c <- 1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     1 #> 2     2 f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4     4 h     <chr [1]>     1 with_tbl(tbl$cd <- 1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     1 e         1 #> 2     2 f         1 #> 3     3 g         1 #> 4     4 h         1 with_tbl(tbl$a <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl$b <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     1     4 <dbl [1]> #> 2     2     3 <int [2]> #> 3     3     2 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl$c <- 4:1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2     2 f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4     4 h     <chr [1]>     1 with_tbl(tbl$cd <- 4:1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <int> #> 1     1 e         4 #> 2     2 f         3 #> 3     3 g         2 #> 4     4 h         1"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"recycling","dir":"Articles > Wip","previous_headings":"$<-","what":"Recycling","title":"Subassignment","text":"Tibbles allow recycling vectors length 1 length data.","code":"with_df(df$a <- 1:2) #>   a b         cd #> 1 1 e          9 #> 2 2 f     10, 11 #> 3 1 g 12, 13, 14 #> 4 2 h       text with_tbl(tbl$a <- 1:2) #> Error in `$<-`: #> ! Assigned data `1:2` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 2 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 2 to size 4. with_df(df$a <- 1:3) #> Error in `$<-.data.frame`(`*tmp*`, a, value = 1:3): replacement has 3 rows, data has 4 with_tbl(tbl$a <- 1:3) #> Error in `$<-`: #> ! Assigned data `1:3` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 3 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 3 to size 4. with_df(df$a <- 1:5) #> Error in `$<-.data.frame`(`*tmp*`, a, value = 1:5): replacement has 5 rows, data has 4 with_tbl(tbl$a <- 1:5) #> Error in `$<-`: #> ! Assigned data `1:5` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 5 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 5 to size 4. with_df(df$c <- 1:2) #>   a b         cd c #> 1 1 e          9 1 #> 2 2 f     10, 11 2 #> 3 3 g 12, 13, 14 1 #> 4 4 h       text 2 with_tbl(tbl$c <- 1:2) #> Error in `$<-`: #> ! Assigned data `1:2` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 2 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 2 to size 4."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"subset-assignment","dir":"Articles > Wip","previous_headings":"$<-","what":"Subset assignment","title":"Subassignment","text":"Updating parts column extracted $ responsibility column vector. Tibbles can’t control happens $ returned. columns stricter \"vctrs_vctr\" class, class implements check, works identically data frames tibbles:","code":"with_tbl(tbl$a[1:2] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_tbl(tbl$b[1:2] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 4     <dbl [1]> #> 2     2 3     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df$c[1:2] <- 4:3) #>   a b         cd          c #> 1 1 e          9          4 #> 2 2 f     10, 11          3 #> 3 3 g 12, 13, 14 12, 13, 14 #> 4 4 h       text       text with_tbl(tbl$c[1:2] <- 4:3) #> Warning: Unknown or uninitialised #> column: `c`. #> Error in `$<-`: #> ! Assigned data `<int>` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 2 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 2 to size 4. with_tbl(tbl$cd[1:2] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <int [1]> #> 2     2 f     <int [1]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df$a[1:3] <- 4:3) #> Warning in df$a[1:3] <- 4:3: number #> of items to replace is not a #> multiple of replacement length #>   a b         cd #> 1 4 e          9 #> 2 3 f     10, 11 #> 3 4 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl$a[1:3] <- 4:3) #> Warning in tbl$a[1:3] <- 4:3: #> number of items to replace is not a #> multiple of replacement length #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     4 g     <int [3]> #> 4     4 h     <chr [1]> with_tbl(tbl$a[1:4] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     4 g     <int [3]> #> 4     3 h     <chr [1]> with_df({ df$v = vctrs::new_vctr(1:4); df$v[1:2] <- vctrs::new_vctr(4:3)}) #>   a b         cd v #> 1 1 e          9 4 #> 2 2 f     10, 11 3 #> 3 3 g 12, 13, 14 3 #> 4 4 h       text 4 with_df({ df$v = vctrs::new_vctr(1:4); df$v[1:2] <- vctrs::new_vctr(letters[4:3])}) #> Error in `[<-`: #> ! Can't convert `value` <vctrs_vctr> to <vctrs_vctr>."},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"scalars-and-full-length-1","dir":"Articles > Wip","previous_headings":"[[<-","what":"Scalars and full length","title":"Subassignment","text":"$ subsetting, columns consistently overwritten, partial matching doesn’t occur. Numeric indexing supported, tibbles don’t support creation new numbered columns good reason.","code":"with_tbl(tbl[[\"a\"]] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[[\"a\"]] <- \"x\") #> # A tibble: 4 × 3 #>   a     b     cd        #>   <chr> <chr> <list>    #> 1 x     e     <dbl [1]> #> 2 x     f     <int [2]> #> 3 x     g     <int [3]> #> 4 x     h     <chr [1]> with_tbl(tbl[[\"b\"]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 x     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 x     <int [3]> #> 4     4 x     <chr [1]> with_tbl(tbl[[\"c\"]] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> x     #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> x     #> 4     4 h     <chr [1]> x with_tbl(tbl[[\"cd\"]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd    #>   <int> <chr> <chr> #> 1     1 e     x     #> 2     2 f     x     #> 3     3 g     x     #> 4     4 h     x with_tbl(tbl[[1]] <- \"x\") #> # A tibble: 4 × 3 #>   a     b     cd        #>   <chr> <chr> <list>    #> 1 x     e     <dbl [1]> #> 2 x     f     <int [2]> #> 3 x     g     <int [3]> #> 4 x     h     <chr [1]> with_tbl(tbl[[2]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 x     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 x     <int [3]> #> 4     4 x     <chr [1]> with_df(df[[4]] <- \"x\") #>   a b         cd V4 #> 1 1 e          9  x #> 2 2 f     10, 11  x #> 3 3 g 12, 13, 14  x #> 4 4 h       text  x with_tbl(tbl[[4]] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        ...4  #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> x     #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> x     #> 4     4 h     <chr [1]> x with_df(df[[5]] <- \"x\") #> Warning in format.data.frame(if #> (omit) x[seq_len(n0), , drop = #> FALSE] else x, : corrupt data #> frame: columns will be truncated or #> padded with NAs #>   a b         cd      V5 #> 1 1 e          9 NULL  x #> 2 2 f     10, 11 <NA>  x #> 3 3 g 12, 13, 14 <NA>  x #> 4 4 h       text <NA>  x with_tbl(tbl[[5]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign to columns beyond the end with non-consecutive locations. #> ℹ Input has size 3. #> ✖ Subscript `5` contains non-consecutive location 5. with_tbl(tbl[[\"a\"]] <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[[\"a\"]] <- letters[4:1]) #> # A tibble: 4 × 3 #>   a     b     cd        #>   <chr> <chr> <list>    #> 1 d     e     <dbl [1]> #> 2 c     f     <int [2]> #> 3 b     g     <int [3]> #> 4 a     h     <chr [1]> with_tbl(tbl[[\"b\"]] <- letters[4:1]) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 d     <dbl [1]> #> 2     2 c     <int [2]> #> 3     3 b     <int [3]> #> 4     4 a     <chr [1]> with_tbl(tbl[[\"c\"]] <- letters[4:1]) #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> d     #> 2     2 f     <int [2]> c     #> 3     3 g     <int [3]> b     #> 4     4 h     <chr [1]> a with_tbl(tbl[[\"cd\"]] <- letters[4:1]) #> # A tibble: 4 × 3 #>       a b     cd    #>   <int> <chr> <chr> #> 1     1 e     d     #> 2     2 f     c     #> 3     3 g     b     #> 4     4 h     a"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"cells","dir":"Articles > Wip","previous_headings":"[[<-","what":"Cells","title":"Subassignment","text":"Tibbles stricter updating single cells, value must coercible existing contents. Updating list column requires contents wrapped list, consistently [[ subsetting returns list cell list column accessed:","code":"with_df(df[[2, \"a\"]] <- 1) #>   a b         cd #> 1 1 e          9 #> 2 1 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"a\"]] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, \"a\"]] <- 1.5) #>     a b         cd #> 1 1.0 e          9 #> 2 1.5 f     10, 11 #> 3 3.0 g 12, 13, 14 #> 4 4.0 h       text with_tbl(tbl[[2, \"a\"]] <- 1.5) #> Error in `[[<-`: #> ! Assigned data `1.5` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert from <double> to <integer> due to loss of precision. #> • Locations: 1 with_df(df[[2, \"a\"]] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"a\"]] <- \"x\") #> Error in `[[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[[2, \"b\"]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, 1]] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, 1]] <- \"x\") #> Error in `[[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[[2, 2]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, \"cd\"]] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"cd\"]] <- \"x\") #> Error in `[[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_df(df[[2, \"cd\"]] <- list(\"x\")) #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"cd\"]] <- list(\"x\")) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <chr [1]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, \"c\"]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 2, \"c\", value = \"x\"): replacing element in non-existent column: c with_tbl(tbl[[2, \"c\"]] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> NA    #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> NA    #> 4     4 h     <chr [1]> NA with_df(df[[1:2, \"cd\"]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 1:2, \"cd\", value = \"x\"): only a single element should be replaced with_tbl(tbl[[1:2, \"cd\"]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. with_df(df[[1:2, \"c\"]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 1:2, \"c\", value = \"x\"): replacing element in non-existent column: c with_tbl(tbl[[1:2, \"c\"]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. with_df(df[[2, c(\"cd\", \"d\")]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 2, c(\"cd\", \"d\"), value = \"x\"): replacing element in non-existent column: d with_tbl(tbl[[2, c(\"cd\", \"d\")]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign column with `c(\"cd\", \"d\")`. #> ✖ Subscript `c(\"cd\", \"d\")` must be size 1, not 2."},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"scalars","dir":"Articles > Wip","previous_headings":"[<-","what":"Scalars","title":"Subassignment","text":"","code":"with_df(df[2, \"a\"] <- 1) #>   a b         cd #> 1 1 e          9 #> 2 1 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"a\"] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, \"a\"] <- 1.5) #>     a b         cd #> 1 1.0 e          9 #> 2 1.5 f     10, 11 #> 3 3.0 g 12, 13, 14 #> 4 4.0 h       text with_tbl(tbl[2, \"a\"] <- 1.5) #> Error in `[<-`: #> ! Assigned data `1.5` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert from <double> to <integer> due to loss of precision. #> • Locations: 1 with_df(df[2, \"a\"] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"a\"] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[2, \"b\"] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, \"cd\"] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"cd\"] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_df(df[2, \"cd\"] <- list(\"x\")) #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"cd\"] <- list(\"x\")) #> Error in `[<-`: #> ! Assigned data `list(\"x\")` #>   must be compatible with existing #>   data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_tbl(tbl[2, \"c\"] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> NA    #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> NA    #> 4     4 h     <chr [1]> NA with_df(df[2, 1] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, 1] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[2, 2] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, 3] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, 3] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_df(df[2, 4] <- \"x\") #>   a b         cd   V4 #> 1 1 e          9 <NA> #> 2 2 f     10, 11    x #> 3 3 g 12, 13, 14 <NA> #> 4 4 h       text <NA> with_tbl(tbl[2, 4] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        ...4  #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> NA    #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> NA    #> 4     4 h     <chr [1]> NA"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"full-length-columns","dir":"Articles > Wip","previous_headings":"[<-","what":"Full length columns","title":"Subassignment","text":"","code":"with_tbl(tbl[, \"a\"] <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, \"b\"] <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     1     4 <dbl [1]> #> 2     2     3 <int [2]> #> 3     3     2 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, \"c\"] <- 4:1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2     2 f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4     4 h     <chr [1]>     1 with_tbl(tbl[, \"cd\"] <- 4:1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <int> #> 1     1 e         4 #> 2     2 f         3 #> 3     3 g         2 #> 4     4 h         1 with_tbl(tbl[, 1] <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, 2] <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     1     4 <dbl [1]> #> 2     2     3 <int [2]> #> 3     3     2 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, 3] <- 4:1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <int> #> 1     1 e         4 #> 2     2 f         3 #> 3     3 g         2 #> 4     4 h         1 with_df(df[, 4] <- 4:1) #>   a b         cd V4 #> 1 1 e          9  4 #> 2 2 f     10, 11  3 #> 3 3 g 12, 13, 14  2 #> 4 4 h       text  1 with_tbl(tbl[, 4] <- 4:1) #> # A tibble: 4 × 4 #>       a b     cd         ...4 #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2     2 f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4     4 h     <chr [1]>     1 with_tbl(tbl[, \"a\"] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, \"b\"] <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     2     1 <int [2]> #> 3     3     1 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, \"c\"] <- 1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     1 #> 2     2 f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4     4 h     <chr [1]>     1 with_tbl(tbl[, \"cd\"] <- 1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     1 e         1 #> 2     2 f         1 #> 3     3 g         1 #> 4     4 h         1 with_tbl(tbl[, 1] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, 2] <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     2     1 <int [2]> #> 3     3     1 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, 3] <- 1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     1 e         1 #> 2     2 f         1 #> 3     3 g         1 #> 4     4 h         1 with_df(df[, 4] <- 1) #>   a b         cd V4 #> 1 1 e          9  1 #> 2 2 f     10, 11  1 #> 3 3 g 12, 13, 14  1 #> 4 4 h       text  1 with_tbl(tbl[, 4] <- 1) #> # A tibble: 4 × 4 #>       a b     cd         ...4 #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     1 #> 2     2 f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4     4 h     <chr [1]>     1"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"multiple-full-length-columns","dir":"Articles > Wip","previous_headings":"[<-","what":"Multiple full length columns","title":"Subassignment","text":"","code":"with_tbl(tbl[, c(\"a\", \"b\")] <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     4     4 <dbl [1]> #> 2     3     3 <int [2]> #> 3     2     2 <int [3]> #> 4     1     1 <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <dbl> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     1     1 <int [2]> #> 3     1     1 <int [3]> #> 4     1     1 <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(a = 4:1, b = letters[4:1])) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 d     <dbl [1]> #> 2     3 c     <int [2]> #> 3     2 b     <int [3]> #> 4     1 a     <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(b = 4:1, a = letters[4:1])) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 d     <dbl [1]> #> 2     3 c     <int [2]> #> 3     2 b     <int [3]> #> 4     1 a     <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(c = 4:1, d = letters[4:1])) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 d     <dbl [1]> #> 2     3 c     <int [2]> #> 3     2 b     <int [3]> #> 4     1 a     <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(a = 4:1)) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     4     4 <dbl [1]> #> 2     3     3 <int [2]> #> 3     2     2 <int [3]> #> 4     1     1 <chr [1]> with_df(df[, c(\"a\", \"b\")] <- data.frame(a = 4:1, b = letters[4:1], c = 1:4)) #> Warning in #> `[<-.data.frame`(`*tmp*`, , c(\"a\", #> \"b\"), value = structure(list(: #> provided 3 variables to replace 2 #> variables #>   a b         cd #> 1 4 d          9 #> 2 3 c     10, 11 #> 3 2 b 12, 13, 14 #> 4 1 a       text with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(a = 4:1, b = letters[4:1], c = 1:4)) #> Error in `[<-`: #> ! Can't recycle input of size 3 to size 2. with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(4:1, 1)) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     4     1 <dbl [1]> #> 2     3     1 <int [2]> #> 3     2     1 <int [3]> #> 4     1     1 <chr [1]> with_df(df[, c(\"a\", \"b\", \"c\")] <- data.frame(4:1, letters[4:1])) #>   a b         cd c #> 1 4 d          9 4 #> 2 3 c     10, 11 3 #> 3 2 b 12, 13, 14 2 #> 4 1 a       text 1 with_tbl(tbl[, c(\"a\", \"b\", \"c\")] <- data.frame(4:1, letters[4:1])) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_df(df[, c(\"a\", \"b\", \"cd\")] <- data.frame(4:1, letters[4:1])) #>   a b cd #> 1 4 d  4 #> 2 3 c  3 #> 3 2 b  2 #> 4 1 a  1 with_tbl(tbl[, c(\"a\", \"b\", \"cd\")] <- data.frame(4:1, letters[4:1])) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"full-length-rows","dir":"Articles > Wip","previous_headings":"[<-","what":"Full length rows","title":"Subassignment","text":"","code":"with_df(df[2, ] <- 1) #>   a b         cd #> 1 1 e          9 #> 2 1 1          1 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- 1) #> Error in `[<-`: #> ! Assigned data `1` must be #>   compatible with existing data. #> ℹ Error occurred for column `b`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double> to <character>. with_df(df[2, ] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x x          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_df(df[2, ] <- tibble(a = 1, b = \"x\")) #>   a b         cd #> 1 1 e          9 #> 2 1 x          1 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- tibble(a = 1, b = \"x\")) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_df(df[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b         cd #> 1 1 e          9 #> 2 1 x          y #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 x     <chr [1]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2, , #> value = structure(list(a = 1, : #> provided 4 variables to replace 3 #> variables #>   a b         cd #> 1 1 e          9 #> 2 1 x          y #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_df(df[0, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b         cd #> 1 1 e          9 #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[0, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> Error in `[<-`: #> ! Can't assign rows with `0`. #> ✖ Subscript `0` can't contain `0` values. #> ℹ It has a `0` value at location 1. with_df(df[5, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b         cd #> 1 1 e          9 #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text #> 5 1 x          y with_tbl(tbl[5, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> # A tibble: 5 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> #> 5     1 x     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"multiple-full-length-rows","dir":"Articles > Wip","previous_headings":"[<-","what":"Multiple full length rows","title":"Subassignment","text":"","code":"with_df(df[2:3, ] <- 1) #>   a b   cd #> 1 1 e    9 #> 2 1 1    1 #> 3 1 1    1 #> 4 4 h text with_tbl(tbl[2:3, ] <- 1) #> Error in `[<-`: #> ! Assigned data `1` must be #>   compatible with existing data. #> ℹ Error occurred for column `b`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double> to <character>. with_df(df[2:3, ] <- 1:2) #>   a b   cd #> 1 1 e    9 #> 2 1 1    1 #> 3 2 2    2 #> 4 4 h text with_tbl(tbl[2:3, ] <- 1:2) #> Error in `[<-`: #> ! Assigned data `1:2` must #>   be compatible with existing data. #> ℹ Error occurred for column `b`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <integer> to <character>. with_df(df[2:3, ] <- c(\"x\", \"y\")) #>   a b   cd #> 1 1 e    9 #> 2 x x    x #> 3 y y    y #> 4 4 h text with_tbl(tbl[2:3, ] <- c(\"x\", \"y\")) #> Error in `[<-`: #> ! Assigned data `c(\"x\", #>   \"y\")` must be compatible with #>   existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_df(df[2:3, ] <- tibble(a = 1:2, b = c(\"x\", \"y\"))) #>   a b   cd #> 1 1 e    9 #> 2 1 x    1 #> 3 2 y    2 #> 4 4 h text with_tbl(tbl[2:3, ] <- tibble(a = 1:2, b = c(\"x\", \"y\"))) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_df(df[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b   cd #> 1 1 e    9 #> 2 1 x    y #> 3 1 x    y #> 4 4 h text with_tbl(tbl[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 x     <chr [1]> #> 3     1 x     <chr [1]> #> 4     4 h     <chr [1]> with_tbl(tbl[2:3, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 x     <chr [1]> #> 3     2 x     <chr [1]> #> 4     4 h     <chr [1]> with_df(df[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2:3, , #> value = structure(list(a = 1, : #> provided 4 variables to replace 3 #> variables #>   a b   cd #> 1 1 e    9 #> 2 1 x    y #> 3 1 x    y #> 4 4 h text with_tbl(tbl[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_tbl(tbl[-(1:2), ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     1 x     <chr [1]> #> 4     2 x     <chr [1]> with_df(df[0:1, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> Warning in #> `[<-.data.frame`(`*tmp*`, 0:1, , #> value = structure(list(a = 1:2, : #> replacement element 1 has 2 rows to #> replace 1 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 0:1, , #> value = structure(list(a = 1:2, : #> replacement element 2 has 2 rows to #> replace 1 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 0:1, , #> value = structure(list(a = 1:2, : #> replacement element 3 has 2 rows to #> replace 1 rows #>   a b         cd #> 1 1 x          y #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[0:1, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> Error in `[<-`: #> ! Can't assign rows with `0:1`. #> ✖ Subscript `0:1` can't contain `0` values. #> ℹ It has a `0` value at location 1. with_tbl(tbl[4:5, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> # A tibble: 5 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     1 x     <chr [1]> #> 5     2 x     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"unspecified","dir":"Articles > Wip","previous_headings":"[<-","what":"Unspecified","title":"Subassignment","text":"","code":"with_tbl(tbl[] <- 1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <dbl> <dbl> <dbl> #> 1     1     1     1 #> 2     1     1     1 #> 3     1     1     1 #> 4     1     1     1 with_tbl(tbl[] <- 4:1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <int> <int> <int> #> 1     4     4     4 #> 2     3     3     3 #> 3     2     2     2 #> 4     1     1     1 with_df(df[] <- 3:1) #>   a b cd #> 1 3 2  1 #> 2 2 1  3 #> 3 1 3  2 #> 4 3 2  1 with_tbl(tbl[] <- 3:1) #> Error in `[<-`: #> ! Assigned data `3:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 3 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 3 to size 4. with_df(df[] <- 5:1) #> Error in `[<-.data.frame`(`*tmp*`, , value = 5:1): replacement has 5 items, need 12 with_tbl(tbl[] <- 5:1) #> Error in `[<-`: #> ! Assigned data `5:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 5 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 5 to size 4. with_df(df[] <- data.frame(1, \"x\")) #>   a b cd #> 1 1 x  1 #> 2 1 x  1 #> 3 1 x  1 #> 4 1 x  1 with_tbl(tbl[] <- data.frame(1, \"x\")) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_tbl(tbl[] <- data.frame(4:1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     4 x         2 #> 2     3 x         2 #> 3     2 x         2 #> 4     1 x         2 with_tbl(tbl[] <- data.frame(1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <dbl> <chr> <dbl> #> 1     1 x         2 #> 2     1 x         2 #> 3     1 x         2 #> 4     1 x         2 with_df(df[] <- data.frame(1, \"x\", 2, 3)) #> Warning in #> `[<-.data.frame`(`*tmp*`, , value = #> structure(list(X1 = 1, X.x. = \"x\", #> : provided 4 variables to replace 3 #> variables #>   a b cd #> 1 1 x  2 #> 2 1 x  2 #> 3 1 x  2 #> 4 1 x  2 with_tbl(tbl[] <- data.frame(1, \"x\", 2, 3)) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_tbl(tbl[] <- tbl) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_tbl(tbl[,] <- 1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <dbl> <dbl> <dbl> #> 1     1     1     1 #> 2     1     1     1 #> 3     1     1     1 #> 4     1     1     1 with_tbl(tbl[,] <- 4:1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <int> <int> <int> #> 1     4     4     4 #> 2     3     3     3 #> 3     2     2     2 #> 4     1     1     1 with_df(df[,] <- 3:1) #>   a b cd #> 1 3 2  1 #> 2 2 1  3 #> 3 1 3  2 #> 4 3 2  1 with_tbl(tbl[,] <- 3:1) #> Error in `[<-`: #> ! Assigned data `3:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 3 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 3 to size 4. with_df(df[,] <- 5:1) #> Error in `[<-.data.frame`(`*tmp*`, , , value = 5:1): replacement has 5 items, need 12 with_tbl(tbl[,] <- 5:1) #> Error in `[<-`: #> ! Assigned data `5:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 5 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 5 to size 4. with_df(df[,] <- data.frame(1, \"x\")) #>   a b cd #> 1 1 x  1 #> 2 1 x  1 #> 3 1 x  1 #> 4 1 x  1 with_tbl(tbl[,] <- data.frame(1, \"x\")) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_tbl(tbl[,] <- data.frame(4:1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     4 x         2 #> 2     3 x         2 #> 3     2 x         2 #> 4     1 x         2 with_tbl(tbl[,] <- data.frame(1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <dbl> <chr> <dbl> #> 1     1 x         2 #> 2     1 x         2 #> 3     1 x         2 #> 4     1 x         2 with_df(df[,] <- data.frame(1, \"x\", 2, 3)) #> Warning in #> `[<-.data.frame`(`*tmp*`, , , value #> = structure(list(X1 = 1, : provided #> 4 variables to replace 3 variables #>   a b cd #> 1 1 x  2 #> 2 1 x  2 #> 3 1 x  2 #> 4 1 x  2 with_tbl(tbl[,] <- data.frame(1, \"x\", 2, 3)) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_tbl(tbl[,] <- tbl) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"subset-assignment-1","dir":"Articles > Wip","previous_headings":"[<-","what":"Subset assignment","title":"Subassignment","text":"Due tibble’s default drop = FALSE, updating portion [ subset still safe, tibble still control. one example given .","code":"with_df(df[\"a\"][1, ] <- \"b\") #>   a b         cd #> 1 b e          9 #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[\"a\"][1, ] <- \"b\") #> Error in `[<-`: #> ! Assigned data `\"b\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subset.html","id":"section","dir":"Articles > Wip","previous_headings":"","what":"$","title":"Subsetting","text":"$ subsetting, accessing missing column gives warning. Inexact matching supported:","code":"tbl$a #> [1] 1 2 3 4 tbl$\"a\" #> [1] 1 2 3 4 tbl$a[2:3] #> [1] 2 3 tbl$cd #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" df$c #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl$c #> Warning: Unknown or uninitialised #> column: `c`. #> NULL df$d #> NULL tbl$d #> Warning: Unknown or uninitialised #> column: `d`. #> NULL"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subset.html","id":"section-1","dir":"Articles > Wip","previous_headings":"","what":"[[","title":"Subsetting","text":"exact argument supported tibbles. two indexes, single element returned. List columns unpacked tibbles, [[ unpacks columns. Exotic variants like recursive indexing deprecated tibbles.","code":"tbl[[\"a\"]] #> [1] 1 2 3 4 tbl[[\"cd\", exact = TRUE]] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" df[[\"cd\", exact = FALSE]] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[[\"cd\", exact = FALSE]] #> Warning: `exact` ignored. #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[[\"c\", exact = TRUE]] #> NULL df[[\"c\", exact = FALSE]] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[[\"c\", exact = FALSE]] #> Warning: `exact` ignored. #> NULL tbl[[2, \"a\"]] #> [1] 2 df[[2, \"cd\"]] #> [1] 10 11 tbl[[2, \"cd\"]] #> [[1]] #> [1] 10 11 df[[1:2, \"cd\"]] #> Error in col[[i, exact = exact]]: subscript out of bounds tbl[[1:2, \"cd\"]] #> Error in `tbl[[1:2, \"cd\"]]`: #> ! Can't extract row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. tbl[[2, \"c\"]] #> NULL df[[1:2, \"c\"]] #> NULL tbl[[1:2, \"c\"]] #> Error in `tbl[[1:2, \"c\"]]`: #> ! Can't extract row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. df[[c(1, 2)]] #> [1] 2 tbl[[c(1, 2)]] #> Error: #> ! The `j` argument of #>   `[[.tbl_df()` can't be a vector #>   of length 2 as of tibble 3.0.0. #> ℹ Recursive subsetting is #>   deprecated for tibbles."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subset.html","id":"section-2","dir":"Articles > Wip","previous_headings":"","what":"[","title":"Subsetting","text":"[ subsetting, tibbles always return tibble. drop argument supported different defaults: examples repeated two-dimensional indexing omitting row index: Multiple columns can queried passing vector column indexes (names, positions, even logical vector). latter option, tibbles tad stricter: examples repeated two-dimensional indexing omitting row index: Row subsetting integer indexes works almost identical. --bounds subsetting recommended may lead error future versions. Another special case subsetting [1, , drop = TRUE] data frame implementation returns list. Row subsetting logical indexes also works almost identical, index vector must length one number rows tibbles. Indexing row column works less , except drop: Indexes can omitted altogether, differences :","code":"tbl[\"a\"] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 df[\"a\", drop = FALSE] #> Warning in `[.data.frame`(df, \"a\", #> drop = FALSE): 'drop' argument will #> be ignored #>   a #> 1 1 #> 2 2 #> 3 3 #> 4 4 tbl[\"a\", drop = FALSE] #> Warning: `drop` argument ignored #> for subsetting a tibble with #> `x[j]`, it has an effect only for #> `x[i, j]`. #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 df[\"a\", drop = TRUE] #> Warning in `[.data.frame`(df, \"a\", #> drop = TRUE): 'drop' argument will #> be ignored #>   a #> 1 1 #> 2 2 #> 3 3 #> 4 4 tbl[\"a\", drop = TRUE] #> Warning: `drop` argument ignored #> for subsetting a tibble with #> `x[j]`, it has an effect only for #> `x[i, j]`. #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[1] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[0] #> # A tibble: 4 × 0 df[4] #> Error in `[.data.frame`(df, 4): undefined columns selected tbl[4] #> Error in `tbl[4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. df[NA] #> Error in `[.data.frame`(df, NA): undefined columns selected tbl[NA] #> Error in `tbl[NA]`: #> ! Can't subset columns with `NA`. #> ✖ Subscript `NA` can't contain missing values. #> ✖ It has a missing value at location 1. df[NA_character_] #> Error in `[.data.frame`(df, NA_character_): undefined columns selected tbl[NA_character_] #> Error in `tbl[NA_character_]`: #> ! Can't subset columns with `NA_character_`. #> ✖ Subscript `NA_character_` can't contain missing values. #> ✖ It has a missing value at location 1. df[NA_integer_] #> Error in `[.data.frame`(df, NA_integer_): undefined columns selected tbl[NA_integer_] #> Error in `tbl[NA_integer_]`: #> ! Can't subset columns with `NA_integer_`. #> ✖ Subscript `NA_integer_` can't contain missing values. #> ✖ It has a missing value at location 1. df[, \"a\"] #> [1] 1 2 3 4 tbl[, \"a\"] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[, \"a\", drop = FALSE] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[, \"a\", drop = TRUE] #> [1] 1 2 3 4 df[, 1] #> [1] 1 2 3 4 tbl[, 1] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[, 0] #> # A tibble: 4 × 0 df[, 4] #> Error in `[.data.frame`(df, , 4): undefined columns selected tbl[, 4] #> Error in `tbl[, 4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. df[, NA] #> Error in `[.data.frame`(df, , NA): undefined columns selected tbl[, NA] #> Error in `tbl[, NA]`: #> ! Can't subset columns with `NA`. #> ✖ Subscript `NA` can't contain missing values. #> ✖ It has a missing value at location 1. df[, NA_character_] #> Error in `[.data.frame`(df, , NA_character_): undefined columns selected tbl[, NA_character_] #> Error in `tbl[, NA_character_]`: #> ! Can't subset columns with `NA_character_`. #> ✖ Subscript `NA_character_` can't contain missing values. #> ✖ It has a missing value at location 1. df[, NA_integer_] #> Error in `[.data.frame`(df, , NA_integer_): undefined columns selected tbl[, NA_integer_] #> Error in `tbl[, NA_integer_]`: #> ! Can't subset columns with `NA_integer_`. #> ✖ Subscript `NA_integer_` can't contain missing values. #> ✖ It has a missing value at location 1. tbl[c(\"a\", \"b\")] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[character()] #> # A tibble: 4 × 0 tbl[1:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[1:3] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> df[1:4] #> Error in `[.data.frame`(df, 1:4): undefined columns selected tbl[1:4] #> Error in `tbl[1:4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. tbl[0:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h df[-1:2] #> Error in `[.default`(df, -1:2): only 0's may be mixed with negative subscripts tbl[-1:2] #> Error in `tbl[-1:2]`: #> ! Can't subset columns with `-1:2`. #> ✖ Negative and positive locations can't be mixed. #> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4. tbl[-1] #> # A tibble: 4 × 2 #>   b     cd        #>   <chr> <list>    #> 1 e     <dbl [1]> #> 2 f     <int [2]> #> 3 g     <int [3]> #> 4 h     <chr [1]> tbl[-(1:2)] #> # A tibble: 4 × 1 #>   cd        #>   <list>    #> 1 <dbl [1]> #> 2 <int [2]> #> 3 <int [3]> #> 4 <chr [1]> tbl[integer()] #> # A tibble: 4 × 0 tbl[TRUE] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[FALSE] #> # A tibble: 4 × 0 tbl[c(TRUE, TRUE, FALSE)] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[c(FALSE, TRUE, FALSE)] #> # A tibble: 4 × 1 #>   b     #>   <chr> #> 1 e     #> 2 f     #> 3 g     #> 4 h df[c(FALSE, TRUE)] #>   b #> 1 e #> 2 f #> 3 g #> 4 h tbl[c(FALSE, TRUE)] #> Error in `tbl[c(FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE)` must be size 1 or 3, not 2. df[c(FALSE, TRUE, FALSE, TRUE)] #> Error in `[.data.frame`(df, c(FALSE, TRUE, FALSE, TRUE)): undefined columns selected tbl[c(FALSE, TRUE, FALSE, TRUE)] #> Error in `tbl[c(FALSE, TRUE, FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE, FALSE, TRUE)` must be size 1 or 3, not 4. tbl[, c(\"a\", \"b\")] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[, character()] #> # A tibble: 4 × 0 tbl[, 1:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[, 1:3] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> df[, 1:4] #> Error in `[.data.frame`(df, , 1:4): undefined columns selected tbl[, 1:4] #> Error in `tbl[, 1:4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. tbl[, 0:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h df[, -1:2] #> Error in .subset(x, j): only 0's may be mixed with negative subscripts tbl[, -1:2] #> Error in `tbl[, -1:2]`: #> ! Can't subset columns with `-1:2`. #> ✖ Negative and positive locations can't be mixed. #> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4. tbl[, -1] #> # A tibble: 4 × 2 #>   b     cd        #>   <chr> <list>    #> 1 e     <dbl [1]> #> 2 f     <int [2]> #> 3 g     <int [3]> #> 4 h     <chr [1]> df[, -(1:2)] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[, -(1:2)] #> # A tibble: 4 × 1 #>   cd        #>   <list>    #> 1 <dbl [1]> #> 2 <int [2]> #> 3 <int [3]> #> 4 <chr [1]> tbl[, integer()] #> # A tibble: 4 × 0 tbl[, TRUE] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[, FALSE] #> # A tibble: 4 × 0 tbl[, c(TRUE, TRUE, FALSE)] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h df[, c(FALSE, TRUE, FALSE)] #> [1] \"e\" \"f\" \"g\" \"h\" tbl[, c(FALSE, TRUE, FALSE)] #> # A tibble: 4 × 1 #>   b     #>   <chr> #> 1 e     #> 2 f     #> 3 g     #> 4 h df[, c(FALSE, TRUE)] #> [1] \"e\" \"f\" \"g\" \"h\" tbl[, c(FALSE, TRUE)] #> Error in `tbl[, c(FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE)` must be size 1 or 3, not 2. df[, c(FALSE, TRUE, FALSE, TRUE)] #> Error in `[.data.frame`(df, , c(FALSE, TRUE, FALSE, TRUE)): undefined columns selected tbl[, c(FALSE, TRUE, FALSE, TRUE)] #> Error in `tbl[, c(FALSE, TRUE, FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE, FALSE, TRUE)` must be size 1 or 3, not 4. tbl[1, ] #> # A tibble: 1 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> df[1, , drop = TRUE] #> $a #> [1] 1 #>  #> $b #> [1] \"e\" #>  #> $cd #> $cd[[1]] #> [1] 9 tbl[1, , drop = TRUE] #> # A tibble: 1 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> tbl[1:2, ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> tbl[0, ] #> # A tibble: 0 × 3 #> # ℹ 3 variables: a <int>, b <chr>, #> #   cd <list> tbl[integer(), ] #> # A tibble: 0 × 3 #> # ℹ 3 variables: a <int>, b <chr>, #> #   cd <list> tbl[5, ] #> # A tibble: 1 × 3 #>       a b     cd     #>   <int> <chr> <list> #> 1    NA NA    <NULL> tbl[4:5, ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 h     <chr [1]> #> 2    NA NA    <NULL> tbl[-1, ] #> # A tibble: 3 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     2 f     <int [2]> #> 2     3 g     <int [3]> #> 3     4 h     <chr [1]> df[-1:2, ] #> Error in xj[i]: only 0's may be mixed with negative subscripts tbl[-1:2, ] #> Error in `tbl[-1:2, ]`: #> ! Can't subset rows with `-1:2`. #> ✖ Negative and positive locations can't be mixed. #> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4. tbl[NA, ] #> # A tibble: 4 × 3 #>       a b     cd     #>   <int> <chr> <list> #> 1    NA NA    <NULL> #> 2    NA NA    <NULL> #> 3    NA NA    <NULL> #> 4    NA NA    <NULL> tbl[NA_integer_, ] #> # A tibble: 1 × 3 #>       a b     cd     #>   <int> <chr> <list> #> 1    NA NA    <NULL> tbl[c(NA, 1), ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1    NA NA    <NULL>    #> 2     1 e     <dbl [1]> tbl[TRUE, ] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[FALSE, ] #> # A tibble: 0 × 3 #> # ℹ 3 variables: a <int>, b <chr>, #> #   cd <list> df[c(TRUE, FALSE), ] #>   a b         cd #> 1 1 e          9 #> 3 3 g 12, 13, 14 tbl[c(TRUE, FALSE), ] #> Error in `tbl[c(TRUE, FALSE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE)`. #> ✖ Logical subscript `c(TRUE, FALSE)` must be size 1 or 4, not 2. df[c(TRUE, FALSE, TRUE), ] #>   a b         cd #> 1 1 e          9 #> 3 3 g 12, 13, 14 #> 4 4 h       text tbl[c(TRUE, FALSE, TRUE), ] #> Error in `tbl[c(TRUE, FALSE, TRUE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(TRUE, FALSE, TRUE)` must be size 1 or 4, not 3. tbl[c(TRUE, FALSE, TRUE, FALSE), ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     3 g     <int [3]> df[c(TRUE, FALSE, TRUE, FALSE, TRUE), ] #>     a    b         cd #> 1   1    e          9 #> 3   3    g 12, 13, 14 #> NA NA <NA>       NULL tbl[c(TRUE, FALSE, TRUE, FALSE, TRUE), ] #> Error in `tbl[c(TRUE, FALSE, TRUE, FALSE, #>     TRUE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE, TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(TRUE, FALSE, TRUE, FALSE, TRUE)` must be size 1 or 4, not 5. df[1, \"a\"] #> [1] 1 tbl[1, \"a\"] #> # A tibble: 1 × 1 #>       a #>   <int> #> 1     1 tbl[1, \"a\", drop = FALSE] #> # A tibble: 1 × 1 #>       a #>   <int> #> 1     1 tbl[1, \"a\", drop = TRUE] #> [1] 1 df[1:2, \"a\"] #> [1] 1 2 tbl[1:2, \"a\"] #> # A tibble: 2 × 1 #>       a #>   <int> #> 1     1 #> 2     2 tbl[1:2, \"a\", drop = FALSE] #> # A tibble: 2 × 1 #>       a #>   <int> #> 1     1 #> 2     2 tbl[1:2, \"a\", drop = TRUE] #> [1] 1 2 tbl[1, c(\"a\", \"b\")] #> # A tibble: 1 × 2 #>       a b     #>   <int> <chr> #> 1     1 e tbl[1, c(\"a\", \"b\"), drop = FALSE] #> # A tibble: 1 × 2 #>       a b     #>   <int> <chr> #> 1     1 e df[1, c(\"a\", \"b\"), drop = TRUE] #> $a #> [1] 1 #>  #> $b #> [1] \"e\" tbl[1, c(\"a\", \"b\"), drop = TRUE] #> # A tibble: 1 × 2 #>       a b     #>   <int> <chr> #> 1     1 e tbl[1:2, c(\"a\", \"b\")] #> # A tibble: 2 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f tbl[] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[,] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kirill Müller. Author, maintainer. Hadley Wickham. Author. Romain Francois. Contributor. Jennifer Bryan. Contributor. . Copyright holder, funder.","code":""},{"path":"https://tibble.tidyverse.org/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Müller K, Wickham H (2024). tibble: Simple Data Frames. R package version 3.2.1.9033, https://github.com/tidyverse/tibble, https://tibble.tidyverse.org/.","code":"@Manual{,   title = {tibble: Simple Data Frames},   author = {Kirill Müller and Hadley Wickham},   year = {2024},   note = {R package version 3.2.1.9033,     https://github.com/tidyverse/tibble},   url = {https://tibble.tidyverse.org/}, }"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Simple Data Frames","text":"tibble, tbl_df, modern reimagining data.frame, keeping time proven effective, throwing . Tibbles data.frames lazy surly: less (.e. don’t change variable names types, don’t partial matching) complain (e.g. variable exist). forces confront problems earlier, typically leading cleaner, expressive code. Tibbles also enhanced print() method makes easier use large datasets containing complex objects. new tibbles, best place start tibbles chapter R data science.","code":""},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Simple Data Frames","text":"","code":"# The easiest way to get tibble is to install the whole tidyverse: install.packages(\"tidyverse\")  # Alternatively, install just tibble: install.packages(\"tibble\")  # Or the the development version from GitHub: # install.packages(\"devtools\") devtools::install_github(\"tidyverse/tibble\")"},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Simple Data Frames","text":"Create tibble existing object as_tibble(): work reasonable inputs already data.frames, lists, matrices, tables. can also create new tibble column vectors tibble(): tibble() much less data.frame(): never changes type inputs (e.g. keeps list columns ), never changes names variables, recycles inputs length 1, never creates row.names(). can read features vignette(\"tibble\"). can define tibble row--row tribble():","code":"library(tibble) data <- data.frame(a = 1:3, b = letters[1:3], c = Sys.Date() - 1:3) data #>   a b          c #> 1 1 a 2023-10-07 #> 2 2 b 2023-10-06 #> 3 3 c 2023-10-05  as_tibble(data) #> # A tibble: 3 × 3 #>       a b     c          #>   <int> <chr> <date>     #> 1     1 a     2023-10-07 #> 2     2 b     2023-10-06 #> 3     3 c     2023-10-05 tibble(x = 1:5, y = 1, z = x^2 + y) #> # A tibble: 5 × 3 #>       x     y     z #>   <int> <dbl> <dbl> #> 1     1     1     2 #> 2     2     1     5 #> 3     3     1    10 #> 4     4     1    17 #> 5     5     1    26 tribble(   ~x, ~y,  ~z,   \"a\", 2,  3.6,   \"b\", 1,  8.5 ) #> # A tibble: 2 × 3 #>   x         y     z #>   <chr> <dbl> <dbl> #> 1 a         2   3.6 #> 2 b         1   8.5"},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"related-work","dir":"","previous_headings":"","what":"Related work","title":"Simple Data Frames","text":"tibble print method draws inspiration data.table, frame. Like data.table::data.table(), tibble() doesn’t change column names doesn’t use rownames.","code":""},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Simple Data Frames","text":"Please note tibble project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Add columns to a data frame — add_column","title":"Add columns to a data frame — add_column","text":"convenient way add one columns existing data frame.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add columns to a data frame — add_column","text":"","code":"add_column(   .data,   ...,   .before = NULL,   .after = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )"},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add columns to a data frame — add_column","text":".data Data frame append . ... <dynamic-dots> Name-value pairs, passed tibble(). values must size .data size 1. ., .One-based column index column name add new columns, default: last column. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add columns to a data frame — add_column","text":"","code":"# add_column --------------------------------- df <- tibble(x = 1:3, y = 3:1)  df %>% add_column(z = -1:1, w = 0) #> # A tibble: 3 × 4 #>       x     y     z     w #>   <int> <int> <int> <dbl> #> 1     1     3    -1     0 #> 2     2     2     0     0 #> 3     3     1     1     0 df %>% add_column(z = -1:1, .before = \"y\") #> # A tibble: 3 × 3 #>       x     z     y #>   <int> <int> <int> #> 1     1    -1     3 #> 2     2     0     2 #> 3     3     1     1  # You can't overwrite existing columns try(df %>% add_column(x = 4:6)) #> Error in add_column(., x = 4:6) :  #>   Column name `x` must not be duplicated. #> Use `.name_repair` to specify repair. #> Caused by error in `repaired_names()` at tibble/R/names.R:14:3: #> ! Names must be unique. #> ✖ These names are duplicated: #>   * \"x\" at locations 1 and 3.  # You can't create new observations try(df %>% add_column(z = 1:5)) #> Error in add_column(., z = 1:5) :  #>   New columns must be compatible with `.data`. #> ✖ New column has 5 rows. #> ℹ `.data` has 3 rows."},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":null,"dir":"Reference","previous_headings":"","what":"Add rows to a data frame — add_row","title":"Add rows to a data frame — add_row","text":"convenient way add one rows data existing data frame. See tribble() easy way create complete data frame row--row. Use tibble_row() ensure new data one row. add_case() alias add_row().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add rows to a data frame — add_row","text":"","code":"add_row(.data, ..., .before = NULL, .after = NULL)"},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add rows to a data frame — add_row","text":".data Data frame append . ... <dynamic-dots> Name-value pairs, passed tibble(). Values can defined columns already exist .data unset columns get NA value. ., .One-based row index add new rows, default: last row.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add rows to a data frame — add_row","text":"","code":"# add_row --------------------------------- df <- tibble(x = 1:3, y = 3:1)  df %>% add_row(x = 4, y = 0) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4     0  # You can specify where to add the new rows df %>% add_row(x = 4, y = 0, .before = 2) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3 #> 2     4     0 #> 3     2     2 #> 4     3     1  # You can supply vectors, to add multiple rows (this isn't # recommended because it's a bit hard to read) df %>% add_row(x = 4:5, y = 0:-1) #> # A tibble: 5 × 2 #>       x     y #>   <int> <int> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4     0 #> 5     5    -1  # Use tibble_row() to add one row only df %>% add_row(tibble_row(x = 4, y = 0)) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4     0 try(df %>% add_row(tibble_row(x = 4:5, y = 0:-1))) #> Error in tibble_quos(xs, .rows = 1, .name_repair = .name_repair, single_row = TRUE) :  #>   All vectors must be size one, use `list()` to wrap. #> ✖ Column `x` is of size 2.  # Absent variables get missing values df %>% add_row(x = 4) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <int> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4    NA  # You can't create new variables try(df %>% add_row(z = 10)) #> Error in add_row(., z = 10) : New rows can't add columns. #> ✖ Can't find column `z` in `.data`."},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce lists, matrices, and more to data frames — as_tibble","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"as_tibble() turns existing object, data frame matrix, -called tibble, data frame class tbl_df. contrast tibble(), builds tibble individual columns. as_tibble() tibble() base::.data.frame() base::data.frame(). as_tibble() S3 generic, methods : data.frame: Thin wrapper around list method implements tibble's treatment rownames. matrix, poly, ts, table Default: inputs first coerced base::.data.frame(). as_tibble_row() converts vector tibble one row. input list, elements must size one. as_tibble_col() converts vector tibble one column.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"","code":"as_tibble(   x,   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\"),   rownames = pkgconfig::get_config(\"tibble::rownames\", NULL) )  # S3 method for class 'data.frame' as_tibble(   x,   validate = NULL,   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\"),   rownames = pkgconfig::get_config(\"tibble::rownames\", NULL) )  # S3 method for class 'list' as_tibble(   x,   validate = NULL,   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )  # S3 method for class 'matrix' as_tibble(x, ..., validate = NULL, .name_repair = NULL)  # S3 method for class 'table' as_tibble(x, `_n` = \"n\", ..., n = `_n`, .name_repair = \"check_unique\")  # S3 method for class '`NULL`' as_tibble(x, ...)  # Default S3 method as_tibble(x, ...)  as_tibble_row(   x,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )  as_tibble_col(x, column_name = \"value\")"},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"x data frame, list, matrix, object reasonably coerced tibble. ... Unused, extensibility. .rows number rows, useful create 0-column tibble just additional check. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce . rownames treat existing row names data frame matrix: NULL: remove row names. default. NA: keep row names. string: name new column. Existing rownames transferred column row.names attribute deleted. name repair applied new column name, even x already contains column name. Use as_tibble(rownames_to_column(...)) safeguard case. Read rownames. _n, validate  compatibility , use new code. n Name count column, default: \"n\". column_name Name column.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"row-names","dir":"Reference","previous_headings":"","what":"Row names","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"default behavior silently remove row names. New code explicitly convert row names new column using rownames argument. existing code relies retention row names, call pkgconfig::set_config(\"tibble::rownames\" = NA) script package's .onLoad()  function.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"life-cycle","dir":"Reference","previous_headings":"","what":"Life cycle","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"Using as_tibble() vectors superseded version 3.0.0, prefer expressive as_tibble_row() as_tibble_col() variants new code.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"","code":"m <- matrix(rnorm(50), ncol = 5) colnames(m) <- c(\"a\", \"b\", \"c\", \"d\", \"e\") df <- as_tibble(m)  as_tibble_row(c(a = 1, b = 2)) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     1     2 as_tibble_row(list(c = \"three\", d = list(4:5))) #> # A tibble: 1 × 2 #>   c     d         #>   <chr> <list>    #> 1 three <int [2]> as_tibble_row(1:3, .name_repair = \"unique\") #> New names: #> • `` -> `...1` #> • `` -> `...2` #> • `` -> `...3` #> # A tibble: 1 × 3 #>    ...1  ...2  ...3 #>   <int> <int> <int> #> 1     1     2     3  as_tibble_col(1:3) #> # A tibble: 3 × 1 #>   value #>   <int> #> 1     1 #> 2     2 #> 3     3 as_tibble_col(   list(c = \"three\", d = list(4:5)),   column_name = \"data\" ) #> # A tibble: 2 × 1 #>   data         #>   <named list> #> 1 <chr [1]>    #> 2 <list [1]>"},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":null,"dir":"Reference","previous_headings":"","what":"Format a character vector — char","title":"Format a character vector — char","text":"Constructs character vector can formatted predefined minimum width without width restrictions, abbreviation style can configured. formatting applied vector printed formatted, also tibble column. set_char_opts() adds formatting options arbitrary character vector, useful composing types.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format a character vector — char","text":"","code":"char(   x,   ...,   min_chars = NULL,   shorten = c(\"back\", \"front\", \"mid\", \"abbreviate\") )  set_char_opts(   x,   ...,   min_chars = NULL,   shorten = c(\"back\", \"front\", \"mid\", \"abbreviate\") )"},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format a character vector — char","text":"x character vector. ... dots future extensions must empty. min_chars minimum width allocate column, defaults 15. \"pillar.min_chars\" option consulted. shorten abbreviate data necessary: \"back\" (default): add ellipsis end \"front\": add ellipsis front \"mid\": add ellipsis middle \"abbreviate\": use abbreviate()","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format a character vector — char","text":"","code":"# Display as a vector: char(letters[1:3]) #> <pillar_char[3]> #> [1] a b c # Space constraints: rand_strings <- stringi::stri_rand_strings(10, seq(40, 22, by = -2))  # Plain character vectors get truncated if space is limited: data_with_id <- function(id) {   tibble(     id,     some_number_1 = 1, some_number_2 = 2, some_number_3 = 3,     some_number_4 = 4, some_number_5 = 5, some_number_6 = 6,     some_number_7 = 7, some_number_8 = 8, some_number_9 = 9   ) } data_with_id(rand_strings) #> # A tibble: 10 × 10 #>    id              some_number_1 some_number_2 some_number_3 some_number_4 #>    <chr>                   <dbl>         <dbl>         <dbl>         <dbl> #>  1 WJSsxf5RPiDjFR…             1             2             3             4 #>  2 HQOlqjZavreUgr…             1             2             3             4 #>  3 GR8hYmPYrwJG9U…             1             2             3             4 #>  4 dO0k5CLEp9Mcps…             1             2             3             4 #>  5 3yKhjZZbegjEYO…             1             2             3             4 #>  6 uyWa9TGFPzGkVA…             1             2             3             4 #>  7 UaiZMcOCjm7Lh2…             1             2             3             4 #>  8 z2qYwlo7fldemm…             1             2             3             4 #>  9 ek4O7hFxIud6K9…             1             2             3             4 #> 10 YCRHBt1kj2i9Jn…             1             2             3             4 #> # ℹ 5 more variables: some_number_5 <dbl>, some_number_6 <dbl>, #> #   some_number_7 <dbl>, some_number_8 <dbl>, some_number_9 <dbl>  # Use char() to avoid or control truncation data_with_id(char(rand_strings, min_chars = 24)) #> # A tibble: 10 × 10 #>    id                            some_number_1 some_number_2 some_number_3 #>    <char>                                <dbl>         <dbl>         <dbl> #>  1 WJSsxf5RPiDjFRy7iVoadHgnz4uv…             1             2             3 #>  2 HQOlqjZavreUgr3GXLoDX6mpkxih…             1             2             3 #>  3 GR8hYmPYrwJG9UsJm9A47KvwLTo2…             1             2             3 #>  4 dO0k5CLEp9McpsCkmBt5wkYOioyg…             1             2             3 #>  5 3yKhjZZbegjEYOJm1HIxNkWvYx8O…             1             2             3 #>  6 uyWa9TGFPzGkVAZsJlCaIHvalJsv…             1             2             3 #>  7 UaiZMcOCjm7Lh2Gthq6NgOGsJbR2              1             2             3 #>  8 z2qYwlo7fldemmrE772uCYLrI6                1             2             3 #>  9 ek4O7hFxIud6K9YsvhQTB6Oe                  1             2             3 #> 10 YCRHBt1kj2i9JnNgyaa5MB                    1             2             3 #> # ℹ 6 more variables: some_number_4 <dbl>, some_number_5 <dbl>, #> #   some_number_6 <dbl>, some_number_7 <dbl>, some_number_8 <dbl>, #> #   some_number_9 <dbl> data_with_id(char(rand_strings, min_chars = Inf)) #> # A tibble: 10 × 10 #>    id                                       some_number_1 some_number_2 #>    <char>                                           <dbl>         <dbl> #>  1 WJSsxf5RPiDjFRy7iVoadHgnz4uveIpMlA7Qaqjz             1             2 #>  2 HQOlqjZavreUgr3GXLoDX6mpkxihE20qyTAB45               1             2 #>  3 GR8hYmPYrwJG9UsJm9A47KvwLTo2FQEkuqMT                 1             2 #>  4 dO0k5CLEp9McpsCkmBt5wkYOioyg9COiuR                   1             2 #>  5 3yKhjZZbegjEYOJm1HIxNkWvYx8Ozl85                     1             2 #>  6 uyWa9TGFPzGkVAZsJlCaIHvalJsvMC                       1             2 #>  7 UaiZMcOCjm7Lh2Gthq6NgOGsJbR2                         1             2 #>  8 z2qYwlo7fldemmrE772uCYLrI6                           1             2 #>  9 ek4O7hFxIud6K9YsvhQTB6Oe                             1             2 #> 10 YCRHBt1kj2i9JnNgyaa5MB                               1             2 #> # ℹ 7 more variables: some_number_3 <dbl>, some_number_4 <dbl>, #> #   some_number_5 <dbl>, some_number_6 <dbl>, some_number_7 <dbl>, #> #   some_number_8 <dbl>, some_number_9 <dbl> data_with_id(char(rand_strings, min_chars = 24, shorten = \"mid\")) #> # A tibble: 10 × 10 #>    id                            some_number_1 some_number_2 some_number_3 #>    <char>                                <dbl>         <dbl>         <dbl> #>  1 WJSsxf5RPiDjFR…uveIpMlA7Qaqjz             1             2             3 #>  2 HQOlqjZavreUgr…kxihE20qyTAB45             1             2             3 #>  3 GR8hYmPYrwJG9U…vwLTo2FQEkuqMT             1             2             3 #>  4 dO0k5CLEp9Mcps…wkYOioyg9COiuR             1             2             3 #>  5 3yKhjZZbegjEYO…IxNkWvYx8Ozl85             1             2             3 #>  6 uyWa9TGFPzGkVA…JlCaIHvalJsvMC             1             2             3 #>  7 UaiZMcOCjm7Lh2Gthq6NgOGsJbR2              1             2             3 #>  8 z2qYwlo7fldemmrE772uCYLrI6                1             2             3 #>  9 ek4O7hFxIud6K9YsvhQTB6Oe                  1             2             3 #> 10 YCRHBt1kj2i9JnNgyaa5MB                    1             2             3 #> # ℹ 6 more variables: some_number_4 <dbl>, some_number_5 <dbl>, #> #   some_number_6 <dbl>, some_number_7 <dbl>, some_number_8 <dbl>, #> #   some_number_9 <dbl>  # Lorem Ipsum, one sentence per row. lipsum <- unlist(strsplit(stringi::stri_rand_lipsum(1), \"(?<=[.]) +\", perl = TRUE)) tibble(   back = char(lipsum, shorten = \"back\"),   front = char(lipsum, shorten = \"front\"),   mid = char(lipsum, shorten = \"mid\") ) #> # A tibble: 10 × 3 #>    back                                                        front mid   #>    <char>                                                      <cha> <cha> #>  1 Lorem ipsum dolor sit amet, sit et auctor lacinia etiam co… …iis. Lo…s. #>  2 Laoreet in nec diam ut suspendisse adipiscing suspendisse … …pis. La…s. #>  3 Eleifend eleifend, metus nunc turpis.                       …pis. El…s. #>  4 Augue massa lobortis faucibus velit efficitur ac faucibus.  …bus. Au…s. #>  5 Elit egestas egestas sit amet ut conubia faucibus facilisi. …isi. El…i. #>  6 Tempor urna nisi feugiat senectus.                          …tus. Te…s. #>  7 Nunc in pretium dui ac.                                     … ac. Nu…c. #>  8 Elit semper eu.                                             … eu. El…u. #>  9 Sapien congue, dictum massa sed posuere sagittis.           …tis. Sa…s. #> 10 Interdum ante aenean, nisi dapibus lacinia nulla laoreet, … …bus. In…s. tibble(abbr = char(lipsum, shorten = \"abbreviate\")) #> # A tibble: 10 × 1 #>    abbr                                                                    #>    <char>                                                                  #>  1 Loremipsumdolorsitamet,sitetauctorlacinetiamcommodo!Tincidntd,nslvtscs. #>  2 Laoreetinnecdiamutsuspendissadipiscingsuspendissconubtortorrissdnctrps. #>  3 Eleifend eleifend, metus nunc turpis.                                   #>  4 Augue massa lobortis faucibus velit efficitur ac faucibus.              #>  5 Elit egestas egestas sit amet ut conubia faucibus facilisi.             #>  6 Tempor urna nisi feugiat senectus.                                      #>  7 Nunc in pretium dui ac.                                                 #>  8 Elit semper eu.                                                         #>  9 Sapien congue, dictum massa sed posuere sagittis.                       #> 10 Interdumanteaenean,nisidapibuslacinianulllaoreet,felisultriciesdapibus."},{"path":"https://tibble.tidyverse.org/dev/reference/deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions — deprecated","title":"Deprecated functions — deprecated","text":"Use tibble() instead data_frame(). Use rlang::quasiquotation instead tibble_(), data_frame_(), lst_(). Use as_tibble() instead as_data_frame() .tibble(), mind new signature semantics. Use tribble() instead frame_data().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions — deprecated","text":"","code":"data_frame(...)  tibble_(xs)  data_frame_(xs)  lst_(xs)  as_data_frame(x, ...)  as.tibble(x, ...)  frame_data(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Converting vectors to data frames, and vice versa — enframe","title":"Converting vectors to data frames, and vice versa — enframe","text":"enframe() converts named atomic vectors lists one- two-column data frames. list, result nested tibble column type list. unnamed vectors, natural sequence used name column. deframe() converts two-column data frames named vector list, using first column name second column value. input one column, unnamed vector returned.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converting vectors to data frames, and vice versa — enframe","text":"","code":"enframe(x, name = \"name\", value = \"value\")  deframe(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converting vectors to data frames, and vice versa — enframe","text":"x vector (enframe()) data frame one two columns (deframe()). name, value Names columns store names values. name NULL, one-column tibble returned; value NULL.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converting vectors to data frames, and vice versa — enframe","text":"enframe(), tibble two columns (name NULL, default) one column (otherwise). deframe(), vector (named unnamed).","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converting vectors to data frames, and vice versa — enframe","text":"","code":"enframe(1:3) #> # A tibble: 3 × 2 #>    name value #>   <int> <int> #> 1     1     1 #> 2     2     2 #> 3     3     3 enframe(c(a = 5, b = 7)) #> # A tibble: 2 × 2 #>   name  value #>   <chr> <dbl> #> 1 a         5 #> 2 b         7 enframe(list(one = 1, two = 2:3, three = 4:6)) #> # A tibble: 3 × 2 #>   name  value     #>   <chr> <list>    #> 1 one   <dbl [1]> #> 2 two   <int [2]> #> 3 three <int [3]> deframe(enframe(3:1)) #> 1 2 3  #> 3 2 1  deframe(tibble(a = 1:3)) #> [1] 1 2 3 deframe(tibble(a = as.list(1:3))) #> [[1]] #> [1] 1 #>  #> [[2]] #> [1] 2 #>  #> [[3]] #> [1] 3 #>"},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":null,"dir":"Reference","previous_headings":"","what":"Printing tibbles — formatting","title":"Printing tibbles — formatting","text":"One main features tbl_df class printing: Tibbles print many rows columns fit one screen, supplemented summary remaining rows columns. Tibble reveals type column, keeps user informed whether variable , e.g., <chr> <fct> (character versus factor). See vignette(\"types\") overview common type abbreviations. Printing can tweaked one-call calling print() explicitly setting arguments like n width. persistent control available setting options described pillar::pillar_options. See also vignette(\"digits\") comparison base options, vignette(\"numbers\") showcases num() char() creating columns custom formatting options. tibble 3.1.0, printing handled entirely pillar package. implement package extends tibble, printed output can customized various ways. See vignette(\"extending\", package = \"pillar\") details, pillar::pillar_options options control display console.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Printing tibbles — formatting","text":"","code":"# S3 method for class 'tbl_df' print(   x,   width = NULL,   ...,   n = NULL,   max_extra_cols = NULL,   max_footer_lines = NULL )  # S3 method for class 'tbl_df' format(   x,   width = NULL,   ...,   n = NULL,   max_extra_cols = NULL,   max_footer_lines = NULL )"},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Printing tibbles — formatting","text":"x Object format print. width Width text output generate. defaults NULL, means use width option. ... Passed pillar::tbl_format_setup(). n Number rows show. NULL, default, print rows less print_max option. Otherwise, print many rows specified print_min option. max_extra_cols Number extra columns print abbreviated information , width small entire tibble. NULL, max_extra_cols option used. previously defined n_extra argument soft-deprecated. max_footer_lines Maximum number footer lines. NULL, max_footer_lines option used.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Printing tibbles — formatting","text":"","code":"print(as_tibble(mtcars)) #> # A tibble: 32 × 11 #>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4 #>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4 #>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1 #>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1 #>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 #>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 #>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 #>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2 #>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2 #> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 #> # ℹ 22 more rows print(as_tibble(mtcars), n = 1) #> # A tibble: 32 × 11 #>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1    21     6   160   110   3.9  2.62  16.5     0     1     4     4 #> # ℹ 31 more rows print(as_tibble(mtcars), n = 3) #> # A tibble: 32 × 11 #>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1  21       6   160   110  3.9   2.62  16.5     0     1     4     4 #> 2  21       6   160   110  3.9   2.88  17.0     0     1     4     4 #> 3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1 #> # ℹ 29 more rows  print(as_tibble(trees), n = 100) #> # A tibble: 31 × 3 #>    Girth Height Volume #>    <dbl>  <dbl>  <dbl> #>  1   8.3     70   10.3 #>  2   8.6     65   10.3 #>  3   8.8     63   10.2 #>  4  10.5     72   16.4 #>  5  10.7     81   18.8 #>  6  10.8     83   19.7 #>  7  11       66   15.6 #>  8  11       75   18.2 #>  9  11.1     80   22.6 #> 10  11.2     75   19.9 #> 11  11.3     79   24.2 #> 12  11.4     76   21   #> 13  11.4     76   21.4 #> 14  11.7     69   21.3 #> 15  12       75   19.1 #> 16  12.9     74   22.2 #> 17  12.9     85   33.8 #> 18  13.3     86   27.4 #> 19  13.7     71   25.7 #> 20  13.8     64   24.9 #> 21  14       78   34.5 #> 22  14.2     80   31.7 #> 23  14.5     74   36.3 #> 24  16       72   38.3 #> 25  16.3     77   42.6 #> 26  17.3     81   55.4 #> 27  17.5     82   55.7 #> 28  17.9     80   58.3 #> 29  18       80   51.5 #> 30  18       80   51   #> 31  20.6     87   77    print(mtcars, width = 10) #>                      mpg #> Mazda RX4           21.0 #> Mazda RX4 Wag       21.0 #> Datsun 710          22.8 #> Hornet 4 Drive      21.4 #> Hornet Sportabout   18.7 #> Valiant             18.1 #> Duster 360          14.3 #> Merc 240D           24.4 #> Merc 230            22.8 #> Merc 280            19.2 #> Merc 280C           17.8 #> Merc 450SE          16.4 #> Merc 450SL          17.3 #> Merc 450SLC         15.2 #> Cadillac Fleetwood  10.4 #> Lincoln Continental 10.4 #> Chrysler Imperial   14.7 #> Fiat 128            32.4 #> Honda Civic         30.4 #> Toyota Corolla      33.9 #> Toyota Corona       21.5 #> Dodge Challenger    15.5 #> AMC Javelin         15.2 #> Camaro Z28          13.3 #> Pontiac Firebird    19.2 #> Fiat X1-9           27.3 #> Porsche 914-2       26.0 #> Lotus Europa        30.4 #> Ford Pantera L      15.8 #> Ferrari Dino        19.7 #> Maserati Bora       15.0 #> Volvo 142E          21.4 #>                     cyl #> Mazda RX4             6 #> Mazda RX4 Wag         6 #> Datsun 710            4 #> Hornet 4 Drive        6 #> Hornet Sportabout     8 #> Valiant               6 #> Duster 360            8 #> Merc 240D             4 #> Merc 230              4 #> Merc 280              6 #> Merc 280C             6 #> Merc 450SE            8 #> Merc 450SL            8 #> Merc 450SLC           8 #> Cadillac Fleetwood    8 #> Lincoln Continental   8 #> Chrysler Imperial     8 #> Fiat 128              4 #> Honda Civic           4 #> Toyota Corolla        4 #> Toyota Corona         4 #> Dodge Challenger      8 #> AMC Javelin           8 #> Camaro Z28            8 #> Pontiac Firebird      8 #> Fiat X1-9             4 #> Porsche 914-2         4 #> Lotus Europa          4 #> Ford Pantera L        8 #> Ferrari Dino          6 #> Maserati Bora         8 #> Volvo 142E            4 #>                      disp #> Mazda RX4           160.0 #> Mazda RX4 Wag       160.0 #> Datsun 710          108.0 #> Hornet 4 Drive      258.0 #> Hornet Sportabout   360.0 #> Valiant             225.0 #> Duster 360          360.0 #> Merc 240D           146.7 #> Merc 230            140.8 #> Merc 280            167.6 #> Merc 280C           167.6 #> Merc 450SE          275.8 #> Merc 450SL          275.8 #> Merc 450SLC         275.8 #> Cadillac Fleetwood  472.0 #> Lincoln Continental 460.0 #> Chrysler Imperial   440.0 #> Fiat 128             78.7 #> Honda Civic          75.7 #> Toyota Corolla       71.1 #> Toyota Corona       120.1 #> Dodge Challenger    318.0 #> AMC Javelin         304.0 #> Camaro Z28          350.0 #> Pontiac Firebird    400.0 #> Fiat X1-9            79.0 #> Porsche 914-2       120.3 #> Lotus Europa         95.1 #> Ford Pantera L      351.0 #> Ferrari Dino        145.0 #> Maserati Bora       301.0 #> Volvo 142E          121.0 #>                      hp #> Mazda RX4           110 #> Mazda RX4 Wag       110 #> Datsun 710           93 #> Hornet 4 Drive      110 #> Hornet Sportabout   175 #> Valiant             105 #> Duster 360          245 #> Merc 240D            62 #> Merc 230             95 #> Merc 280            123 #> Merc 280C           123 #> Merc 450SE          180 #> Merc 450SL          180 #> Merc 450SLC         180 #> Cadillac Fleetwood  205 #> Lincoln Continental 215 #> Chrysler Imperial   230 #> Fiat 128             66 #> Honda Civic          52 #> Toyota Corolla       65 #> Toyota Corona        97 #> Dodge Challenger    150 #> AMC Javelin         150 #> Camaro Z28          245 #> Pontiac Firebird    175 #> Fiat X1-9            66 #> Porsche 914-2        91 #> Lotus Europa        113 #> Ford Pantera L      264 #> Ferrari Dino        175 #> Maserati Bora       335 #> Volvo 142E          109 #>                     drat #> Mazda RX4           3.90 #> Mazda RX4 Wag       3.90 #> Datsun 710          3.85 #> Hornet 4 Drive      3.08 #> Hornet Sportabout   3.15 #> Valiant             2.76 #> Duster 360          3.21 #> Merc 240D           3.69 #> Merc 230            3.92 #> Merc 280            3.92 #> Merc 280C           3.92 #> Merc 450SE          3.07 #> Merc 450SL          3.07 #> Merc 450SLC         3.07 #> Cadillac Fleetwood  2.93 #> Lincoln Continental 3.00 #> Chrysler Imperial   3.23 #> Fiat 128            4.08 #> Honda Civic         4.93 #> Toyota Corolla      4.22 #> Toyota Corona       3.70 #> Dodge Challenger    2.76 #> AMC Javelin         3.15 #> Camaro Z28          3.73 #> Pontiac Firebird    3.08 #> Fiat X1-9           4.08 #> Porsche 914-2       4.43 #> Lotus Europa        3.77 #> Ford Pantera L      4.22 #> Ferrari Dino        3.62 #> Maserati Bora       3.54 #> Volvo 142E          4.11 #>                        wt #> Mazda RX4           2.620 #> Mazda RX4 Wag       2.875 #> Datsun 710          2.320 #> Hornet 4 Drive      3.215 #> Hornet Sportabout   3.440 #> Valiant             3.460 #> Duster 360          3.570 #> Merc 240D           3.190 #> Merc 230            3.150 #> Merc 280            3.440 #> Merc 280C           3.440 #> Merc 450SE          4.070 #> Merc 450SL          3.730 #> Merc 450SLC         3.780 #> Cadillac Fleetwood  5.250 #> Lincoln Continental 5.424 #> Chrysler Imperial   5.345 #> Fiat 128            2.200 #> Honda Civic         1.615 #> Toyota Corolla      1.835 #> Toyota Corona       2.465 #> Dodge Challenger    3.520 #> AMC Javelin         3.435 #> Camaro Z28          3.840 #> Pontiac Firebird    3.845 #> Fiat X1-9           1.935 #> Porsche 914-2       2.140 #> Lotus Europa        1.513 #> Ford Pantera L      3.170 #> Ferrari Dino        2.770 #> Maserati Bora       3.570 #> Volvo 142E          2.780 #>                      qsec #> Mazda RX4           16.46 #> Mazda RX4 Wag       17.02 #> Datsun 710          18.61 #> Hornet 4 Drive      19.44 #> Hornet Sportabout   17.02 #> Valiant             20.22 #> Duster 360          15.84 #> Merc 240D           20.00 #> Merc 230            22.90 #> Merc 280            18.30 #> Merc 280C           18.90 #> Merc 450SE          17.40 #> Merc 450SL          17.60 #> Merc 450SLC         18.00 #> Cadillac Fleetwood  17.98 #> Lincoln Continental 17.82 #> Chrysler Imperial   17.42 #> Fiat 128            19.47 #> Honda Civic         18.52 #> Toyota Corolla      19.90 #> Toyota Corona       20.01 #> Dodge Challenger    16.87 #> AMC Javelin         17.30 #> Camaro Z28          15.41 #> Pontiac Firebird    17.05 #> Fiat X1-9           18.90 #> Porsche 914-2       16.70 #> Lotus Europa        16.90 #> Ford Pantera L      14.50 #> Ferrari Dino        15.50 #> Maserati Bora       14.60 #> Volvo 142E          18.60 #>                     vs #> Mazda RX4            0 #> Mazda RX4 Wag        0 #> Datsun 710           1 #> Hornet 4 Drive       1 #> Hornet Sportabout    0 #> Valiant              1 #> Duster 360           0 #> Merc 240D            1 #> Merc 230             1 #> Merc 280             1 #> Merc 280C            1 #> Merc 450SE           0 #> Merc 450SL           0 #> Merc 450SLC          0 #> Cadillac Fleetwood   0 #> Lincoln Continental  0 #> Chrysler Imperial    0 #> Fiat 128             1 #> Honda Civic          1 #> Toyota Corolla       1 #> Toyota Corona        1 #> Dodge Challenger     0 #> AMC Javelin          0 #> Camaro Z28           0 #> Pontiac Firebird     0 #> Fiat X1-9            1 #> Porsche 914-2        0 #> Lotus Europa         1 #> Ford Pantera L       0 #> Ferrari Dino         0 #> Maserati Bora        0 #> Volvo 142E           1 #>                     am #> Mazda RX4            1 #> Mazda RX4 Wag        1 #> Datsun 710           1 #> Hornet 4 Drive       0 #> Hornet Sportabout    0 #> Valiant              0 #> Duster 360           0 #> Merc 240D            0 #> Merc 230             0 #> Merc 280             0 #> Merc 280C            0 #> Merc 450SE           0 #> Merc 450SL           0 #> Merc 450SLC          0 #> Cadillac Fleetwood   0 #> Lincoln Continental  0 #> Chrysler Imperial    0 #> Fiat 128             1 #> Honda Civic          1 #> Toyota Corolla       1 #> Toyota Corona        0 #> Dodge Challenger     0 #> AMC Javelin          0 #> Camaro Z28           0 #> Pontiac Firebird     0 #> Fiat X1-9            1 #> Porsche 914-2        1 #> Lotus Europa         1 #> Ford Pantera L       1 #> Ferrari Dino         1 #> Maserati Bora        1 #> Volvo 142E           1 #>                     gear #> Mazda RX4              4 #> Mazda RX4 Wag          4 #> Datsun 710             4 #> Hornet 4 Drive         3 #> Hornet Sportabout      3 #> Valiant                3 #> Duster 360             3 #> Merc 240D              4 #> Merc 230               4 #> Merc 280               4 #> Merc 280C              4 #> Merc 450SE             3 #> Merc 450SL             3 #> Merc 450SLC            3 #> Cadillac Fleetwood     3 #> Lincoln Continental    3 #> Chrysler Imperial      3 #> Fiat 128               4 #> Honda Civic            4 #> Toyota Corolla         4 #> Toyota Corona          3 #> Dodge Challenger       3 #> AMC Javelin            3 #> Camaro Z28             3 #> Pontiac Firebird       3 #> Fiat X1-9              4 #> Porsche 914-2          5 #> Lotus Europa           5 #> Ford Pantera L         5 #> Ferrari Dino           5 #> Maserati Bora          5 #> Volvo 142E             4 #>                     carb #> Mazda RX4              4 #> Mazda RX4 Wag          4 #> Datsun 710             1 #> Hornet 4 Drive         1 #> Hornet Sportabout      2 #> Valiant                1 #> Duster 360             4 #> Merc 240D              2 #> Merc 230               2 #> Merc 280               4 #> Merc 280C              4 #> Merc 450SE             3 #> Merc 450SL             3 #> Merc 450SLC            3 #> Cadillac Fleetwood     4 #> Lincoln Continental    4 #> Chrysler Imperial      4 #> Fiat 128               1 #> Honda Civic            2 #> Toyota Corolla         1 #> Toyota Corona          1 #> Dodge Challenger       2 #> AMC Javelin            2 #> Camaro Z28             4 #> Pontiac Firebird       2 #> Fiat X1-9              1 #> Porsche 914-2          2 #> Lotus Europa           2 #> Ford Pantera L         4 #> Ferrari Dino           6 #> Maserati Bora          8 #> Volvo 142E             2  mtcars2 <- as_tibble(cbind(mtcars, mtcars), .name_repair = \"unique\") #> New names: #> • `mpg` -> `mpg...1` #> • `cyl` -> `cyl...2` #> • `disp` -> `disp...3` #> • `hp` -> `hp...4` #> • `drat` -> `drat...5` #> • `wt` -> `wt...6` #> • `qsec` -> `qsec...7` #> • `vs` -> `vs...8` #> • `am` -> `am...9` #> • `gear` -> `gear...10` #> • `carb` -> `carb...11` #> • `mpg` -> `mpg...12` #> • `cyl` -> `cyl...13` #> • `disp` -> `disp...14` #> • `hp` -> `hp...15` #> • `drat` -> `drat...16` #> • `wt` -> `wt...17` #> • `qsec` -> `qsec...18` #> • `vs` -> `vs...19` #> • `am` -> `am...20` #> • `gear` -> `gear...21` #> • `carb` -> `carb...22` print(mtcars2, n = 25, max_extra_cols = 3) #> # A tibble: 32 × 22 #>    mpg...1 cyl...2 disp...3 hp...4 drat...5 wt...6 qsec...7 vs...8 am...9 #>      <dbl>   <dbl>    <dbl>  <dbl>    <dbl>  <dbl>    <dbl>  <dbl>  <dbl> #>  1    21         6    160      110     3.9    2.62     16.5      0      1 #>  2    21         6    160      110     3.9    2.88     17.0      0      1 #>  3    22.8       4    108       93     3.85   2.32     18.6      1      1 #>  4    21.4       6    258      110     3.08   3.22     19.4      1      0 #>  5    18.7       8    360      175     3.15   3.44     17.0      0      0 #>  6    18.1       6    225      105     2.76   3.46     20.2      1      0 #>  7    14.3       8    360      245     3.21   3.57     15.8      0      0 #>  8    24.4       4    147.      62     3.69   3.19     20        1      0 #>  9    22.8       4    141.      95     3.92   3.15     22.9      1      0 #> 10    19.2       6    168.     123     3.92   3.44     18.3      1      0 #> 11    17.8       6    168.     123     3.92   3.44     18.9      1      0 #> 12    16.4       8    276.     180     3.07   4.07     17.4      0      0 #> 13    17.3       8    276.     180     3.07   3.73     17.6      0      0 #> 14    15.2       8    276.     180     3.07   3.78     18        0      0 #> 15    10.4       8    472      205     2.93   5.25     18.0      0      0 #> 16    10.4       8    460      215     3      5.42     17.8      0      0 #> 17    14.7       8    440      230     3.23   5.34     17.4      0      0 #> 18    32.4       4     78.7     66     4.08   2.2      19.5      1      1 #> 19    30.4       4     75.7     52     4.93   1.62     18.5      1      1 #> 20    33.9       4     71.1     65     4.22   1.84     19.9      1      1 #> 21    21.5       4    120.      97     3.7    2.46     20.0      1      0 #> 22    15.5       8    318      150     2.76   3.52     16.9      0      0 #> 23    15.2       8    304      150     3.15   3.44     17.3      0      0 #> 24    13.3       8    350      245     3.73   3.84     15.4      0      0 #> 25    19.2       8    400      175     3.08   3.84     17.0      0      0 #> # ℹ 7 more rows #> # ℹ 13 more variables: gear...10 <dbl>, carb...11 <dbl>, mpg...12 <dbl>, #> #   …  print(nycflights13::flights, max_footer_lines = 1) #> # A tibble: 336,776 × 19 #>     year month   day dep_time sched_dep_time dep_delay arr_time #>    <int> <int> <int>    <int>          <int>     <dbl>    <int> #>  1  2013     1     1      517            515         2      830 #>  2  2013     1     1      533            529         4      850 #>  3  2013     1     1      542            540         2      923 #>  4  2013     1     1      544            545        -1     1004 #>  5  2013     1     1      554            600        -6      812 #>  6  2013     1     1      554            558        -4      740 #>  7  2013     1     1      555            600        -5      913 #>  8  2013     1     1      557            600        -3      709 #>  9  2013     1     1      557            600        -3      838 #> 10  2013     1     1      558            600        -2      753 #> # ℹ 336,766 more rows print(nycflights13::flights, width = Inf) #> # A tibble: 336,776 × 19 #>     year month   day dep_time sched_dep_time dep_delay arr_time #>    <int> <int> <int>    <int>          <int>     <dbl>    <int> #>  1  2013     1     1      517            515         2      830 #>  2  2013     1     1      533            529         4      850 #>  3  2013     1     1      542            540         2      923 #>  4  2013     1     1      544            545        -1     1004 #>  5  2013     1     1      554            600        -6      812 #>  6  2013     1     1      554            558        -4      740 #>  7  2013     1     1      555            600        -5      913 #>  8  2013     1     1      557            600        -3      709 #>  9  2013     1     1      557            600        -3      838 #> 10  2013     1     1      558            600        -2      753 #>    sched_arr_time arr_delay carrier flight tailnum origin dest  air_time #>             <int>     <dbl> <chr>    <int> <chr>   <chr>  <chr>    <dbl> #>  1            819        11 UA        1545 N14228  EWR    IAH        227 #>  2            830        20 UA        1714 N24211  LGA    IAH        227 #>  3            850        33 AA        1141 N619AA  JFK    MIA        160 #>  4           1022       -18 B6         725 N804JB  JFK    BQN        183 #>  5            837       -25 DL         461 N668DN  LGA    ATL        116 #>  6            728        12 UA        1696 N39463  EWR    ORD        150 #>  7            854        19 B6         507 N516JB  EWR    FLL        158 #>  8            723       -14 EV        5708 N829AS  LGA    IAD         53 #>  9            846        -8 B6          79 N593JB  JFK    MCO        140 #> 10            745         8 AA         301 N3ALAA  LGA    ORD        138 #>    distance  hour minute time_hour           #>       <dbl> <dbl>  <dbl> <dttm>              #>  1     1400     5     15 2013-01-01 05:00:00 #>  2     1416     5     29 2013-01-01 05:00:00 #>  3     1089     5     40 2013-01-01 05:00:00 #>  4     1576     5     45 2013-01-01 05:00:00 #>  5      762     6      0 2013-01-01 06:00:00 #>  6      719     5     58 2013-01-01 05:00:00 #>  7     1065     6      0 2013-01-01 06:00:00 #>  8      229     6      0 2013-01-01 06:00:00 #>  9      944     6      0 2013-01-01 06:00:00 #> 10      733     6      0 2013-01-01 06:00:00 #> # ℹ 336,766 more rows"},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-wise matrix creation — frame_matrix","title":"Row-wise matrix creation — frame_matrix","text":"Create matrices laying data rows, similar matrix(..., byrow = TRUE), nicer--read syntax. useful small matrices, e.g. covariance matrices, readability important. syntax inspired tribble().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row-wise matrix creation — frame_matrix","text":"","code":"frame_matrix(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row-wise matrix creation — frame_matrix","text":"... <dynamic-dots> Arguments specifying structure frame_matrix. Column names formulas, may appear data. arguments processed rlang::list2() support unquote via rlang::!! unquote-splice via rlang::!!!.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row-wise matrix creation — frame_matrix","text":"matrix.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-wise matrix creation — frame_matrix","text":"","code":"frame_matrix(   ~col1, ~col2,   1,     3,   5,     2 ) #>      col1 col2 #> [1,]    1    3 #> [2,]    5    2"},{"path":"https://tibble.tidyverse.org/dev/reference/is.tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated test for tibble-ness — is.tibble","title":"Deprecated test for tibble-ness — is.tibble","text":"Please use is_tibble() instead.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is.tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated test for tibble-ness — is.tibble","text":"","code":"is.tibble(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/is.tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated test for tibble-ness — is.tibble","text":"x object","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Test if the object is a tibble — is_tibble","title":"Test if the object is a tibble — is_tibble","text":"function returns TRUE tibbles subclasses thereof, FALSE objects, including regular data frames.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test if the object is a tibble — is_tibble","text":"","code":"is_tibble(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test if the object is a tibble — is_tibble","text":"x object","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test if the object is a tibble — is_tibble","text":"TRUE object inherits tbl_df class.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":null,"dir":"Reference","previous_headings":"","what":"Build a list — lst","title":"Build a list — lst","text":"lst() constructs list, similar base::list(), features tibble(). lst() builds components sequentially. defining component, can refer components created earlier call. lst() also generates missing names automatically. See rlang::list2() simpler faster alternative without tibble's evaluation auto-name semantics.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build a list — lst","text":"","code":"lst(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build a list — lst","text":"... <dynamic-dots> set name-value pairs. arguments processed rlang::quos() support unquote via rlang::!! unquote-splice via rlang::!!!. Use := create columns start dot. Arguments evaluated sequentially. can refer previously created elements directly using rlang::.data pronoun. refer explicitly objects calling environment, use rlang::!! rlang::.env, e.g. !!.data .env$.data special case object named .data.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build a list — lst","text":"named list.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build a list — lst","text":"","code":"# the value of n can be used immediately in the definition of x lst(n = 5, x = runif(n)) #> $n #> [1] 5 #>  #> $x #> [1] 0.2220037 0.1208247 0.5305426 0.7951219 0.3532806 #>   # missing names are constructed from user's input lst(1:3, z = letters[4:6], runif(3)) #> $`1:3` #> [1] 1 2 3 #>  #> $z #> [1] \"d\" \"e\" \"f\" #>  #> $`runif(3)` #> [1] 0.9371862 0.6326068 0.6565604 #>   a <- 1:3 b <- letters[4:6] lst(a, b) #> $a #> [1] 1 2 3 #>  #> $b #> [1] \"d\" \"e\" \"f\" #>   # pre-formed quoted expressions can be used with lst() and then # unquoted (with !!) or unquoted and spliced (with !!!) n1 <- 2 n2 <- 3 n_stuff <- quote(n1 + n2) x_stuff <- quote(seq_len(n)) lst(!!!list(n = n_stuff, x = x_stuff)) #> $n #> [1] 5 #>  #> $x #> [1] 1 2 3 4 5 #>  lst(n = !!n_stuff, x = !!x_stuff) #> $n #> [1] 5 #>  #> $x #> [1] 1 2 3 4 5 #>  lst(n = 4, x = !!x_stuff) #> $n #> [1] 4 #>  #> $x #> [1] 1 2 3 4 #>  lst(!!!list(n = 2, x = x_stuff)) #> $n #> [1] 2 #>  #> $x #> [1] 1 2 #>"},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":null,"dir":"Reference","previous_headings":"","what":"Superseded functions for name repair — name-repair-superseded","title":"Superseded functions for name repair — name-repair-superseded","text":"tidy_names(), set_tidy_names(), repair_names() early efforts facilitate post hoc name repair tibble, given tibble() as_tibble() . tibble v2.0.0, .name_repair argument gives direct access three specific levels name repair: minimal, unique, universal. See vctrs::vec_as_names() implementation underlying logic.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Superseded functions for name repair — name-repair-superseded","text":"","code":"tidy_names(name, syntactic = FALSE, quiet = FALSE)  set_tidy_names(x, syntactic = FALSE, quiet = FALSE)  repair_names(x, prefix = \"V\", sep = \"\")"},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Superseded functions for name repair — name-repair-superseded","text":"name names attribute, usually character vector. syntactic names made syntactically valid? FALSE, uses logic .name_repair = \"unique\". TRUE, uses logic .name_repair = \"universal\". quiet Whether suppress messages name repair. x vector. prefix string, prefix use new column names. sep string inserted column name de-duplicating number.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Superseded functions for name repair — name-repair-superseded","text":"x repaired names repaired version name.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"life-cycle","dir":"Reference","previous_headings":"","what":"Life cycle","title":"Superseded functions for name repair — name-repair-superseded","text":"functions superseded. repair_names() logic also remain available vctrs::vec_as_names_legacy().","code":"tibble(..., `.name_repair = \"unique\"`) ## is preferred to df <- tibble(...) set_tidy_names(df, syntactic = FALSE)  tibble(..., `.name_repair = \"universal\"`) ## is preferred to df <- tibble(...) set_tidy_names(df, syntactic = TRUE)"},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair.html","id":null,"dir":"Reference","previous_headings":"","what":"Name repair — name-repair","title":"Name repair — name-repair","text":"Please review vctrs::vec_as_names().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Tibble constructor and validator — new_tibble","title":"Tibble constructor and validator — new_tibble","text":"Creates validates subclass tibble. function mostly useful package authors implement subclasses tibble, like sf tsibble. new_tibble() creates new object subclass tbl_df, tbl data.frame. function optimized performance, checks reduced minimum. See vctrs::new_data_frame() details. validate_tibble() checks tibble internal consistency. Correct behavior can guaranteed function runs without raising error.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tibble constructor and validator — new_tibble","text":"","code":"new_tibble(x, ..., nrow = NULL, class = NULL, subclass = NULL)  validate_tibble(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tibble constructor and validator — new_tibble","text":"x tibble-like object. ... Name-value pairs additional attributes. nrow number rows, inferred x omitted. class Subclasses assign new object, default: none. subclass Deprecated, retained compatibility. Please use class argument.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"construction","dir":"Reference","previous_headings":"","what":"Construction","title":"Tibble constructor and validator — new_tibble","text":"new_tibble(), x must list. nrow argument may omitted tibble 3.1.4. present, every element list x vctrs::vec_size() equal value. (checked constructor). takes place \"row.names\" attribute data frame. x must names (empty), names checked correctness.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"validation","dir":"Reference","previous_headings":"","what":"Validation","title":"Tibble constructor and validator — new_tibble","text":"validate_tibble() checks \"minimal\" names columns vectors, data frames matrices. also makes sure columns length, vctrs::vec_size() consistent data.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tibble constructor and validator — new_tibble","text":"","code":"# The nrow argument can be omitted: new_tibble(list(a = 1:3, b = 4:6)) #> # A tibble: 3 × 2 #>       a     b #>   <int> <int> #> 1     1     4 #> 2     2     5 #> 3     3     6  # Existing row.names attributes are ignored: try(validate_tibble(new_tibble(trees, nrow = 3))) #> Error in validate_nrow(names(x), col_lengths(x), vec_size(x)) :  #>   Tibble columns must have compatible sizes. #> • Size 3: Requested with `nrow` argument. #> • Size 31: Columns `Girth`, `Height`, and `Volume`. #> ℹ Only values of size one are recycled.  # The length of all columns must be compatible with the nrow argument: try(validate_tibble(new_tibble(list(a = 1:3, b = 4:6), nrow = 2))) #> Error in validate_nrow(names(x), col_lengths(x), vec_size(x)) :  #>   Tibble columns must have compatible sizes. #> • Size 2: Requested with `nrow` argument. #> • Size 3: Columns `a` and `b`. #> ℹ Only values of size one are recycled."},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":null,"dir":"Reference","previous_headings":"","what":"Format a numeric vector — num","title":"Format a numeric vector — num","text":"Constructs numeric vector can formatted predefined significant digits, maximum fixed number digits decimal point. Scaling supported, well forcing decimal, scientific engineering notation. label given, shown header column. formatting applied vector printed formatted, also tibble column. formatting annotation class survives arithmetic transformations, notable exceptions var() sd(). set_num_opts() adds formatting options arbitrary numeric vector, useful composing types.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format a numeric vector — num","text":"","code":"num(   x,   ...,   sigfig = NULL,   digits = NULL,   label = NULL,   scale = NULL,   notation = c(\"fit\", \"dec\", \"sci\", \"eng\", \"si\"),   fixed_exponent = NULL,   extra_sigfig = NULL )  set_num_opts(   x,   ...,   sigfig = NULL,   digits = NULL,   label = NULL,   scale = NULL,   notation = c(\"fit\", \"dec\", \"sci\", \"eng\", \"si\"),   fixed_exponent = NULL,   extra_sigfig = NULL )"},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format a numeric vector — num","text":"x numeric vector. ... dots future extensions must empty. sigfig Define number significant digits show. Must one greater. \"pillar.sigfig\" option consulted. combined digits. digits Number digits decimal points show. Positive numbers specify exact number digits show. Negative numbers specify (negation) maximum number digits show. digits = 2, numbers 1.2 1.234 printed 1.20 1.23, digits = -2 1.2 1.23, respectively. combined sigfig. label label show instead type description. scale Multiplier apply data showing. Useful displaying e.g. percentages. Must combined label. notation One \"fit\", \"dec\", \"sci\", \"eng\", \"si\". \"fit\": Use decimal notation fits consumes 13 digits less, otherwise use scientific notation. (default numeric pillars.) \"dec\": Use decimal notation, regardless width. \"sci\": Use scientific notation. \"eng\": Use engineering notation, .e. scientific notation using exponents multiple three. \"si\": Use SI notation, prefixes 1e-24 1e24 supported. fixed_exponent Use exponent numbers scientific, engineering SI notation. -Inf uses smallest, +Inf largest fixed_exponent present data. default use varying exponents. extra_sigfig TRUE, increase number significant digits data consists numbers magnitude subtle differences.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format a numeric vector — num","text":"","code":"# Display as a vector num(9:11 * 100 + 0.5) #> <pillar_num[3]> #> [1]  900. 1000. 1100.  # Significant figures tibble(   x3 = num(9:11 * 100 + 0.5, sigfig = 3),   x4 = num(9:11 * 100 + 0.5, sigfig = 4),   x5 = num(9:11 * 100 + 0.5, sigfig = 5), ) #> # A tibble: 3 × 3 #>        x3      x4      x5 #>   <num:3> <num:4> <num:5> #> 1    900.   900.5   900.5 #> 2   1000.  1000.   1000.5 #> 3   1100.  1100.   1100.5  # Maximum digits after the decimal points tibble(   x0 = num(9:11 * 100 + 0.5, digits = 0),   x1 = num(9:11 * 100 + 0.5, digits = -1),   x2 = num(9:11 * 100 + 0.5, digits = -2), ) #> # A tibble: 3 × 3 #>         x0       x1       x2 #>   <num:.0> <num:.1> <num:.2> #> 1     900.    900.5    900.5 #> 2    1000.   1000.5   1000.5 #> 3    1100.   1100.5   1100.5  # Use fixed digits and a currency label tibble(   usd = num(9:11 * 100 + 0.5, digits = 2, label = \"USD\"),   gbp = num(9:11 * 100 + 0.5, digits = 2, label = \"£\"),   chf = num(9:11 * 100 + 0.5, digits = 2, label = \"SFr\") ) #> # A tibble: 3 × 3 #>       usd     gbp     chf #>       USD       £     SFr #> 1  900.50  900.50  900.50 #> 2 1000.50 1000.50 1000.50 #> 3 1100.50 1100.50 1100.50  # Scale tibble(   small  = num(9:11 / 1000 + 0.00005, label = \"%\", scale = 100),   medium = num(9:11 / 100 + 0.0005, label = \"%\", scale = 100),   large  = num(9:11 / 10 + 0.005, label = \"%\", scale = 100) ) #> # A tibble: 3 × 3 #>   small medium large #>       %      %     % #> 1 0.905   9.05  90.5 #> 2 1.00   10.0  100.  #> 3 1.10   11.0  110.   # Notation tibble(   sci = num(10^(-13:6), notation = \"sci\"),   eng = num(10^(-13:6), notation = \"eng\"),   si  = num(10^(-13:6), notation = \"si\"),   dec = num(10^(-13:6), notation = \"dec\") ) #> # A tibble: 20 × 4 #>      sci     eng   si                   dec #>    <sci>   <eng> <si>                 <dec> #>  1 1e-13 100e-15 100f       0.0000000000001 #>  2 1e-12   1e-12   1p       0.000000000001  #>  3 1e-11  10e-12  10p       0.00000000001   #>  4 1e-10 100e-12 100p       0.0000000001    #>  5 1e- 9   1e- 9   1n       0.000000001     #>  6 1e- 8  10e- 9  10n       0.00000001      #>  7 1e- 7 100e- 9 100n       0.0000001       #>  8 1e- 6   1e- 6   1µ       0.000001        #>  9 1e- 5  10e- 6  10µ       0.00001         #> 10 1e- 4 100e- 6 100µ       0.0001          #> 11 1e- 3   1e- 3   1m       0.001           #> 12 1e- 2  10e- 3  10m       0.01            #> 13 1e- 1 100e- 3 100m       0.1             #> 14 1e+ 0   1e+ 0   1        1               #> 15 1e+ 1  10e+ 0  10       10               #> 16 1e+ 2 100e+ 0 100      100               #> 17 1e+ 3   1e+ 3   1k    1000               #> 18 1e+ 4  10e+ 3  10k   10000               #> 19 1e+ 5 100e+ 3 100k  100000               #> 20 1e+ 6   1e+ 6   1M 1000000                # Fixed exponent tibble(   scimin = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = -Inf),   engmin = num(10^(-7:6) * 123, notation = \"eng\", fixed_exponent = -Inf),   simin  = num(10^(-7:6) * 123, notation = \"si\", fixed_exponent = -Inf) ) #> # A tibble: 14 × 3 #>               scimin            engmin             simin #>                [e-5]             [e-5]               [µ] #>  1              1.23              12.3              12.3 #>  2             12.3              123               123   #>  3            123               1230              1230   #>  4           1230              12300             12300   #>  5          12300             123000            123000   #>  6         123000            1230000           1230000   #>  7        1230000           12300000          12300000   #>  8       12300000          123000000         123000000   #>  9      123000000         1230000000        1230000000   #> 10     1230000000        12300000000       12300000000   #> 11    12300000000       123000000000      123000000000   #> 12   123000000000      1230000000000     1230000000000   #> 13  1230000000000     12300000000000    12300000000000   #> 14 12300000000000    123000000000000   123000000000000    tibble(   scismall = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = -3),   scilarge = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = 3),   scimax   = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = Inf) ) #> # A tibble: 14 × 3 #>             scismall          scilarge            scimax #>                [e-3]              [e3]              [e8] #>  1            0.0123      0.0000000123 0.000000000000123 #>  2            0.123       0.000000123  0.00000000000123  #>  3            1.23        0.00000123   0.0000000000123   #>  4           12.3         0.0000123    0.000000000123    #>  5          123           0.000123     0.00000000123     #>  6         1230           0.00123      0.0000000123      #>  7        12300           0.0123       0.000000123       #>  8       123000           0.123        0.00000123        #>  9      1230000           1.23         0.0000123         #> 10     12300000          12.3          0.000123          #> 11    123000000         123            0.00123           #> 12   1230000000        1230            0.0123            #> 13  12300000000       12300            0.123             #> 14 123000000000      123000            1.23               #' Extra significant digits tibble(   default = num(100 + 1:3 * 0.001),   extra1 = num(100 + 1:3 * 0.001, extra_sigfig = TRUE),   extra2 = num(100 + 1:3 * 0.0001, extra_sigfig = TRUE),   extra3 = num(10000 + 1:3 * 0.00001, extra_sigfig = TRUE) ) #> # A tibble: 3 × 4 #>   default  extra1   extra2      extra3 #>     <num>   <num>    <num>       <num> #> 1    100. 100.001 100.0001 10000.00001 #> 2    100. 100.002 100.0002 10000.00002 #> 3    100. 100.003 100.0003 10000.00003"},{"path":"https://tibble.tidyverse.org/dev/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. magrittr %>% pillar glimpse, obj_sum, size_sum, tbl_sum, type_sum rlang has_name","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for working with row names — rownames","title":"Tools for working with row names — rownames","text":"tibble can row names (e.g., converting regular data frame), removed subsetting [ operator. warning raised attempting assign non-NULL row names tibble. Generally, best avoid row names, basically character column different semantics every column. functions allow detect data frame row names (has_rownames()), remove (remove_rownames()), convert back--forth explicit column (rownames_to_column() column_to_rownames()). Also included rowid_to_column(), adds column start dataframe ascending sequential row ids starting 1. Note remove existing row names.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for working with row names — rownames","text":"","code":"has_rownames(.data)  remove_rownames(.data)  rownames_to_column(.data, var = \"rowname\")  rowid_to_column(.data, var = \"rowid\")  column_to_rownames(.data, var = \"rowname\")"},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for working with row names — rownames","text":".data data frame. var Name column use rownames.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tools for working with row names — rownames","text":"column_to_rownames() always returns data frame. has_rownames() returns scalar logical. functions return object class input.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for working with row names — rownames","text":"","code":"# Detect row names ---------------------------------------------------- has_rownames(mtcars) #> [1] TRUE has_rownames(trees) #> [1] FALSE  # Remove row names ---------------------------------------------------- remove_rownames(mtcars) %>% has_rownames() #> [1] FALSE  # Convert between row names and column -------------------------------- mtcars_tbl <- rownames_to_column(mtcars, var = \"car\") %>% as_tibble() mtcars_tbl #> # A tibble: 32 × 12 #>    car     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Mazd…  21       6  160    110  3.9   2.62  16.5     0     1     4     4 #>  2 Mazd…  21       6  160    110  3.9   2.88  17.0     0     1     4     4 #>  3 Dats…  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1 #>  4 Horn…  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1 #>  5 Horn…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 #>  6 Vali…  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 #>  7 Dust…  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 #>  8 Merc…  24.4     4  147.    62  3.69  3.19  20       1     0     4     2 #>  9 Merc…  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2 #> 10 Merc…  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 #> # ℹ 22 more rows column_to_rownames(mtcars_tbl, var = \"car\") %>% head() #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  # Adding rowid as a column -------------------------------------------- rowid_to_column(trees) %>% head() #>   rowid Girth Height Volume #> 1     1   8.3     70   10.3 #> 2     2   8.6     65   10.3 #> 3     3   8.8     63   10.2 #> 4     4  10.5     72   16.4 #> 5     5  10.7     81   18.8 #> 6     6  10.8     83   19.7"},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":null,"dir":"Reference","previous_headings":"","what":"Subsetting tibbles — subsetting","title":"Subsetting tibbles — subsetting","text":"Accessing columns, rows, cells via $, [[, [ mostly similar regular data frames. However, behavior different tibbles data frames cases: [ always returns tibble default, even one column accessed. Partial matching column names $ [[ supported, NULL returned. $, warning given. scalars (vectors length one) vectors length number rows can used assignment. Rows outside tibble's boundaries accessed. updating [[<- [<-, type changes entire columns supported, updating part column requires new value coercible existing type. See vctrs::vec_slice() underlying implementation. Unstable return type implicit partial matching can lead surprises bugs hard catch. rely code requires original data frame behavior, coerce data frame via .data.frame().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subsetting tibbles — subsetting","text":"","code":"# S3 method for class 'tbl_df' x$name  # S3 method for class 'tbl_df' x[[i, j, ..., exact = TRUE]]  # S3 method for class 'tbl_df' x[i, j, drop = FALSE, ...]  # S3 method for class 'tbl_df' x$name <- value  # S3 method for class 'tbl_df' x[[i, j, ...]] <- value  # S3 method for class 'tbl_df' x[i, j, ...] <- value"},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subsetting tibbles — subsetting","text":"x tibble. name name string. , j Row column indices. j omitted, used column index. ... Ignored. exact Ignored, warning. drop Coerce vector fetching one column via tbl[, j] . Default FALSE, ignored accessing column via tbl[j] . value value store row, column, range cell. Tibbles stricter data frames accepted .","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subsetting tibbles — subsetting","text":"better compatibility older code written regular data frames, [ supports drop argument defaults FALSE. New code use [[ turn column vector.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subsetting tibbles — subsetting","text":"","code":"df <- data.frame(a = 1:3, bc = 4:6) tbl <- tibble(a = 1:3, bc = 4:6)  # Subsetting single columns: df[, \"a\"] #> [1] 1 2 3 tbl[, \"a\"] #> # A tibble: 3 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 tbl[, \"a\", drop = TRUE] #> [1] 1 2 3 as.data.frame(tbl)[, \"a\"] #> [1] 1 2 3  # Subsetting single rows with the drop argument: df[1, , drop = TRUE] #> $a #> [1] 1 #>  #> $bc #> [1] 4 #>  tbl[1, , drop = TRUE] #> # A tibble: 1 × 2 #>       a    bc #>   <int> <int> #> 1     1     4 as.list(tbl[1, ]) #> $a #> [1] 1 #>  #> $bc #> [1] 4 #>   # Accessing non-existent columns: df$b #> [1] 4 5 6 tbl$b #> Warning: Unknown or uninitialised column: `b`. #> NULL  df[[\"b\", exact = FALSE]] #> [1] 4 5 6 tbl[[\"b\", exact = FALSE]] #> Warning: `exact` ignored. #> NULL  df$bd <- c(\"n\", \"e\", \"w\") tbl$bd <- c(\"n\", \"e\", \"w\") df$b #> NULL tbl$b #> Warning: Unknown or uninitialised column: `b`. #> NULL  df$b <- 7:9 tbl$b <- 7:9 df$b #> [1] 7 8 9 tbl$b #> [1] 7 8 9  # Identical behavior: tbl[1, ] #> # A tibble: 1 × 4 #>       a    bc bd        b #>   <int> <int> <chr> <int> #> 1     1     4 n         7 tbl[1, c(\"bc\", \"a\")] #> # A tibble: 1 × 2 #>      bc     a #>   <int> <int> #> 1     4     1 tbl[, c(\"bc\", \"a\")] #> # A tibble: 3 × 2 #>      bc     a #>   <int> <int> #> 1     4     1 #> 2     5     2 #> 3     6     3 tbl[c(\"bc\", \"a\")] #> # A tibble: 3 × 2 #>      bc     a #>   <int> <int> #> 1     4     1 #> 2     5     2 #> 3     6     3 tbl[\"a\"] #> # A tibble: 3 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 tbl$a #> [1] 1 2 3 tbl[[\"a\"]] #> [1] 1 2 3"},{"path":"https://tibble.tidyverse.org/dev/reference/tbl_df-class.html","id":null,"dir":"Reference","previous_headings":"","what":"tbl_df class — tbl_df-class","title":"tbl_df class — tbl_df-class","text":"tbl_df class subclass data.frame, created order different default behaviour. colloquial term \"tibble\" refers data frame tbl_df class. Tibble central data structure set packages known tidyverse, including dplyr, ggplot2, tidyr, readr. general ethos tibbles lazy surly: less complain base data.frames. forces problems tackled earlier explicitly, typically leading code expressive robust.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tbl_df-class.html","id":"properties-of-tbl-df","dir":"Reference","previous_headings":"","what":"Properties of tbl_df","title":"tbl_df class — tbl_df-class","text":"Objects class tbl_df : class attribute c(\"tbl_df\", \"tbl\", \"data.frame\"). base type \"list\", element list vctrs::vec_size(). names attribute character vector length underlying list. row.names attribute, included compatibility data.frame. attribute consulted query number rows, row names might stored ignored tibble methods.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tbl_df-class.html","id":"behavior-of-tbl-df","dir":"Reference","previous_headings":"","what":"Behavior of tbl_df","title":"tbl_df class — tbl_df-class","text":"default behaviour tibbles differs data.frames, creation access: Column data coerced. character vector turned factor. List-columns expressly anticipated require special tricks. Internal names never stripped column data. Read tibble(). Recycling happens length 1 input. Read vctrs::vec_recycle(). Column names munged, although missing names auto-populated. Empty duplicated column names strongly discouraged, user must indicate resolve. Read vctrs::vec_as_names(). Row names added strongly discouraged, favor storing info column. Read rownames. df[, j] returns tibble; automatically extract column inside. df[, j, drop = FALSE] default. Read subsetting. partial matching $ used index name. df$name nonexistent name generates warning. Read subsetting. See vignette(\"invariants\") detailed description behavior. Furthermore, printing inspection high priority. goal convey much information possible, concise way, even large complex tibbles. Read formatting.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tibble-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tibble: Simple Data Frames — tibble-package","title":"tibble: Simple Data Frames — tibble-package","text":"Provides 'tbl_df' class ('tibble') stricter checking better formatting traditional data frame.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"tibble: Simple Data Frames — tibble-package","text":"tibble package provides utilities handling tibbles, \"tibble\" colloquial term S3 tbl_df class. tbl_df class special case base data.frame class, developed response lessons learned many years data analysis data frames. Tibble central data structure set packages known tidyverse, including dplyr, ggplot2, tidyr, readr. General resources: Website tibble package: https://tibble.tidyverse.org Vectors chapter Advanced R (2nd edition), specifically Data frames tibbles section Resources specific topics: Create tibble: tibble(), as_tibble(), tribble(), enframe() Inspect tibble: print.tbl(), glimpse() Details S3 tbl_df class: tbl_df Package options: tibble_options","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tibble-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tibble: Simple Data Frames — tibble-package","text":"Maintainer: Kirill Müller kirill@cynkra.com (ORCID) Authors: Hadley Wickham hadley@rstudio.com contributors: Romain Francois romain@r-enthusiasts.com [contributor] Jennifer Bryan jenny@rstudio.com [contributor] RStudio [copyright holder, funder]","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Build a data frame — tibble","title":"Build a data frame — tibble","text":"tibble() constructs data frame. used like base::data.frame(), couple notable differences: returned data frame class tbl_df, addition data.frame. allows -called \"tibbles\" exhibit special behaviour, enhanced printing. Tibbles fully described tbl_df. tibble() much lazier base::data.frame() terms transforming user's input. Character vectors coerced factor. List-columns expressly anticipated require special tricks. Column names modified. Inner names columns left unchanged. tibble() builds columns sequentially. defining column, can refer columns created earlier call. columns length one recycled. column evaluates data frame tibble, nested spliced. evaluates matrix array, remains matrix array, respectively. See examples. tibble_row() constructs data frame guaranteed occupy one row. Vector columns required size one, non-vector columns wrapped list.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build a data frame — tibble","text":"","code":"tibble(   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )  tibble_row(   ...,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )"},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build a data frame — tibble","text":"... <dynamic-dots> set name-value pairs. arguments processed rlang::quos() support unquote via rlang::!! unquote-splice via rlang::!!!. Use := create columns start dot. Arguments evaluated sequentially. can refer previously created elements directly using rlang::.data pronoun. refer explicitly objects calling environment, use rlang::!! rlang::.env, e.g. !!.data .env$.data special case object named .data. .rows number rows, useful create 0-column tibble just additional check. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build a data frame — tibble","text":"tibble, colloquial term object class tbl_df. tbl_df object also data frame, .e. class data.frame.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build a data frame — tibble","text":"","code":"# Unnamed arguments are named with their expression: a <- 1:5 tibble(a, a * 2) #> # A tibble: 5 × 2 #>       a `a * 2` #>   <int>   <dbl> #> 1     1       2 #> 2     2       4 #> 3     3       6 #> 4     4       8 #> 5     5      10  # Scalars (vectors of length one) are recycled: tibble(a, b = a * 2, c = 1) #> # A tibble: 5 × 3 #>       a     b     c #>   <int> <dbl> <dbl> #> 1     1     2     1 #> 2     2     4     1 #> 3     3     6     1 #> 4     4     8     1 #> 5     5    10     1  # Columns are available in subsequent expressions: tibble(x = runif(10), y = x * 2) #> # A tibble: 10 × 2 #>         x     y #>     <dbl> <dbl> #>  1 0.900  1.80  #>  2 0.617  1.23  #>  3 0.704  1.41  #>  4 0.546  1.09  #>  5 0.807  1.61  #>  6 0.184  0.368 #>  7 0.725  1.45  #>  8 0.623  1.25  #>  9 0.0574 0.115 #> 10 0.636  1.27   # tibble() never coerces its inputs, str(tibble(letters)) #> tibble [26 × 1] (S3: tbl_df/tbl/data.frame) #>  $ letters: chr [1:26] \"a\" \"b\" \"c\" \"d\" ... str(tibble(x = list(diag(1), diag(2)))) #> tibble [2 × 1] (S3: tbl_df/tbl/data.frame) #>  $ x:List of 2 #>   ..$ : num [1, 1] 1 #>   ..$ : num [1:2, 1:2] 1 0 0 1  # or munges column names (unless requested), tibble(`a + b` = 1:5) #> # A tibble: 5 × 1 #>   `a + b` #>     <int> #> 1       1 #> 2       2 #> 3       3 #> 4       4 #> 5       5  # but it forces you to take charge of names, if they need repair: try(tibble(x = 1, x = 2)) #> Error in tibble(x = 1, x = 2) :  #>   Column name `x` must not be duplicated. #> Use `.name_repair` to specify repair. #> Caused by error in `repaired_names()` at tibble/R/names.R:14:3: #> ! Names must be unique. #> ✖ These names are duplicated: #>   * \"x\" at locations 1 and 2. tibble(x = 1, x = 2, .name_repair = \"unique\") #> New names: #> • `x` -> `x...1` #> • `x` -> `x...2` #> # A tibble: 1 × 2 #>   x...1 x...2 #>   <dbl> <dbl> #> 1     1     2 tibble(x = 1, x = 2, .name_repair = \"minimal\") #> # A tibble: 1 × 2 #>       x     x #>   <dbl> <dbl> #> 1     1     2  ## By default, non-syntactic names are allowed, df <- tibble(`a 1` = 1, `a 2` = 2) ## because you can still index by name: df[[\"a 1\"]] #> [1] 1 df$`a 1` #> [1] 1 with(df, `a 1`) #> [1] 1  ## Syntactic names are easier to work with, though, and you can request them: df <- tibble(`a 1` = 1, `a 2` = 2, .name_repair = \"universal\") #> New names: #> • `a 1` -> `a.1` #> • `a 2` -> `a.2` df$a.1 #> [1] 1  ## You can specify your own name repair function: tibble(x = 1, x = 2, .name_repair = make.unique) #> # A tibble: 1 × 2 #>       x   x.1 #>   <dbl> <dbl> #> 1     1     2  fix_names <- function(x) gsub(\"\\\\s+\", \"_\", x) tibble(`year 1` = 1, `year 2` = 2, .name_repair = fix_names) #> # A tibble: 1 × 2 #>   year_1 year_2 #>    <dbl>  <dbl> #> 1      1      2  ## purrr-style anonymous functions and constants ## are also supported tibble(x = 1, x = 2, .name_repair = ~ make.names(., unique = TRUE)) #> # A tibble: 1 × 2 #>       x   x.1 #>   <dbl> <dbl> #> 1     1     2  tibble(x = 1, x = 2, .name_repair = ~ c(\"a\", \"b\")) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     1     2  # Tibbles can contain columns that are tibbles or matrices # if the number of rows is compatible. Unnamed tibbled are # spliced, i.e. the inner columns are inserted into the # tibble under construction. tibble(   a = 1:3,   tibble(     b = 4:6,     c = 7:9   ),   d = tibble(     e = tibble(       f = b     )   ) ) #> # A tibble: 3 × 4 #>       a     b     c d$e$f #>   <int> <int> <int> <int> #> 1     1     4     7     4 #> 2     2     5     8     5 #> 3     3     6     9     6 tibble(   a = 1:3,   b = diag(3),   c = cor(trees),   d = Titanic[1:3, , , ] ) #> # A tibble: 3 × 4 #>       a b[,1]  [,2]  [,3] c[,\"Girth\"] [,\"Height\"] [,\"Volume\"]            d #>   <int> <dbl> <dbl> <dbl>       <dbl>       <dbl>       <dbl> <table[,2,2> #> 1     1     1     0     0       1           0.519       0.967          0 … #> 2     2     0     1     0       0.519       1           0.598          0 … #> 3     3     0     0     1       0.967       0.598       1             35 …  # Data can not contain tibbles or matrices with incompatible number of rows: try(tibble(a = 1:3, b = tibble(c = 4:7))) #> Error in tibble(a = 1:3, b = tibble(c = 4:7)) :  #>   Tibble columns must have compatible sizes. #> • Size 3: Existing data. #> • Size 4: Column `b`. #> ℹ Only values of size one are recycled.  # Use := to create columns with names that start with a dot: tibble(.dotted := 3) #> # A tibble: 1 × 1 #>   .dotted #>     <dbl> #> 1       3  # This also works, but might break in the future: tibble(.dotted = 3) #> # A tibble: 1 × 1 #>   .dotted #>     <dbl> #> 1       3  # You can unquote an expression: x <- 3 tibble(x = 1, y = x) #> # A tibble: 1 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     1 tibble(x = 1, y = !!x) #> # A tibble: 1 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3  # You can splice-unquote a list of quosures and expressions: tibble(!!!list(x = rlang::quo(1:10), y = quote(x * 2))) #> # A tibble: 10 × 2 #>        x     y #>    <int> <dbl> #>  1     1     2 #>  2     2     4 #>  3     3     6 #>  4     4     8 #>  5     5    10 #>  6     6    12 #>  7     7    14 #>  8     8    16 #>  9     9    18 #> 10    10    20  # Use .data, .env and !! to refer explicitly to columns or outside objects a <- 1 tibble(a = 2, b = a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     2 tibble(a = 2, b = .data$a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     2 tibble(a = 2, b = .env$a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     1 tibble(a = 2, b = !!a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     1 try(tibble(a = 2, b = .env$bogus)) #> Error : object 'bogus' not found try(tibble(a = 2, b = !!bogus)) #> Error in eval(expr, envir) : object 'bogus' not found  # Use tibble_row() to construct a one-row tibble: tibble_row(a = 1, lm = lm(Height ~ Girth + Volume, data = trees)) #> # A tibble: 1 × 2 #>       a lm     #>   <dbl> <list> #> 1     1 <lm>"},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — tibble_options","title":"Package options — tibble_options","text":"Options affect interactive display. See pillar::pillar_options options affect display console, cli::num_ansi_colors() enabling disabling colored output via ANSI sequences like [3m[38;5;246m[39m[23m.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Package options — tibble_options","text":"","code":"tibble_options"},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Package options — tibble_options","text":"options can set via options() queried via getOption(). , add tibble. prefix (package name dot) option name. Example: option foo, use options(tibble.foo = value) set getOption(\"tibble.foo\") retrieve current value. option value NULL means default used.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"options-for-the-tibble-package","dir":"Reference","previous_headings":"","what":"Options for the tibble package","title":"Package options — tibble_options","text":"view_max: Maximum number rows shown view() input data frame, passed head(). Default: 1000.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Package options — tibble_options","text":"","code":"# Default setting: getOption(\"tibble.view_max\") #> NULL  # Change for the duration of the session: old <- options(tibble.view_max = 100)  # view() would show only 100 rows e.g. for a lazy data frame  # Change back to the original value: options(old)  # Local scope: local({   rlang::local_options(tibble.view_max = 100)   # view() would show only 100 rows e.g. for a lazy data frame }) # view() would show the default 1000 rows e.g. for a lazy data frame"},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-wise tibble creation — tribble","title":"Row-wise tibble creation — tribble","text":"Create tibbles using easier read row--row layout. useful small tables data readability important.  Please see tibble-package general introduction.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row-wise tibble creation — tribble","text":"","code":"tribble(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row-wise tibble creation — tribble","text":"... <dynamic-dots> Arguments specifying structure tibble. Variable names formulas, may appear data. arguments processed rlang::list2() support unquote via rlang::!! unquote-splice via rlang::!!!.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row-wise tibble creation — tribble","text":"tibble.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-wise tibble creation — tribble","text":"","code":"tribble(   ~colA, ~colB,   \"a\",   1,   \"b\",   2,   \"c\",   3 ) #> # A tibble: 3 × 2 #>   colA   colB #>   <chr> <dbl> #> 1 a         1 #> 2 b         2 #> 3 c         3  # tribble will create a list column if the value in any cell is # not a scalar tribble(   ~x,  ~y,   \"a\", 1:3,   \"b\", 4:6 ) #> # A tibble: 2 × 2 #>   x     y         #>   <chr> <list>    #> 1 a     <int [3]> #> 2 b     <int [3]>  # Use dplyr::mutate(dplyr::across(...)) to assign an explicit type tribble(   ~a, ~b, ~c,   1, \"2000-01-01\", \"1.5\" ) %>%   dplyr::mutate(     dplyr::across(a, as.integer),     dplyr::across(b, as.Date)   ) #> # A tibble: 1 × 3 #>       a b          c     #>   <int> <date>     <chr> #> 1     1 2000-01-01 1.5"},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy printing — trunc_mat","title":"Legacy printing — trunc_mat","text":"tibble 3.1.0, printing handled entirely pillar package. use function. implement package extend tibble, printed output can customized various ways. See vignette(\"extending\", package = \"pillar\") details.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy printing — trunc_mat","text":"","code":"trunc_mat(x, n = NULL, width = NULL, n_extra = NULL)"},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy printing — trunc_mat","text":"x Object format print. n Number rows show. NULL, default, print rows less option tibble.print_max. Otherwise, print tibble.print_min rows. width Width text output generate. defaults NULL, means use getOption(\"tibble.width\") (also NULL) getOption(\"width\"); latter displays columns fit one screen. can also set options(tibble.width = Inf) override default always print columns, may slow wide tibbles. n_extra Number extra columns print abbreviated information , width small entire tibble. NULL, default, print information tibble.max_extra_cols extra columns.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Legacy printing — trunc_mat","text":"object print() method print input similarly tibble. internal data format implementation detail, rely .","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":null,"dir":"Reference","previous_headings":"","what":"View an object — view","title":"View an object — view","text":"Calls utils::View() input returns , invisibly. input data frame, processed using variant .data.frame(head(x, n)). message printed number rows exceeds n. function effect noninteractive sessions.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View an object — view","text":"","code":"view(x, title = NULL, ..., n = NULL)"},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View an object — view","text":"x object display. title title use display, default deparsed expression used. ... Unused, must empty. n Maximum number rows display. used x data frame. Uses view_max option default.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"View an object — view","text":"RStudio IDE overrides utils::View(), picked correctly.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9033","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9033","text":"Use pkgdown branch (#1604). ci: Use pkgdown branch ci: Updates duckdb ci: Trigger run","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-2-1-9032","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.2.1.9032","text":"Rework view() better work RStudio Positron (@DavisVaughan, #1551, #1603).","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9031","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9031","text":"Install via R CMD INSTALL ., pak (#1601). ci: Install via R CMD INSTALL ., pak ci: Bump version upload-artifact action","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9030","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9030","text":"Install local package pkgdown builds. Improve support protected branches fledge. Improve support protected branches, without fledge.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9029","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9029","text":"Auto-update GitHub Actions. 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Run: https://github.com/tidyverse/tibble/actions/runs/9687521438 Replace non-API SET_S4_OBJECT() Rf_asS4() (@olivroy, #1588).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9019","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9019","text":"Fix. Remove dead workflow. Recent updates.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9018","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9018","text":"Snapshot updates (#1580).","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-2-1-9017","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.2.1.9017","text":"new_tibble() as_tibble() support attributes named \"n\" \"x\" (#1573, #1574).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219016","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9016","title":"tibble 3.2.1.9016","text":"Internal changes .","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9015","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9015","text":"Revert “Fix as_tibble() usage”. reverts commit 49d0b63504c4e3f77beaf2889ace12cad7d3f293.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219014","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9014","title":"tibble 3.2.1.9014","text":"Internal changes .","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-2-1-9013","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.2.1.9013","text":"as_tibble() calls .data.frame() objects subclasses \"tbl_df\" (@TimTaylor, #1556, #1557).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9013","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9013","text":"Check fast path first.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9012","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9012","text":"Skip tests packages missing.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"testing-3-2-1-9012","dir":"Changelog","previous_headings":"","what":"Testing","title":"tibble 3.2.1.9012","text":"Skip tests packages missing.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219011","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9011","title":"tibble 3.2.1.9011","text":"Merge pull request #1565 tidyverse/snapshot-main-rcc-smoke-null.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219010","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9010","title":"tibble 3.2.1.9010","text":"Merge pull request #1562 tidyverse/snapshot-main-rcc-smoke-null. Merge pull request #1561 tidyverse/snapshot-main-rcc-full-config-os-macos-latest-r-release.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219009","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9009","title":"tibble 3.2.1.9009","text":"Merge pull request #1560 tidyverse/snapshot-main-R-CMD-check-base-null.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-2-1-9008","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.2.1.9008","text":"Fix rendering suggested packages missing.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"testing-3-2-1-9007","dir":"Changelog","previous_headings":"","what":"Testing","title":"tibble 3.2.1.9007","text":"Override :: avoid failures tests without suggested packages.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219006","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9006","title":"tibble 3.2.1.9006","text":"Merge branch ‘docs’.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9005","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9005","text":"Snapshot updates rcc-smoke (null) (#1548).","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-2-1-9004","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.2.1.9004","text":"Alt text (@maelle, #1528, #1539).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219003","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9003","title":"tibble 3.2.1.9003","text":"Merged cran-3.2.1 main.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219002","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9002","title":"tibble 3.2.1.9002","text":"Internal changes .","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219001","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9001","title":"tibble 3.2.1.9001","text":"Merge pull request #1527 tidyverse/snapshot-main-rcc-smoke-null.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219000","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9000","title":"tibble 3.2.1.9000","text":"Internal changes .","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-321","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1","title":"tibble 3.2.1","text":"CRAN release: 2023-03-20","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-2-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.2.1","text":"Use symbol instead string .Call().","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-320","dir":"Changelog","previous_headings":"","what":"tibble 3.2.0","title":"tibble 3.2.0","text":"CRAN release: 2023-03-08","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-2-0","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.2.0","text":"Accurate location source error error messages (#1379, #1065, #1508). as_data_frame() now also refers .data.frame() deprecation message (#1149, #1506).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-3-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 3.2.0","text":"Deprecated functions arguments detect usage CRAN packages (#1515): data_frame_(), lst_(), frame_data() as_tibble(validate = ), as_tibble(NULL), new_tibble(subclass = ) add_row() add_column() non-data-frame input add_column() input non-unique names corner cases tbl[[x]] Breaking change: Remove knit_print.trunc_mat() method (#1516). Forward trunc_mat() new-style pillar methods (#1517).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.2.0","text":"Allow glue() classed characters subassignment (#1150, #1503).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-2-0","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.2.0","text":"Reduce overhead single-column subset assignment (#1363).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-2-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.2.0","text":"New vignette(\"extending\") (#275, #1512). Minor updates (#1151, #1070, #1512, #1485). Update example nrow argument new_tibble() (@heavywatal, #1394). Fix display mermaid diagrams vignette(\"formats\") (@maelle, #1497, #1498). 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Mention median() Recovery section vignette(\"numbers\") (#1197).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-317","dir":"Changelog","previous_headings":"","what":"tibble 3.1.7","title":"tibble 3.1.7","text":"CRAN release: 2022-05-03","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-change-3-1-7","dir":"Changelog","previous_headings":"","what":"Breaking change","title":"tibble 3.1.7","text":"trunc_mat() now returns value different structure. considered implementation detail can change future, rely . guarantee calling print() display input like tibble (#1059).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-1-7","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.1.7","text":"Avoid listing dim_desc() reexports. Add examples data frame matrix columns (#978, #1012).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-7","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.7","text":"Require rlang 1.0.1 pillar 1.7.0 (#1063). Prefer class .subclass rlang::error_cnd() (#1015, #1060).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-316","dir":"Changelog","previous_headings":"","what":"tibble 3.1.6","title":"tibble 3.1.6","text":"CRAN release: 2021-11-07 set_num_opts() set_char_opts() reexported pillar (#959). view() uses rlang::expr_deparse(width = Inf) avoid errors long |> pipes (#957). new_tibble() checks nrow argument nonnegative less 2^31 (#916). tbl_sum.tbl_df() ellipsis formals extensibility.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-315","dir":"Changelog","previous_headings":"","what":"tibble 3.1.5","title":"tibble 3.1.5","text":"CRAN release: 2021-09-30 Avoid necessity set \"tibble.view_max\" option lazy tables (#954). Avoid blanket import lifecycle package compatibility upcoming rlang (#955, @romainfrancois).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-314","dir":"Changelog","previous_headings":"","what":"tibble 3.1.4","title":"tibble 3.1.4","text":"CRAN release: 2021-08-25","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-1-4","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.1.4","text":".data.frame.tbl_df() strips inner column names (#837). new_tibble() allows omitting nrow argument (#781).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-1-4","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.1.4","text":"Move vignette(\"digits\"), vignette(\"numbers\"), ?num ?char pillar package (#913). Replace iris trees (#943). Various documentation improvements. New ?tibble_options help page (#912).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-1-4","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.1.4","text":"x[, j] <- one_row_value avoids explicit recycling right-hand side, recycling happens implicitly vctrs::vec_assign() performance (#922).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-4","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.4","text":"Vignettes now tested snapshot test (#919). new_tibble() uses vctrs::new_data_frame() internally (#726, @DavisVaughan). Adapt pillar 1.6.2. Fix tests compatibility pillar 1.6.2.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-313","dir":"Changelog","previous_headings":"","what":"tibble 3.1.3","title":"tibble 3.1.3","text":"CRAN release: 2021-07-23","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-1-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.1.3","text":"tbl[row, col] <- rhs treats -NA logical vector missing value existing data (#773) right-hand side value (#868). means column initialized NA (type logical) change type row updated value different type. [[<-() supports symbols (#893).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-1-3","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.1.3","text":"as_tibble_row() supports arbitrary vectors (#797). enframe() deframe() support arbitrary vectors (#730). tibble() tibble_row() ignore columns evaluate NULL, verbatim NULL passed (#895, #900). new_tibble() now faster (#901, @mgirlich).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-3","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.3","text":"Establish compatibility rlang > 0.4.11 (#908). Use pillar::dim_desc() (#859). Establish compatibility testthat > 3.0.3 (#896, @lionel-). Bump required versions ellipsis vctrs avoid warning package load.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-312","dir":"Changelog","previous_headings":"","what":"tibble 3.1.2","title":"tibble 3.1.2","text":"CRAN release: 2021-05-16 Bump required versions ellipsis vctrs avoid warning package load.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-311","dir":"Changelog","previous_headings":"","what":"tibble 3.1.1","title":"tibble 3.1.1","text":"CRAN release: 2021-04-18 num() char() reexported pillar (#880). tribble() frame_matrix() give error values named (#871, @lorenzwalthert). Document cli.num_colors option (#410). Fix new_tibble() examples compatibility pillar 1.6.0.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-310","dir":"Changelog","previous_headings":"","what":"tibble 3.1.0","title":"tibble 3.1.0","text":"CRAN release: 2021-02-25","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.1.0","text":"has_rownames() now works correctly data frames \"row.names\" attribute malformed due problem structure() (#852). tbl[FALSE, \"column\"] <- x adds new column (#846).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-1-0","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.1.0","text":"Importing pillar 1.5.0, cli crayon now suggested packages (#475). size_sum() now reexported pillar (#850, @topepo). as_tibble() hints often use .name_repair argument column names invalid (#855). as_tibble.table() mentions .name_repair argument error message (#839).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.0","text":"Remove compatibility code pillar < 1.5.0 (#861). Moved functions “stable” lifecycle (#860).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-306","dir":"Changelog","previous_headings":"","what":"tibble 3.0.6","title":"tibble 3.0.6","text":"CRAN release: 2021-01-29 vec_ptype_abbr.tbl_df() type_sum.tbl_df() now uses name topmost class subclasses \"tbl_df\" (#843). Ignore errors formats.Rmd vignette. Avoid tidy evaluation pillar compatibility code.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-305","dir":"Changelog","previous_headings":"","what":"tibble 3.0.5","title":"tibble 3.0.5","text":"CRAN release: 2021-01-15 Use testthat edition 3, compatible testthat 3.0.1 (#827, #832).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-304","dir":"Changelog","previous_headings":"","what":"tibble 3.0.4","title":"tibble 3.0.4","text":"CRAN release: 2020-10-12","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"compatibility-3-0-4","dir":"Changelog","previous_headings":"","what":"Compatibility","title":"tibble 3.0.4","text":"Establish compatibility upcoming pillar 1.5.0 (#818). tbl_sum() shows “data frame” instead “tibble” objects inheriting \"tbl\" \"tbl_df\" (#818). Register format.tbl() print.tbl() methods pillar doesn’t (#816). Use vctrs::num_as_location() internally subset assignment rows columns better error messages (#746). Adapt tests development version testthat.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-0-4","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.0.4","text":"Fix documentation link base::Extract. add_row(df) adds empty row (#809, @DavisVaughan).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-303","dir":"Changelog","previous_headings":"","what":"tibble 3.0.3","title":"tibble 3.0.3","text":"CRAN release: 2020-07-10 Fix test compatibility rlang 0.4.7. Fix warning needs_dots arguments pillar >= 1.4.5 (#798).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-302","dir":"Changelog","previous_headings":"","what":"tibble 3.0.2","title":"tibble 3.0.2","text":"CRAN release: 2020-07-07","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-0-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.0.2","text":"[[ works classed indexes , e.g. created glue::glue() (#778). add_column() works without warning 0-column data frames (#786). tribble() now better handles named inputs (#775) objects non-vtrs classes like lubridate::Period (#784) formattable::formattable (#785).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-0-2","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.0.2","text":"Subsetting subassignment faster (#780, #790, #794). .null() preferred is_null() speed. Implement continuous benchmarking (#793).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"compatibility-3-0-2","dir":"Changelog","previous_headings":"","what":"Compatibility","title":"tibble 3.0.2","text":"is_vector_s3() longer reexported pillar (#789).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-301","dir":"Changelog","previous_headings":"","what":"tibble 3.0.1","title":"tibble 3.0.1","text":"CRAN release: 2020-04-20","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"compatibility-fixes-3-0-1","dir":"Changelog","previous_headings":"","what":"Compatibility fixes","title":"tibble 3.0.1","text":"[<-.tbl_df() coerces matrices data frames (#762). Use delayed import cli work around unload problems downstream packages (#754).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-0-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.0.1","text":"soft-deprecation warnings actually visible. .name_repair function, repair messages shown (#763). Remove superseded signal as_tibble.list(), as_tibble_row() works size 1.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"enhancements-3-0-1","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tibble 3.0.1","text":"as_tibble(validate = ) now always triggers deprecation warning. Subsetting subassignment rows one-column matrices work , deprecation warning (#760). Attempts update tibble row atomic vector give clearer error message. Recycling message subassignment appears target size != 1. Tweak title “Invariants” vignette.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-300","dir":"Changelog","previous_headings":"","what":"tibble 3.0.0","title":"tibble 3.0.0","text":"CRAN release: 2020-03-30","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"major-breaking-changes-3-0-0","dir":"Changelog","previous_headings":"","what":"Major breaking changes","title":"tibble 3.0.0","text":"Subset assignment (“subassignment”) also subsetting become stricter. Symptoms: Error: common type … Error: Assigned data ... must compatible … must one dimension, 2 Error: Lossy cast … … “invariants” article https://tibble.tidyverse.org/dev/articles/invariants.html describes invariants operations follow tibble, important differences data frames. tried make subsetting subassignment safe possible, errors caught early , introducing little friction possible. List classes longer automatically treated vectors. Symptoms: Error: columns tibble must vectors Error: Expected vector, ... object implement class wraps list S3 vector, need include \"list\" class: Alternatively, implement vec_proxy() method described https://vctrs.r-lib.org/reference/vec_data.html, construct class list_of(). Added experimental support inner names columns, form tibble(= c(b = 1)). Inner names longer stripped creating tibble. maintained slicing operations yet updated assigning row subscript. change may break existing comparison tests don’t expect names columns (#630). Symptoms: “names target current” comparing","code":"structure(x, class = c(\"your_s3_class\", \"list\"))"},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-3-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 3.0.0","text":"tibble() now splices anonymous data frames, tibble(tibble(= 1), b = ) equivalent tibble(= 1, b = ). means tibble(trees) now three columns, use tibble(trees = trees) intention create packed data frame (#581). name-repair help topic gone, refer ?vctrs::vec_as_names instead. expression() columns converted lists workaround lacking support vctrs (#657). tribble() now stricter combining values. values column must compatible, otherwise error occurs (#204). criteria wrapping list column now based vctrs principles: non-vectors vectors vctrs::vec_size() unequal 1 wrapped lists. $ warns unconditionally column found, [[ doesn’t warn. add_row() now uses vctrs::vec_rbind() hood, means columns combined vctrs::vec_c(). particular, factor columns converted character one columns character column.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"soft-deprecations-3-0-0","dir":"Changelog","previous_headings":"","what":"Soft deprecations","title":"tibble 3.0.0","text":"Soft-deprecate subclass argument new_tibble(). Soft-deprecate as_tibble() without arguments (#683). Preparing move glimpse() tbl_sum() pillar package. package implements methods, please import generics pillar soon become available .","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-0-0","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.0.0","text":"Internals now make heavy use vctrs package, following invariants defined . Name repair responsibility vctrs now (#464). errors emitted directly package inherit \"tibble_error\" \"rlang_error\" classes. cases, \"vctrs_error\" errors may passed . exact subclass subject change. Example: tibble(= quote(b)) raises error inherits \"tibble_error_column_must_be_vector\", \"tibble_error\" \"rlang_error\", \"error\" \"condition\" like errors. rely wording \"tibble_error_column_must_be_vector\", likely change. Use following pattern catch errors emitted tibble: New tibble_row() constructs tibbles exactly one row, fails. Non-vector objects automatically wrapped list, vectors (including lists) must length one (#205). New as_tibble_row() as_tibble_col() convert bare vector one-row one-column tibble, respectively. as_tibble_col() also works non-bare vectors. Using as_tibble() bare vectors superseded (#447). as_tibble.data.frame() uses implicit row names asked create column row names. allows lossless direct conversion matrices row names tibbles (#567, @stufield). Implement str.tbl_df() (#480). tribble() now returns columns \"unspecified\" type 0-row tibbles. add_row() add_column() now restore attributes avoid errors appending sf objects tibble subclasses (#662). add_column() gains .name_repair argument. given, .data must unique columns, deprecation message. Allow POSIXlt columns, now better supported dplyr tools thanks vctrs (#626). tibble() ignores NULL arguments, named unnamed (#580). view() works remote data sources applying strategy print() glimpse(). maximum number rows case can specified using new n argument, default taken new \"tibble.view_max\" option (#695).","code":"tryCatch(   your_code(),   tibble_error = function(cnd) {   } )"},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"output-3-0-0","dir":"Changelog","previous_headings":"","what":"Output","title":"tibble 3.0.0","text":"Formatting dimensions never uses scientific notation. glimpse() uses “Rows” “Columns” instead “Variables” “Observations”, ’re sure data tidy (#614). view() now uses created (passed) title argument (#610, @xvrdm).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-0-0","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.0.0","text":"Construction subsetting large data frames faster now (#717, @romainfrancois).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-0-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.0.0","text":"Import lifecycle package (#669). new_tibble() removes redundant subclasses \"class\" attribute. Using classed conditions. classes start \"tibble_error_\" also contain \"tibble_error\" (#659). magrittr pipe %>% reexported.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-213","dir":"Changelog","previous_headings":"","what":"tibble 2.1.3","title":"tibble 2.1.3","text":"CRAN release: 2019-06-06 Fix compatibility R 3.5 earlier, regression introduced tibble 2.1.2.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-212","dir":"Changelog","previous_headings":"","what":"tibble 2.1.2","title":"tibble 2.1.2","text":"CRAN release: 2019-05-29 Relax version requirements. Fix test failing pillar upgrade.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-211","dir":"Changelog","previous_headings":"","what":"tibble 2.1.1","title":"tibble 2.1.1","text":"CRAN release: 2019-03-16 Three dots used even \"unique\" name repair (#566). add_row(), add_case() add_column() now signal warning per session input data frame (#575). Fix view() case object named x exists global environment (#579).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-201","dir":"Changelog","previous_headings":"","what":"tibble 2.0.1","title":"tibble 2.0.1","text":"CRAN release: 2019-01-12 tibble names can set NULL within RStudio, R routines within RStudio relied behaviour (#563, @kevinushey). as_tibble.matrix(validate = TRUE) works , lifecycle warning (#558). Replace new_list_along() rep_along() support rlang 0.3.1 (#557, @lionel-).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-200","dir":"Changelog","previous_headings":"","what":"tibble 2.0.0","title":"tibble 2.0.0","text":"CRAN release: 2019-01-04","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-2-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 2.0.0","text":"tibble() as_tibble() functions, low-level new_tibble() constructor, undergone major overhaul improve consistency. suspect package code affected analysis code. improve compatibility existing code, breaking changes reduced minimum cases replaced warning appears per session. Call tibble:::scoped_lifecycle_errors() updating packages scripts new semantics API turn warnings errors. compatibility code removed tibble 3.0.0. optional arguments moved past ellipsis, must specified named arguments. affects mostly n argument as_tibble.table(), passing n unnamed still works (warning). new_tibble() optimized performance, function longer strips dimensions 1d arrays longer checks correctness names column lengths. (still checks object named, except zero-length input.) Use new validate_tibble() need checks (#471). nrow argument new_tibble() now mandatory. class argument replaces now deprecated subclass argument, latter supported quietly time (#518). Setting names tibble via names(df) <- ... now also requires minimal names, otherwise warning issued per session (#466). as_tibble(), checking names also enabled default, even tibbles, matrices matrix-like objects: names must exist, NA names allowed. Coercing matrix without column names trigger warning per session. (corresponds \"minimal\" checks described .). validate argument as_tibble() deprecated, see alternatives. (as_tibble.tbl_df() method removed, as_tibble.data.frame() method used tibbles.) as_tibble() always checks columns 1D 2D vectors type POSIXlt, even validate = FALSE (now deprecated). Calling as_tibble() vector now warns per session. Use enframe(name = NULL) converting vector one-column tibble, enframe() converting named vector two-column tibble. data_frame() frame_data() soft-deprecated, please use tibble() tribble() (#111). tibble_(), data_frame_(), lst_() soft-deprecated. Please use tibble() lst() (#111, #509). .tibble() as_data_frame() officially deprecated generic anymore, please use/implement as_tibble() (#111). as_tibble.data.frame() (also as_tibble.matrix()) strip row names default. Code relies tibbles keeping row names now see: different result calling rownames() row.names(), rows full NA values subsetting rows character vector, e.g. as_tibble(mtcars)[\"Mazda RX4\", ]. Call pkgconfig::set_config(\"tibble::rownames\", NA) revert old behavior keeping row names. Packages import tibble can call set_config() .onLoad() function (#114). as_tibble() drops extra classes, particular as_tibble.grouped_df() now removes grouping (#535). column_to_rownames() now always coerces data frame, row names longer supported tibbles (#114). *_rownames() functions, first argument renamed .data consistency (#412). Subsetting one row [..., , drop = TRUE] returns tibble (#442). print.tbl_df() method removed, print.tbl() method handles printing (#519).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-features-2-0-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tibble 2.0.0","text":"tibble() supports columns matrices data frames (#416). new .rows argument tibble() as_tibble() allows specifying expected number rows explicitly, even ’s evident data. allows writing defensive code. Column name repair direct support, via new .name_repair argument tibble() as_tibble(). takes following values: \"minimal\": name repair checks, beyond basic existence. \"unique\": Make sure names unique empty. \"check_unique\": (default value), name repair, check unique. \"universal\": Make names unique syntactic. function: apply custom name repair (e.g., .name_repair = make.names .name_repair = ~make.names(., unique = TRUE) names style base R). validate argument as_tibble() deprecated supported (emits message per session). Use .name_repair = \"minimal\" instead validate = FALSE, .name_repair = \"check_unique\" instead validate = TRUE. need support older versions tibble, pass .name_repair validate arguments consistent way, message emitted case (#469, @jennybc). Row name handling stricter. Row names never (never ) supported tibble() new_tibble(), now stripped default as_tibble(). rownames argument as_tibble() supports: NULL: remove row names (default), NA: keep row names, string: name new column contain existing row names, longer present result. old default can restored calling pkgconfig::set_config(\"tibble::rownames\", NA), also works packages import tibble. new_tibble() as_tibble() now also strip \"dim\" attribute columns one-dimensional arrays. (tibble() already .) Internally, as_tibble() implementation forward extra arguments ... as_tibble.list() handled. means common .rows .name_repair can used inputs. suggest implementations method . enframe() (name = NULL) deframe() now support one-column tibbles (#449). Improved S4 support adding exportClass(tbl_df) NAMESPACE (#436, @jeffreyhanson @javierfajnolla). New validate_tibble() checks tibble internal consistency (#471). Bring error message invalid column type line allowed matrix/df cols (#465, @maxheld83).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-functions-2-0-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"tibble 2.0.0","text":"Added experimental view() function always returns input invisibly calls utils::View() interactive mode (#373).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"output-2-0-0","dir":"Changelog","previous_headings":"","what":"Output","title":"tibble 2.0.0","text":"set_tidy_names() tidy_names() helpers list new names using bullet list six items (#406). one-character ellipse (cli::symbol$ellipsis) printed instead \"...\" available, affects glimpse() output truncated lists (#403). Column names types now formatted identically glimpse() print.tbl_df(). tidy_names() quotes variable names reporting repair (#407). error messages now follow tidyverse style guide (#223). as_tibble() prints informative error message using rownames argument input data frame matrix row names (#388, @anhqle). column_to_rownames() uses real variable name error message (#399, @alexwhan). Lazy tibbles exactly 10 rows longer show “…rows” (#371). glimpse() shows information obtained tbl_sum(), e.g. grouping information grouped_df dplyr (#550).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-2-0-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 2.0.0","text":"glimpse() takes coloring account computing column width, output longer truncated prematurely coloring enabled. glimpse() disambiguates outputs factors levels contain commas (#384, @anhqle). print.tbl_df() negative value n behaves n omitted (#371). Fixed output extra column names contain spaces.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-2-0-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 2.0.0","text":"Skip dplyr tests unavailable (#420, @QuLogic). Skip mockr tests unavailable (#454, @Enchufa2). Use fansi::strwrap_ctl() instead string wrapping routine. tibble() uses recycled values construction unrecycled values validation. tibble() now faster wide tibbles. Subsetting [ operator faster (#544). Avoid use stop() examples packages installed (#453, @Enchufa2). Fix as_tibble() examples using correct argument names requireNamespace() call (#424, @michaelweylandt). as_tibble() checks column length (#365, @anhqle). Using rlang::list2() (#391, @lionel-).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-142","dir":"Changelog","previous_headings":"","what":"tibble 1.4.2","title":"tibble 1.4.2","text":"CRAN release: 2018-01-22","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-4-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.4.2","text":"Fix OS X builds. tibble.width option honored (#369). tbl[1, , drop = TRUE] now behaves identically data frames (#367). Fix error message accessing columns using logical index vector (#337, @mundl). glimpse() returns input zero-column data frames.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-1-4-2","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 1.4.2","text":"enframe(NULL) now returns enframe(logical()) (#352). tribble() now ignores trailing commas (#342, @anhqle). Updated vignettes website documentation.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-1-4-2","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 1.4.2","text":"Faster printing wide tibbles (#360). Faster construction subsetting tibbles (#353). call nrow() head() glimpse(), ncol().","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-141","dir":"Changelog","previous_headings":"","what":"tibble 1.4.1","title":"tibble 1.4.1","text":"CRAN release: 2017-12-25","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-formatting-1-4-1","dir":"Changelog","previous_headings":"","what":"New formatting","title":"tibble 1.4.1","text":"new pillar package now responsible formatting tibbles. Pillar try display many columns possible, necessary truncating shortening output. Colored output highlights important information guides eye. vignette tibble package describes adapt custom data types optimal display tibble.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-features-1-4-1","dir":"Changelog","previous_headings":"","what":"New features","title":"tibble 1.4.1","text":"Make add_case() alias add_row() (#324, @LaDilettante). as_tibble() gains rownames argument (#288, #289). as_tibble.matrix() repairs column names. Tibbles now support character subsetting (#312). `[.tbl_df`() supports drop = TRUE omits warning j passed. calls df[, j, drop = TRUE] df[j, drop = TRUE] now compatible data frames (#307, #311).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-4-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.4.1","text":"Improved compatibility remote data sources glimpse() (#328). Logical indexes supported, warning raised length match number rows 1 (#318). Fixed width word wrapping extra information (#301). Prevent add_column() dropping classes attributes removing use cbind(). Additionally ensures add_column() can used grouped data frames (#303, @DavisVaughan). add_column() empty zero-row tibble variable nonzero length now produces correct error message (#319).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-changes-1-4-1","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"tibble 1.4.1","text":"Reexporting has_name() rlang, instead forwarding, avoid warning importing rlang tibble. Compatible R 3.1 (#323). Remove Rcpp dependency (#313, @patperry).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-134","dir":"Changelog","previous_headings":"","what":"tibble 1.3.4","title":"tibble 1.3.4","text":"CRAN release: 2017-08-22","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-4","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.4","text":"Values length 1 tibble() call recycled prior evaluating subsequent arguments, improving consistency mutate() (#213). Recycling values length 1 tibble() call maintains class (#284). add_row() now always preserves column data types input data frame way rbind() (#296). lst() now handles duplicate names, value defined last used case clash. Adding columns zero-row data frames now also works mixing lengths 1 0 new columns (#167). validate argument now also supported as_tibble.tbl_df(), default FALSE (#278). must passed named argument, as_tibble(validate = TRUE).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"formatting-1-3-4","dir":"Changelog","previous_headings":"","what":"Formatting","title":"tibble 1.3.4","text":"format_v() now always surrounds lists [] brackets, even length one. affects glimpse() output list columns (#106). Factor levels escaped printing (#277). Non-syntactic names now also escaped glimpse() (#280). tibble() gives consistent error message case duplicate column names (#291).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-133","dir":"Changelog","previous_headings":"","what":"tibble 1.3.3","title":"tibble 1.3.3","text":"CRAN release: 2017-05-28","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.3","text":"Added format() print() methods tbl tbl_df classes, protect malformed tibbles inherit \"tbl_df\" \"tbl\", created e.g. ungroup() dplyr 0.5.0 earlier (#256, #263). column width non-syntactic columns computed correctly (#258). Printing tibble doesn’t apply quote escaping list columns. Fix error tidy_names(syntactic = TRUE, quiet = FALSE) names fixed (#260, @imanuelcostigan). Remove unused import declaration assertthat.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-131","dir":"Changelog","previous_headings":"","what":"tibble 1.3.1","title":"tibble 1.3.1","text":"CRAN release: 2017-05-17","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.1","text":"Subsetting zero columns longer returns wrong number rows (#241, @echasnovski).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"interface-changes-1-3-1","dir":"Changelog","previous_headings":"","what":"Interface changes","title":"tibble 1.3.1","text":"New set_tidy_names() tidy_names(), simpler version repair_names() works unchanged now (#217). New rowid_to_column() adds rowid column first column removes row names (#243, @barnettjacob). .equal.tbl_df() method removed, calling .equal() now forwards base::.equal.data.frame(). compare tibbles ignoring row column order, please use dplyr::all_equal() (#247).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"formatting-1-3-1","dir":"Changelog","previous_headings":"","what":"Formatting","title":"tibble 1.3.1","text":"Printing now uses x instead Unicode multiplication sign, avoid encoding issues (#216). String values now quoted printing contain non-printable characters quotes (#253). print(), format(), tbl_sum() methods now implemented class \"tbl\" \"tbl_df\". allows subclasses use tibble’s formatting facilities. formatting header can tweaked implementing tbl_sum() subclass, expected return named character vector. print.tbl_df() method still implemented compatibility downstream packages, calls NextMethod(). printing routine, relying print.data.frame() anymore. Now providing format.tbl_df() full support Unicode characters names data, also glimpse() (#235).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"misc-1-3-1","dir":"Changelog","previous_headings":"","what":"Misc","title":"tibble 1.3.1","text":"Improve formatting error messages (#223). Using rlang instead lazyeval (#225, @lionel-), rlang functions (#244). tribble() now handles values class (#237, @NikNakk). Minor efficiency gains replacing (.na()) anyNA() (#229, @csgillespie). microbenchmark package now used conditionally (#245). pkgdown website.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-130","dir":"Changelog","previous_headings":"","what":"tibble 1.3.0","title":"tibble 1.3.0","text":"CRAN release: 2017-04-01","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.0","text":"Time series matrices (objects class mts ts) now supported as_tibble() (#184). all_equal() function (called .equal.tbl_df()) now forwards dplyr fails helpful message installed. Data frames list columns compared anymore, differences declared class (data.frame vs. tbl_df) ignored. .equal.tbl_df() method gives warning forwards NextMethod() dplyr installed; call .equal(.data.frame(...), ...) avoid warning. ensures consistent behavior function, regardless dplyr loaded (#198).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"interface-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Interface changes","title":"tibble 1.3.0","text":"Now requiring R 3.1.0 instead R 3.1.3 (#189). Add .tibble() alias as_tibble() (#160, @LaDilettante). New frame_matrix(), similar frame_data() matrices (#140, #168, @LaDilettante). New deframe() reverse operation enframe() (#146, #214). Removed unused dependency assertthat.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"general-1-3-0","dir":"Changelog","previous_headings":"Features","what":"General","title":"tibble 1.3.0","text":"Keep column classes adding row empty tibble (#171, #177, @LaDilettante). Singular plural variants error messages mention list objects (#116, #138, @LaDilettante). add_column() can add columns length 1 (#162, #164, @LaDilettante).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"input-validation-1-3-0","dir":"Changelog","previous_headings":"Features","what":"Input validation","title":"tibble 1.3.0","text":"attempt read update missing column now throws clearer warning (#199). attempt call add_row() grouped data frame results helpful error message (#179).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"printing-1-3-0","dir":"Changelog","previous_headings":"Features","what":"Printing","title":"tibble 1.3.0","text":"Render Unicode multiplication sign x represented current locale (#192, @ncarchedi). Backtick NA names printing (#206, #207, @jennybc). glimpse() now uses type_sum() also S3 objects (#185, #186, @holstius). max.print option ignored printing tibble (#194, #195, @t-kalinowski).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-1-3-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 1.3.0","text":"Fix typo obj_sum documentation (#193, @etiennebr). Reword documentation tribble() (#191, @kwstat). Now explicitly stating minimum Rcpp version 0.12.3.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-1-3-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 1.3.0","text":"Using registration native routines.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-12","dir":"Changelog","previous_headings":"","what":"tibble 1.2","title":"tibble 1.2","text":"CRAN release: 2016-08-26","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.2","text":"tibble.width option used glimpse() finite (#153, @kwstat). New as_tibble.poly() support conversion poly object tibble (#110). add_row() now correctly handles existing columns type list updated (#148). .equal() doesn’t throw error anymore one columns named na.last, decreasing method (#107, @BillDunlap).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"interface-changes-1-2","dir":"Changelog","previous_headings":"","what":"Interface changes","title":"tibble 1.2","text":"New add_column(), analogously add_row() (#99). print.tbl_df() gains n_extra method interface trunc_mat() now . add_row() add_column() gain ..arguments indicate row (number) column (number name) new data inserted. Updated added columns named ..(#99). Rename frame_data() tribble(), stands “transposed tibble”. former still available alias (#132, #143).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-1-2","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 1.2","text":"add_row() now can add multiple rows, recycling (#142, @jennybc). Use multiply character × instead x printing dimensions (#126). Output tests disabled Windows. Back-tick non-semantic column names output (#131). Use dttm instead time POSIXt values (#133), now used columns difftime class. Better output 0-row results total number rows unknown (e.g., SQL data sources).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-1-2","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 1.2","text":"New object summary vignette shows methods define custom vector classes used tibble columns (#151). Added examples print.tbl_df(), now using data nycflights13 instead Lahman (#121), guidance install nycflights13 package necessary (#152). Minor changes vignette (#115, @helix123).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-11","dir":"Changelog","previous_headings":"","what":"tibble 1.1","title":"tibble 1.1","text":"CRAN release: 2016-07-04 Follow-release.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-1","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 1.1","text":"tibble() longer alias frame_data() (#82). Remove tbl_df() (#57). $ returns NULL column found, without partial matching. warning given (#109). [[ returns NULL column found (#109).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"output-1-1","dir":"Changelog","previous_headings":"","what":"Output","title":"tibble 1.1","text":"Reworked output: concise summary (begins hash # contains text (#95)), removed empty line, showing number hidden rows columns (#51). trailing metadata also begins hash # (#101). Presence row names indicated star printed output (#72). Format NA values character columns <NA>, like print.data.frame() (#69). number printed extra cols now option (#68, @lionel-). Computation column width properly handles wide (e.g., Chinese) characters, tests still fail Windows (#100). glimpse() shows nesting structure lists uses angle brackets type (#98). Tibbles POSIXlt columns can printed now, text <POSIXlt> shown placeholder encourage usage POSIXct (#86). type_sum() shows topmost class S3 objects.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"error-reporting-1-1","dir":"Changelog","previous_headings":"","what":"Error reporting","title":"tibble 1.1","text":"Strict checking integer logical column indexes. integers, passing non-integer index --bounds index raises error. logicals, vectors length 1 ncol supported. Passing matrix array now raises error case (#83). Warn setting non-NULL row names (#75). Consistently surround variable names single quotes error messages. Use “Unknown column ‘x’” error message column found, like base R (#94). stop() warning() now always called call. = FALSE.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"coercion-1-1","dir":"Changelog","previous_headings":"","what":"Coercion","title":"tibble 1.1","text":".Dim attribute silently stripped columns 1d matrices (#84). Converting tibble without row names regular data frame add explicit row names. as_tibble.data.frame() preserves attributes, uses as_tibble.list() calling overriden methods may lead endless recursion.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-features-1-1","dir":"Changelog","previous_headings":"","what":"New features","title":"tibble 1.1","text":"New has_name() (#102). Prefer tibble() as_tibble() data_frame() as_data_frame() code documentation (#82). New .tibble() is_tibble() (#79). New enframe() converts vectors two-column tibbles (#31, #74). obj_sum() type_sum() show \"tibble\" instead \"tbl_df\" tibbles (#82). as_tibble.data.frame() gains validate argument (as_tibble.list()), TRUE input validated. Implement as_tibble.default() (#71, hadley/dplyr#1752). has_rownames() supports arguments data frames.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.1","text":"Two-dimensional indexing [[ works (#58, #63). Subsetting empty index (e.g., x[]) also removes row names.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-1-1","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 1.1","text":"Document behavior as_tibble.tbl_df() subclasses (#60). Document test subsetting removes row names.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-1-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 1.1","text":"Don’t rely knitr internals testing (#78). Fix compatibility knitr 1.13 (#76). Enhance knit_print() tests. Provide default implementation tbl_sum.tbl_sql() tbl_sum.tbl_grouped_df() allow dplyr release tibble release. Explicit tests format_v() (#98). Test output NULL value tbl_sum(). Test subsetting variants (#62). Add missing test dplyr. Use new expect_output_file() testthat.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"version-10","dir":"Changelog","previous_headings":"","what":"Version 1.0","title":"Version 1.0","text":"CRAN release: 2016-03-23 Initial CRAN release Extracted dplyr 0.4.3 Exported functions: tbl_df() as_data_frame() data_frame(), data_frame_() frame_data(), tibble() glimpse() trunc_mat(), knit_print.trunc_mat() type_sum() New lst() lst_() create lists way data_frame() data_frame_() create data frames (hadley/dplyr#1290). lst(NULL) doesn’t raise error (#17, @jennybc), always uses deparsed expression name (even NULL). New add_row() makes easy add new row data frame (hadley/dplyr#1021). New rownames_to_column() column_to_rownames() (#11, @zhilongjia). New has_rownames() remove_rownames() (#44). New repair_names() fixes missing duplicate names (#10, #15, @r2evans). New is_vector_s3(). Features New as_data_frame.table() argument n control name count column (#22, #23). Use tibble prefix options (#13, #36). glimpse() now (invisibly) returns argument (hadley/dplyr#1570). now generic, default method dispatches str() (hadley/dplyr#1325). default width obtained tibble.width option (#35, #56). as_data_frame() now S3 generic methods lists (old as_data_frame()), data frames (trivial), matrices (efficient C++ implementation) (hadley/dplyr#876), NULL (returns 0-row 0-column data frame) (#17, @jennybc). Non-scalar input frame_data() tibble() (including lists) creates list-valued columns (#7). functions return 0-row n-col data frame data. Bug fixes frame_data() properly constructs rectangular tables (hadley/dplyr#1377, @kevinushey). Minor modifications Uses setOldClass(c(\"tbl_df\", \"tbl\", \"data.frame\")) help S4 (hadley/dplyr#969). tbl_df() automatically generates column names (hadley/dplyr#1606). tbl_dfs gain $ [[ methods ~5x faster defaults, never partial matching (hadley/dplyr#1504), throw error variable exist. [[.tbl_df() falls back regular subsetting used anything single string (#29). base::getElement() now works tibbles (#9). all_equal() allows compare data frames ignoring row column order, optionally ignoring minor differences type (e.g. int vs. double) (hadley/dplyr#821). Used .equal() tibbles. (package contains pure R implementation all_equal(), dplyr code identical behavior written C++ thus faster.) internals data_frame() as_data_frame() aligned, as_data_frame() now automatically recycle length-1 vectors. functions give informative error messages attempting create invalid data frame. can longer create data frame duplicated names (hadley/dplyr#820). functions now check don’t POSIXlt columns, tell use POSIXct (hadley/dplyr#813). data_frame(NULL) raises error “must 1d atomic vector list”. trunc_mat() print.tbl_df() considerably faster wide data frames. now also list first 100 additional variables already screen - control new n_extra parameter print() (hadley/dplyr#1161). type list columns printed correctly (hadley/dplyr#1379). width argument used also 0-row 0-column data frames (#18). used list-columns, S4 objects print class name rather full class hierarchy (#33). Add test [.tbl_df() change class (#41, @jennybc). Improve [.tbl_df() error message. Documentation Update README, edits (#52, @bhive01) enhancements (#54, @jennybc). vignette(\"tibble\") describes difference tbl_dfs regular data frames (hadley/dplyr#1468). Code quality Test using new-style Travis-CI AppVeyor. Full test coverage (#24, #53). Regression tests load known output file (#49). Renamed obj_type() obj_sum(), improvements, better integration type_sum(). Internal cleanup.","code":""}]
+[{"path":"https://tibble.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behavior participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behavior may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://www.contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/.","code":""},{"path":"https://tibble.tidyverse.org/dev/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to tibble","title":"Contributing to tibble","text":"outlines propose change tibble. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://tibble.tidyverse.org/dev/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to tibble","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://tibble.tidyverse.org/dev/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to tibble","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://tibble.tidyverse.org/dev/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to tibble","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"batpigandme/tibble\", fork = TRUE). Install development dependences devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. Please edit NEWS.md .","code":""},{"path":"https://tibble.tidyverse.org/dev/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to tibble","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://tibble.tidyverse.org/dev/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to tibble","text":"Please note tibble project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://tibble.tidyverse.org/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2019 RStudio others. Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with tibble","title":"Getting help with tibble","text":"Thanks using tibble! filing issue, places explore pieces put together make process smooth possible.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":"make-a-reprex","dir":"","previous_headings":"","what":"Make a reprex","title":"Getting help with tibble","text":"Start making minimal reproducible example using reprex package. haven’t heard used reprex , ’re treat! Seriously, reprex make R-question-asking endeavors easier (pretty insane ROI five ten minutes ’ll take learn ’s ). additional reprex pointers, check Get help! section tidyverse site.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":"where-to-ask","dir":"","previous_headings":"","what":"Where to ask?","title":"Getting help with tibble","text":"Armed reprex, next step figure ask. ’s question: start community.rstudio.com, /StackOverflow. people answer questions. ’s bug: ’re right place, file issue. ’re sure: let community help figure ! problem bug feature request, can easily return report . opening new issue, sure search issues pull requests make sure bug hasn’t reported /already fixed development version. default, search pre-populated :issue :open. can edit qualifiers (e.g. :pr, :closed) needed. example, ’d simply remove :open search issues repo, open closed.","code":""},{"path":"https://tibble.tidyverse.org/dev/SUPPORT.html","id":"what-happens-next","dir":"","previous_headings":"","what":"What happens next?","title":"Getting help with tibble","text":"efficient possible, development tidyverse packages tends bursty, shouldn’t worry don’t get immediate response. Typically don’t look repo sufficient quantity issues accumulates, ’s burst intense activity focus efforts. makes development efficient avoids expensive context switching problems, cost taking longer get back . process makes good reprex particularly important might multiple months initial report start working . can’t reproduce bug, can’t fix !","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"basic-differences","dir":"Articles","previous_headings":"Digits","what":"Basic differences","title":"Comparing display with data frames","text":"default getOption(\"digits\") 7, whereas \"pillar.sigfig\" option defaults 3. default setting, pillar prints first three digits (.e. digits represent > 99.9% value number). Another difference pillar show specified number significant digits, even space available. rationale allow quick glance significant digits number, without spending much horizontal space without distraction insignificant digits.","code":"options(digits = 3) c(1.2345, 12.345, 123.45, 1234.5, 12345) #> [1]     1.23    12.35   123.45  1234.50 12345.00 tibble(x = c(1.2345, 12.345, 123.45, 1234.5, 12345)) #> # A tibble: 5 × 1 #>          x #>      <dbl> #> 1     1.23 #> 2    12.3  #> 3   123.   #> 4  1234.   #> 5 12345"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"terminal-zeros","dir":"Articles","previous_headings":"Digits","what":"Terminal zeros","title":"Comparing display with data frames","text":"Terminal zeros shown pillar nonzero value past significant digits shown. contrast base R terminal zeros always shown space, hidden value insignificant:","code":"c(1, 1.00001) #> [1] 1 1 tibble(x = c(1, 1.00001)) #> # A tibble: 2 × 1 #>       x #>   <dbl> #> 1  1    #> 2  1.00"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"trailing-dot","dir":"Articles","previous_headings":"Digits","what":"Trailing dot","title":"Comparing display with data frames","text":"trailing decimal separator shown fractional part integer part already exceeds significant digits. presence decimal separator indicate number larger, exists nonzero fractional part:","code":"c(123, 123.45, 567.89) #> [1] 123 123 568 tibble(x = c(123, 123.45, 567.89)) #> # A tibble: 3 × 1 #>       x #>   <dbl> #> 1  123  #> 2  123. #> 3  568."},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"showing-more-digits","dir":"Articles","previous_headings":"Digits","what":"Showing more digits","title":"Comparing display with data frames","text":"show significant digits, set \"pillar.sigfig\" option larger value: Setting \"pillar.sigfig\" larger value enhance display digits deemed insignificant:","code":"options(digits = 7) options(pillar.sigfig = 7) c(1.2345, 12.345, 123.45, 1234.5, 12345) #> [1]     1.2345    12.3450   123.4500  1234.5000 12345.0000 tibble(x = c(1.2345, 12.345, 123.45, 1234.5, 12345)) #> # A tibble: 5 × 1 #>            x #>        <dbl> #> 1     1.2345 #> 2    12.345  #> 3   123.45   #> 4  1234.5    #> 5 12345 options(digits = 7) options(pillar.sigfig = 7) c(1.2345, 12.3456, 123.4567, 1234.5678, 12345.6789) #> [1]     1.2345    12.3456   123.4567  1234.5678 12345.6789 tibble(x = c(1.2345, 12.3456, 123.4567, 1234.5678, 12345.6789)) #> # A tibble: 5 × 1 #>            x #>        <dbl> #> 1     1.2345 #> 2    12.3456 #> 3   123.4567 #> 4  1234.568  #> 5 12345.68"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"fixed-number-of-digits","dir":"Articles","previous_headings":"Digits","what":"Fixed number of digits","title":"Comparing display with data frames","text":"show fixed number decimal digits, use num() digits argument: See vignette(\"numbers\") details.","code":"num(c(1.2345, 12.345, 123.45, 1234.5, 12345), digits = 2) #> <pillar_num:.2![5]> #> [1]     1.23    12.35   123.45  1234.50 12345.00"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"when-is-it-used","dir":"Articles","previous_headings":"Scientific notation","what":"When is it used?","title":"Comparing display with data frames","text":"base R pillar switch scientific notation decimal representation becomes wide. larger getOption(\"scipen\"), stronger resistance switching scientific notation. default 0 seems anchored 13 digits integer part. \"pillar.max_dec_width\" option similar, indicates width must exceeded switch scientific notation happen. width includes decimal separator.","code":"123456789012 #> [1] 123456789012 123456789012.3 #> [1] 123456789012 1234567890123 #> [1] 1.234568e+12 1234567890123.4 #> [1] 1.234568e+12 options(scipen = 1) 1234567890123 #> [1] 1234567890123 12345678901234 #> [1] 1.234568e+13 12345678901234.5 #> [1] 1.234568e+13 tibble(x = 123456789012) #> # A tibble: 1 × 1 #>              x #>          <dbl> #> 1 123456789012 tibble(x = 123456789012.3) #> # A tibble: 1 × 1 #>               x #>           <dbl> #> 1 123456789012. tibble(x = 1234567890123) #> # A tibble: 1 × 1 #>               x #>           <dbl> #> 1 1234567890123 tibble(x = 1234567890123.4) #> # A tibble: 1 × 1 #>             x #>         <dbl> #> 1 1.234568e12 options(pillar.max_dec_width = 14) tibble(x = 1234567890123) #> # A tibble: 1 × 1 #>               x #>           <dbl> #> 1 1234567890123 tibble(x = 12345678901234) #> # A tibble: 1 × 1 #>                x #>            <dbl> #> 1 12345678901234 tibble(x = 12345678901234.5) #> # A tibble: 1 × 1 #>             x #>         <dbl> #> 1 1.234568e13"},{"path":"https://tibble.tidyverse.org/dev/articles/digits.html","id":"enforce-notation","dir":"Articles","previous_headings":"Scientific notation","what":"Enforce notation","title":"Comparing display with data frames","text":"avoid switching scientific notation, set \"pillar.max_dec_width\" option large value. Note required width available show column, shown case. notation argument num() offers options:","code":"num(12345678901234567, notation = \"dec\") #> <pillar_num(dec)[1]> #> [1] 12345678901234568 num(12345678901234567, notation = \"sci\") #> <pillar_num(sci)[1]> #> [1] 1.234568e16 num(12345678901234567, notation = \"eng\") #> <pillar_num(eng)[1]> #> [1] 12.34568e15 num(12345678901234567, notation = \"si\") #> <pillar_num(si)[1]> #> [1] 12.34568P"},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"topics-documented-elsewhere","dir":"Articles","previous_headings":"","what":"Topics documented elsewhere","title":"Extending tibble","text":"Change tweak way tibble prints: vignette(\"extending\", package = \"pillar\") Change tweak way column type printed tibble: vignette(\"pillar\", package = \"vctrs\") Implement new column data type: vignette(\"s3-vector\", package = \"vctrs\") Making tibble subclass work well dplyr: ?dplyr::dplyr_extending","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"data-frame-subclasses","dir":"Articles","previous_headings":"","what":"Data frame subclasses","title":"Extending tibble","text":"tibble >= 3.0.0, \"tbl\" class responsible printing, \"tbl_df\" class adds tibble’s sturdy subsetting subset assignment behavior (see vignette(\"invariants\") details). means data frame class like (mostly) print like tibble keep behavior base R’s data frames can inherit c(\"tbl\", \"data.frame\") just \"tbl\". example \"tbl_sql\" class dbplyr package used implement lazy database tables.","code":"library(tibble)"},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"tibble-example","dir":"Articles","previous_headings":"Data frame subclasses","what":"Tibble example","title":"Extending tibble","text":"","code":"my_tbl_df <- new_tibble(   list(a = 1:3, b = 2:4),   class = \"my_tbl_df\" )  tbl_sum.my_tbl_df <- function(x, ...) {   c(     \"My custom tibble\" = \"Some info about it\",     NextMethod()   ) }  my_tbl_df ## # My custom tibble: Some info about it ## # A tibble:         3 × 2 ##       a     b ##   <int> <int> ## 1     1     2 ## 2     2     3 ## 3     3     4 my_tbl_df[, \"a\"] ## # My custom tibble: Some info about it ## # A tibble:         3 × 1 ##       a ##   <int> ## 1     1 ## 2     2 ## 3     3"},{"path":"https://tibble.tidyverse.org/dev/articles/extending.html","id":"data-frame-example","dir":"Articles","previous_headings":"Data frame subclasses","what":"Data frame example","title":"Extending tibble","text":"","code":"my_tbl <- vctrs::new_data_frame(   list(a = 1:3, b = 2:4),   class = c(\"my_tbl\", \"tbl\") )  tbl_sum.my_tbl <- function(x, ...) {   c(     \"My custom data frame\" = \"Some info about it\",     NextMethod()   ) }  my_tbl ## # My custom data frame: Some info about it ## # A data frame:         3 × 2 ##       a     b ##   <int> <int> ## 1     1     2 ## 2     2     3 ## 3     3     4 my_tbl[, \"a\"] ## [1] 1 2 3"},{"path":"https://tibble.tidyverse.org/dev/articles/formats.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Column formats","text":"vignette shows decorate columns custom formatting. use formattable package demonstration already contains useful vector classes apply custom formatting numbers. x column tibble regular number formatting method. always shown three digits decimal point. also applies columns derived x. Summaries also maintain formatting. pivoting operations. ggplot2 need work show apply formatting scales.  pays specify formatting early process. diagram shows principal stages data analysis exploration “R data science”. subsequent diagram adds data formats, communication options, explicit data formatting. original r4ds transitions highlighted bold. two principal options apply formatting results: right communicating , right importing. Applying formatting early process gives added benefit showing data useful format “Tidy”, “Transform”, “Visualize” stages. useful, need ensure formatting options applied early: easy set keep sticky process data analysis exploration support analyst asking right questions data convey critical information glance, support go greater detail easier applied various communication options support everything necessary present data desired way Ensuring stickiness difficult, insufficient dbplyr workflow parts “Tidy”, “Transform” even “Visualize” stages run database. Often ’s possible derive rule-based approach formatting. rules can stored quos(): poses drawbacks: syntax repetitive intuitive Rules match multiple columns must given reverse order due way mutate() works, executed multiple times good API rule-based formatting look like?","code":"library(formattable)  tbl <- tibble(x = digits(9:11, 3)) tbl #> # A tibble: 3 × 1 #>   x          #>   <formttbl> #> 1 9.000      #> 2 10.000     #> 3 11.000 library(dplyr) tbl2 <-   tbl %>%   mutate(     y = x + 1,     z = x * x,     v = y + z,     lag = lag(x, default = x[[1]]),     sin = sin(x),     mean = mean(v),     var = var(x)   )  tbl2 #> # A tibble: 3 × 8 #>           x         y         z         v       lag      sin    mean   var #>   <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fm> <dbl:f> <dbl> #> 1     9.000    10.000    81.000    91.000     9.000    0.412 111.667     1 #> 2    10.000    11.000   100.000   111.000     9.000   -0.544 111.667     1 #> 3    11.000    12.000   121.000   133.000    10.000   -1.000 111.667     1 tbl2 %>%   group_by(lag) %>%   summarize(z = mean(z)) %>%   ungroup() #> # A tibble: 2 × 2 #>         lag         z #>   <dbl:fmt> <dbl:fmt> #> 1     9.000    90.500 #> 2    10.000   121.000 library(tidyr)  stocks <-   expand_grid(id = factor(1:4), year = 2018:2022) %>%   mutate(stock = currency(runif(20) * 10000))  stocks %>%   pivot_wider(id_cols = id, names_from = year, values_from = stock) #> # A tibble: 4 × 6 #>   id       `2018`    `2019`    `2020`    `2021`    `2022` #>   <fct> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> #> 1 1       $807.50 $8,343.33 $6,007.61 $1,572.08    $73.99 #> 2 2     $4,663.93 $4,977.77 $2,897.67 $7,328.82 $7,725.22 #> 3 3     $8,746.01 $1,749.41   $342.41 $3,203.86 $4,023.28 #> 4 4     $1,956.70 $4,035.38   $636.61 $3,887.01 $9,755.48 library(ggplot2)  # Needs https://github.com/tidyverse/ggplot2/pull/4065 or similar stocks %>%   ggplot(aes(x = year, y = stock, color = id)) +   geom_line() tbl3 <-   tibble(id = letters[1:3], x = 9:11) %>%   mutate(     y = x + 1,     z = x * x,     v = y + z,     lag = lag(x, default = x[[1]]),     sin = sin(x),     mean = mean(v),     var = var(x)   )  tbl3 #> # A tibble: 3 × 9 #>   id        x     y     z     v   lag    sin  mean   var #>   <chr> <int> <dbl> <int> <dbl> <int>  <dbl> <dbl> <dbl> #> 1 a         9    10    81    91     9  0.412  112.     1 #> 2 b        10    11   100   111     9 -0.544  112.     1 #> 3 c        11    12   121   133    10 -1.00   112.     1  tbl3 %>%   mutate(     across(where(is.numeric), ~ digits(.x, 3)),     across(where(~ is.numeric(.x) && mean(.x) > 50), ~ digits(.x, 1))   ) #> # A tibble: 3 × 9 #>   id            x         y         z         v     lag    sin  mean   var #>   <chr> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:f> <dbl:> <dbl> <dbl> #> 1 a         9.000    10.000      81.0      91.0   9.000  0.412 111.7 1.000 #> 2 b        10.000    11.000     100.0     111.0   9.000 -0.544 111.7 1.000 #> 3 c        11.000    12.000     121.0     133.0  10.000 -1.000 111.7 1.000 rules <- quos(   across(where(is.numeric), ~ digits(.x, 3)),   across(where(~ is.numeric(.x) && mean(.x) > 50), ~ digits(.x, 1)) )  tbl3 %>%   mutate(!!!rules) #> # A tibble: 3 × 9 #>   id            x         y         z         v     lag    sin  mean   var #>   <chr> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:fmt> <dbl:f> <dbl:> <dbl> <dbl> #> 1 a         9.000    10.000      81.0      91.0   9.000  0.412 111.7 1.000 #> 2 b        10.000    11.000     100.0     111.0   9.000 -0.544 111.7 1.000 #> 3 c        11.000    12.000     121.0     133.0  10.000 -1.000 111.7 1.000"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"conventions","dir":"Articles","previous_headings":"","what":"Conventions","title":"Invariants: Comparing behavior with data frames","text":"article, behaviors demonstrated using one example data frame tibble equivalent: Results code data frames tibbles presented side side: results identical (converting data frame necessary), tibble result shown. Subsetting operations read-. objects reused examples: needed, also show examples hierarchical columns containing data frame matrix: subset assignment (subassignment, short), need fresh copy data test. with_*() functions (omitted brevity) allow concise notation. functions take assignment expression, execute fresh copy data, return data printing. first example prints ’s really executed, examples omit output.","code":"library(tibble) library(vctrs) new_df <- function() {   df <- data.frame(n = c(1L, NA, 3L, NA))   df$c <- letters[5:8]   df$li <- list(9, 10:11, 12:14, \"text\")   df } new_tbl <- function() {   as_tibble(new_df()) } new_df() #>    n c         li #> 1  1 e          9 #> 2 NA f     10, 11 #> 3  3 g 12, 13, 14 #> 4 NA h       text new_tbl() #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> df <- new_df() tbl <- new_tbl() new_tbl2 <- function() {   tibble(     tb = tbl,     m = diag(4)   ) } new_df2 <- function() {   df2 <- new_tbl2()   class(df2) <- \"data.frame\"   class(df2$tb) <- \"data.frame\"   df2 } df2 <- new_df2() tbl2 <- new_tbl2() new_tbl() #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> with_df(df$n <- rev(df$n), verbose = TRUE) #> { #>   df <- new_df() #>   df$n <- rev(df$n) #>   df #> } #>    n c         li #> 1 NA e          9 #> 2  3 f     10, 11 #> 3 NA g 12, 13, 14 #> 4  1 h       text with_tbl(tbl$n <- rev(tbl$n), verbose = TRUE) #> { #>   tbl <- new_tbl() #>   tbl$n <- rev(tbl$n) #>   tbl #> } #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1    NA e     <dbl [1]> #> 2     3 f     <int [2]> #> 3    NA g     <int [3]> #> 4     1 h     <chr [1]>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xj","dir":"Articles","previous_headings":"Column extraction","what":"Definition of x[[j]]","title":"Invariants: Comparing behavior with data frames","text":"x[[j]] equal .subset2(x, j). NB: x[[j]] always returns object size nrow(x) column exists. j must single number string, enforced .subset2(x, j). NA indexes, numeric --bounds (OOB) values, non-integers throw error: Character OOB access silent common package idiom check absence column .null(df[[var]]).","code":"tbl[[1]] #> [1]  1 NA  3 NA .subset2(tbl, 1) #> [1]  1 NA  3 NA df[[1:2]] #> [1] NA tbl[[1:2]] #> Error: #> ! The `j` argument of #>   `[[.tbl_df()` can't be a vector #>   of length 2 as of tibble 3.0.0. #> ℹ Recursive subsetting is #>   deprecated for tibbles. df[[c(\"n\", \"c\")]] #> Error in .subset2(x, i, exact = exact): subscript out of bounds tbl[[c(\"n\", \"c\")]] #> Error in `tbl[[c(\"n\", \"c\")]]`: #> ! Can't extract column with `c(\"n\", \"c\")`. #> ✖ Subscript `c(\"n\", \"c\")` must be size 1, not 2. df[[TRUE]] #> [1]  1 NA  3 NA tbl[[TRUE]] #> Error in `tbl[[TRUE]]`: #> ! Can't extract column with `TRUE`. #> ✖ `TRUE` must be numeric or character, not `TRUE`. df[[mean]] #> Error in .subset2(x, i, exact = exact): invalid subscript type 'closure' tbl[[mean]] #> Error in `tbl[[mean]]`: #> ! Can't extract column with `mean`. #> ✖ `mean` must be numeric or character, not a function. df[[NA]] #> NULL tbl[[NA]] #> Error in `tbl[[NA]]`: #> ! Can't extract column with `NA`. #> ✖ Subscript `NA` must be a location, not an integer `NA`. df[[NA_character_]] #> NULL tbl[[NA_character_]] #> Error in `tbl[[NA_character_]]`: #> ! Can't extract column with `NA_character_`. #> ✖ Subscript `NA_character_` must be a location, not a character `NA`. df[[NA_integer_]] #> NULL tbl[[NA_integer_]] #> Error in `tbl[[NA_integer_]]`: #> ! Can't extract column with `NA_integer_`. #> ✖ Subscript `NA_integer_` must be a location, not an integer `NA`. df[[-1]] #> Error in .subset2(x, i, exact = exact): invalid negative subscript in get1index <real> tbl[[-1]] #> Error in `tbl[[-1]]`: #> ! Can't extract column with `-1`. #> ✖ Subscript `-1` must be a positive location, not -1. df[[4]] #> Error in .subset2(x, i, exact = exact): subscript out of bounds tbl[[4]] #> Error in `tbl[[4]]`: #> ! Can't extract columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. df[[1.5]] #> [1]  1 NA  3 NA tbl[[1.5]] #> Error in `tbl[[1.5]]`: #> ! Can't extract column with `1.5`. #> ✖ Can't convert from `j` <double> to <integer> due to loss of precision. df[[Inf]] #> NULL tbl[[Inf]] #> Error in `tbl[[Inf]]`: #> ! Can't extract column with `Inf`. #> ✖ Can't convert from `j` <double> to <integer> due to loss of precision. tbl[[\"x\"]] #> NULL"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xname","dir":"Articles","previous_headings":"Column extraction","what":"Definition of x$name","title":"Invariants: Comparing behavior with data frames","text":"x$name x$\"name\" equal x[[\"name\"]]. Unlike data frames, tibbles partially match names. df$x rarely used packages, can raise warning:","code":"tbl$n #> [1]  1 NA  3 NA tbl$\"n\" #> [1]  1 NA  3 NA tbl[[\"n\"]] #> [1]  1 NA  3 NA df$l #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl$l #> Warning: Unknown or uninitialised #> column: `l`. #> NULL df$not_present #> NULL tbl$not_present #> Warning: Unknown or uninitialised #> column: `not_present`. #> NULL"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xj-1","dir":"Articles","previous_headings":"Column subsetting","what":"Definition of x[j]","title":"Invariants: Comparing behavior with data frames","text":"j converted integer vector vec_as_index(j, ncol(x), names = names(x)). x[c(j_1, j_2, ..., j_n)] equivalent tibble(x[[j_1]], x[[j_2]], ..., x[[j_n]]), keeping corresponding column names. implies j must numeric character vector, logical vector length 1 ncol(x).1 subsetting repeated indexes, resulting column names undefined, rely . tibbles repeated column names, subsetting name uses first matching column. nrow(df[j]) equals nrow(df). Tibbles support indexing logical matrix, values returned vector compatible.","code":"tbl[1:2] #> # A tibble: 4 × 2 #>       n c     #>   <int> <chr> #> 1     1 e     #> 2    NA f     #> 3     3 g     #> 4    NA h df[c(1, 1)] #>    n n.1 #> 1  1   1 #> 2 NA  NA #> 3  3   3 #> 4 NA  NA tbl[c(1, 1)] #> # A tibble: 4 × 2 #>       n     n #>   <int> <int> #> 1     1     1 #> 2    NA    NA #> 3     3     3 #> 4    NA    NA tbl[integer()] #> # A tibble: 4 × 0 df[is.na(df)] #> [[1]] #> [1] NA #>  #> [[2]] #> [1] NA tbl[is.na(tbl)] #> [1] NA NA df[!is.na(df)] #> [[1]] #> [1] 1 #>  #> [[2]] #> [1] 3 #>  #> [[3]] #> [1] \"e\" #>  #> [[4]] #> [1] \"f\" #>  #> [[5]] #> [1] \"g\" #>  #> [[6]] #> [1] \"h\" #>  #> [[7]] #> [1] 9 #>  #> [[8]] #> [1] 10 11 #>  #> [[9]] #> [1] 12 13 14 #>  #> [[10]] #> [1] \"text\" tbl[!is.na(tbl)] #> Error in `vec_c()` at tibble/R/subsetting-matrix.R:12:3: #> ! Can't combine `n` <integer> and `c` <character>."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-x-j","dir":"Articles","previous_headings":"Column subsetting","what":"Definition of x[, j]","title":"Invariants: Comparing behavior with data frames","text":"x[, j] equal x[j]. Tibbles perform column extraction x[j] yield single column.","code":"df[, 1] #> [1]  1 NA  3 NA tbl[, 1] #> # A tibble: 4 × 1 #>       n #>   <int> #> 1     1 #> 2    NA #> 3     3 #> 4    NA tbl[, 1:2] #> # A tibble: 4 × 2 #>       n c     #>   <int> <chr> #> 1     1 e     #> 2    NA f     #> 3     3 g     #> 4    NA h"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-x-j-drop-true","dir":"Articles","previous_headings":"Column subsetting","what":"Definition of x[, j, drop = TRUE]","title":"Invariants: Comparing behavior with data frames","text":"backward compatiblity, x[, j, drop = TRUE] performs column extraction, returning x[j][[1]] ncol(x[j]) 1.","code":"tbl[, 1, drop = TRUE] #> [1]  1 NA  3 NA"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi","dir":"Articles","previous_headings":"Row subsetting","what":"Definition of x[i, ]","title":"Invariants: Comparing behavior with data frames","text":"x[, ] equal tibble(vec_slice(x[[1]], ), vec_slice(x[[2]], ), ...).2 means must numeric vector, logical vector length nrow(x) 1. compatibility, can also character vector containing positive numbers. Exception: OOB values generate warnings instead errors: Unlike data frames, logical vectors length 1 recycled. NB: scalar logicals recycled, scalar numerics . makes x[NA, ] x[NA_integer_, ] return different results.","code":"tbl[3, ] #> # A tibble: 1 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     3 g     <int [3]> df[mean, ] #> Error in xj[i]: invalid subscript type 'closure' tbl[mean, ] #> Error in `tbl[mean, ]`: #> ! Can't subset rows with `mean`. #> ✖ `mean` must be logical, numeric, or character, not a function. df[list(1), ] #> Error in xj[i]: invalid subscript type 'list' tbl[list(1), ] #> Error in `tbl[list(1), ]`: #> ! Can't subset rows with `list(1)`. #> ✖ `list(1)` must be logical, numeric, or character, not a list. tbl[\"1\", ] #> # A tibble: 1 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> tbl[10, ] #> # A tibble: 1 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL> tbl[\"x\", ] #> # A tibble: 1 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL> df[c(TRUE, FALSE), ] #>   n c         li #> 1 1 e          9 #> 3 3 g 12, 13, 14 tbl[c(TRUE, FALSE), ] #> Error in `tbl[c(TRUE, FALSE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE)`. #> ✖ Logical subscript `c(TRUE, FALSE)` must be size 1 or 4, not 2. tbl[NA, ] #> # A tibble: 4 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL> #> 2    NA NA    <NULL> #> 3    NA NA    <NULL> #> 4    NA NA    <NULL> tbl[NA_integer_, ] #> # A tibble: 1 × 3 #>       n c     li     #>   <int> <chr> <list> #> 1    NA NA    <NULL>"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-drop-true","dir":"Articles","previous_headings":"Row subsetting","what":"Definition of x[i, , drop = TRUE]","title":"Invariants: Comparing behavior with data frames","text":"drop = TRUE effect selecting single row:","code":"df[1, , drop = TRUE] #> $n #> [1] 1 #>  #> $c #> [1] \"e\" #>  #> $li #> $li[[1]] #> [1] 9 tbl[1, , drop = TRUE] #> # A tibble: 1 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-x-and-x","dir":"Articles","previous_headings":"Row and column subsetting","what":"Definition of x[] and x[,]","title":"Invariants: Comparing behavior with data frames","text":"x[] x[,] equivalent x.3","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j","dir":"Articles","previous_headings":"Row and column subsetting","what":"Definition of x[i, j]","title":"Invariants: Comparing behavior with data frames","text":"x[, j] equal x[, ][j].4","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j-1","dir":"Articles","previous_headings":"Row and column subsetting","what":"Definition of x[[i, j]]","title":"Invariants: Comparing behavior with data frames","text":"must numeric vector length 1. x[[, j]] equal x[, ][[j]], vctrs::vec_slice(x[[j]], ).5 implies j must numeric character vector length 1. NB: vec_size(x[[, j]]) always equals 1. Unlike x[, ], x[[, ]] valid.","code":"df[[1, 1]] #> [1] 1 df[[1, 3]] #> [1] 9"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xj---a","dir":"Articles","previous_headings":"Column update","what":"Definition of x[[j]] <- a","title":"Invariants: Comparing behavior with data frames","text":"vector x[[j]] <- replaces jth column value . recycled size x must size nrow(x) 1. (exception NULL, described .) Recycling also works list, data frame, matrix columns. j must scalar numeric string, NA. j OOB, new column added right hand side, name repair needed. df[[j]] <- replaces complete column can change type. [[<- supports removing column assigning NULL . Removing nonexistent column -op.","code":"with_tbl(tbl[[\"li\"]] <- list(0)) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <dbl [1]> #> 3     3 g     <dbl [1]> #> 4    NA h     <dbl [1]> with_df2(df2[[\"tb\"]] <- df[1, ]) #> Error in `[[<-.data.frame`(`*tmp*`, \"tb\", value = structure(list(n = 1L, : replacement has 1 row, data has 4 with_tbl2(tbl2[[\"tb\"]] <- tbl[1, ]) #> # A tibble: 4 × 2 #>    tb$n $c    $li       m[,1]  [,2] #>   <int> <chr> <list>    <dbl> <dbl> #> 1     1 e     <dbl [1]>     1     0 #> 2     1 e     <dbl [1]>     0     1 #> 3     1 e     <dbl [1]>     0     0 #> 4     1 e     <dbl [1]>     0     0 #> # ℹ 1 more variable: m[3:4] <dbl> with_df2(df2[[\"m\"]] <- df2[[\"m\"]][1, , drop = FALSE]) #> Error in `[[<-.data.frame`(`*tmp*`, \"m\", value = structure(c(1, 0, 0, : replacement has 1 row, data has 4 with_tbl2(tbl2[[\"m\"]] <- tbl2[[\"m\"]][1, , drop = FALSE]) #> # A tibble: 4 × 2 #>    tb$n $c    $li       m[,1]  [,2] #>   <int> <chr> <list>    <dbl> <dbl> #> 1     1 e     <dbl [1]>     1     0 #> 2    NA f     <int [2]>     1     0 #> 3     3 g     <int [3]>     1     0 #> 4    NA h     <chr [1]>     1     0 #> # ℹ 1 more variable: m[3:4] <dbl> with_tbl(tbl[[\"x\"]] <- 0) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0 with_df(df[[4]] <- 0) #>    n c         li V4 #> 1  1 e          9  0 #> 2 NA f     10, 11  0 #> 3  3 g 12, 13, 14  0 #> 4 NA h       text  0 with_tbl(tbl[[4]] <- 0) #> # A tibble: 4 × 4 #>       n c     li         ...4 #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0 with_df(df[[5]] <- 0) #> Warning in format.data.frame(if #> (omit) x[seq_len(n0), , drop = #> FALSE] else x, : corrupt data #> frame: columns will be truncated or #> padded with NAs #>    n c         li      V5 #> 1  1 e          9 NULL  0 #> 2 NA f     10, 11 <NA>  0 #> 3  3 g 12, 13, 14 <NA>  0 #> 4 NA h       text <NA>  0 with_tbl(tbl[[5]] <- 0) #> Error in `[[<-`: #> ! Can't assign to columns beyond the end with non-consecutive locations. #> ℹ Input has size 3. #> ✖ Subscript `5` contains non-consecutive location 5."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xname---a","dir":"Articles","previous_headings":"Column update","what":"Definition of x$name <- a","title":"Invariants: Comparing behavior with data frames","text":"x$name <- x$\"name\" <- equivalent x[[\"name\"]] <- .6 $<- perform partial matching.","code":"with_tbl(tbl$n <- 0) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl[[\"n\"]] <- 0) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl$l <- 0) #> # A tibble: 4 × 4 #>       n c     li            l #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0 with_tbl(tbl[[\"l\"]] <- 0) #> # A tibble: 4 × 4 #>       n c     li            l #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     0 #> 2    NA f     <int [2]>     0 #> 3     3 g     <int [3]>     0 #> 4    NA h     <chr [1]>     0"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"column-subassignment-xj---a","dir":"Articles","previous_headings":"","what":"Column subassignment: x[j] <- a","title":"Invariants: Comparing behavior with data frames","text":"j missing, ’s replaced seq_along(x) j logical vector, ’s converted numeric seq_along(x)[j].","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-a-list-or-data-frame","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is a list or data frame","title":"Invariants: Comparing behavior with data frames","text":"inherits(, \"list\") inherits(, \"data.frame\") TRUE, x[j] <- equivalent x[[j[[1]]] <- [[1]], x[[j[[2]]]] <- [[2]], … length() equals 1, recycled length j. attempt update column twice gives error. contains NULL values, corresponding columns removed updating (.e. position indexes refer columns modifications). NA indexes supported. Just like column updates, [<- supports changing type existing column. Appending columns end (without gaps) supported. name new columns determined LHS, RHS, name repair (order precedence). Tibbles support indexing logical matrix, scalar RHS, columns updated compatible value assigned.","code":"with_tbl(tbl[1:2] <- list(\"x\", 4:1)) #> # A tibble: 4 × 3 #>   n         c li        #>   <chr> <int> <list>    #> 1 x         4 <dbl [1]> #> 2 x         3 <int [2]> #> 3 x         2 <int [3]> #> 4 x         1 <chr [1]> with_tbl(tbl[c(\"li\", \"x\", \"c\")] <- list(\"x\", 4:1, NULL)) #> # A tibble: 4 × 3 #>       n li        x #>   <int> <chr> <int> #> 1     1 x         4 #> 2    NA x         3 #> 3     3 x         2 #> 4    NA x         1 with_tbl(tbl[1:2] <- list(1)) #> # A tibble: 4 × 3 #>       n     c li        #>   <dbl> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     1     1 <int [2]> #> 3     1     1 <int [3]> #> 4     1     1 <chr [1]> with_df(df[1:2] <- list(0, 0, 0)) #> Warning in #> `[<-.data.frame`(`*tmp*`, 1:2, #> value = list(0, 0, 0)): provided 3 #> variables to replace 2 variables #>   n c         li #> 1 0 0          9 #> 2 0 0     10, 11 #> 3 0 0 12, 13, 14 #> 4 0 0       text with_tbl(tbl[1:2] <- list(0, 0, 0)) #> Error in `[<-`: #> ! Can't recycle `list(0, 0, 0)` (size 3) to size 2. with_df(df[1:3] <- list(0, 0)) #>   n c li #> 1 0 0  0 #> 2 0 0  0 #> 3 0 0  0 #> 4 0 0  0 with_tbl(tbl[1:3] <- list(0, 0)) #> Error in `[<-`: #> ! Can't recycle `list(0, 0)` (size 2) to size 3. with_df(df[c(1, 1)] <- list(1, 2)) #> Error in `[<-.data.frame`(`*tmp*`, c(1, 1), value = list(1, 2)): duplicate subscripts for columns with_tbl(tbl[c(1, 1)] <- list(1, 2)) #> Error in `[<-`: #> ! Column index 1 is used #>   more than once for assignment. with_tbl(tbl[1:2] <- list(NULL, 4:1)) #> # A tibble: 4 × 2 #>       c li        #>   <int> <list>    #> 1     4 <dbl [1]> #> 2     3 <int [2]> #> 3     2 <int [3]> #> 4     1 <chr [1]> with_tbl(tbl[c(\"x\", \"y\")] <- tibble(\"x\", x = 4:1)) #> # A tibble: 4 × 5 #>       n c     li        x         y #>   <int> <chr> <list>    <chr> <int> #> 1     1 e     <dbl [1]> x         4 #> 2    NA f     <int [2]> x         3 #> 3     3 g     <int [3]> x         2 #> 4    NA h     <chr [1]> x         1 with_tbl(tbl[3:4] <- list(\"x\", x = 4:1)) #> # A tibble: 4 × 4 #>       n c     li        x #>   <int> <chr> <chr> <int> #> 1     1 e     x         4 #> 2    NA f     x         3 #> 3     3 g     x         2 #> 4    NA h     x         1 with_df(df[4] <- list(4:1)) #>    n c         li V4 #> 1  1 e          9  4 #> 2 NA f     10, 11  3 #> 3  3 g 12, 13, 14  2 #> 4 NA h       text  1 with_tbl(tbl[4] <- list(4:1)) #> # A tibble: 4 × 4 #>       n c     li         ...4 #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2    NA f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4    NA h     <chr [1]>     1 with_df(df[5] <- list(4:1)) #> Error in `[<-.data.frame`(`*tmp*`, 5, value = list(4:1)): new columns would leave holes after existing columns with_tbl(tbl[5] <- list(4:1)) #> Error in `[<-`: #> ! Can't assign to columns beyond the end with non-consecutive locations. #> ℹ Input has size 3. #> ✖ Subscript `5` contains non-consecutive location 5. with_df(df[is.na(df)] <- 4) #>   n c         li #> 1 1 e          9 #> 2 4 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[is.na(tbl)] <- 4) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     4 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[is.na(df)] <- 1:2) #>   n c         li #> 1 1 e          9 #> 2 1 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 2 h       text with_tbl(tbl[is.na(tbl)] <- 1:2) #> Error in `[<-`: #> ! Subscript `is.na(tbl)` is #>   a matrix, the data `1:2` must #>   have size 1. with_df(df[matrix(c(rep(TRUE, 5), rep(FALSE, 7)), ncol = 3)] <- 4) #>   n c         li #> 1 4 4          9 #> 2 4 f     10, 11 #> 3 4 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[matrix(c(rep(TRUE, 5), rep(FALSE, 7)), ncol = 3)] <- 4) #> Error in `[<-`: #> ! Assigned data `4` must be #>   compatible with existing data. #> ℹ Error occurred for column `c`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double> to <character>."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-a-matrix-or-array","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is a matrix or array","title":"Invariants: Comparing behavior with data frames","text":".matrix(), coerced data frame .data.frame() assigning. rows assigned, matrix type must compatible columns. .array() (dim()[-1:-2] != 1), error thrown.","code":"with_tbl(tbl[1:2] <- matrix(8:1, ncol = 2)) #> # A tibble: 4 × 3 #>       n     c li        #>   <int> <int> <list>    #> 1     8     4 <dbl [1]> #> 2     7     3 <int [2]> #> 3     6     2 <int [3]> #> 4     5     1 <chr [1]> with_df(df[1:3, 1:2] <- matrix(6:1, ncol = 2)) #>    n c         li #> 1  6 3          9 #> 2  5 2     10, 11 #> 3  4 1 12, 13, 14 #> 4 NA h       text with_tbl(tbl[1:3, 1:2] <- matrix(6:1, ncol = 2)) #> Error in `[<-`: #> ! Assigned data `matrix(6:1, #>   ncol = 2)` must be compatible #>   with existing data. #> ℹ Error occurred for column `c`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <integer> to <character>. with_tbl(tbl[1:2] <- array(4:1, dim = c(4, 1, 1))) #> # A tibble: 4 × 3 #>       n     c li        #>   <int> <int> <list>    #> 1     4     4 <dbl [1]> #> 2     3     3 <int [2]> #> 3     2     2 <int [3]> #> 4     1     1 <chr [1]> with_tbl(tbl[1:2] <- array(8:1, dim = c(4, 2, 1))) #> # A tibble: 4 × 3 #>       n     c li        #>   <int> <int> <list>    #> 1     8     4 <dbl [1]> #> 2     7     3 <int [2]> #> 3     6     2 <int [3]> #> 4     5     1 <chr [1]> with_df(df[1:2] <- array(8:1, dim = c(2, 1, 4))) #>   n c         li #> 1 8 4          9 #> 2 7 3     10, 11 #> 3 6 2 12, 13, 14 #> 4 5 1       text with_tbl(tbl[1:2] <- array(8:1, dim = c(2, 1, 4))) #> Error in `[<-`: #> ! `array(8:1, dim = c(2, 1, #>   4))` must be a vector, a bare #>   list, a data frame, a matrix, or #>   NULL. with_df(df[1:2] <- array(8:1, dim = c(4, 1, 2))) #>   n c         li #> 1 8 4          9 #> 2 7 3     10, 11 #> 3 6 2 12, 13, 14 #> 4 5 1       text with_tbl(tbl[1:2] <- array(8:1, dim = c(4, 1, 2))) #> Error in `[<-`: #> ! `array(8:1, dim = c(4, 1, #>   2))` must be a vector, a bare #>   list, a data frame, a matrix, or #>   NULL."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-another-type-of-vector","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is another type of vector","title":"Invariants: Comparing behavior with data frames","text":"vec_is(), x[j] <- equivalent x[j] <- list(). primarily provided backward compatibility. Matrices must wrapped list() assignment create matrix column.","code":"with_tbl(tbl[1] <- 0) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl[1] <- list(0)) #> # A tibble: 4 × 3 #>       n c     li        #>   <dbl> <chr> <list>    #> 1     0 e     <dbl [1]> #> 2     0 f     <int [2]> #> 3     0 g     <int [3]> #> 4     0 h     <chr [1]> with_tbl(tbl[1] <- list(matrix(1:8, ncol = 2))) #> # A tibble: 4 × 3 #>   n[,1]  [,2] c     li        #>   <int> <int> <chr> <list>    #> 1     1     5 e     <dbl [1]> #> 2     2     6 f     <int [2]> #> 3     3     7 g     <int [3]> #> 4     4     8 h     <chr [1]> with_tbl(tbl[1:2] <- list(matrix(1:8, ncol = 2))) #> # A tibble: 4 × 3 #>   n[,1]  [,2] c[,1]  [,2] li        #>   <int> <int> <int> <int> <list>    #> 1     1     5     1     5 <dbl [1]> #> 2     2     6     2     6 <int [2]> #> 3     3     7     3     7 <int [3]> #> 4     4     8     4     8 <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-null","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is NULL","title":"Invariants: Comparing behavior with data frames","text":"Entire columns can removed. Specifying error.","code":"with_tbl(tbl[1] <- NULL) #> # A tibble: 4 × 2 #>   c     li        #>   <chr> <list>    #> 1 e     <dbl [1]> #> 2 f     <int [2]> #> 3 g     <int [3]> #> 4 h     <chr [1]> with_tbl(tbl[, 2:3] <- NULL) #> # A tibble: 4 × 1 #>       n #>   <int> #> 1     1 #> 2    NA #> 3     3 #> 4    NA with_df(df[1, 2:3] <- NULL) #> Error in x[[jj]][iseq] <- vjj: replacement has length zero with_tbl(tbl[1, 2:3] <- NULL) #> Error in `[<-`: #> ! `NULL` must be a vector, a #>   bare list, a data frame or a #>   matrix."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"a-is-not-a-vector","dir":"Articles","previous_headings":"Column subassignment: x[j] <- a","what":"a is not a vector","title":"Invariants: Comparing behavior with data frames","text":"type error. Note .list() TRUE, inherits(, \"list\") FALSE, considered scalar. See ?vec_is ?vec_proxy details.","code":"with_df(df[1] <- mean) #> Error in rep(value, length.out = n): attempt to replicate an object of type 'closure' with_tbl(tbl[1] <- mean) #> Error in `[<-`: #> ! `mean` must be a vector, a #>   bare list, a data frame, a #>   matrix, or NULL. with_df(df[1] <- lm(mpg ~ wt, data = mtcars)) #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 2 has 32 rows #> to replace 4 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 3 has 32 rows #> to replace 4 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 5 has 32 rows #> to replace 4 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 1, value #> = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, #> : replacement element 7 has 5 rows #> to replace 4 rows #> Error in `[<-.data.frame`(`*tmp*`, 1, value = structure(list(coefficients = c(`(Intercept)` = 37.285126167342, : replacement element 10 has 3 rows, need 4 with_tbl(tbl[1] <- lm(mpg ~ wt, data = mtcars)) #> Error in `[<-`: #> ! `lm(mpg ~ wt, data = #>   mtcars)` must be a vector, a bare #>   list, a data frame, a matrix, or #>   NULL."},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"row-subassignment-xi---list---","dir":"Articles","previous_headings":"","what":"Row subassignment: x[i, ] <- list(...)","title":"Invariants: Comparing behavior with data frames","text":"x[, ] <- vec_slice(x[[j_1]], ) <- [[1]], vec_slice(x[[j_2]], ) <- [[2]], … .7 values size one can recycled. compatibility, warning issued indexing beyond number rows. Appending rows right end existing data supported, without warning. compatibility, can also character vector containing positive numbers.","code":"with_tbl(tbl[2:3, ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]> with_tbl(tbl[c(FALSE, TRUE, TRUE, FALSE), ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]> with_tbl(tbl[2:3, ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]> with_tbl(tbl[2:3, ] <- list(tbl$n[1], tbl$c[1:2], tbl$li[1])) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 f     <dbl [1]> #> 4    NA h     <chr [1]> with_df(df[2:4, ] <- df[1:2, ]) #> Error in `[<-.data.frame`(`*tmp*`, 2:4, , value = structure(list(n = c(1L, : replacement element 1 has 2 rows, need 3 with_tbl(tbl[2:4, ] <- tbl[1:2, ]) #> Error in `[<-`: #> ! Assigned data `tbl[1:2, ]` #>   must be compatible with row #>   subscript `2:4`. #> ✖ 3 rows must be assigned. #> ✖ Element 1 of assigned data has 2 #>   rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:169:7: #> ! Can't recycle input of size 2 to size 3. with_tbl(tbl[5, ] <- tbl[1, ]) #> # A tibble: 5 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> #> 5     1 e     <dbl [1]> with_tbl(tbl[5:7, ] <- tbl[1, ]) #> # A tibble: 7 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> #> 5     1 e     <dbl [1]> #> 6     1 e     <dbl [1]> #> 7     1 e     <dbl [1]> with_df(df[6, ] <- df[1, ]) #>    n    c         li #> 1  1    e          9 #> 2 NA    f     10, 11 #> 3  3    g 12, 13, 14 #> 4 NA    h       text #> 5 NA <NA>       NULL #> 6  1    e          9 with_tbl(tbl[6, ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't assign to rows beyond the end with non-consecutive locations. #> ℹ Input has size 4. #> ✖ Subscript `6` contains non-consecutive location 6. with_df(df[-5, ] <- df[1, ]) #>   n c li #> 1 1 e  9 #> 2 1 e  9 #> 3 1 e  9 #> 4 1 e  9 with_tbl(tbl[-5, ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't negate rows past the end. #> ℹ Location 5 doesn't exist. #> ℹ There are only 4 rows. with_df(df[-(5:7), ] <- df[1, ]) #>   n c li #> 1 1 e  9 #> 2 1 e  9 #> 3 1 e  9 #> 4 1 e  9 with_tbl(tbl[-(5:7), ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't negate rows past the end. #> ℹ Locations 5, 6, and 7 #>   don't exist. #> ℹ There are only 4 rows. with_df(df[-6, ] <- df[1, ]) #>   n c li #> 1 1 e  9 #> 2 1 e  9 #> 3 1 e  9 #> 4 1 e  9 with_tbl(tbl[-6, ] <- tbl[1, ]) #> Error in `[<-`: #> ! Can't negate rows past the end. #> ℹ Location 6 doesn't exist. #> ℹ There are only 4 rows. with_tbl(tbl[as.character(1:3), ] <- tbl[1, ]) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 e     <dbl [1]> #> 3     1 e     <dbl [1]> #> 4    NA h     <chr [1]>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j---a","dir":"Articles","previous_headings":"Row and column subassignment","what":"Definition of x[i, j] <- a","title":"Invariants: Comparing behavior with data frames","text":"x[, j] <- equivalent x[, ][j] <- .8 Subassignment x[, j] stricter tibbles data frames. x[, j] <- can’t change data type existing columns. notable exception population column full NA (type logical), use NA right-hand side assignment. programming, always safer (faster) use correct type NA initialize columns. new columns, x[, j] <- fills unassigned rows NA. Likewise, new rows, x[, j] <- fills unassigned columns NA.","code":"with_df(df[2:3, 1] <- df[1:2, 2]) #>      n c         li #> 1    1 e          9 #> 2    e f     10, 11 #> 3    f g 12, 13, 14 #> 4 <NA> h       text with_tbl(tbl[2:3, 1] <- tbl[1:2, 2]) #> Error in `[<-`: #> ! Assigned data `tbl[1:2, #>   2]` must be compatible with #>   existing data. #> ℹ Error occurred for column `n`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_df(df[2:3, 2] <- df[1:2, 3]) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2:3, 2, #> value = list(9, 10:11)): provided 2 #> variables to replace 1 variables #>    n c         li #> 1  1 e          9 #> 2 NA 9     10, 11 #> 3  3 9 12, 13, 14 #> 4 NA h       text with_tbl(tbl[2:3, 2] <- tbl[1:2, 3]) #> Error in `[<-`: #> ! Assigned data `tbl[1:2, #>   3]` must be compatible with #>   existing data. #> ℹ Error occurred for column `c`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <list> to <character>. with_df(df[2:3, 3] <- df2[1:2, 1]) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2:3, 3, #> value = structure(list(n = c(1L, : #> provided 3 variables to replace 1 #> variables #>    n c   li #> 1  1 e    9 #> 2 NA f    1 #> 3  3 g   NA #> 4 NA h text with_tbl(tbl[2:3, 3] <- tbl2[1:2, 1]) #> Error in `[<-`: #> ! Assigned data `tbl2[1:2, #>   1]` must be compatible with #>   existing data. #> ℹ Error occurred for column `li`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <tbl_df> to <list>. with_df2(df2[2:3, 1] <- df2[1:2, 2]) #> Warning in matrix(value, n, p): #> data length [8] is not a #> sub-multiple or multiple of the #> number of columns [3] #>   tb.n tb.c tb.li m.1 m.2 m.3 m.4 #> 1    1    e     9   1   0   0   0 #> 2    1    0     0   0   1   0   0 #> 3    0    1     0   0   0   1   0 #> 4   NA    h  text   0   0   0   1 with_tbl2(tbl2[2:3, 1] <- tbl2[1:2, 2]) #> Error in `[<-`: #> ! Assigned data `tbl2[1:2, #>   2]` must be compatible with #>   existing data. #> ℹ Error occurred for column `tb`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double[,4]> to <tbl_df>. with_tbl2(tbl2[2:3, 2] <- tbl[1:2, 1]) #> # A tibble: 4 × 2 #>    tb$n $c    $li       m[,1]  [,2] #>   <int> <chr> <list>    <dbl> <dbl> #> 1     1 e     <dbl [1]>     1     0 #> 2    NA f     <int [2]>     1     1 #> 3     3 g     <int [3]>    NA    NA #> 4    NA h     <chr [1]>     0     0 #> # ℹ 1 more variable: m[3:4] <dbl> with_tbl({tbl$x <- NA; tbl[2:3, \"x\"] <- 3:2}) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>    NA #> 2    NA f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4    NA h     <chr [1]>    NA with_df({df[2:3, 2:3] <- NA}) #>    n    c   li #> 1  1    e    9 #> 2 NA <NA>   NA #> 3  3 <NA>   NA #> 4 NA    h text with_tbl({tbl[2:3, 2:3] <- NA}) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA NA    <NULL>    #> 3     3 NA    <NULL>    #> 4    NA h     <chr [1]> with_tbl({tbl$x <- NA_integer_; tbl[2:3, \"x\"] <- 3:2}) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>    NA #> 2    NA f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4    NA h     <chr [1]>    NA with_df(df[2:3, \"n\"] <- 1) #>    n c         li #> 1  1 e          9 #> 2  1 f     10, 11 #> 3  1 g 12, 13, 14 #> 4 NA h       text with_tbl(tbl[2:3, \"n\"] <- 1) #> # A tibble: 4 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4    NA h     <chr [1]> with_tbl(tbl[2:3, \"x\"] <- 1) #> # A tibble: 4 × 4 #>       n c     li            x #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>    NA #> 2    NA f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4    NA h     <chr [1]>    NA with_df(df[2:3, \"n\"] <- NULL) #> Error in x[[jj]][iseq] <- vjj: replacement has length zero with_tbl(tbl[2:3, \"n\"] <- NULL) #> Error in `[<-`: #> ! `NULL` must be a vector, a #>   bare list, a data frame or a #>   matrix. with_tbl(tbl[5, \"n\"] <- list(0L)) #> # A tibble: 5 × 3 #>       n c     li        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2    NA f     <int [2]> #> 3     3 g     <int [3]> #> 4    NA h     <chr [1]> #> 5     0 NA    <NULL>"},{"path":"https://tibble.tidyverse.org/dev/articles/invariants.html","id":"definition-of-xi-j---a-1","dir":"Articles","previous_headings":"Row and column subassignment","what":"Definition of x[[i, j]] <- a","title":"Invariants: Comparing behavior with data frames","text":"must numeric vector length 1. x[[, j]] <- equivalent x[, ][[j]] <- .9 NB: vec_size() must equal 1. Unlike x[, ] <-, x[[, ]] <- valid.","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"options","dir":"Articles","previous_headings":"","what":"Options","title":"Controlling display of numbers","text":"easiest way customize display numbers data tibble define options. See ?pillar::pillar_options comprehensive overview. changes display columns. Read see specify display options column--column basis.","code":"tibble(x = 123.4567) #> # A tibble: 1 × 1 #>       x #>   <dbl> #> 1  123. old <- options(pillar.sigfig = 7) tibble(x = 123.4567) #> # A tibble: 1 × 1 #>          x #>      <dbl> #> 1 123.4567 # Restore old options, see also rlang::local_options() for a more elegant way options(old)"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"per-column-number-formatting","dir":"Articles","previous_headings":"","what":"Per-column number formatting","title":"Controlling display of numbers","text":"new num() constructor allows creating vectors behave like numbers allow customizing display. examples shown, see ?num comprehensive overview. Similarly, char() allows customizing display character columns. pillar package responsible display tibbles tries hard get number display right, however impossible accommodate use cases. Whenever default formatting suit application, num() char() allow redefining formatting individual columns. formatting survives data transformations.","code":"num(-1:3, notation = \"sci\") #> <pillar_num(sci)[5]> #> [1] -1e0  0    1e0  2e0  3e0 tibble(   x4 = num(8:12 * 100 + 0.5, digits = 4),   x1 = num(8:12 * 100 + 0.5, digits = -1),   usd = num(8:12 * 100 + 0.5, digits = 2, label = \"USD\"),   percent = num(8:12 / 100 + 0.0005, label = \"%\", scale = 100),   eng = num(10^(-3:1), notation = \"eng\", fixed_exponent = -Inf),   si = num(10^(-3:1) * 123, notation = \"si\"),   char = char(paste(LETTERS, collapse = \" \"), shorten = \"mid\") ) #> # A tibble: 5 × 7 #>          x4       x1     usd percent   eng      si char                    #>   <num:.4!> <num:.1>     USD       % [e-3]    <si> <char>                  #> 1  800.5000    800.5  800.50    8.05     1 123   m A B C D E F…U V W X Y Z #> 2  900.5000    900.5  900.50    9.05    10   1.23  A B C D E F…U V W X Y Z #> 3 1000.5000   1000.5 1000.50   10.0    100  12.3   A B C D E F…U V W X Y Z #> 4 1100.5000   1100.5 1100.50   11.0   1000 123     A B C D E F…U V W X Y Z #> 5 1200.5000   1200.5 1200.50   12.0  10000   1.23k A B C D E F…U V W X Y Z"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"rule-based-number-formatting","dir":"Articles","previous_headings":"","what":"Rule-based number formatting","title":"Controlling display of numbers","text":"Currently, formatting must applied manually column. following pattern may help consistently columns tibble, columns based name.","code":"library(dplyr, warn.conflicts = FALSE)  markets <-   as_tibble(EuStockMarkets) %>%   mutate(time = time(EuStockMarkets), .before = 1)  markets #> # A tibble: 1,860 × 5 #>     time   DAX   SMI   CAC  FTSE #>    <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 1991. 1629. 1678. 1773. 2444. #>  2 1992. 1614. 1688. 1750. 2460. #>  3 1992. 1607. 1679. 1718  2448. #>  4 1992. 1621. 1684. 1708. 2470. #>  5 1992. 1618. 1687. 1723. 2485. #>  6 1992. 1611. 1672. 1714. 2467. #>  7 1992. 1631. 1683. 1734. 2488. #>  8 1992. 1640. 1704. 1757. 2508. #>  9 1992. 1635. 1698. 1754  2510. #> 10 1992. 1646. 1716. 1754. 2497. #> # ℹ 1,850 more rows markets %>%   mutate(across(-time, ~ num(.x, digits = 3))) #> # A tibble: 1,860 × 5 #>     time       DAX       SMI       CAC      FTSE #>    <dbl> <num:.3!> <num:.3!> <num:.3!> <num:.3!> #>  1 1991.  1628.750  1678.100  1772.800  2443.600 #>  2 1992.  1613.630  1688.500  1750.500  2460.200 #>  3 1992.  1606.510  1678.600  1718.000  2448.200 #>  4 1992.  1621.040  1684.100  1708.100  2470.400 #>  5 1992.  1618.160  1686.600  1723.100  2484.700 #>  6 1992.  1610.610  1671.600  1714.300  2466.800 #>  7 1992.  1630.750  1682.900  1734.500  2487.900 #>  8 1992.  1640.170  1703.600  1757.400  2508.400 #>  9 1992.  1635.470  1697.500  1754.000  2510.500 #> 10 1992.  1645.890  1716.300  1754.300  2497.400 #> # ℹ 1,850 more rows"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"computing-on-num","dir":"Articles","previous_headings":"","what":"Computing on num","title":"Controlling display of numbers","text":"Formatting numbers useful presentation results. defined early analysis, formatting options survive operations. worth defining output options suit data early process, benefit formatting throughout analysis. working seamlessly applying formatting final presentation (plots, tables, …).","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"arithmetics","dir":"Articles","previous_headings":"Computing on num","what":"Arithmetics","title":"Controlling display of numbers","text":"applying arithmetic operations numbers created num(), result inherits formatting first num object.","code":"num(1) + 2 #> <pillar_num[1]> #> [1] 3 1 + num(2) #> <pillar_num[1]> #> [1] 3 1L + num(2) #> <pillar_num[1]> #> [1] 3 num(3.23456, sigfig = 4) - num(2) #> <pillar_num:4[1]> #> [1] 1.235 num(4, sigfig = 2) * num(3, digits = 2) #> <pillar_num:2[1]> #> [1] 12 num(3, digits = 2) * num(4, sigfig = 2) #> <pillar_num:.2![1]> #> [1] 12.00 -num(2) #> <pillar_num[1]> #> [1] -2"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"mathematics","dir":"Articles","previous_headings":"Computing on num","what":"Mathematics","title":"Controlling display of numbers","text":"Similarly, mathematical operations, formatting inherited.","code":"min(num(1:3, label = \"$\")) #> <pillar_num{$}[1]> #> [1] 1 mean(num(1:3, notation = \"eng\")) #> <pillar_num(eng)[1]> #> [1] 2e0 sin(num(1:3, label = \"%\", scale = 100)) #> <pillar_num{%}*100[3]> #> [1] 84.1 90.9 14.1"},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"override","dir":"Articles","previous_headings":"Computing on num","what":"Override","title":"Controlling display of numbers","text":"cases, ideal formatting changes transformation. num() can applied repeatedly, last setting wins.","code":"num(1:3 + 0.125, digits = 4) #> <pillar_num:.4![3]> #> [1] 1.1250 2.1250 3.1250 transf <- 10^num(1:3 + 0.125, digits = 4) transf #> <pillar_num:.4![3]> #> [1]   13.3352  133.3521 1333.5214 num(transf, sigfig = 3) #> <pillar_num:3[3]> #> [1]   13.3  133.  1334."},{"path":"https://tibble.tidyverse.org/dev/articles/numbers.html","id":"recovery","dir":"Articles","previous_headings":"Computing on num","what":"Recovery","title":"Controlling display of numbers","text":"var() function one examples formatting lost: median() function worse, breaks num() objects: One way recover apply num() result: automatic recovery, can also define version var(), even overwrite base implementation. Note pattern still experimental may subject change: pattern can applied functions lose formatting. make_restore() function defined function factory consumes function returns derived function:","code":"x <- num(c(1, 2, 4), notation = \"eng\") var(x) #> [1] 2.333333 median(x) #> Error in `median()`: #> ! `median.pillar_num()` not implemented. num(var(x), notation = \"eng\") #> <pillar_num(eng)[1]> #> [1] 2.33e0 num(median(as.numeric(x)), notation = \"eng\") #> <pillar_num(eng)[1]> #> [1] 2e0 var_ <- function(x, ...) {   out <- var(vctrs::vec_proxy(x), ...)   vctrs::vec_restore(out, x) } var_(x) #> <pillar_num(eng)[1]> #> [1] 2.33e0 make_restore <- function(fun) {   force(fun)   function(x, ...) {     out <- fun(vctrs::vec_proxy(x), ...)     vctrs::vec_restore(out, x)   } }  var_ <- make_restore(var) sd_ <- make_restore(sd)  var_(x) #> <pillar_num(eng)[1]> #> [1] 2.33e0 sd_(x) #> <pillar_num(eng)[1]> #> [1] 1.53e0"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"creating","dir":"Articles","previous_headings":"","what":"Creating","title":"Tibbles","text":"tibble() nice way create data frames. encapsulates best practices data frames: never changes input’s type (.e., stringsAsFactors = FALSE!). makes easier use list-columns: List-columns often created tidyr::nest(), can useful create hand. never adjusts names variables: evaluates arguments lazily sequentially: never uses row.names(). whole point tidy data store variables consistent way. never stores variable special attribute. recycles vectors length 1. recycling vectors greater lengths frequent source bugs.","code":"tibble(x = letters) #> # A tibble: 26 × 1 #>    x     #>    <chr> #>  1 a     #>  2 b     #>  3 c     #>  4 d     #>  5 e     #>  6 f     #>  7 g     #>  8 h     #>  9 i     #> 10 j     #> # ℹ 16 more rows tibble(x = 1:3, y = list(1:5, 1:10, 1:20)) #> # A tibble: 3 × 2 #>       x y          #>   <int> <list>     #> 1     1 <int [5]>  #> 2     2 <int [10]> #> 3     3 <int [20]> names(data.frame(`crazy name` = 1)) #> [1] \"crazy.name\" names(tibble(`crazy name` = 1)) #> [1] \"crazy name\" tibble(x = 1:5, y = x ^ 2) #> # A tibble: 5 × 2 #>       x     y #>   <int> <dbl> #> 1     1     1 #> 2     2     4 #> 3     3     9 #> 4     4    16 #> 5     5    25"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"coercion","dir":"Articles","previous_headings":"","what":"Coercion","title":"Tibbles","text":"complement tibble(), tibble provides as_tibble() coerce objects tibbles. Generally, as_tibble() methods much simpler .data.frame() methods. method lists written eye performance: speed .data.frame() usually bottleneck used interactively, can problem combining thousands messy inputs one tidy data frame.","code":"l <- replicate(26, sample(100), simplify = FALSE) names(l) <- letters  timing <- bench::mark(   as_tibble(l),   as.data.frame(l),   check = FALSE )  timing #> # A tibble: 2 × 14 #>   expression    min         mean         median      max         `itr/sec` #>   <chr>         <bench_tm>  <bench_tm>   <bench_tm>  <bench_tm>      <dbl> #> 1 as_tibble(l)  0.000287696 0.0006251376 0.000327178 0.004508219     1600. #> 2 as.data.fram… 0.000791522 0.0016640039 0.001098172 0.007652914      601. #> # ℹ 8 more variables: mem_alloc <bnch_byt>, n_gc <dbl>, n_itr <int>, #> #   total_time <bench_tm>, result <list>, memory <list>, time <list>, #> #   gc <list>"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"tibbles-vs-data-frames","dir":"Articles","previous_headings":"","what":"Tibbles vs data frames","title":"Tibbles","text":"three key differences tibbles data frames: printing, subsetting, recycling rules.","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"printing","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Printing","title":"Tibbles","text":"print tibble, shows first ten rows columns fit one screen. also prints abbreviated description column type, uses font styles color highlighting: Numbers displayed three significant figures default, trailing dot indicates existence fractional component. can control default appearance options: options(pillar.print_max = n, pillar.print_min = m): n rows, print first m rows. Use options(pillar.print_max = Inf) always show rows. options(pillar.width = n): use n character slots horizontally show data. n > getOption(\"width\"), result multiple tiers. Use options(pillar.width = Inf) always print columns, regardless width screen. See ?pillar::pillar_options ?tibble_options available options, vignette(\"types\") overview type abbreviations, vignette(\"numbers\") details formatting numbers, vignette(\"digits\") comparison data frame printing.","code":"tibble(x = -5:100, y = 123.456 * (3^x)) #> # A tibble: 106 × 2 #>        x         y #>    <int>     <dbl> #>  1    -5     0.508 #>  2    -4     1.52  #>  3    -3     4.57  #>  4    -2    13.7   #>  5    -1    41.2   #>  6     0   123.    #>  7     1   370.    #>  8     2  1111.    #>  9     3  3333.    #> 10     4 10000.    #> # ℹ 96 more rows"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"subsetting","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Subsetting","title":"Tibbles","text":"Tibbles quite strict subsetting. [ always returns another tibble. Contrast data frame: sometimes [ returns data frame sometimes just returns vector: extract single column use [[ $: Tibbles also stricter $. Tibbles never partial matching, throw warning return NULL column exist: However, tibbles respect drop argument provided: Tibbles support row names. removed converting tibble subsetting: See vignette(\"invariants\") detailed comparison tibbles data frames.","code":"df1 <- data.frame(x = 1:3, y = 3:1) class(df1[, 1:2]) #> [1] \"data.frame\" class(df1[, 1]) #> [1] \"integer\"  df2 <- tibble(x = 1:3, y = 3:1) class(df2[, 1:2]) #> [1] \"tbl_df\"     \"tbl\"        \"data.frame\" class(df2[, 1]) #> [1] \"tbl_df\"     \"tbl\"        \"data.frame\" class(df2[[1]]) #> [1] \"integer\" class(df2$x) #> [1] \"integer\" df <- data.frame(abc = 1) df$a #> [1] 1  df2 <- tibble(abc = 1) df2$a #> Warning: Unknown or uninitialised column: `a`. #> NULL data.frame(a = 1:3)[, \"a\", drop = TRUE] #> [1] 1 2 3 tibble(a = 1:3)[, \"a\", drop = TRUE] #> [1] 1 2 3 df <- data.frame(a = 1:3, row.names = letters[1:3]) rownames(df) #> [1] \"a\" \"b\" \"c\" rownames(as_tibble(df)) #> [1] \"1\" \"2\" \"3\"  tbl <- tibble(a = 1:3) rownames(tbl) <- letters[1:3] #> Warning: Setting row names on a tibble is deprecated. rownames(tbl) #> [1] \"a\" \"b\" \"c\" rownames(tbl[1, ]) #> [1] \"1\""},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"recycling","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Recycling","title":"Tibbles","text":"constructing tibble, values length 1 recycled. first column length different one determines number rows tibble, conflicts lead error: also extends tibbles zero rows, sometimes important programming:","code":"tibble(a = 1, b = 1:3) #> # A tibble: 3 × 2 #>       a     b #>   <dbl> <int> #> 1     1     1 #> 2     1     2 #> 3     1     3 tibble(a = 1:3, b = 1) #> # A tibble: 3 × 2 #>       a     b #>   <int> <dbl> #> 1     1     1 #> 2     2     1 #> 3     3     1 tibble(a = 1:3, c = 1:2) #> Error in `tibble()`: #> ! Tibble columns must have compatible sizes. #> • Size 3: Existing data. #> • Size 2: Column `c`. #> ℹ Only values of size one are recycled. tibble(a = 1, b = integer()) #> # A tibble: 0 × 2 #> # ℹ 2 variables: a <dbl>, b <int> tibble(a = integer(), b = 1) #> # A tibble: 0 × 2 #> # ℹ 2 variables: a <int>, b <dbl>"},{"path":"https://tibble.tidyverse.org/dev/articles/tibble.html","id":"arithmetic-operations","dir":"Articles","previous_headings":"Tibbles vs data frames","what":"Arithmetic operations","title":"Tibbles","text":"Unlike data frames, tibbles don’t support arithmetic operations columns. result silently coerced data frame. rely behavior, may become error forthcoming version.","code":"tbl <- tibble(a = 1:3, b = 4:6) tbl * 2 #>   a  b #> 1 2  8 #> 2 4 10 #> 3 6 12"},{"path":"https://tibble.tidyverse.org/dev/articles/types.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Column types","text":"vignette shows overview known data types abbreviations, origin. example, <int> header column indicates integer column, <chr> denotes character column.","code":""},{"path":"https://tibble.tidyverse.org/dev/articles/types.html","id":"example-values","dir":"Articles","previous_headings":"","what":"Example values","title":"Column types","text":"following overview contains example values type:","code":"#> Rows: 1 #> Columns: 23 #> $ logical       <lgl> TRUE #> $ integer       <int> 1 #> $ double        <dbl> 1.5 #> $ character     <chr> \"A\" #> $ complex       <cpl> 0+1i #> $ raw           <raw> 01 #> $ list          <list> 1 #> $ `named list`  <named list> 1 #> $ factor        <fct> A #> $ ordered       <ord> a #> $ Date          <date> 2024-11-17 #> $ POSIXt        <dttm> 2024-11-17 06:10:28 #> $ difftime      <drtn> 1 secs #> $ hms           <time> 00:00:01 #> $ integer64     <int64> 10000000000 #> $ blob          <blob> 00 #> $ data.frame    <df[,1]> <data.frame[1 x 1]> #> $ tbl_df        <tibble[,1]> <tbl_df[1 x 1]> #> $ AsIs          <I<int>> 1 #> $ unspecified   <???> NA #> $ vctrs_list_of <list<int>> 1 #> $ vctrs_vctr    <vctrs_vc> 1 #> $ quosures      <quos> <~1>"},{"path":"https://tibble.tidyverse.org/dev/articles/types.html","id":"implementation","dir":"Articles","previous_headings":"","what":"Implementation","title":"Column types","text":"column header obtained calling pillar::type_sum() column. S3 method can overridden, time useful override vctrs::vec_ptype_abbr():","code":"pillar:::type_sum.default #> function (x)  #> { #>     pillar_attr <- attr(x, \"pillar\", exact = TRUE) #>     label <- pillar_attr$label #>     if (!is.null(label)) { #>         return(I(label)) #>     } #>     if (is.object(x) || vec_is(x)) { #>         return(vec_ptype_abbr(x)) #>     } #>     switch(typeof(x), builtin = , special = , closure = \"fn\",  #>         environment = \"env\", symbol = if (is_missing(x)) { #>             \"missing\" #>         } else { #>             \"sym\" #>         }, typeof(x)) #> } #> <bytecode: 0x55c4303a0070> #> <environment: namespace:pillar>"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"scalars-and-full-length","dir":"Articles > Wip","previous_headings":"$<-","what":"Scalars and full length","title":"Subassignment","text":"Assigning scalar full-length vector column consistently overwrites existing data appends new column end. Partial matching doesn’t happen:","code":"with_tbl(tbl$a <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl$b <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     2     1 <int [2]> #> 3     3     1 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl$c <- 1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     1 #> 2     2 f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4     4 h     <chr [1]>     1 with_tbl(tbl$cd <- 1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     1 e         1 #> 2     2 f         1 #> 3     3 g         1 #> 4     4 h         1 with_tbl(tbl$a <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl$b <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     1     4 <dbl [1]> #> 2     2     3 <int [2]> #> 3     3     2 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl$c <- 4:1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2     2 f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4     4 h     <chr [1]>     1 with_tbl(tbl$cd <- 4:1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <int> #> 1     1 e         4 #> 2     2 f         3 #> 3     3 g         2 #> 4     4 h         1"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"recycling","dir":"Articles > Wip","previous_headings":"$<-","what":"Recycling","title":"Subassignment","text":"Tibbles allow recycling vectors length 1 length data.","code":"with_df(df$a <- 1:2) #>   a b         cd #> 1 1 e          9 #> 2 2 f     10, 11 #> 3 1 g 12, 13, 14 #> 4 2 h       text with_tbl(tbl$a <- 1:2) #> Error in `$<-`: #> ! Assigned data `1:2` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 2 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 2 to size 4. with_df(df$a <- 1:3) #> Error in `$<-.data.frame`(`*tmp*`, a, value = 1:3): replacement has 3 rows, data has 4 with_tbl(tbl$a <- 1:3) #> Error in `$<-`: #> ! Assigned data `1:3` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 3 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 3 to size 4. with_df(df$a <- 1:5) #> Error in `$<-.data.frame`(`*tmp*`, a, value = 1:5): replacement has 5 rows, data has 4 with_tbl(tbl$a <- 1:5) #> Error in `$<-`: #> ! Assigned data `1:5` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 5 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 5 to size 4. with_df(df$c <- 1:2) #>   a b         cd c #> 1 1 e          9 1 #> 2 2 f     10, 11 2 #> 3 3 g 12, 13, 14 1 #> 4 4 h       text 2 with_tbl(tbl$c <- 1:2) #> Error in `$<-`: #> ! Assigned data `1:2` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 2 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 2 to size 4."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"subset-assignment","dir":"Articles > Wip","previous_headings":"$<-","what":"Subset assignment","title":"Subassignment","text":"Updating parts column extracted $ responsibility column vector. Tibbles can’t control happens $ returned. columns stricter \"vctrs_vctr\" class, class implements check, works identically data frames tibbles:","code":"with_tbl(tbl$a[1:2] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_tbl(tbl$b[1:2] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 4     <dbl [1]> #> 2     2 3     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df$c[1:2] <- 4:3) #>   a b         cd          c #> 1 1 e          9          4 #> 2 2 f     10, 11          3 #> 3 3 g 12, 13, 14 12, 13, 14 #> 4 4 h       text       text with_tbl(tbl$c[1:2] <- 4:3) #> Warning: Unknown or uninitialised #> column: `c`. #> Error in `$<-`: #> ! Assigned data `<int>` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 2 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 2 to size 4. with_tbl(tbl$cd[1:2] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <int [1]> #> 2     2 f     <int [1]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df$a[1:3] <- 4:3) #> Warning in df$a[1:3] <- 4:3: number #> of items to replace is not a #> multiple of replacement length #>   a b         cd #> 1 4 e          9 #> 2 3 f     10, 11 #> 3 4 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl$a[1:3] <- 4:3) #> Warning in tbl$a[1:3] <- 4:3: #> number of items to replace is not a #> multiple of replacement length #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     4 g     <int [3]> #> 4     4 h     <chr [1]> with_tbl(tbl$a[1:4] <- 4:3) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     4 g     <int [3]> #> 4     3 h     <chr [1]> with_df({ df$v = vctrs::new_vctr(1:4); df$v[1:2] <- vctrs::new_vctr(4:3)}) #>   a b         cd v #> 1 1 e          9 4 #> 2 2 f     10, 11 3 #> 3 3 g 12, 13, 14 3 #> 4 4 h       text 4 with_df({ df$v = vctrs::new_vctr(1:4); df$v[1:2] <- vctrs::new_vctr(letters[4:3])}) #> Error in `[<-`: #> ! Can't convert `value` <vctrs_vctr> to <vctrs_vctr>."},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"scalars-and-full-length-1","dir":"Articles > Wip","previous_headings":"[[<-","what":"Scalars and full length","title":"Subassignment","text":"$ subsetting, columns consistently overwritten, partial matching doesn’t occur. Numeric indexing supported, tibbles don’t support creation new numbered columns good reason.","code":"with_tbl(tbl[[\"a\"]] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[[\"a\"]] <- \"x\") #> # A tibble: 4 × 3 #>   a     b     cd        #>   <chr> <chr> <list>    #> 1 x     e     <dbl [1]> #> 2 x     f     <int [2]> #> 3 x     g     <int [3]> #> 4 x     h     <chr [1]> with_tbl(tbl[[\"b\"]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 x     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 x     <int [3]> #> 4     4 x     <chr [1]> with_tbl(tbl[[\"c\"]] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> x     #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> x     #> 4     4 h     <chr [1]> x with_tbl(tbl[[\"cd\"]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd    #>   <int> <chr> <chr> #> 1     1 e     x     #> 2     2 f     x     #> 3     3 g     x     #> 4     4 h     x with_tbl(tbl[[1]] <- \"x\") #> # A tibble: 4 × 3 #>   a     b     cd        #>   <chr> <chr> <list>    #> 1 x     e     <dbl [1]> #> 2 x     f     <int [2]> #> 3 x     g     <int [3]> #> 4 x     h     <chr [1]> with_tbl(tbl[[2]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 x     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 x     <int [3]> #> 4     4 x     <chr [1]> with_df(df[[4]] <- \"x\") #>   a b         cd V4 #> 1 1 e          9  x #> 2 2 f     10, 11  x #> 3 3 g 12, 13, 14  x #> 4 4 h       text  x with_tbl(tbl[[4]] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        ...4  #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> x     #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> x     #> 4     4 h     <chr [1]> x with_df(df[[5]] <- \"x\") #> Warning in format.data.frame(if #> (omit) x[seq_len(n0), , drop = #> FALSE] else x, : corrupt data #> frame: columns will be truncated or #> padded with NAs #>   a b         cd      V5 #> 1 1 e          9 NULL  x #> 2 2 f     10, 11 <NA>  x #> 3 3 g 12, 13, 14 <NA>  x #> 4 4 h       text <NA>  x with_tbl(tbl[[5]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign to columns beyond the end with non-consecutive locations. #> ℹ Input has size 3. #> ✖ Subscript `5` contains non-consecutive location 5. with_tbl(tbl[[\"a\"]] <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[[\"a\"]] <- letters[4:1]) #> # A tibble: 4 × 3 #>   a     b     cd        #>   <chr> <chr> <list>    #> 1 d     e     <dbl [1]> #> 2 c     f     <int [2]> #> 3 b     g     <int [3]> #> 4 a     h     <chr [1]> with_tbl(tbl[[\"b\"]] <- letters[4:1]) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 d     <dbl [1]> #> 2     2 c     <int [2]> #> 3     3 b     <int [3]> #> 4     4 a     <chr [1]> with_tbl(tbl[[\"c\"]] <- letters[4:1]) #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> d     #> 2     2 f     <int [2]> c     #> 3     3 g     <int [3]> b     #> 4     4 h     <chr [1]> a with_tbl(tbl[[\"cd\"]] <- letters[4:1]) #> # A tibble: 4 × 3 #>       a b     cd    #>   <int> <chr> <chr> #> 1     1 e     d     #> 2     2 f     c     #> 3     3 g     b     #> 4     4 h     a"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"cells","dir":"Articles > Wip","previous_headings":"[[<-","what":"Cells","title":"Subassignment","text":"Tibbles stricter updating single cells, value must coercible existing contents. Updating list column requires contents wrapped list, consistently [[ subsetting returns list cell list column accessed:","code":"with_df(df[[2, \"a\"]] <- 1) #>   a b         cd #> 1 1 e          9 #> 2 1 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"a\"]] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, \"a\"]] <- 1.5) #>     a b         cd #> 1 1.0 e          9 #> 2 1.5 f     10, 11 #> 3 3.0 g 12, 13, 14 #> 4 4.0 h       text with_tbl(tbl[[2, \"a\"]] <- 1.5) #> Error in `[[<-`: #> ! Assigned data `1.5` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert from <double> to <integer> due to loss of precision. #> • Locations: 1 with_df(df[[2, \"a\"]] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"a\"]] <- \"x\") #> Error in `[[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[[2, \"b\"]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, 1]] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, 1]] <- \"x\") #> Error in `[[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[[2, 2]] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, \"cd\"]] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"cd\"]] <- \"x\") #> Error in `[[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_df(df[[2, \"cd\"]] <- list(\"x\")) #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[[2, \"cd\"]] <- list(\"x\")) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <chr [1]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[[2, \"c\"]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 2, \"c\", value = \"x\"): replacing element in non-existent column: c with_tbl(tbl[[2, \"c\"]] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> NA    #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> NA    #> 4     4 h     <chr [1]> NA with_df(df[[1:2, \"cd\"]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 1:2, \"cd\", value = \"x\"): only a single element should be replaced with_tbl(tbl[[1:2, \"cd\"]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. with_df(df[[1:2, \"c\"]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 1:2, \"c\", value = \"x\"): replacing element in non-existent column: c with_tbl(tbl[[1:2, \"c\"]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. with_df(df[[2, c(\"cd\", \"d\")]] <- \"x\") #> Error in `[[<-.data.frame`(`*tmp*`, 2, c(\"cd\", \"d\"), value = \"x\"): replacing element in non-existent column: d with_tbl(tbl[[2, c(\"cd\", \"d\")]] <- \"x\") #> Error in `[[<-`: #> ! Can't assign column with `c(\"cd\", \"d\")`. #> ✖ Subscript `c(\"cd\", \"d\")` must be size 1, not 2."},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"scalars","dir":"Articles > Wip","previous_headings":"[<-","what":"Scalars","title":"Subassignment","text":"","code":"with_df(df[2, \"a\"] <- 1) #>   a b         cd #> 1 1 e          9 #> 2 1 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"a\"] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, \"a\"] <- 1.5) #>     a b         cd #> 1 1.0 e          9 #> 2 1.5 f     10, 11 #> 3 3.0 g 12, 13, 14 #> 4 4.0 h       text with_tbl(tbl[2, \"a\"] <- 1.5) #> Error in `[<-`: #> ! Assigned data `1.5` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert from <double> to <integer> due to loss of precision. #> • Locations: 1 with_df(df[2, \"a\"] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"a\"] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[2, \"b\"] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, \"cd\"] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"cd\"] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_df(df[2, \"cd\"] <- list(\"x\")) #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, \"cd\"] <- list(\"x\")) #> Error in `[<-`: #> ! Assigned data `list(\"x\")` #>   must be compatible with existing #>   data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_tbl(tbl[2, \"c\"] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        c     #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> NA    #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> NA    #> 4     4 h     <chr [1]> NA with_df(df[2, 1] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, 1] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_tbl(tbl[2, 2] <- \"x\") #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 x     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, 3] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 2 f          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, 3] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `cd`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <list>. with_df(df[2, 4] <- \"x\") #>   a b         cd   V4 #> 1 1 e          9 <NA> #> 2 2 f     10, 11    x #> 3 3 g 12, 13, 14 <NA> #> 4 4 h       text <NA> with_tbl(tbl[2, 4] <- \"x\") #> # A tibble: 4 × 4 #>       a b     cd        ...4  #>   <int> <chr> <list>    <chr> #> 1     1 e     <dbl [1]> NA    #> 2     2 f     <int [2]> x     #> 3     3 g     <int [3]> NA    #> 4     4 h     <chr [1]> NA"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"full-length-columns","dir":"Articles > Wip","previous_headings":"[<-","what":"Full length columns","title":"Subassignment","text":"","code":"with_tbl(tbl[, \"a\"] <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, \"b\"] <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     1     4 <dbl [1]> #> 2     2     3 <int [2]> #> 3     3     2 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, \"c\"] <- 4:1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2     2 f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4     4 h     <chr [1]>     1 with_tbl(tbl[, \"cd\"] <- 4:1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <int> #> 1     1 e         4 #> 2     2 f         3 #> 3     3 g         2 #> 4     4 h         1 with_tbl(tbl[, 1] <- 4:1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 e     <dbl [1]> #> 2     3 f     <int [2]> #> 3     2 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, 2] <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     1     4 <dbl [1]> #> 2     2     3 <int [2]> #> 3     3     2 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, 3] <- 4:1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <int> #> 1     1 e         4 #> 2     2 f         3 #> 3     3 g         2 #> 4     4 h         1 with_df(df[, 4] <- 4:1) #>   a b         cd V4 #> 1 1 e          9  4 #> 2 2 f     10, 11  3 #> 3 3 g 12, 13, 14  2 #> 4 4 h       text  1 with_tbl(tbl[, 4] <- 4:1) #> # A tibble: 4 × 4 #>       a b     cd         ...4 #>   <int> <chr> <list>    <int> #> 1     1 e     <dbl [1]>     4 #> 2     2 f     <int [2]>     3 #> 3     3 g     <int [3]>     2 #> 4     4 h     <chr [1]>     1 with_tbl(tbl[, \"a\"] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, \"b\"] <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     2     1 <int [2]> #> 3     3     1 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, \"c\"] <- 1) #> # A tibble: 4 × 4 #>       a b     cd            c #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     1 #> 2     2 f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4     4 h     <chr [1]>     1 with_tbl(tbl[, \"cd\"] <- 1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     1 e         1 #> 2     2 f         1 #> 3     3 g         1 #> 4     4 h         1 with_tbl(tbl[, 1] <- 1) #> # A tibble: 4 × 3 #>       a b     cd        #>   <dbl> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 f     <int [2]> #> 3     1 g     <int [3]> #> 4     1 h     <chr [1]> with_tbl(tbl[, 2] <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     2     1 <int [2]> #> 3     3     1 <int [3]> #> 4     4     1 <chr [1]> with_tbl(tbl[, 3] <- 1) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     1 e         1 #> 2     2 f         1 #> 3     3 g         1 #> 4     4 h         1 with_df(df[, 4] <- 1) #>   a b         cd V4 #> 1 1 e          9  1 #> 2 2 f     10, 11  1 #> 3 3 g 12, 13, 14  1 #> 4 4 h       text  1 with_tbl(tbl[, 4] <- 1) #> # A tibble: 4 × 4 #>       a b     cd         ...4 #>   <int> <chr> <list>    <dbl> #> 1     1 e     <dbl [1]>     1 #> 2     2 f     <int [2]>     1 #> 3     3 g     <int [3]>     1 #> 4     4 h     <chr [1]>     1"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"multiple-full-length-columns","dir":"Articles > Wip","previous_headings":"[<-","what":"Multiple full length columns","title":"Subassignment","text":"","code":"with_tbl(tbl[, c(\"a\", \"b\")] <- 4:1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     4     4 <dbl [1]> #> 2     3     3 <int [2]> #> 3     2     2 <int [3]> #> 4     1     1 <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- 1) #> # A tibble: 4 × 3 #>       a     b cd        #>   <dbl> <dbl> <list>    #> 1     1     1 <dbl [1]> #> 2     1     1 <int [2]> #> 3     1     1 <int [3]> #> 4     1     1 <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(a = 4:1, b = letters[4:1])) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 d     <dbl [1]> #> 2     3 c     <int [2]> #> 3     2 b     <int [3]> #> 4     1 a     <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(b = 4:1, a = letters[4:1])) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 d     <dbl [1]> #> 2     3 c     <int [2]> #> 3     2 b     <int [3]> #> 4     1 a     <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(c = 4:1, d = letters[4:1])) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 d     <dbl [1]> #> 2     3 c     <int [2]> #> 3     2 b     <int [3]> #> 4     1 a     <chr [1]> with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(a = 4:1)) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <int> <list>    #> 1     4     4 <dbl [1]> #> 2     3     3 <int [2]> #> 3     2     2 <int [3]> #> 4     1     1 <chr [1]> with_df(df[, c(\"a\", \"b\")] <- data.frame(a = 4:1, b = letters[4:1], c = 1:4)) #> Warning in #> `[<-.data.frame`(`*tmp*`, , c(\"a\", #> \"b\"), value = structure(list(: #> provided 3 variables to replace 2 #> variables #>   a b         cd #> 1 4 d          9 #> 2 3 c     10, 11 #> 3 2 b 12, 13, 14 #> 4 1 a       text with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(a = 4:1, b = letters[4:1], c = 1:4)) #> Error in `[<-`: #> ! Can't recycle input of size 3 to size 2. with_tbl(tbl[, c(\"a\", \"b\")] <- data.frame(4:1, 1)) #> # A tibble: 4 × 3 #>       a     b cd        #>   <int> <dbl> <list>    #> 1     4     1 <dbl [1]> #> 2     3     1 <int [2]> #> 3     2     1 <int [3]> #> 4     1     1 <chr [1]> with_df(df[, c(\"a\", \"b\", \"c\")] <- data.frame(4:1, letters[4:1])) #>   a b         cd c #> 1 4 d          9 4 #> 2 3 c     10, 11 3 #> 3 2 b 12, 13, 14 2 #> 4 1 a       text 1 with_tbl(tbl[, c(\"a\", \"b\", \"c\")] <- data.frame(4:1, letters[4:1])) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_df(df[, c(\"a\", \"b\", \"cd\")] <- data.frame(4:1, letters[4:1])) #>   a b cd #> 1 4 d  4 #> 2 3 c  3 #> 3 2 b  2 #> 4 1 a  1 with_tbl(tbl[, c(\"a\", \"b\", \"cd\")] <- data.frame(4:1, letters[4:1])) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"full-length-rows","dir":"Articles > Wip","previous_headings":"[<-","what":"Full length rows","title":"Subassignment","text":"","code":"with_df(df[2, ] <- 1) #>   a b         cd #> 1 1 e          9 #> 2 1 1          1 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- 1) #> Error in `[<-`: #> ! Assigned data `1` must be #>   compatible with existing data. #> ℹ Error occurred for column `b`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double> to <character>. with_df(df[2, ] <- \"x\") #>   a b         cd #> 1 1 e          9 #> 2 x x          x #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- \"x\") #> Error in `[<-`: #> ! Assigned data `\"x\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_df(df[2, ] <- tibble(a = 1, b = \"x\")) #>   a b         cd #> 1 1 e          9 #> 2 1 x          1 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- tibble(a = 1, b = \"x\")) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_df(df[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b         cd #> 1 1 e          9 #> 2 1 x          y #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 x     <chr [1]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_df(df[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2, , #> value = structure(list(a = 1, : #> provided 4 variables to replace 3 #> variables #>   a b         cd #> 1 1 e          9 #> 2 1 x          y #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[2, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_df(df[0, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b         cd #> 1 1 e          9 #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[0, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> Error in `[<-`: #> ! Can't assign rows with `0`. #> ✖ Subscript `0` can't contain `0` values. #> ℹ It has a `0` value at location 1. with_df(df[5, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b         cd #> 1 1 e          9 #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text #> 5 1 x          y with_tbl(tbl[5, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> # A tibble: 5 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> #> 5     1 x     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"multiple-full-length-rows","dir":"Articles > Wip","previous_headings":"[<-","what":"Multiple full length rows","title":"Subassignment","text":"","code":"with_df(df[2:3, ] <- 1) #>   a b   cd #> 1 1 e    9 #> 2 1 1    1 #> 3 1 1    1 #> 4 4 h text with_tbl(tbl[2:3, ] <- 1) #> Error in `[<-`: #> ! Assigned data `1` must be #>   compatible with existing data. #> ℹ Error occurred for column `b`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <double> to <character>. with_df(df[2:3, ] <- 1:2) #>   a b   cd #> 1 1 e    9 #> 2 1 1    1 #> 3 2 2    2 #> 4 4 h text with_tbl(tbl[2:3, ] <- 1:2) #> Error in `[<-`: #> ! Assigned data `1:2` must #>   be compatible with existing data. #> ℹ Error occurred for column `b`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <integer> to <character>. with_df(df[2:3, ] <- c(\"x\", \"y\")) #>   a b   cd #> 1 1 e    9 #> 2 x x    x #> 3 y y    y #> 4 4 h text with_tbl(tbl[2:3, ] <- c(\"x\", \"y\")) #> Error in `[<-`: #> ! Assigned data `c(\"x\", #>   \"y\")` must be compatible with #>   existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>. with_df(df[2:3, ] <- tibble(a = 1:2, b = c(\"x\", \"y\"))) #>   a b   cd #> 1 1 e    9 #> 2 1 x    1 #> 3 2 y    2 #> 4 4 h text with_tbl(tbl[2:3, ] <- tibble(a = 1:2, b = c(\"x\", \"y\"))) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_df(df[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #>   a b   cd #> 1 1 e    9 #> 2 1 x    y #> 3 1 x    y #> 4 4 h text with_tbl(tbl[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 x     <chr [1]> #> 3     1 x     <chr [1]> #> 4     4 h     <chr [1]> with_tbl(tbl[2:3, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     1 x     <chr [1]> #> 3     2 x     <chr [1]> #> 4     4 h     <chr [1]> with_df(df[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Warning in #> `[<-.data.frame`(`*tmp*`, 2:3, , #> value = structure(list(a = 1, : #> provided 4 variables to replace 3 #> variables #>   a b   cd #> 1 1 e    9 #> 2 1 x    y #> 3 1 x    y #> 4 4 h text with_tbl(tbl[2:3, ] <- tibble(a = 1, b = \"x\", c = list(\"y\"), d = \"z\")) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_tbl(tbl[-(1:2), ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     1 x     <chr [1]> #> 4     2 x     <chr [1]> with_df(df[0:1, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> Warning in #> `[<-.data.frame`(`*tmp*`, 0:1, , #> value = structure(list(a = 1:2, : #> replacement element 1 has 2 rows to #> replace 1 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 0:1, , #> value = structure(list(a = 1:2, : #> replacement element 2 has 2 rows to #> replace 1 rows #> Warning in #> `[<-.data.frame`(`*tmp*`, 0:1, , #> value = structure(list(a = 1:2, : #> replacement element 3 has 2 rows to #> replace 1 rows #>   a b         cd #> 1 1 x          y #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[0:1, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> Error in `[<-`: #> ! Can't assign rows with `0:1`. #> ✖ Subscript `0:1` can't contain `0` values. #> ℹ It has a `0` value at location 1. with_tbl(tbl[4:5, ] <- tibble(a = 1:2, b = \"x\", c = list(\"y\"))) #> # A tibble: 5 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     1 x     <chr [1]> #> 5     2 x     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"unspecified","dir":"Articles > Wip","previous_headings":"[<-","what":"Unspecified","title":"Subassignment","text":"","code":"with_tbl(tbl[] <- 1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <dbl> <dbl> <dbl> #> 1     1     1     1 #> 2     1     1     1 #> 3     1     1     1 #> 4     1     1     1 with_tbl(tbl[] <- 4:1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <int> <int> <int> #> 1     4     4     4 #> 2     3     3     3 #> 3     2     2     2 #> 4     1     1     1 with_df(df[] <- 3:1) #>   a b cd #> 1 3 2  1 #> 2 2 1  3 #> 3 1 3  2 #> 4 3 2  1 with_tbl(tbl[] <- 3:1) #> Error in `[<-`: #> ! Assigned data `3:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 3 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 3 to size 4. with_df(df[] <- 5:1) #> Error in `[<-.data.frame`(`*tmp*`, , value = 5:1): replacement has 5 items, need 12 with_tbl(tbl[] <- 5:1) #> Error in `[<-`: #> ! Assigned data `5:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 5 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 5 to size 4. with_df(df[] <- data.frame(1, \"x\")) #>   a b cd #> 1 1 x  1 #> 2 1 x  1 #> 3 1 x  1 #> 4 1 x  1 with_tbl(tbl[] <- data.frame(1, \"x\")) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_tbl(tbl[] <- data.frame(4:1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     4 x         2 #> 2     3 x         2 #> 3     2 x         2 #> 4     1 x         2 with_tbl(tbl[] <- data.frame(1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <dbl> <chr> <dbl> #> 1     1 x         2 #> 2     1 x         2 #> 3     1 x         2 #> 4     1 x         2 with_df(df[] <- data.frame(1, \"x\", 2, 3)) #> Warning in #> `[<-.data.frame`(`*tmp*`, , value = #> structure(list(X1 = 1, X.x. = \"x\", #> : provided 4 variables to replace 3 #> variables #>   a b cd #> 1 1 x  2 #> 2 1 x  2 #> 3 1 x  2 #> 4 1 x  2 with_tbl(tbl[] <- data.frame(1, \"x\", 2, 3)) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_tbl(tbl[] <- tbl) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> with_tbl(tbl[,] <- 1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <dbl> <dbl> <dbl> #> 1     1     1     1 #> 2     1     1     1 #> 3     1     1     1 #> 4     1     1     1 with_tbl(tbl[,] <- 4:1) #> # A tibble: 4 × 3 #>       a     b    cd #>   <int> <int> <int> #> 1     4     4     4 #> 2     3     3     3 #> 3     2     2     2 #> 4     1     1     1 with_df(df[,] <- 3:1) #>   a b cd #> 1 3 2  1 #> 2 2 1  3 #> 3 1 3  2 #> 4 3 2  1 with_tbl(tbl[,] <- 3:1) #> Error in `[<-`: #> ! Assigned data `3:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 3 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 3 to size 4. with_df(df[,] <- 5:1) #> Error in `[<-.data.frame`(`*tmp*`, , , value = 5:1): replacement has 5 items, need 12 with_tbl(tbl[,] <- 5:1) #> Error in `[<-`: #> ! Assigned data `5:1` must #>   be compatible with existing data. #> ✖ Existing data has 4 rows. #> ✖ Assigned data has 5 rows. #> ℹ Only vectors of size 1 are #>   recycled. #> Caused by error in `vectbl_recycle_rhs_rows()` at tibble/R/subassign-backend.R:35:5: #> ! Can't recycle input of size 5 to size 4. with_df(df[,] <- data.frame(1, \"x\")) #>   a b cd #> 1 1 x  1 #> 2 1 x  1 #> 3 1 x  1 #> 4 1 x  1 with_tbl(tbl[,] <- data.frame(1, \"x\")) #> Error in `[<-`: #> ! Can't recycle input of size 2 to size 3. with_tbl(tbl[,] <- data.frame(4:1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <int> <chr> <dbl> #> 1     4 x         2 #> 2     3 x         2 #> 3     2 x         2 #> 4     1 x         2 with_tbl(tbl[,] <- data.frame(1, \"x\", 2)) #> # A tibble: 4 × 3 #>       a b        cd #>   <dbl> <chr> <dbl> #> 1     1 x         2 #> 2     1 x         2 #> 3     1 x         2 #> 4     1 x         2 with_df(df[,] <- data.frame(1, \"x\", 2, 3)) #> Warning in #> `[<-.data.frame`(`*tmp*`, , , value #> = structure(list(X1 = 1, : provided #> 4 variables to replace 3 variables #>   a b cd #> 1 1 x  2 #> 2 1 x  2 #> 3 1 x  2 #> 4 1 x  2 with_tbl(tbl[,] <- data.frame(1, \"x\", 2, 3)) #> Error in `[<-`: #> ! Can't recycle input of size 4 to size 3. with_tbl(tbl[,] <- tbl) #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subassign.html","id":"subset-assignment-1","dir":"Articles > Wip","previous_headings":"[<-","what":"Subset assignment","title":"Subassignment","text":"Due tibble’s default drop = FALSE, updating portion [ subset still safe, tibble still control. one example given .","code":"with_df(df[\"a\"][1, ] <- \"b\") #>   a b         cd #> 1 b e          9 #> 2 2 f     10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h       text with_tbl(tbl[\"a\"][1, ] <- \"b\") #> Error in `[<-`: #> ! Assigned data `\"b\"` must #>   be compatible with existing data. #> ℹ Error occurred for column `a`. #> Caused by error in `vec_assign()` at tibble/R/subassign-backend.R:193:3: #> ! Can't convert <character> to <integer>."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subset.html","id":"section","dir":"Articles > Wip","previous_headings":"","what":"$","title":"Subsetting","text":"$ subsetting, accessing missing column gives warning. Inexact matching supported:","code":"tbl$a #> [1] 1 2 3 4 tbl$\"a\" #> [1] 1 2 3 4 tbl$a[2:3] #> [1] 2 3 tbl$cd #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" df$c #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl$c #> Warning: Unknown or uninitialised #> column: `c`. #> NULL df$d #> NULL tbl$d #> Warning: Unknown or uninitialised #> column: `d`. #> NULL"},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subset.html","id":"section-1","dir":"Articles > Wip","previous_headings":"","what":"[[","title":"Subsetting","text":"exact argument supported tibbles. two indexes, single element returned. List columns unpacked tibbles, [[ unpacks columns. Exotic variants like recursive indexing deprecated tibbles.","code":"tbl[[\"a\"]] #> [1] 1 2 3 4 tbl[[\"cd\", exact = TRUE]] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" df[[\"cd\", exact = FALSE]] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[[\"cd\", exact = FALSE]] #> Warning: `exact` ignored. #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[[\"c\", exact = TRUE]] #> NULL df[[\"c\", exact = FALSE]] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[[\"c\", exact = FALSE]] #> Warning: `exact` ignored. #> NULL tbl[[2, \"a\"]] #> [1] 2 df[[2, \"cd\"]] #> [1] 10 11 tbl[[2, \"cd\"]] #> [[1]] #> [1] 10 11 df[[1:2, \"cd\"]] #> Error in col[[i, exact = exact]]: subscript out of bounds tbl[[1:2, \"cd\"]] #> Error in `tbl[[1:2, \"cd\"]]`: #> ! Can't extract row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. tbl[[2, \"c\"]] #> NULL df[[1:2, \"c\"]] #> NULL tbl[[1:2, \"c\"]] #> Error in `tbl[[1:2, \"c\"]]`: #> ! Can't extract row with `1:2`. #> ✖ Subscript `1:2` must be size 1, not 2. df[[c(1, 2)]] #> [1] 2 tbl[[c(1, 2)]] #> Error: #> ! The `j` argument of #>   `[[.tbl_df()` can't be a vector #>   of length 2 as of tibble 3.0.0. #> ℹ Recursive subsetting is #>   deprecated for tibbles."},{"path":"https://tibble.tidyverse.org/dev/articles/wip/subset.html","id":"section-2","dir":"Articles > Wip","previous_headings":"","what":"[","title":"Subsetting","text":"[ subsetting, tibbles always return tibble. drop argument supported different defaults: examples repeated two-dimensional indexing omitting row index: Multiple columns can queried passing vector column indexes (names, positions, even logical vector). latter option, tibbles tad stricter: examples repeated two-dimensional indexing omitting row index: Row subsetting integer indexes works almost identical. --bounds subsetting recommended may lead error future versions. Another special case subsetting [1, , drop = TRUE] data frame implementation returns list. Row subsetting logical indexes also works almost identical, index vector must length one number rows tibbles. Indexing row column works less , except drop: Indexes can omitted altogether, differences :","code":"tbl[\"a\"] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 df[\"a\", drop = FALSE] #> Warning in `[.data.frame`(df, \"a\", #> drop = FALSE): 'drop' argument will #> be ignored #>   a #> 1 1 #> 2 2 #> 3 3 #> 4 4 tbl[\"a\", drop = FALSE] #> Warning: `drop` argument ignored #> for subsetting a tibble with #> `x[j]`, it has an effect only for #> `x[i, j]`. #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 df[\"a\", drop = TRUE] #> Warning in `[.data.frame`(df, \"a\", #> drop = TRUE): 'drop' argument will #> be ignored #>   a #> 1 1 #> 2 2 #> 3 3 #> 4 4 tbl[\"a\", drop = TRUE] #> Warning: `drop` argument ignored #> for subsetting a tibble with #> `x[j]`, it has an effect only for #> `x[i, j]`. #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[1] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[0] #> # A tibble: 4 × 0 df[4] #> Error in `[.data.frame`(df, 4): undefined columns selected tbl[4] #> Error in `tbl[4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. df[NA] #> Error in `[.data.frame`(df, NA): undefined columns selected tbl[NA] #> Error in `tbl[NA]`: #> ! Can't subset columns with `NA`. #> ✖ Subscript `NA` can't contain missing values. #> ✖ It has a missing value at location 1. df[NA_character_] #> Error in `[.data.frame`(df, NA_character_): undefined columns selected tbl[NA_character_] #> Error in `tbl[NA_character_]`: #> ! Can't subset columns with `NA_character_`. #> ✖ Subscript `NA_character_` can't contain missing values. #> ✖ It has a missing value at location 1. df[NA_integer_] #> Error in `[.data.frame`(df, NA_integer_): undefined columns selected tbl[NA_integer_] #> Error in `tbl[NA_integer_]`: #> ! Can't subset columns with `NA_integer_`. #> ✖ Subscript `NA_integer_` can't contain missing values. #> ✖ It has a missing value at location 1. df[, \"a\"] #> [1] 1 2 3 4 tbl[, \"a\"] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[, \"a\", drop = FALSE] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[, \"a\", drop = TRUE] #> [1] 1 2 3 4 df[, 1] #> [1] 1 2 3 4 tbl[, 1] #> # A tibble: 4 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 #> 4     4 tbl[, 0] #> # A tibble: 4 × 0 df[, 4] #> Error in `[.data.frame`(df, , 4): undefined columns selected tbl[, 4] #> Error in `tbl[, 4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. df[, NA] #> Error in `[.data.frame`(df, , NA): undefined columns selected tbl[, NA] #> Error in `tbl[, NA]`: #> ! Can't subset columns with `NA`. #> ✖ Subscript `NA` can't contain missing values. #> ✖ It has a missing value at location 1. df[, NA_character_] #> Error in `[.data.frame`(df, , NA_character_): undefined columns selected tbl[, NA_character_] #> Error in `tbl[, NA_character_]`: #> ! Can't subset columns with `NA_character_`. #> ✖ Subscript `NA_character_` can't contain missing values. #> ✖ It has a missing value at location 1. df[, NA_integer_] #> Error in `[.data.frame`(df, , NA_integer_): undefined columns selected tbl[, NA_integer_] #> Error in `tbl[, NA_integer_]`: #> ! Can't subset columns with `NA_integer_`. #> ✖ Subscript `NA_integer_` can't contain missing values. #> ✖ It has a missing value at location 1. tbl[c(\"a\", \"b\")] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[character()] #> # A tibble: 4 × 0 tbl[1:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[1:3] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> df[1:4] #> Error in `[.data.frame`(df, 1:4): undefined columns selected tbl[1:4] #> Error in `tbl[1:4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. tbl[0:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h df[-1:2] #> Error in `[.default`(df, -1:2): only 0's may be mixed with negative subscripts tbl[-1:2] #> Error in `tbl[-1:2]`: #> ! Can't subset columns with `-1:2`. #> ✖ Negative and positive locations can't be mixed. #> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4. tbl[-1] #> # A tibble: 4 × 2 #>   b     cd        #>   <chr> <list>    #> 1 e     <dbl [1]> #> 2 f     <int [2]> #> 3 g     <int [3]> #> 4 h     <chr [1]> tbl[-(1:2)] #> # A tibble: 4 × 1 #>   cd        #>   <list>    #> 1 <dbl [1]> #> 2 <int [2]> #> 3 <int [3]> #> 4 <chr [1]> tbl[integer()] #> # A tibble: 4 × 0 tbl[TRUE] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[FALSE] #> # A tibble: 4 × 0 tbl[c(TRUE, TRUE, FALSE)] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[c(FALSE, TRUE, FALSE)] #> # A tibble: 4 × 1 #>   b     #>   <chr> #> 1 e     #> 2 f     #> 3 g     #> 4 h df[c(FALSE, TRUE)] #>   b #> 1 e #> 2 f #> 3 g #> 4 h tbl[c(FALSE, TRUE)] #> Error in `tbl[c(FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE)` must be size 1 or 3, not 2. df[c(FALSE, TRUE, FALSE, TRUE)] #> Error in `[.data.frame`(df, c(FALSE, TRUE, FALSE, TRUE)): undefined columns selected tbl[c(FALSE, TRUE, FALSE, TRUE)] #> Error in `tbl[c(FALSE, TRUE, FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE, FALSE, TRUE)` must be size 1 or 3, not 4. tbl[, c(\"a\", \"b\")] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[, character()] #> # A tibble: 4 × 0 tbl[, 1:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h tbl[, 1:3] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> df[, 1:4] #> Error in `[.data.frame`(df, , 1:4): undefined columns selected tbl[, 1:4] #> Error in `tbl[, 1:4]`: #> ! Can't subset columns past the end. #> ℹ Location 4 doesn't exist. #> ℹ There are only 3 columns. tbl[, 0:2] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h df[, -1:2] #> Error in .subset(x, j): only 0's may be mixed with negative subscripts tbl[, -1:2] #> Error in `tbl[, -1:2]`: #> ! Can't subset columns with `-1:2`. #> ✖ Negative and positive locations can't be mixed. #> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4. tbl[, -1] #> # A tibble: 4 × 2 #>   b     cd        #>   <chr> <list>    #> 1 e     <dbl [1]> #> 2 f     <int [2]> #> 3 g     <int [3]> #> 4 h     <chr [1]> df[, -(1:2)] #> [[1]] #> [1] 9 #>  #> [[2]] #> [1] 10 11 #>  #> [[3]] #> [1] 12 13 14 #>  #> [[4]] #> [1] \"text\" tbl[, -(1:2)] #> # A tibble: 4 × 1 #>   cd        #>   <list>    #> 1 <dbl [1]> #> 2 <int [2]> #> 3 <int [3]> #> 4 <chr [1]> tbl[, integer()] #> # A tibble: 4 × 0 tbl[, TRUE] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[, FALSE] #> # A tibble: 4 × 0 tbl[, c(TRUE, TRUE, FALSE)] #> # A tibble: 4 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f     #> 3     3 g     #> 4     4 h df[, c(FALSE, TRUE, FALSE)] #> [1] \"e\" \"f\" \"g\" \"h\" tbl[, c(FALSE, TRUE, FALSE)] #> # A tibble: 4 × 1 #>   b     #>   <chr> #> 1 e     #> 2 f     #> 3 g     #> 4 h df[, c(FALSE, TRUE)] #> [1] \"e\" \"f\" \"g\" \"h\" tbl[, c(FALSE, TRUE)] #> Error in `tbl[, c(FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE)` must be size 1 or 3, not 2. df[, c(FALSE, TRUE, FALSE, TRUE)] #> Error in `[.data.frame`(df, , c(FALSE, TRUE, FALSE, TRUE)): undefined columns selected tbl[, c(FALSE, TRUE, FALSE, TRUE)] #> Error in `tbl[, c(FALSE, TRUE, FALSE, TRUE)]`: #> ! Can't subset columns with `c(FALSE, TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(FALSE, TRUE, FALSE, TRUE)` must be size 1 or 3, not 4. tbl[1, ] #> # A tibble: 1 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> df[1, , drop = TRUE] #> $a #> [1] 1 #>  #> $b #> [1] \"e\" #>  #> $cd #> $cd[[1]] #> [1] 9 tbl[1, , drop = TRUE] #> # A tibble: 1 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> tbl[1:2, ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> tbl[0, ] #> # A tibble: 0 × 3 #> # ℹ 3 variables: a <int>, b <chr>, #> #   cd <list> tbl[integer(), ] #> # A tibble: 0 × 3 #> # ℹ 3 variables: a <int>, b <chr>, #> #   cd <list> tbl[5, ] #> # A tibble: 1 × 3 #>       a b     cd     #>   <int> <chr> <list> #> 1    NA NA    <NULL> tbl[4:5, ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     4 h     <chr [1]> #> 2    NA NA    <NULL> tbl[-1, ] #> # A tibble: 3 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     2 f     <int [2]> #> 2     3 g     <int [3]> #> 3     4 h     <chr [1]> df[-1:2, ] #> Error in xj[i]: only 0's may be mixed with negative subscripts tbl[-1:2, ] #> Error in `tbl[-1:2, ]`: #> ! Can't subset rows with `-1:2`. #> ✖ Negative and positive locations can't be mixed. #> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4. tbl[NA, ] #> # A tibble: 4 × 3 #>       a b     cd     #>   <int> <chr> <list> #> 1    NA NA    <NULL> #> 2    NA NA    <NULL> #> 3    NA NA    <NULL> #> 4    NA NA    <NULL> tbl[NA_integer_, ] #> # A tibble: 1 × 3 #>       a b     cd     #>   <int> <chr> <list> #> 1    NA NA    <NULL> tbl[c(NA, 1), ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1    NA NA    <NULL>    #> 2     1 e     <dbl [1]> tbl[TRUE, ] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[FALSE, ] #> # A tibble: 0 × 3 #> # ℹ 3 variables: a <int>, b <chr>, #> #   cd <list> df[c(TRUE, FALSE), ] #>   a b         cd #> 1 1 e          9 #> 3 3 g 12, 13, 14 tbl[c(TRUE, FALSE), ] #> Error in `tbl[c(TRUE, FALSE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE)`. #> ✖ Logical subscript `c(TRUE, FALSE)` must be size 1 or 4, not 2. df[c(TRUE, FALSE, TRUE), ] #>   a b         cd #> 1 1 e          9 #> 3 3 g 12, 13, 14 #> 4 4 h       text tbl[c(TRUE, FALSE, TRUE), ] #> Error in `tbl[c(TRUE, FALSE, TRUE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(TRUE, FALSE, TRUE)` must be size 1 or 4, not 3. tbl[c(TRUE, FALSE, TRUE, FALSE), ] #> # A tibble: 2 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     3 g     <int [3]> df[c(TRUE, FALSE, TRUE, FALSE, TRUE), ] #>     a    b         cd #> 1   1    e          9 #> 3   3    g 12, 13, 14 #> NA NA <NA>       NULL tbl[c(TRUE, FALSE, TRUE, FALSE, TRUE), ] #> Error in `tbl[c(TRUE, FALSE, TRUE, FALSE, #>     TRUE), ]`: #> ! Can't subset rows with `c(TRUE, FALSE, TRUE, FALSE, TRUE)`. #> ✖ Logical subscript `c(TRUE, FALSE, TRUE, FALSE, TRUE)` must be size 1 or 4, not 5. df[1, \"a\"] #> [1] 1 tbl[1, \"a\"] #> # A tibble: 1 × 1 #>       a #>   <int> #> 1     1 tbl[1, \"a\", drop = FALSE] #> # A tibble: 1 × 1 #>       a #>   <int> #> 1     1 tbl[1, \"a\", drop = TRUE] #> [1] 1 df[1:2, \"a\"] #> [1] 1 2 tbl[1:2, \"a\"] #> # A tibble: 2 × 1 #>       a #>   <int> #> 1     1 #> 2     2 tbl[1:2, \"a\", drop = FALSE] #> # A tibble: 2 × 1 #>       a #>   <int> #> 1     1 #> 2     2 tbl[1:2, \"a\", drop = TRUE] #> [1] 1 2 tbl[1, c(\"a\", \"b\")] #> # A tibble: 1 × 2 #>       a b     #>   <int> <chr> #> 1     1 e tbl[1, c(\"a\", \"b\"), drop = FALSE] #> # A tibble: 1 × 2 #>       a b     #>   <int> <chr> #> 1     1 e df[1, c(\"a\", \"b\"), drop = TRUE] #> $a #> [1] 1 #>  #> $b #> [1] \"e\" tbl[1, c(\"a\", \"b\"), drop = TRUE] #> # A tibble: 1 × 2 #>       a b     #>   <int> <chr> #> 1     1 e tbl[1:2, c(\"a\", \"b\")] #> # A tibble: 2 × 2 #>       a b     #>   <int> <chr> #> 1     1 e     #> 2     2 f tbl[] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]> tbl[,] #> # A tibble: 4 × 3 #>       a b     cd        #>   <int> <chr> <list>    #> 1     1 e     <dbl [1]> #> 2     2 f     <int [2]> #> 3     3 g     <int [3]> #> 4     4 h     <chr [1]>"},{"path":"https://tibble.tidyverse.org/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kirill Müller. Author, maintainer. Hadley Wickham. Author. Romain Francois. Contributor. Jennifer Bryan. Contributor. . Copyright holder, funder.","code":""},{"path":"https://tibble.tidyverse.org/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Müller K, Wickham H (2024). tibble: Simple Data Frames. R package version 3.2.1.9034, https://github.com/tidyverse/tibble, https://tibble.tidyverse.org/.","code":"@Manual{,   title = {tibble: Simple Data Frames},   author = {Kirill Müller and Hadley Wickham},   year = {2024},   note = {R package version 3.2.1.9034,     https://github.com/tidyverse/tibble},   url = {https://tibble.tidyverse.org/}, }"},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Simple Data Frames","text":"tibble, tbl_df, modern reimagining data.frame, keeping time proven effective, throwing . Tibbles data.frames lazy surly: less (.e. don’t change variable names types, don’t partial matching) complain (e.g. variable exist). forces confront problems earlier, typically leading cleaner, expressive code. Tibbles also enhanced print() method makes easier use large datasets containing complex objects. new tibbles, best place start tibbles chapter R data science.","code":""},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Simple Data Frames","text":"","code":"# The easiest way to get tibble is to install the whole tidyverse: install.packages(\"tidyverse\")  # Alternatively, install just tibble: install.packages(\"tibble\")  # Or the the development version from GitHub: # install.packages(\"devtools\") devtools::install_github(\"tidyverse/tibble\")"},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Simple Data Frames","text":"Create tibble existing object as_tibble(): work reasonable inputs already data.frames, lists, matrices, tables. can also create new tibble column vectors tibble(): tibble() much less data.frame(): never changes type inputs (e.g. keeps list columns ), never changes names variables, recycles inputs length 1, never creates row.names(). can read features vignette(\"tibble\"). can define tibble row--row tribble():","code":"library(tibble) data <- data.frame(a = 1:3, b = letters[1:3], c = Sys.Date() - 1:3) data #>   a b          c #> 1 1 a 2023-10-07 #> 2 2 b 2023-10-06 #> 3 3 c 2023-10-05  as_tibble(data) #> # A tibble: 3 × 3 #>       a b     c          #>   <int> <chr> <date>     #> 1     1 a     2023-10-07 #> 2     2 b     2023-10-06 #> 3     3 c     2023-10-05 tibble(x = 1:5, y = 1, z = x^2 + y) #> # A tibble: 5 × 3 #>       x     y     z #>   <int> <dbl> <dbl> #> 1     1     1     2 #> 2     2     1     5 #> 3     3     1    10 #> 4     4     1    17 #> 5     5     1    26 tribble(   ~x, ~y,  ~z,   \"a\", 2,  3.6,   \"b\", 1,  8.5 ) #> # A tibble: 2 × 3 #>   x         y     z #>   <chr> <dbl> <dbl> #> 1 a         2   3.6 #> 2 b         1   8.5"},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"related-work","dir":"","previous_headings":"","what":"Related work","title":"Simple Data Frames","text":"tibble print method draws inspiration data.table, frame. Like data.table::data.table(), tibble() doesn’t change column names doesn’t use rownames.","code":""},{"path":"https://tibble.tidyverse.org/dev/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Simple Data Frames","text":"Please note tibble project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Add columns to a data frame — add_column","title":"Add columns to a data frame — add_column","text":"convenient way add one columns existing data frame.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add columns to a data frame — add_column","text":"","code":"add_column(   .data,   ...,   .before = NULL,   .after = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )"},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add columns to a data frame — add_column","text":".data Data frame append . ... <dynamic-dots> Name-value pairs, passed tibble(). values must size .data size 1. ., .One-based column index column name add new columns, default: last column. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/add_column.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add columns to a data frame — add_column","text":"","code":"# add_column --------------------------------- df <- tibble(x = 1:3, y = 3:1)  df %>% add_column(z = -1:1, w = 0) #> # A tibble: 3 × 4 #>       x     y     z     w #>   <int> <int> <int> <dbl> #> 1     1     3    -1     0 #> 2     2     2     0     0 #> 3     3     1     1     0 df %>% add_column(z = -1:1, .before = \"y\") #> # A tibble: 3 × 3 #>       x     z     y #>   <int> <int> <int> #> 1     1    -1     3 #> 2     2     0     2 #> 3     3     1     1  # You can't overwrite existing columns try(df %>% add_column(x = 4:6)) #> Error in add_column(., x = 4:6) :  #>   Column name `x` must not be duplicated. #> Use `.name_repair` to specify repair. #> Caused by error in `repaired_names()` at tibble/R/names.R:14:3: #> ! Names must be unique. #> ✖ These names are duplicated: #>   * \"x\" at locations 1 and 3.  # You can't create new observations try(df %>% add_column(z = 1:5)) #> Error in add_column(., z = 1:5) :  #>   New columns must be compatible with `.data`. #> ✖ New column has 5 rows. #> ℹ `.data` has 3 rows."},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":null,"dir":"Reference","previous_headings":"","what":"Add rows to a data frame — add_row","title":"Add rows to a data frame — add_row","text":"convenient way add one rows data existing data frame. See tribble() easy way create complete data frame row--row. Use tibble_row() ensure new data one row. add_case() alias add_row().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add rows to a data frame — add_row","text":"","code":"add_row(.data, ..., .before = NULL, .after = NULL)"},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add rows to a data frame — add_row","text":".data Data frame append . ... <dynamic-dots> Name-value pairs, passed tibble(). Values can defined columns already exist .data unset columns get NA value. ., .One-based row index add new rows, default: last row.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/add_row.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add rows to a data frame — add_row","text":"","code":"# add_row --------------------------------- df <- tibble(x = 1:3, y = 3:1)  df %>% add_row(x = 4, y = 0) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4     0  # You can specify where to add the new rows df %>% add_row(x = 4, y = 0, .before = 2) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3 #> 2     4     0 #> 3     2     2 #> 4     3     1  # You can supply vectors, to add multiple rows (this isn't # recommended because it's a bit hard to read) df %>% add_row(x = 4:5, y = 0:-1) #> # A tibble: 5 × 2 #>       x     y #>   <int> <int> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4     0 #> 5     5    -1  # Use tibble_row() to add one row only df %>% add_row(tibble_row(x = 4, y = 0)) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4     0 try(df %>% add_row(tibble_row(x = 4:5, y = 0:-1))) #> Error in tibble_quos(xs, .rows = 1, .name_repair = .name_repair, single_row = TRUE) :  #>   All vectors must be size one, use `list()` to wrap. #> ✖ Column `x` is of size 2.  # Absent variables get missing values df %>% add_row(x = 4) #> # A tibble: 4 × 2 #>       x     y #>   <dbl> <int> #> 1     1     3 #> 2     2     2 #> 3     3     1 #> 4     4    NA  # You can't create new variables try(df %>% add_row(z = 10)) #> Error in add_row(., z = 10) : New rows can't add columns. #> ✖ Can't find column `z` in `.data`."},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce lists, matrices, and more to data frames — as_tibble","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"as_tibble() turns existing object, data frame matrix, -called tibble, data frame class tbl_df. contrast tibble(), builds tibble individual columns. as_tibble() tibble() base::.data.frame() base::data.frame(). as_tibble() S3 generic, methods : data.frame: Thin wrapper around list method implements tibble's treatment rownames. matrix, poly, ts, table Default: inputs first coerced base::.data.frame(). as_tibble_row() converts vector tibble one row. input list, elements must size one. as_tibble_col() converts vector tibble one column.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"","code":"as_tibble(   x,   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\"),   rownames = pkgconfig::get_config(\"tibble::rownames\", NULL) )  # S3 method for class 'data.frame' as_tibble(   x,   validate = NULL,   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\"),   rownames = pkgconfig::get_config(\"tibble::rownames\", NULL) )  # S3 method for class 'list' as_tibble(   x,   validate = NULL,   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )  # S3 method for class 'matrix' as_tibble(x, ..., validate = NULL, .name_repair = NULL)  # S3 method for class 'table' as_tibble(x, `_n` = \"n\", ..., n = `_n`, .name_repair = \"check_unique\")  # S3 method for class '`NULL`' as_tibble(x, ...)  # Default S3 method as_tibble(x, ...)  as_tibble_row(   x,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )  as_tibble_col(x, column_name = \"value\")"},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"x data frame, list, matrix, object reasonably coerced tibble. ... Unused, extensibility. .rows number rows, useful create 0-column tibble just additional check. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce . rownames treat existing row names data frame matrix: NULL: remove row names. default. NA: keep row names. string: name new column. Existing rownames transferred column row.names attribute deleted. name repair applied new column name, even x already contains column name. Use as_tibble(rownames_to_column(...)) safeguard case. Read rownames. _n, validate  compatibility , use new code. n Name count column, default: \"n\". column_name Name column.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"row-names","dir":"Reference","previous_headings":"","what":"Row names","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"default behavior silently remove row names. New code explicitly convert row names new column using rownames argument. existing code relies retention row names, call pkgconfig::set_config(\"tibble::rownames\" = NA) script package's .onLoad()  function.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"life-cycle","dir":"Reference","previous_headings":"","what":"Life cycle","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"Using as_tibble() vectors superseded version 3.0.0, prefer expressive as_tibble_row() as_tibble_col() variants new code.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/as_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce lists, matrices, and more to data frames — as_tibble","text":"","code":"m <- matrix(rnorm(50), ncol = 5) colnames(m) <- c(\"a\", \"b\", \"c\", \"d\", \"e\") df <- as_tibble(m)  as_tibble_row(c(a = 1, b = 2)) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     1     2 as_tibble_row(list(c = \"three\", d = list(4:5))) #> # A tibble: 1 × 2 #>   c     d         #>   <chr> <list>    #> 1 three <int [2]> as_tibble_row(1:3, .name_repair = \"unique\") #> New names: #> • `` -> `...1` #> • `` -> `...2` #> • `` -> `...3` #> # A tibble: 1 × 3 #>    ...1  ...2  ...3 #>   <int> <int> <int> #> 1     1     2     3  as_tibble_col(1:3) #> # A tibble: 3 × 1 #>   value #>   <int> #> 1     1 #> 2     2 #> 3     3 as_tibble_col(   list(c = \"three\", d = list(4:5)),   column_name = \"data\" ) #> # A tibble: 2 × 1 #>   data         #>   <named list> #> 1 <chr [1]>    #> 2 <list [1]>"},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":null,"dir":"Reference","previous_headings":"","what":"Format a character vector — char","title":"Format a character vector — char","text":"Constructs character vector can formatted predefined minimum width without width restrictions, abbreviation style can configured. formatting applied vector printed formatted, also tibble column. set_char_opts() adds formatting options arbitrary character vector, useful composing types.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format a character vector — char","text":"","code":"char(   x,   ...,   min_chars = NULL,   shorten = c(\"back\", \"front\", \"mid\", \"abbreviate\") )  set_char_opts(   x,   ...,   min_chars = NULL,   shorten = c(\"back\", \"front\", \"mid\", \"abbreviate\") )"},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format a character vector — char","text":"x character vector. ... dots future extensions must empty. min_chars minimum width allocate column, defaults 15. \"pillar.min_chars\" option consulted. shorten abbreviate data necessary: \"back\" (default): add ellipsis end \"front\": add ellipsis front \"mid\": add ellipsis middle \"abbreviate\": use abbreviate()","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/char.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format a character vector — char","text":"","code":"# Display as a vector: char(letters[1:3]) #> <pillar_char[3]> #> [1] a b c # Space constraints: rand_strings <- stringi::stri_rand_strings(10, seq(40, 22, by = -2))  # Plain character vectors get truncated if space is limited: data_with_id <- function(id) {   tibble(     id,     some_number_1 = 1, some_number_2 = 2, some_number_3 = 3,     some_number_4 = 4, some_number_5 = 5, some_number_6 = 6,     some_number_7 = 7, some_number_8 = 8, some_number_9 = 9   ) } data_with_id(rand_strings) #> # A tibble: 10 × 10 #>    id              some_number_1 some_number_2 some_number_3 some_number_4 #>    <chr>                   <dbl>         <dbl>         <dbl>         <dbl> #>  1 WJSsxf5RPiDjFR…             1             2             3             4 #>  2 HQOlqjZavreUgr…             1             2             3             4 #>  3 GR8hYmPYrwJG9U…             1             2             3             4 #>  4 dO0k5CLEp9Mcps…             1             2             3             4 #>  5 3yKhjZZbegjEYO…             1             2             3             4 #>  6 uyWa9TGFPzGkVA…             1             2             3             4 #>  7 UaiZMcOCjm7Lh2…             1             2             3             4 #>  8 z2qYwlo7fldemm…             1             2             3             4 #>  9 ek4O7hFxIud6K9…             1             2             3             4 #> 10 YCRHBt1kj2i9Jn…             1             2             3             4 #> # ℹ 5 more variables: some_number_5 <dbl>, some_number_6 <dbl>, #> #   some_number_7 <dbl>, some_number_8 <dbl>, some_number_9 <dbl>  # Use char() to avoid or control truncation data_with_id(char(rand_strings, min_chars = 24)) #> # A tibble: 10 × 10 #>    id                            some_number_1 some_number_2 some_number_3 #>    <char>                                <dbl>         <dbl>         <dbl> #>  1 WJSsxf5RPiDjFRy7iVoadHgnz4uv…             1             2             3 #>  2 HQOlqjZavreUgr3GXLoDX6mpkxih…             1             2             3 #>  3 GR8hYmPYrwJG9UsJm9A47KvwLTo2…             1             2             3 #>  4 dO0k5CLEp9McpsCkmBt5wkYOioyg…             1             2             3 #>  5 3yKhjZZbegjEYOJm1HIxNkWvYx8O…             1             2             3 #>  6 uyWa9TGFPzGkVAZsJlCaIHvalJsv…             1             2             3 #>  7 UaiZMcOCjm7Lh2Gthq6NgOGsJbR2              1             2             3 #>  8 z2qYwlo7fldemmrE772uCYLrI6                1             2             3 #>  9 ek4O7hFxIud6K9YsvhQTB6Oe                  1             2             3 #> 10 YCRHBt1kj2i9JnNgyaa5MB                    1             2             3 #> # ℹ 6 more variables: some_number_4 <dbl>, some_number_5 <dbl>, #> #   some_number_6 <dbl>, some_number_7 <dbl>, some_number_8 <dbl>, #> #   some_number_9 <dbl> data_with_id(char(rand_strings, min_chars = Inf)) #> # A tibble: 10 × 10 #>    id                                       some_number_1 some_number_2 #>    <char>                                           <dbl>         <dbl> #>  1 WJSsxf5RPiDjFRy7iVoadHgnz4uveIpMlA7Qaqjz             1             2 #>  2 HQOlqjZavreUgr3GXLoDX6mpkxihE20qyTAB45               1             2 #>  3 GR8hYmPYrwJG9UsJm9A47KvwLTo2FQEkuqMT                 1             2 #>  4 dO0k5CLEp9McpsCkmBt5wkYOioyg9COiuR                   1             2 #>  5 3yKhjZZbegjEYOJm1HIxNkWvYx8Ozl85                     1             2 #>  6 uyWa9TGFPzGkVAZsJlCaIHvalJsvMC                       1             2 #>  7 UaiZMcOCjm7Lh2Gthq6NgOGsJbR2                         1             2 #>  8 z2qYwlo7fldemmrE772uCYLrI6                           1             2 #>  9 ek4O7hFxIud6K9YsvhQTB6Oe                             1             2 #> 10 YCRHBt1kj2i9JnNgyaa5MB                               1             2 #> # ℹ 7 more variables: some_number_3 <dbl>, some_number_4 <dbl>, #> #   some_number_5 <dbl>, some_number_6 <dbl>, some_number_7 <dbl>, #> #   some_number_8 <dbl>, some_number_9 <dbl> data_with_id(char(rand_strings, min_chars = 24, shorten = \"mid\")) #> # A tibble: 10 × 10 #>    id                            some_number_1 some_number_2 some_number_3 #>    <char>                                <dbl>         <dbl>         <dbl> #>  1 WJSsxf5RPiDjFR…uveIpMlA7Qaqjz             1             2             3 #>  2 HQOlqjZavreUgr…kxihE20qyTAB45             1             2             3 #>  3 GR8hYmPYrwJG9U…vwLTo2FQEkuqMT             1             2             3 #>  4 dO0k5CLEp9Mcps…wkYOioyg9COiuR             1             2             3 #>  5 3yKhjZZbegjEYO…IxNkWvYx8Ozl85             1             2             3 #>  6 uyWa9TGFPzGkVA…JlCaIHvalJsvMC             1             2             3 #>  7 UaiZMcOCjm7Lh2Gthq6NgOGsJbR2              1             2             3 #>  8 z2qYwlo7fldemmrE772uCYLrI6                1             2             3 #>  9 ek4O7hFxIud6K9YsvhQTB6Oe                  1             2             3 #> 10 YCRHBt1kj2i9JnNgyaa5MB                    1             2             3 #> # ℹ 6 more variables: some_number_4 <dbl>, some_number_5 <dbl>, #> #   some_number_6 <dbl>, some_number_7 <dbl>, some_number_8 <dbl>, #> #   some_number_9 <dbl>  # Lorem Ipsum, one sentence per row. lipsum <- unlist(strsplit(stringi::stri_rand_lipsum(1), \"(?<=[.]) +\", perl = TRUE)) tibble(   back = char(lipsum, shorten = \"back\"),   front = char(lipsum, shorten = \"front\"),   mid = char(lipsum, shorten = \"mid\") ) #> # A tibble: 10 × 3 #>    back                                                        front mid   #>    <char>                                                      <cha> <cha> #>  1 Lorem ipsum dolor sit amet, sit et auctor lacinia etiam co… …iis. Lo…s. #>  2 Laoreet in nec diam ut suspendisse adipiscing suspendisse … …pis. La…s. #>  3 Eleifend eleifend, metus nunc turpis.                       …pis. El…s. #>  4 Augue massa lobortis faucibus velit efficitur ac faucibus.  …bus. Au…s. #>  5 Elit egestas egestas sit amet ut conubia faucibus facilisi. …isi. El…i. #>  6 Tempor urna nisi feugiat senectus.                          …tus. Te…s. #>  7 Nunc in pretium dui ac.                                     … ac. Nu…c. #>  8 Elit semper eu.                                             … eu. El…u. #>  9 Sapien congue, dictum massa sed posuere sagittis.           …tis. Sa…s. #> 10 Interdum ante aenean, nisi dapibus lacinia nulla laoreet, … …bus. In…s. tibble(abbr = char(lipsum, shorten = \"abbreviate\")) #> # A tibble: 10 × 1 #>    abbr                                                                    #>    <char>                                                                  #>  1 Loremipsumdolorsitamet,sitetauctorlacinetiamcommodo!Tincidntd,nslvtscs. #>  2 Laoreetinnecdiamutsuspendissadipiscingsuspendissconubtortorrissdnctrps. #>  3 Eleifend eleifend, metus nunc turpis.                                   #>  4 Augue massa lobortis faucibus velit efficitur ac faucibus.              #>  5 Elit egestas egestas sit amet ut conubia faucibus facilisi.             #>  6 Tempor urna nisi feugiat senectus.                                      #>  7 Nunc in pretium dui ac.                                                 #>  8 Elit semper eu.                                                         #>  9 Sapien congue, dictum massa sed posuere sagittis.                       #> 10 Interdumanteaenean,nisidapibuslacinianulllaoreet,felisultriciesdapibus."},{"path":"https://tibble.tidyverse.org/dev/reference/deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions — deprecated","title":"Deprecated functions — deprecated","text":"Use tibble() instead data_frame(). Use rlang::quasiquotation instead tibble_(), data_frame_(), lst_(). Use as_tibble() instead as_data_frame() .tibble(), mind new signature semantics. Use tribble() instead frame_data().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions — deprecated","text":"","code":"data_frame(...)  tibble_(xs)  data_frame_(xs)  lst_(xs)  as_data_frame(x, ...)  as.tibble(x, ...)  frame_data(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Converting vectors to data frames, and vice versa — enframe","title":"Converting vectors to data frames, and vice versa — enframe","text":"enframe() converts named atomic vectors lists one- two-column data frames. list, result nested tibble column type list. unnamed vectors, natural sequence used name column. deframe() converts two-column data frames named vector list, using first column name second column value. input one column, unnamed vector returned.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converting vectors to data frames, and vice versa — enframe","text":"","code":"enframe(x, name = \"name\", value = \"value\")  deframe(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converting vectors to data frames, and vice versa — enframe","text":"x vector (enframe()) data frame one two columns (deframe()). name, value Names columns store names values. name NULL, one-column tibble returned; value NULL.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converting vectors to data frames, and vice versa — enframe","text":"enframe(), tibble two columns (name NULL, default) one column (otherwise). deframe(), vector (named unnamed).","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/enframe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converting vectors to data frames, and vice versa — enframe","text":"","code":"enframe(1:3) #> # A tibble: 3 × 2 #>    name value #>   <int> <int> #> 1     1     1 #> 2     2     2 #> 3     3     3 enframe(c(a = 5, b = 7)) #> # A tibble: 2 × 2 #>   name  value #>   <chr> <dbl> #> 1 a         5 #> 2 b         7 enframe(list(one = 1, two = 2:3, three = 4:6)) #> # A tibble: 3 × 2 #>   name  value     #>   <chr> <list>    #> 1 one   <dbl [1]> #> 2 two   <int [2]> #> 3 three <int [3]> deframe(enframe(3:1)) #> 1 2 3  #> 3 2 1  deframe(tibble(a = 1:3)) #> [1] 1 2 3 deframe(tibble(a = as.list(1:3))) #> [[1]] #> [1] 1 #>  #> [[2]] #> [1] 2 #>  #> [[3]] #> [1] 3 #>"},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":null,"dir":"Reference","previous_headings":"","what":"Printing tibbles — formatting","title":"Printing tibbles — formatting","text":"One main features tbl_df class printing: Tibbles print many rows columns fit one screen, supplemented summary remaining rows columns. Tibble reveals type column, keeps user informed whether variable , e.g., <chr> <fct> (character versus factor). See vignette(\"types\") overview common type abbreviations. Printing can tweaked one-call calling print() explicitly setting arguments like n width. persistent control available setting options described pillar::pillar_options. See also vignette(\"digits\") comparison base options, vignette(\"numbers\") showcases num() char() creating columns custom formatting options. tibble 3.1.0, printing handled entirely pillar package. implement package extends tibble, printed output can customized various ways. See vignette(\"extending\", package = \"pillar\") details, pillar::pillar_options options control display console.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Printing tibbles — formatting","text":"","code":"# S3 method for class 'tbl_df' print(   x,   width = NULL,   ...,   n = NULL,   max_extra_cols = NULL,   max_footer_lines = NULL )  # S3 method for class 'tbl_df' format(   x,   width = NULL,   ...,   n = NULL,   max_extra_cols = NULL,   max_footer_lines = NULL )"},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Printing tibbles — formatting","text":"x Object format print. width Width text output generate. defaults NULL, means use width option. ... Passed pillar::tbl_format_setup(). n Number rows show. NULL, default, print rows less print_max option. Otherwise, print many rows specified print_min option. max_extra_cols Number extra columns print abbreviated information , width small entire tibble. NULL, max_extra_cols option used. previously defined n_extra argument soft-deprecated. max_footer_lines Maximum number footer lines. NULL, max_footer_lines option used.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/formatting.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Printing tibbles — formatting","text":"","code":"print(as_tibble(mtcars)) #> # A tibble: 32 × 11 #>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4 #>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4 #>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1 #>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1 #>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 #>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 #>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 #>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2 #>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2 #> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 #> # ℹ 22 more rows print(as_tibble(mtcars), n = 1) #> # A tibble: 32 × 11 #>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1    21     6   160   110   3.9  2.62  16.5     0     1     4     4 #> # ℹ 31 more rows print(as_tibble(mtcars), n = 3) #> # A tibble: 32 × 11 #>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1  21       6   160   110  3.9   2.62  16.5     0     1     4     4 #> 2  21       6   160   110  3.9   2.88  17.0     0     1     4     4 #> 3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1 #> # ℹ 29 more rows  print(as_tibble(trees), n = 100) #> # A tibble: 31 × 3 #>    Girth Height Volume #>    <dbl>  <dbl>  <dbl> #>  1   8.3     70   10.3 #>  2   8.6     65   10.3 #>  3   8.8     63   10.2 #>  4  10.5     72   16.4 #>  5  10.7     81   18.8 #>  6  10.8     83   19.7 #>  7  11       66   15.6 #>  8  11       75   18.2 #>  9  11.1     80   22.6 #> 10  11.2     75   19.9 #> 11  11.3     79   24.2 #> 12  11.4     76   21   #> 13  11.4     76   21.4 #> 14  11.7     69   21.3 #> 15  12       75   19.1 #> 16  12.9     74   22.2 #> 17  12.9     85   33.8 #> 18  13.3     86   27.4 #> 19  13.7     71   25.7 #> 20  13.8     64   24.9 #> 21  14       78   34.5 #> 22  14.2     80   31.7 #> 23  14.5     74   36.3 #> 24  16       72   38.3 #> 25  16.3     77   42.6 #> 26  17.3     81   55.4 #> 27  17.5     82   55.7 #> 28  17.9     80   58.3 #> 29  18       80   51.5 #> 30  18       80   51   #> 31  20.6     87   77    print(mtcars, width = 10) #>                      mpg #> Mazda RX4           21.0 #> Mazda RX4 Wag       21.0 #> Datsun 710          22.8 #> Hornet 4 Drive      21.4 #> Hornet Sportabout   18.7 #> Valiant             18.1 #> Duster 360          14.3 #> Merc 240D           24.4 #> Merc 230            22.8 #> Merc 280            19.2 #> Merc 280C           17.8 #> Merc 450SE          16.4 #> Merc 450SL          17.3 #> Merc 450SLC         15.2 #> Cadillac Fleetwood  10.4 #> Lincoln Continental 10.4 #> Chrysler Imperial   14.7 #> Fiat 128            32.4 #> Honda Civic         30.4 #> Toyota Corolla      33.9 #> Toyota Corona       21.5 #> Dodge Challenger    15.5 #> AMC Javelin         15.2 #> Camaro Z28          13.3 #> Pontiac Firebird    19.2 #> Fiat X1-9           27.3 #> Porsche 914-2       26.0 #> Lotus Europa        30.4 #> Ford Pantera L      15.8 #> Ferrari Dino        19.7 #> Maserati Bora       15.0 #> Volvo 142E          21.4 #>                     cyl #> Mazda RX4             6 #> Mazda RX4 Wag         6 #> Datsun 710            4 #> Hornet 4 Drive        6 #> Hornet Sportabout     8 #> Valiant               6 #> Duster 360            8 #> Merc 240D             4 #> Merc 230              4 #> Merc 280              6 #> Merc 280C             6 #> Merc 450SE            8 #> Merc 450SL            8 #> Merc 450SLC           8 #> Cadillac Fleetwood    8 #> Lincoln Continental   8 #> Chrysler Imperial     8 #> Fiat 128              4 #> Honda Civic           4 #> Toyota Corolla        4 #> Toyota Corona         4 #> Dodge Challenger      8 #> AMC Javelin           8 #> Camaro Z28            8 #> Pontiac Firebird      8 #> Fiat X1-9             4 #> Porsche 914-2         4 #> Lotus Europa          4 #> Ford Pantera L        8 #> Ferrari Dino          6 #> Maserati Bora         8 #> Volvo 142E            4 #>                      disp #> Mazda RX4           160.0 #> Mazda RX4 Wag       160.0 #> Datsun 710          108.0 #> Hornet 4 Drive      258.0 #> Hornet Sportabout   360.0 #> Valiant             225.0 #> Duster 360          360.0 #> Merc 240D           146.7 #> Merc 230            140.8 #> Merc 280            167.6 #> Merc 280C           167.6 #> Merc 450SE          275.8 #> Merc 450SL          275.8 #> Merc 450SLC         275.8 #> Cadillac Fleetwood  472.0 #> Lincoln Continental 460.0 #> Chrysler Imperial   440.0 #> Fiat 128             78.7 #> Honda Civic          75.7 #> Toyota Corolla       71.1 #> Toyota Corona       120.1 #> Dodge Challenger    318.0 #> AMC Javelin         304.0 #> Camaro Z28          350.0 #> Pontiac Firebird    400.0 #> Fiat X1-9            79.0 #> Porsche 914-2       120.3 #> Lotus Europa         95.1 #> Ford Pantera L      351.0 #> Ferrari Dino        145.0 #> Maserati Bora       301.0 #> Volvo 142E          121.0 #>                      hp #> Mazda RX4           110 #> Mazda RX4 Wag       110 #> Datsun 710           93 #> Hornet 4 Drive      110 #> Hornet Sportabout   175 #> Valiant             105 #> Duster 360          245 #> Merc 240D            62 #> Merc 230             95 #> Merc 280            123 #> Merc 280C           123 #> Merc 450SE          180 #> Merc 450SL          180 #> Merc 450SLC         180 #> Cadillac Fleetwood  205 #> Lincoln Continental 215 #> Chrysler Imperial   230 #> Fiat 128             66 #> Honda Civic          52 #> Toyota Corolla       65 #> Toyota Corona        97 #> Dodge Challenger    150 #> AMC Javelin         150 #> Camaro Z28          245 #> Pontiac Firebird    175 #> Fiat X1-9            66 #> Porsche 914-2        91 #> Lotus Europa        113 #> Ford Pantera L      264 #> Ferrari Dino        175 #> Maserati Bora       335 #> Volvo 142E          109 #>                     drat #> Mazda RX4           3.90 #> Mazda RX4 Wag       3.90 #> Datsun 710          3.85 #> Hornet 4 Drive      3.08 #> Hornet Sportabout   3.15 #> Valiant             2.76 #> Duster 360          3.21 #> Merc 240D           3.69 #> Merc 230            3.92 #> Merc 280            3.92 #> Merc 280C           3.92 #> Merc 450SE          3.07 #> Merc 450SL          3.07 #> Merc 450SLC         3.07 #> Cadillac Fleetwood  2.93 #> Lincoln Continental 3.00 #> Chrysler Imperial   3.23 #> Fiat 128            4.08 #> Honda Civic         4.93 #> Toyota Corolla      4.22 #> Toyota Corona       3.70 #> Dodge Challenger    2.76 #> AMC Javelin         3.15 #> Camaro Z28          3.73 #> Pontiac Firebird    3.08 #> Fiat X1-9           4.08 #> Porsche 914-2       4.43 #> Lotus Europa        3.77 #> Ford Pantera L      4.22 #> Ferrari Dino        3.62 #> Maserati Bora       3.54 #> Volvo 142E          4.11 #>                        wt #> Mazda RX4           2.620 #> Mazda RX4 Wag       2.875 #> Datsun 710          2.320 #> Hornet 4 Drive      3.215 #> Hornet Sportabout   3.440 #> Valiant             3.460 #> Duster 360          3.570 #> Merc 240D           3.190 #> Merc 230            3.150 #> Merc 280            3.440 #> Merc 280C           3.440 #> Merc 450SE          4.070 #> Merc 450SL          3.730 #> Merc 450SLC         3.780 #> Cadillac Fleetwood  5.250 #> Lincoln Continental 5.424 #> Chrysler Imperial   5.345 #> Fiat 128            2.200 #> Honda Civic         1.615 #> Toyota Corolla      1.835 #> Toyota Corona       2.465 #> Dodge Challenger    3.520 #> AMC Javelin         3.435 #> Camaro Z28          3.840 #> Pontiac Firebird    3.845 #> Fiat X1-9           1.935 #> Porsche 914-2       2.140 #> Lotus Europa        1.513 #> Ford Pantera L      3.170 #> Ferrari Dino        2.770 #> Maserati Bora       3.570 #> Volvo 142E          2.780 #>                      qsec #> Mazda RX4           16.46 #> Mazda RX4 Wag       17.02 #> Datsun 710          18.61 #> Hornet 4 Drive      19.44 #> Hornet Sportabout   17.02 #> Valiant             20.22 #> Duster 360          15.84 #> Merc 240D           20.00 #> Merc 230            22.90 #> Merc 280            18.30 #> Merc 280C           18.90 #> Merc 450SE          17.40 #> Merc 450SL          17.60 #> Merc 450SLC         18.00 #> Cadillac Fleetwood  17.98 #> Lincoln Continental 17.82 #> Chrysler Imperial   17.42 #> Fiat 128            19.47 #> Honda Civic         18.52 #> Toyota Corolla      19.90 #> Toyota Corona       20.01 #> Dodge Challenger    16.87 #> AMC Javelin         17.30 #> Camaro Z28          15.41 #> Pontiac Firebird    17.05 #> Fiat X1-9           18.90 #> Porsche 914-2       16.70 #> Lotus Europa        16.90 #> Ford Pantera L      14.50 #> Ferrari Dino        15.50 #> Maserati Bora       14.60 #> Volvo 142E          18.60 #>                     vs #> Mazda RX4            0 #> Mazda RX4 Wag        0 #> Datsun 710           1 #> Hornet 4 Drive       1 #> Hornet Sportabout    0 #> Valiant              1 #> Duster 360           0 #> Merc 240D            1 #> Merc 230             1 #> Merc 280             1 #> Merc 280C            1 #> Merc 450SE           0 #> Merc 450SL           0 #> Merc 450SLC          0 #> Cadillac Fleetwood   0 #> Lincoln Continental  0 #> Chrysler Imperial    0 #> Fiat 128             1 #> Honda Civic          1 #> Toyota Corolla       1 #> Toyota Corona        1 #> Dodge Challenger     0 #> AMC Javelin          0 #> Camaro Z28           0 #> Pontiac Firebird     0 #> Fiat X1-9            1 #> Porsche 914-2        0 #> Lotus Europa         1 #> Ford Pantera L       0 #> Ferrari Dino         0 #> Maserati Bora        0 #> Volvo 142E           1 #>                     am #> Mazda RX4            1 #> Mazda RX4 Wag        1 #> Datsun 710           1 #> Hornet 4 Drive       0 #> Hornet Sportabout    0 #> Valiant              0 #> Duster 360           0 #> Merc 240D            0 #> Merc 230             0 #> Merc 280             0 #> Merc 280C            0 #> Merc 450SE           0 #> Merc 450SL           0 #> Merc 450SLC          0 #> Cadillac Fleetwood   0 #> Lincoln Continental  0 #> Chrysler Imperial    0 #> Fiat 128             1 #> Honda Civic          1 #> Toyota Corolla       1 #> Toyota Corona        0 #> Dodge Challenger     0 #> AMC Javelin          0 #> Camaro Z28           0 #> Pontiac Firebird     0 #> Fiat X1-9            1 #> Porsche 914-2        1 #> Lotus Europa         1 #> Ford Pantera L       1 #> Ferrari Dino         1 #> Maserati Bora        1 #> Volvo 142E           1 #>                     gear #> Mazda RX4              4 #> Mazda RX4 Wag          4 #> Datsun 710             4 #> Hornet 4 Drive         3 #> Hornet Sportabout      3 #> Valiant                3 #> Duster 360             3 #> Merc 240D              4 #> Merc 230               4 #> Merc 280               4 #> Merc 280C              4 #> Merc 450SE             3 #> Merc 450SL             3 #> Merc 450SLC            3 #> Cadillac Fleetwood     3 #> Lincoln Continental    3 #> Chrysler Imperial      3 #> Fiat 128               4 #> Honda Civic            4 #> Toyota Corolla         4 #> Toyota Corona          3 #> Dodge Challenger       3 #> AMC Javelin            3 #> Camaro Z28             3 #> Pontiac Firebird       3 #> Fiat X1-9              4 #> Porsche 914-2          5 #> Lotus Europa           5 #> Ford Pantera L         5 #> Ferrari Dino           5 #> Maserati Bora          5 #> Volvo 142E             4 #>                     carb #> Mazda RX4              4 #> Mazda RX4 Wag          4 #> Datsun 710             1 #> Hornet 4 Drive         1 #> Hornet Sportabout      2 #> Valiant                1 #> Duster 360             4 #> Merc 240D              2 #> Merc 230               2 #> Merc 280               4 #> Merc 280C              4 #> Merc 450SE             3 #> Merc 450SL             3 #> Merc 450SLC            3 #> Cadillac Fleetwood     4 #> Lincoln Continental    4 #> Chrysler Imperial      4 #> Fiat 128               1 #> Honda Civic            2 #> Toyota Corolla         1 #> Toyota Corona          1 #> Dodge Challenger       2 #> AMC Javelin            2 #> Camaro Z28             4 #> Pontiac Firebird       2 #> Fiat X1-9              1 #> Porsche 914-2          2 #> Lotus Europa           2 #> Ford Pantera L         4 #> Ferrari Dino           6 #> Maserati Bora          8 #> Volvo 142E             2  mtcars2 <- as_tibble(cbind(mtcars, mtcars), .name_repair = \"unique\") #> New names: #> • `mpg` -> `mpg...1` #> • `cyl` -> `cyl...2` #> • `disp` -> `disp...3` #> • `hp` -> `hp...4` #> • `drat` -> `drat...5` #> • `wt` -> `wt...6` #> • `qsec` -> `qsec...7` #> • `vs` -> `vs...8` #> • `am` -> `am...9` #> • `gear` -> `gear...10` #> • `carb` -> `carb...11` #> • `mpg` -> `mpg...12` #> • `cyl` -> `cyl...13` #> • `disp` -> `disp...14` #> • `hp` -> `hp...15` #> • `drat` -> `drat...16` #> • `wt` -> `wt...17` #> • `qsec` -> `qsec...18` #> • `vs` -> `vs...19` #> • `am` -> `am...20` #> • `gear` -> `gear...21` #> • `carb` -> `carb...22` print(mtcars2, n = 25, max_extra_cols = 3) #> # A tibble: 32 × 22 #>    mpg...1 cyl...2 disp...3 hp...4 drat...5 wt...6 qsec...7 vs...8 am...9 #>      <dbl>   <dbl>    <dbl>  <dbl>    <dbl>  <dbl>    <dbl>  <dbl>  <dbl> #>  1    21         6    160      110     3.9    2.62     16.5      0      1 #>  2    21         6    160      110     3.9    2.88     17.0      0      1 #>  3    22.8       4    108       93     3.85   2.32     18.6      1      1 #>  4    21.4       6    258      110     3.08   3.22     19.4      1      0 #>  5    18.7       8    360      175     3.15   3.44     17.0      0      0 #>  6    18.1       6    225      105     2.76   3.46     20.2      1      0 #>  7    14.3       8    360      245     3.21   3.57     15.8      0      0 #>  8    24.4       4    147.      62     3.69   3.19     20        1      0 #>  9    22.8       4    141.      95     3.92   3.15     22.9      1      0 #> 10    19.2       6    168.     123     3.92   3.44     18.3      1      0 #> 11    17.8       6    168.     123     3.92   3.44     18.9      1      0 #> 12    16.4       8    276.     180     3.07   4.07     17.4      0      0 #> 13    17.3       8    276.     180     3.07   3.73     17.6      0      0 #> 14    15.2       8    276.     180     3.07   3.78     18        0      0 #> 15    10.4       8    472      205     2.93   5.25     18.0      0      0 #> 16    10.4       8    460      215     3      5.42     17.8      0      0 #> 17    14.7       8    440      230     3.23   5.34     17.4      0      0 #> 18    32.4       4     78.7     66     4.08   2.2      19.5      1      1 #> 19    30.4       4     75.7     52     4.93   1.62     18.5      1      1 #> 20    33.9       4     71.1     65     4.22   1.84     19.9      1      1 #> 21    21.5       4    120.      97     3.7    2.46     20.0      1      0 #> 22    15.5       8    318      150     2.76   3.52     16.9      0      0 #> 23    15.2       8    304      150     3.15   3.44     17.3      0      0 #> 24    13.3       8    350      245     3.73   3.84     15.4      0      0 #> 25    19.2       8    400      175     3.08   3.84     17.0      0      0 #> # ℹ 7 more rows #> # ℹ 13 more variables: gear...10 <dbl>, carb...11 <dbl>, mpg...12 <dbl>, #> #   …  print(nycflights13::flights, max_footer_lines = 1) #> # A tibble: 336,776 × 19 #>     year month   day dep_time sched_dep_time dep_delay arr_time #>    <int> <int> <int>    <int>          <int>     <dbl>    <int> #>  1  2013     1     1      517            515         2      830 #>  2  2013     1     1      533            529         4      850 #>  3  2013     1     1      542            540         2      923 #>  4  2013     1     1      544            545        -1     1004 #>  5  2013     1     1      554            600        -6      812 #>  6  2013     1     1      554            558        -4      740 #>  7  2013     1     1      555            600        -5      913 #>  8  2013     1     1      557            600        -3      709 #>  9  2013     1     1      557            600        -3      838 #> 10  2013     1     1      558            600        -2      753 #> # ℹ 336,766 more rows print(nycflights13::flights, width = Inf) #> # A tibble: 336,776 × 19 #>     year month   day dep_time sched_dep_time dep_delay arr_time #>    <int> <int> <int>    <int>          <int>     <dbl>    <int> #>  1  2013     1     1      517            515         2      830 #>  2  2013     1     1      533            529         4      850 #>  3  2013     1     1      542            540         2      923 #>  4  2013     1     1      544            545        -1     1004 #>  5  2013     1     1      554            600        -6      812 #>  6  2013     1     1      554            558        -4      740 #>  7  2013     1     1      555            600        -5      913 #>  8  2013     1     1      557            600        -3      709 #>  9  2013     1     1      557            600        -3      838 #> 10  2013     1     1      558            600        -2      753 #>    sched_arr_time arr_delay carrier flight tailnum origin dest  air_time #>             <int>     <dbl> <chr>    <int> <chr>   <chr>  <chr>    <dbl> #>  1            819        11 UA        1545 N14228  EWR    IAH        227 #>  2            830        20 UA        1714 N24211  LGA    IAH        227 #>  3            850        33 AA        1141 N619AA  JFK    MIA        160 #>  4           1022       -18 B6         725 N804JB  JFK    BQN        183 #>  5            837       -25 DL         461 N668DN  LGA    ATL        116 #>  6            728        12 UA        1696 N39463  EWR    ORD        150 #>  7            854        19 B6         507 N516JB  EWR    FLL        158 #>  8            723       -14 EV        5708 N829AS  LGA    IAD         53 #>  9            846        -8 B6          79 N593JB  JFK    MCO        140 #> 10            745         8 AA         301 N3ALAA  LGA    ORD        138 #>    distance  hour minute time_hour           #>       <dbl> <dbl>  <dbl> <dttm>              #>  1     1400     5     15 2013-01-01 05:00:00 #>  2     1416     5     29 2013-01-01 05:00:00 #>  3     1089     5     40 2013-01-01 05:00:00 #>  4     1576     5     45 2013-01-01 05:00:00 #>  5      762     6      0 2013-01-01 06:00:00 #>  6      719     5     58 2013-01-01 05:00:00 #>  7     1065     6      0 2013-01-01 06:00:00 #>  8      229     6      0 2013-01-01 06:00:00 #>  9      944     6      0 2013-01-01 06:00:00 #> 10      733     6      0 2013-01-01 06:00:00 #> # ℹ 336,766 more rows"},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-wise matrix creation — frame_matrix","title":"Row-wise matrix creation — frame_matrix","text":"Create matrices laying data rows, similar matrix(..., byrow = TRUE), nicer--read syntax. useful small matrices, e.g. covariance matrices, readability important. syntax inspired tribble().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row-wise matrix creation — frame_matrix","text":"","code":"frame_matrix(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row-wise matrix creation — frame_matrix","text":"... <dynamic-dots> Arguments specifying structure frame_matrix. Column names formulas, may appear data. arguments processed rlang::list2() support unquote via rlang::!! unquote-splice via rlang::!!!.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row-wise matrix creation — frame_matrix","text":"matrix.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/frame_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-wise matrix creation — frame_matrix","text":"","code":"frame_matrix(   ~col1, ~col2,   1,     3,   5,     2 ) #>      col1 col2 #> [1,]    1    3 #> [2,]    5    2"},{"path":"https://tibble.tidyverse.org/dev/reference/is.tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated test for tibble-ness — is.tibble","title":"Deprecated test for tibble-ness — is.tibble","text":"Please use is_tibble() instead.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is.tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated test for tibble-ness — is.tibble","text":"","code":"is.tibble(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/is.tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated test for tibble-ness — is.tibble","text":"x object","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Test if the object is a tibble — is_tibble","title":"Test if the object is a tibble — is_tibble","text":"function returns TRUE tibbles subclasses thereof, FALSE objects, including regular data frames.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test if the object is a tibble — is_tibble","text":"","code":"is_tibble(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test if the object is a tibble — is_tibble","text":"x object","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/is_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test if the object is a tibble — is_tibble","text":"TRUE object inherits tbl_df class.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":null,"dir":"Reference","previous_headings":"","what":"Build a list — lst","title":"Build a list — lst","text":"lst() constructs list, similar base::list(), features tibble(). lst() builds components sequentially. defining component, can refer components created earlier call. lst() also generates missing names automatically. See rlang::list2() simpler faster alternative without tibble's evaluation auto-name semantics.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build a list — lst","text":"","code":"lst(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build a list — lst","text":"... <dynamic-dots> set name-value pairs. arguments processed rlang::quos() support unquote via rlang::!! unquote-splice via rlang::!!!. Use := create columns start dot. Arguments evaluated sequentially. can refer previously created elements directly using rlang::.data pronoun. refer explicitly objects calling environment, use rlang::!! rlang::.env, e.g. !!.data .env$.data special case object named .data.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build a list — lst","text":"named list.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/lst.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build a list — lst","text":"","code":"# the value of n can be used immediately in the definition of x lst(n = 5, x = runif(n)) #> $n #> [1] 5 #>  #> $x #> [1] 0.2220037 0.1208247 0.5305426 0.7951219 0.3532806 #>   # missing names are constructed from user's input lst(1:3, z = letters[4:6], runif(3)) #> $`1:3` #> [1] 1 2 3 #>  #> $z #> [1] \"d\" \"e\" \"f\" #>  #> $`runif(3)` #> [1] 0.9371862 0.6326068 0.6565604 #>   a <- 1:3 b <- letters[4:6] lst(a, b) #> $a #> [1] 1 2 3 #>  #> $b #> [1] \"d\" \"e\" \"f\" #>   # pre-formed quoted expressions can be used with lst() and then # unquoted (with !!) or unquoted and spliced (with !!!) n1 <- 2 n2 <- 3 n_stuff <- quote(n1 + n2) x_stuff <- quote(seq_len(n)) lst(!!!list(n = n_stuff, x = x_stuff)) #> $n #> [1] 5 #>  #> $x #> [1] 1 2 3 4 5 #>  lst(n = !!n_stuff, x = !!x_stuff) #> $n #> [1] 5 #>  #> $x #> [1] 1 2 3 4 5 #>  lst(n = 4, x = !!x_stuff) #> $n #> [1] 4 #>  #> $x #> [1] 1 2 3 4 #>  lst(!!!list(n = 2, x = x_stuff)) #> $n #> [1] 2 #>  #> $x #> [1] 1 2 #>"},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":null,"dir":"Reference","previous_headings":"","what":"Superseded functions for name repair — name-repair-superseded","title":"Superseded functions for name repair — name-repair-superseded","text":"tidy_names(), set_tidy_names(), repair_names() early efforts facilitate post hoc name repair tibble, given tibble() as_tibble() . tibble v2.0.0, .name_repair argument gives direct access three specific levels name repair: minimal, unique, universal. See vctrs::vec_as_names() implementation underlying logic.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Superseded functions for name repair — name-repair-superseded","text":"","code":"tidy_names(name, syntactic = FALSE, quiet = FALSE)  set_tidy_names(x, syntactic = FALSE, quiet = FALSE)  repair_names(x, prefix = \"V\", sep = \"\")"},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Superseded functions for name repair — name-repair-superseded","text":"name names attribute, usually character vector. syntactic names made syntactically valid? FALSE, uses logic .name_repair = \"unique\". TRUE, uses logic .name_repair = \"universal\". quiet Whether suppress messages name repair. x vector. prefix string, prefix use new column names. sep string inserted column name de-duplicating number.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Superseded functions for name repair — name-repair-superseded","text":"x repaired names repaired version name.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair-superseded.html","id":"life-cycle","dir":"Reference","previous_headings":"","what":"Life cycle","title":"Superseded functions for name repair — name-repair-superseded","text":"functions superseded. repair_names() logic also remain available vctrs::vec_as_names_legacy().","code":"tibble(..., `.name_repair = \"unique\"`) ## is preferred to df <- tibble(...) set_tidy_names(df, syntactic = FALSE)  tibble(..., `.name_repair = \"universal\"`) ## is preferred to df <- tibble(...) set_tidy_names(df, syntactic = TRUE)"},{"path":"https://tibble.tidyverse.org/dev/reference/name-repair.html","id":null,"dir":"Reference","previous_headings":"","what":"Name repair — name-repair","title":"Name repair — name-repair","text":"Please review vctrs::vec_as_names().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Tibble constructor and validator — new_tibble","title":"Tibble constructor and validator — new_tibble","text":"Creates validates subclass tibble. function mostly useful package authors implement subclasses tibble, like sf tsibble. new_tibble() creates new object subclass tbl_df, tbl data.frame. function optimized performance, checks reduced minimum. See vctrs::new_data_frame() details. validate_tibble() checks tibble internal consistency. Correct behavior can guaranteed function runs without raising error.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tibble constructor and validator — new_tibble","text":"","code":"new_tibble(x, ..., nrow = NULL, class = NULL, subclass = NULL)  validate_tibble(x)"},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tibble constructor and validator — new_tibble","text":"x tibble-like object. ... Name-value pairs additional attributes. nrow number rows, inferred x omitted. class Subclasses assign new object, default: none. subclass Deprecated, retained compatibility. Please use class argument.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"construction","dir":"Reference","previous_headings":"","what":"Construction","title":"Tibble constructor and validator — new_tibble","text":"new_tibble(), x must list. nrow argument may omitted tibble 3.1.4. present, every element list x vctrs::vec_size() equal value. (checked constructor). takes place \"row.names\" attribute data frame. x must names (empty), names checked correctness.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"validation","dir":"Reference","previous_headings":"","what":"Validation","title":"Tibble constructor and validator — new_tibble","text":"validate_tibble() checks \"minimal\" names columns vectors, data frames matrices. also makes sure columns length, vctrs::vec_size() consistent data.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/new_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tibble constructor and validator — new_tibble","text":"","code":"# The nrow argument can be omitted: new_tibble(list(a = 1:3, b = 4:6)) #> # A tibble: 3 × 2 #>       a     b #>   <int> <int> #> 1     1     4 #> 2     2     5 #> 3     3     6  # Existing row.names attributes are ignored: try(validate_tibble(new_tibble(trees, nrow = 3))) #> Error in validate_nrow(names(x), col_lengths(x), vec_size(x)) :  #>   Tibble columns must have compatible sizes. #> • Size 3: Requested with `nrow` argument. #> • Size 31: Columns `Girth`, `Height`, and `Volume`. #> ℹ Only values of size one are recycled.  # The length of all columns must be compatible with the nrow argument: try(validate_tibble(new_tibble(list(a = 1:3, b = 4:6), nrow = 2))) #> Error in validate_nrow(names(x), col_lengths(x), vec_size(x)) :  #>   Tibble columns must have compatible sizes. #> • Size 2: Requested with `nrow` argument. #> • Size 3: Columns `a` and `b`. #> ℹ Only values of size one are recycled."},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":null,"dir":"Reference","previous_headings":"","what":"Format a numeric vector — num","title":"Format a numeric vector — num","text":"Constructs numeric vector can formatted predefined significant digits, maximum fixed number digits decimal point. Scaling supported, well forcing decimal, scientific engineering notation. label given, shown header column. formatting applied vector printed formatted, also tibble column. formatting annotation class survives arithmetic transformations, notable exceptions var() sd(). set_num_opts() adds formatting options arbitrary numeric vector, useful composing types.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format a numeric vector — num","text":"","code":"num(   x,   ...,   sigfig = NULL,   digits = NULL,   label = NULL,   scale = NULL,   notation = c(\"fit\", \"dec\", \"sci\", \"eng\", \"si\"),   fixed_exponent = NULL,   extra_sigfig = NULL )  set_num_opts(   x,   ...,   sigfig = NULL,   digits = NULL,   label = NULL,   scale = NULL,   notation = c(\"fit\", \"dec\", \"sci\", \"eng\", \"si\"),   fixed_exponent = NULL,   extra_sigfig = NULL )"},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format a numeric vector — num","text":"x numeric vector. ... dots future extensions must empty. sigfig Define number significant digits show. Must one greater. \"pillar.sigfig\" option consulted. combined digits. digits Number digits decimal points show. Positive numbers specify exact number digits show. Negative numbers specify (negation) maximum number digits show. digits = 2, numbers 1.2 1.234 printed 1.20 1.23, digits = -2 1.2 1.23, respectively. combined sigfig. label label show instead type description. scale Multiplier apply data showing. Useful displaying e.g. percentages. Must combined label. notation One \"fit\", \"dec\", \"sci\", \"eng\", \"si\". \"fit\": Use decimal notation fits consumes 13 digits less, otherwise use scientific notation. (default numeric pillars.) \"dec\": Use decimal notation, regardless width. \"sci\": Use scientific notation. \"eng\": Use engineering notation, .e. scientific notation using exponents multiple three. \"si\": Use SI notation, prefixes 1e-24 1e24 supported. fixed_exponent Use exponent numbers scientific, engineering SI notation. -Inf uses smallest, +Inf largest fixed_exponent present data. default use varying exponents. extra_sigfig TRUE, increase number significant digits data consists numbers magnitude subtle differences.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/num.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format a numeric vector — num","text":"","code":"# Display as a vector num(9:11 * 100 + 0.5) #> <pillar_num[3]> #> [1]  900. 1000. 1100.  # Significant figures tibble(   x3 = num(9:11 * 100 + 0.5, sigfig = 3),   x4 = num(9:11 * 100 + 0.5, sigfig = 4),   x5 = num(9:11 * 100 + 0.5, sigfig = 5), ) #> # A tibble: 3 × 3 #>        x3      x4      x5 #>   <num:3> <num:4> <num:5> #> 1    900.   900.5   900.5 #> 2   1000.  1000.   1000.5 #> 3   1100.  1100.   1100.5  # Maximum digits after the decimal points tibble(   x0 = num(9:11 * 100 + 0.5, digits = 0),   x1 = num(9:11 * 100 + 0.5, digits = -1),   x2 = num(9:11 * 100 + 0.5, digits = -2), ) #> # A tibble: 3 × 3 #>         x0       x1       x2 #>   <num:.0> <num:.1> <num:.2> #> 1     900.    900.5    900.5 #> 2    1000.   1000.5   1000.5 #> 3    1100.   1100.5   1100.5  # Use fixed digits and a currency label tibble(   usd = num(9:11 * 100 + 0.5, digits = 2, label = \"USD\"),   gbp = num(9:11 * 100 + 0.5, digits = 2, label = \"£\"),   chf = num(9:11 * 100 + 0.5, digits = 2, label = \"SFr\") ) #> # A tibble: 3 × 3 #>       usd     gbp     chf #>       USD       £     SFr #> 1  900.50  900.50  900.50 #> 2 1000.50 1000.50 1000.50 #> 3 1100.50 1100.50 1100.50  # Scale tibble(   small  = num(9:11 / 1000 + 0.00005, label = \"%\", scale = 100),   medium = num(9:11 / 100 + 0.0005, label = \"%\", scale = 100),   large  = num(9:11 / 10 + 0.005, label = \"%\", scale = 100) ) #> # A tibble: 3 × 3 #>   small medium large #>       %      %     % #> 1 0.905   9.05  90.5 #> 2 1.00   10.0  100.  #> 3 1.10   11.0  110.   # Notation tibble(   sci = num(10^(-13:6), notation = \"sci\"),   eng = num(10^(-13:6), notation = \"eng\"),   si  = num(10^(-13:6), notation = \"si\"),   dec = num(10^(-13:6), notation = \"dec\") ) #> # A tibble: 20 × 4 #>      sci     eng   si                   dec #>    <sci>   <eng> <si>                 <dec> #>  1 1e-13 100e-15 100f       0.0000000000001 #>  2 1e-12   1e-12   1p       0.000000000001  #>  3 1e-11  10e-12  10p       0.00000000001   #>  4 1e-10 100e-12 100p       0.0000000001    #>  5 1e- 9   1e- 9   1n       0.000000001     #>  6 1e- 8  10e- 9  10n       0.00000001      #>  7 1e- 7 100e- 9 100n       0.0000001       #>  8 1e- 6   1e- 6   1µ       0.000001        #>  9 1e- 5  10e- 6  10µ       0.00001         #> 10 1e- 4 100e- 6 100µ       0.0001          #> 11 1e- 3   1e- 3   1m       0.001           #> 12 1e- 2  10e- 3  10m       0.01            #> 13 1e- 1 100e- 3 100m       0.1             #> 14 1e+ 0   1e+ 0   1        1               #> 15 1e+ 1  10e+ 0  10       10               #> 16 1e+ 2 100e+ 0 100      100               #> 17 1e+ 3   1e+ 3   1k    1000               #> 18 1e+ 4  10e+ 3  10k   10000               #> 19 1e+ 5 100e+ 3 100k  100000               #> 20 1e+ 6   1e+ 6   1M 1000000                # Fixed exponent tibble(   scimin = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = -Inf),   engmin = num(10^(-7:6) * 123, notation = \"eng\", fixed_exponent = -Inf),   simin  = num(10^(-7:6) * 123, notation = \"si\", fixed_exponent = -Inf) ) #> # A tibble: 14 × 3 #>               scimin            engmin             simin #>                [e-5]             [e-5]               [µ] #>  1              1.23              12.3              12.3 #>  2             12.3              123               123   #>  3            123               1230              1230   #>  4           1230              12300             12300   #>  5          12300             123000            123000   #>  6         123000            1230000           1230000   #>  7        1230000           12300000          12300000   #>  8       12300000          123000000         123000000   #>  9      123000000         1230000000        1230000000   #> 10     1230000000        12300000000       12300000000   #> 11    12300000000       123000000000      123000000000   #> 12   123000000000      1230000000000     1230000000000   #> 13  1230000000000     12300000000000    12300000000000   #> 14 12300000000000    123000000000000   123000000000000    tibble(   scismall = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = -3),   scilarge = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = 3),   scimax   = num(10^(-7:6) * 123, notation = \"sci\", fixed_exponent = Inf) ) #> # A tibble: 14 × 3 #>             scismall          scilarge            scimax #>                [e-3]              [e3]              [e8] #>  1            0.0123      0.0000000123 0.000000000000123 #>  2            0.123       0.000000123  0.00000000000123  #>  3            1.23        0.00000123   0.0000000000123   #>  4           12.3         0.0000123    0.000000000123    #>  5          123           0.000123     0.00000000123     #>  6         1230           0.00123      0.0000000123      #>  7        12300           0.0123       0.000000123       #>  8       123000           0.123        0.00000123        #>  9      1230000           1.23         0.0000123         #> 10     12300000          12.3          0.000123          #> 11    123000000         123            0.00123           #> 12   1230000000        1230            0.0123            #> 13  12300000000       12300            0.123             #> 14 123000000000      123000            1.23               #' Extra significant digits tibble(   default = num(100 + 1:3 * 0.001),   extra1 = num(100 + 1:3 * 0.001, extra_sigfig = TRUE),   extra2 = num(100 + 1:3 * 0.0001, extra_sigfig = TRUE),   extra3 = num(10000 + 1:3 * 0.00001, extra_sigfig = TRUE) ) #> # A tibble: 3 × 4 #>   default  extra1   extra2      extra3 #>     <num>   <num>    <num>       <num> #> 1    100. 100.001 100.0001 10000.00001 #> 2    100. 100.002 100.0002 10000.00002 #> 3    100. 100.003 100.0003 10000.00003"},{"path":"https://tibble.tidyverse.org/dev/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. magrittr %>% pillar glimpse, obj_sum, size_sum, tbl_sum, type_sum rlang has_name","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for working with row names — rownames","title":"Tools for working with row names — rownames","text":"tibble can row names (e.g., converting regular data frame), removed subsetting [ operator. warning raised attempting assign non-NULL row names tibble. Generally, best avoid row names, basically character column different semantics every column. functions allow detect data frame row names (has_rownames()), remove (remove_rownames()), convert back--forth explicit column (rownames_to_column() column_to_rownames()). Also included rowid_to_column(), adds column start dataframe ascending sequential row ids starting 1. Note remove existing row names.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for working with row names — rownames","text":"","code":"has_rownames(.data)  remove_rownames(.data)  rownames_to_column(.data, var = \"rowname\")  rowid_to_column(.data, var = \"rowid\")  column_to_rownames(.data, var = \"rowname\")"},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for working with row names — rownames","text":".data data frame. var Name column use rownames.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tools for working with row names — rownames","text":"column_to_rownames() always returns data frame. has_rownames() returns scalar logical. functions return object class input.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/rownames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for working with row names — rownames","text":"","code":"# Detect row names ---------------------------------------------------- has_rownames(mtcars) #> [1] TRUE has_rownames(trees) #> [1] FALSE  # Remove row names ---------------------------------------------------- remove_rownames(mtcars) %>% has_rownames() #> [1] FALSE  # Convert between row names and column -------------------------------- mtcars_tbl <- rownames_to_column(mtcars, var = \"car\") %>% as_tibble() mtcars_tbl #> # A tibble: 32 × 12 #>    car     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb #>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Mazd…  21       6  160    110  3.9   2.62  16.5     0     1     4     4 #>  2 Mazd…  21       6  160    110  3.9   2.88  17.0     0     1     4     4 #>  3 Dats…  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1 #>  4 Horn…  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1 #>  5 Horn…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 #>  6 Vali…  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1 #>  7 Dust…  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 #>  8 Merc…  24.4     4  147.    62  3.69  3.19  20       1     0     4     2 #>  9 Merc…  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2 #> 10 Merc…  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4 #> # ℹ 22 more rows column_to_rownames(mtcars_tbl, var = \"car\") %>% head() #>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb #> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 #> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 #> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  # Adding rowid as a column -------------------------------------------- rowid_to_column(trees) %>% head() #>   rowid Girth Height Volume #> 1     1   8.3     70   10.3 #> 2     2   8.6     65   10.3 #> 3     3   8.8     63   10.2 #> 4     4  10.5     72   16.4 #> 5     5  10.7     81   18.8 #> 6     6  10.8     83   19.7"},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":null,"dir":"Reference","previous_headings":"","what":"Subsetting tibbles — subsetting","title":"Subsetting tibbles — subsetting","text":"Accessing columns, rows, cells via $, [[, [ mostly similar regular data frames. However, behavior different tibbles data frames cases: [ always returns tibble default, even one column accessed. Partial matching column names $ [[ supported, NULL returned. $, warning given. scalars (vectors length one) vectors length number rows can used assignment. Rows outside tibble's boundaries accessed. updating [[<- [<-, type changes entire columns supported, updating part column requires new value coercible existing type. See vctrs::vec_slice() underlying implementation. Unstable return type implicit partial matching can lead surprises bugs hard catch. rely code requires original data frame behavior, coerce data frame via .data.frame().","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subsetting tibbles — subsetting","text":"","code":"# S3 method for class 'tbl_df' x$name  # S3 method for class 'tbl_df' x[[i, j, ..., exact = TRUE]]  # S3 method for class 'tbl_df' x[i, j, drop = FALSE, ...]  # S3 method for class 'tbl_df' x$name <- value  # S3 method for class 'tbl_df' x[[i, j, ...]] <- value  # S3 method for class 'tbl_df' x[i, j, ...] <- value"},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subsetting tibbles — subsetting","text":"x tibble. name name string. , j Row column indices. j omitted, used column index. ... Ignored. exact Ignored, warning. drop Coerce vector fetching one column via tbl[, j] . Default FALSE, ignored accessing column via tbl[j] . value value store row, column, range cell. Tibbles stricter data frames accepted .","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subsetting tibbles — subsetting","text":"better compatibility older code written regular data frames, [ supports drop argument defaults FALSE. New code use [[ turn column vector.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/subsetting.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subsetting tibbles — subsetting","text":"","code":"df <- data.frame(a = 1:3, bc = 4:6) tbl <- tibble(a = 1:3, bc = 4:6)  # Subsetting single columns: df[, \"a\"] #> [1] 1 2 3 tbl[, \"a\"] #> # A tibble: 3 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 tbl[, \"a\", drop = TRUE] #> [1] 1 2 3 as.data.frame(tbl)[, \"a\"] #> [1] 1 2 3  # Subsetting single rows with the drop argument: df[1, , drop = TRUE] #> $a #> [1] 1 #>  #> $bc #> [1] 4 #>  tbl[1, , drop = TRUE] #> # A tibble: 1 × 2 #>       a    bc #>   <int> <int> #> 1     1     4 as.list(tbl[1, ]) #> $a #> [1] 1 #>  #> $bc #> [1] 4 #>   # Accessing non-existent columns: df$b #> [1] 4 5 6 tbl$b #> Warning: Unknown or uninitialised column: `b`. #> NULL  df[[\"b\", exact = FALSE]] #> [1] 4 5 6 tbl[[\"b\", exact = FALSE]] #> Warning: `exact` ignored. #> NULL  df$bd <- c(\"n\", \"e\", \"w\") tbl$bd <- c(\"n\", \"e\", \"w\") df$b #> NULL tbl$b #> Warning: Unknown or uninitialised column: `b`. #> NULL  df$b <- 7:9 tbl$b <- 7:9 df$b #> [1] 7 8 9 tbl$b #> [1] 7 8 9  # Identical behavior: tbl[1, ] #> # A tibble: 1 × 4 #>       a    bc bd        b #>   <int> <int> <chr> <int> #> 1     1     4 n         7 tbl[1, c(\"bc\", \"a\")] #> # A tibble: 1 × 2 #>      bc     a #>   <int> <int> #> 1     4     1 tbl[, c(\"bc\", \"a\")] #> # A tibble: 3 × 2 #>      bc     a #>   <int> <int> #> 1     4     1 #> 2     5     2 #> 3     6     3 tbl[c(\"bc\", \"a\")] #> # A tibble: 3 × 2 #>      bc     a #>   <int> <int> #> 1     4     1 #> 2     5     2 #> 3     6     3 tbl[\"a\"] #> # A tibble: 3 × 1 #>       a #>   <int> #> 1     1 #> 2     2 #> 3     3 tbl$a #> [1] 1 2 3 tbl[[\"a\"]] #> [1] 1 2 3"},{"path":"https://tibble.tidyverse.org/dev/reference/tbl_df-class.html","id":null,"dir":"Reference","previous_headings":"","what":"tbl_df class — tbl_df-class","title":"tbl_df class — tbl_df-class","text":"tbl_df class subclass data.frame, created order different default behaviour. colloquial term \"tibble\" refers data frame tbl_df class. Tibble central data structure set packages known tidyverse, including dplyr, ggplot2, tidyr, readr. general ethos tibbles lazy surly: less complain base data.frames. forces problems tackled earlier explicitly, typically leading code expressive robust.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tbl_df-class.html","id":"properties-of-tbl-df","dir":"Reference","previous_headings":"","what":"Properties of tbl_df","title":"tbl_df class — tbl_df-class","text":"Objects class tbl_df : class attribute c(\"tbl_df\", \"tbl\", \"data.frame\"). base type \"list\", element list vctrs::vec_size(). names attribute character vector length underlying list. row.names attribute, included compatibility data.frame. attribute consulted query number rows, row names might stored ignored tibble methods.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tbl_df-class.html","id":"behavior-of-tbl-df","dir":"Reference","previous_headings":"","what":"Behavior of tbl_df","title":"tbl_df class — tbl_df-class","text":"default behaviour tibbles differs data.frames, creation access: Column data coerced. character vector turned factor. List-columns expressly anticipated require special tricks. Internal names never stripped column data. Read tibble(). Recycling happens length 1 input. Read vctrs::vec_recycle(). Column names munged, although missing names auto-populated. Empty duplicated column names strongly discouraged, user must indicate resolve. Read vctrs::vec_as_names(). Row names added strongly discouraged, favor storing info column. Read rownames. df[, j] returns tibble; automatically extract column inside. df[, j, drop = FALSE] default. Read subsetting. partial matching $ used index name. df$name nonexistent name generates warning. Read subsetting. See vignette(\"invariants\") detailed description behavior. Furthermore, printing inspection high priority. goal convey much information possible, concise way, even large complex tibbles. Read formatting.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tibble-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tibble: Simple Data Frames — tibble-package","title":"tibble: Simple Data Frames — tibble-package","text":"Provides 'tbl_df' class ('tibble') stricter checking better formatting traditional data frame.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"tibble: Simple Data Frames — tibble-package","text":"tibble package provides utilities handling tibbles, \"tibble\" colloquial term S3 tbl_df class. tbl_df class special case base data.frame class, developed response lessons learned many years data analysis data frames. Tibble central data structure set packages known tidyverse, including dplyr, ggplot2, tidyr, readr. General resources: Website tibble package: https://tibble.tidyverse.org Vectors chapter Advanced R (2nd edition), specifically Data frames tibbles section Resources specific topics: Create tibble: tibble(), as_tibble(), tribble(), enframe() Inspect tibble: print.tbl(), glimpse() Details S3 tbl_df class: tbl_df Package options: tibble_options","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tibble-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tibble: Simple Data Frames — tibble-package","text":"Maintainer: Kirill Müller kirill@cynkra.com (ORCID) Authors: Hadley Wickham hadley@rstudio.com contributors: Romain Francois romain@r-enthusiasts.com [contributor] Jennifer Bryan jenny@rstudio.com [contributor] RStudio [copyright holder, funder]","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Build a data frame — tibble","title":"Build a data frame — tibble","text":"tibble() constructs data frame. used like base::data.frame(), couple notable differences: returned data frame class tbl_df, addition data.frame. allows -called \"tibbles\" exhibit special behaviour, enhanced printing. Tibbles fully described tbl_df. tibble() much lazier base::data.frame() terms transforming user's input. Character vectors coerced factor. List-columns expressly anticipated require special tricks. Column names modified. Inner names columns left unchanged. tibble() builds columns sequentially. defining column, can refer columns created earlier call. columns length one recycled. column evaluates data frame tibble, nested spliced. evaluates matrix array, remains matrix array, respectively. See examples. tibble_row() constructs data frame guaranteed occupy one row. Vector columns required size one, non-vector columns wrapped list.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build a data frame — tibble","text":"","code":"tibble(   ...,   .rows = NULL,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )  tibble_row(   ...,   .name_repair = c(\"check_unique\", \"unique\", \"universal\", \"minimal\") )"},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build a data frame — tibble","text":"... <dynamic-dots> set name-value pairs. arguments processed rlang::quos() support unquote via rlang::!! unquote-splice via rlang::!!!. Use := create columns start dot. Arguments evaluated sequentially. can refer previously created elements directly using rlang::.data pronoun. refer explicitly objects calling environment, use rlang::!! rlang::.env, e.g. !!.data .env$.data special case object named .data. .rows number rows, useful create 0-column tibble just additional check. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build a data frame — tibble","text":"tibble, colloquial term object class tbl_df. tbl_df object also data frame, .e. class data.frame.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build a data frame — tibble","text":"","code":"# Unnamed arguments are named with their expression: a <- 1:5 tibble(a, a * 2) #> # A tibble: 5 × 2 #>       a `a * 2` #>   <int>   <dbl> #> 1     1       2 #> 2     2       4 #> 3     3       6 #> 4     4       8 #> 5     5      10  # Scalars (vectors of length one) are recycled: tibble(a, b = a * 2, c = 1) #> # A tibble: 5 × 3 #>       a     b     c #>   <int> <dbl> <dbl> #> 1     1     2     1 #> 2     2     4     1 #> 3     3     6     1 #> 4     4     8     1 #> 5     5    10     1  # Columns are available in subsequent expressions: tibble(x = runif(10), y = x * 2) #> # A tibble: 10 × 2 #>         x     y #>     <dbl> <dbl> #>  1 0.900  1.80  #>  2 0.617  1.23  #>  3 0.704  1.41  #>  4 0.546  1.09  #>  5 0.807  1.61  #>  6 0.184  0.368 #>  7 0.725  1.45  #>  8 0.623  1.25  #>  9 0.0574 0.115 #> 10 0.636  1.27   # tibble() never coerces its inputs, str(tibble(letters)) #> tibble [26 × 1] (S3: tbl_df/tbl/data.frame) #>  $ letters: chr [1:26] \"a\" \"b\" \"c\" \"d\" ... str(tibble(x = list(diag(1), diag(2)))) #> tibble [2 × 1] (S3: tbl_df/tbl/data.frame) #>  $ x:List of 2 #>   ..$ : num [1, 1] 1 #>   ..$ : num [1:2, 1:2] 1 0 0 1  # or munges column names (unless requested), tibble(`a + b` = 1:5) #> # A tibble: 5 × 1 #>   `a + b` #>     <int> #> 1       1 #> 2       2 #> 3       3 #> 4       4 #> 5       5  # but it forces you to take charge of names, if they need repair: try(tibble(x = 1, x = 2)) #> Error in tibble(x = 1, x = 2) :  #>   Column name `x` must not be duplicated. #> Use `.name_repair` to specify repair. #> Caused by error in `repaired_names()` at tibble/R/names.R:14:3: #> ! Names must be unique. #> ✖ These names are duplicated: #>   * \"x\" at locations 1 and 2. tibble(x = 1, x = 2, .name_repair = \"unique\") #> New names: #> • `x` -> `x...1` #> • `x` -> `x...2` #> # A tibble: 1 × 2 #>   x...1 x...2 #>   <dbl> <dbl> #> 1     1     2 tibble(x = 1, x = 2, .name_repair = \"minimal\") #> # A tibble: 1 × 2 #>       x     x #>   <dbl> <dbl> #> 1     1     2  ## By default, non-syntactic names are allowed, df <- tibble(`a 1` = 1, `a 2` = 2) ## because you can still index by name: df[[\"a 1\"]] #> [1] 1 df$`a 1` #> [1] 1 with(df, `a 1`) #> [1] 1  ## Syntactic names are easier to work with, though, and you can request them: df <- tibble(`a 1` = 1, `a 2` = 2, .name_repair = \"universal\") #> New names: #> • `a 1` -> `a.1` #> • `a 2` -> `a.2` df$a.1 #> [1] 1  ## You can specify your own name repair function: tibble(x = 1, x = 2, .name_repair = make.unique) #> # A tibble: 1 × 2 #>       x   x.1 #>   <dbl> <dbl> #> 1     1     2  fix_names <- function(x) gsub(\"\\\\s+\", \"_\", x) tibble(`year 1` = 1, `year 2` = 2, .name_repair = fix_names) #> # A tibble: 1 × 2 #>   year_1 year_2 #>    <dbl>  <dbl> #> 1      1      2  ## purrr-style anonymous functions and constants ## are also supported tibble(x = 1, x = 2, .name_repair = ~ make.names(., unique = TRUE)) #> # A tibble: 1 × 2 #>       x   x.1 #>   <dbl> <dbl> #> 1     1     2  tibble(x = 1, x = 2, .name_repair = ~ c(\"a\", \"b\")) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     1     2  # Tibbles can contain columns that are tibbles or matrices # if the number of rows is compatible. Unnamed tibbled are # spliced, i.e. the inner columns are inserted into the # tibble under construction. tibble(   a = 1:3,   tibble(     b = 4:6,     c = 7:9   ),   d = tibble(     e = tibble(       f = b     )   ) ) #> # A tibble: 3 × 4 #>       a     b     c d$e$f #>   <int> <int> <int> <int> #> 1     1     4     7     4 #> 2     2     5     8     5 #> 3     3     6     9     6 tibble(   a = 1:3,   b = diag(3),   c = cor(trees),   d = Titanic[1:3, , , ] ) #> # A tibble: 3 × 4 #>       a b[,1]  [,2]  [,3] c[,\"Girth\"] [,\"Height\"] [,\"Volume\"]            d #>   <int> <dbl> <dbl> <dbl>       <dbl>       <dbl>       <dbl> <table[,2,2> #> 1     1     1     0     0       1           0.519       0.967          0 … #> 2     2     0     1     0       0.519       1           0.598          0 … #> 3     3     0     0     1       0.967       0.598       1             35 …  # Data can not contain tibbles or matrices with incompatible number of rows: try(tibble(a = 1:3, b = tibble(c = 4:7))) #> Error in tibble(a = 1:3, b = tibble(c = 4:7)) :  #>   Tibble columns must have compatible sizes. #> • Size 3: Existing data. #> • Size 4: Column `b`. #> ℹ Only values of size one are recycled.  # Use := to create columns with names that start with a dot: tibble(.dotted := 3) #> # A tibble: 1 × 1 #>   .dotted #>     <dbl> #> 1       3  # This also works, but might break in the future: tibble(.dotted = 3) #> # A tibble: 1 × 1 #>   .dotted #>     <dbl> #> 1       3  # You can unquote an expression: x <- 3 tibble(x = 1, y = x) #> # A tibble: 1 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     1 tibble(x = 1, y = !!x) #> # A tibble: 1 × 2 #>       x     y #>   <dbl> <dbl> #> 1     1     3  # You can splice-unquote a list of quosures and expressions: tibble(!!!list(x = rlang::quo(1:10), y = quote(x * 2))) #> # A tibble: 10 × 2 #>        x     y #>    <int> <dbl> #>  1     1     2 #>  2     2     4 #>  3     3     6 #>  4     4     8 #>  5     5    10 #>  6     6    12 #>  7     7    14 #>  8     8    16 #>  9     9    18 #> 10    10    20  # Use .data, .env and !! to refer explicitly to columns or outside objects a <- 1 tibble(a = 2, b = a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     2 tibble(a = 2, b = .data$a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     2 tibble(a = 2, b = .env$a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     1 tibble(a = 2, b = !!a) #> # A tibble: 1 × 2 #>       a     b #>   <dbl> <dbl> #> 1     2     1 try(tibble(a = 2, b = .env$bogus)) #> Error : object 'bogus' not found try(tibble(a = 2, b = !!bogus)) #> Error in eval(expr, envir) : object 'bogus' not found  # Use tibble_row() to construct a one-row tibble: tibble_row(a = 1, lm = lm(Height ~ Girth + Volume, data = trees)) #> # A tibble: 1 × 2 #>       a lm     #>   <dbl> <list> #> 1     1 <lm>"},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — tibble_options","title":"Package options — tibble_options","text":"Options affect interactive display. See pillar::pillar_options options affect display console, cli::num_ansi_colors() enabling disabling colored output via ANSI sequences like [3m[38;5;246m[39m[23m.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Package options — tibble_options","text":"","code":"tibble_options"},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Package options — tibble_options","text":"options can set via options() queried via getOption(). , add tibble. prefix (package name dot) option name. Example: option foo, use options(tibble.foo = value) set getOption(\"tibble.foo\") retrieve current value. option value NULL means default used.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"options-for-the-tibble-package","dir":"Reference","previous_headings":"","what":"Options for the tibble package","title":"Package options — tibble_options","text":"view_max: Maximum number rows shown view() input data frame, passed head(). Default: 1000.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tibble_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Package options — tibble_options","text":"","code":"# Default setting: getOption(\"tibble.view_max\") #> NULL  # Change for the duration of the session: old <- options(tibble.view_max = 100)  # view() would show only 100 rows e.g. for a lazy data frame  # Change back to the original value: options(old)  # Local scope: local({   rlang::local_options(tibble.view_max = 100)   # view() would show only 100 rows e.g. for a lazy data frame }) # view() would show the default 1000 rows e.g. for a lazy data frame"},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":null,"dir":"Reference","previous_headings":"","what":"Row-wise tibble creation — tribble","title":"Row-wise tibble creation — tribble","text":"Create tibbles using easier read row--row layout. useful small tables data readability important.  Please see tibble-package general introduction.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row-wise tibble creation — tribble","text":"","code":"tribble(...)"},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row-wise tibble creation — tribble","text":"... <dynamic-dots> Arguments specifying structure tibble. Variable names formulas, may appear data. arguments processed rlang::list2() support unquote via rlang::!! unquote-splice via rlang::!!!.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row-wise tibble creation — tribble","text":"tibble.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/reference/tribble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Row-wise tibble creation — tribble","text":"","code":"tribble(   ~colA, ~colB,   \"a\",   1,   \"b\",   2,   \"c\",   3 ) #> # A tibble: 3 × 2 #>   colA   colB #>   <chr> <dbl> #> 1 a         1 #> 2 b         2 #> 3 c         3  # tribble will create a list column if the value in any cell is # not a scalar tribble(   ~x,  ~y,   \"a\", 1:3,   \"b\", 4:6 ) #> # A tibble: 2 × 2 #>   x     y         #>   <chr> <list>    #> 1 a     <int [3]> #> 2 b     <int [3]>  # Use dplyr::mutate(dplyr::across(...)) to assign an explicit type tribble(   ~a, ~b, ~c,   1, \"2000-01-01\", \"1.5\" ) %>%   dplyr::mutate(     dplyr::across(a, as.integer),     dplyr::across(b, as.Date)   ) #> # A tibble: 1 × 3 #>       a b          c     #>   <int> <date>     <chr> #> 1     1 2000-01-01 1.5"},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy printing — trunc_mat","title":"Legacy printing — trunc_mat","text":"tibble 3.1.0, printing handled entirely pillar package. use function. implement package extend tibble, printed output can customized various ways. See vignette(\"extending\", package = \"pillar\") details.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy printing — trunc_mat","text":"","code":"trunc_mat(x, n = NULL, width = NULL, n_extra = NULL)"},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy printing — trunc_mat","text":"x Object format print. n Number rows show. NULL, default, print rows less option tibble.print_max. Otherwise, print tibble.print_min rows. width Width text output generate. defaults NULL, means use getOption(\"tibble.width\") (also NULL) getOption(\"width\"); latter displays columns fit one screen. can also set options(tibble.width = Inf) override default always print columns, may slow wide tibbles. n_extra Number extra columns print abbreviated information , width small entire tibble. NULL, default, print information tibble.max_extra_cols extra columns.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/trunc_mat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Legacy printing — trunc_mat","text":"object print() method print input similarly tibble. internal data format implementation detail, rely .","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":null,"dir":"Reference","previous_headings":"","what":"View an object — view","title":"View an object — view","text":"Calls utils::View() input returns , invisibly. input data frame, processed using variant .data.frame(head(x, n)). message printed number rows exceeds n. function effect noninteractive sessions.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View an object — view","text":"","code":"view(x, title = NULL, ..., n = NULL)"},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View an object — view","text":"x object display. title title use display, default deparsed expression used. ... Unused, must empty. n Maximum number rows display. used x data frame. Uses view_max option default.","code":""},{"path":"https://tibble.tidyverse.org/dev/reference/view.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"View an object — view","text":"RStudio IDE overrides utils::View(), picked correctly.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9034","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9034","text":"Trigger run (#1606). ci: Trigger run ci: Latest changes","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9033","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9033","text":"Use pkgdown branch (#1604). ci: Use pkgdown branch ci: Updates duckdb ci: Trigger run","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-2-1-9032","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.2.1.9032","text":"Rework view() better work RStudio Positron (@DavisVaughan, #1551, #1603).","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9031","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9031","text":"Install via R CMD INSTALL ., pak (#1601). ci: Install via R CMD INSTALL ., pak ci: Bump version upload-artifact action","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9030","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9030","text":"Install local package pkgdown builds. Improve support protected branches fledge. Improve support protected branches, without fledge.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9029","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9029","text":"Auto-update GitHub Actions. 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Run: https://github.com/tidyverse/tibble/actions/runs/9687521438 Replace non-API SET_S4_OBJECT() Rf_asS4() (@olivroy, #1588).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"continuous-integration-3-2-1-9019","dir":"Changelog","previous_headings":"","what":"Continuous integration","title":"tibble 3.2.1.9019","text":"Fix. Remove dead workflow. Recent updates.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9018","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9018","text":"Snapshot updates (#1580).","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-2-1-9017","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.2.1.9017","text":"new_tibble() as_tibble() support attributes named \"n\" \"x\" (#1573, #1574).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219016","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9016","title":"tibble 3.2.1.9016","text":"Internal changes .","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9015","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9015","text":"Revert “Fix as_tibble() usage”. reverts commit 49d0b63504c4e3f77beaf2889ace12cad7d3f293.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219014","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9014","title":"tibble 3.2.1.9014","text":"Internal changes .","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-2-1-9013","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.2.1.9013","text":"as_tibble() calls .data.frame() objects subclasses \"tbl_df\" (@TimTaylor, #1556, #1557).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9013","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9013","text":"Check fast path first.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9012","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9012","text":"Skip tests packages missing.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"testing-3-2-1-9012","dir":"Changelog","previous_headings":"","what":"Testing","title":"tibble 3.2.1.9012","text":"Skip tests packages missing.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219011","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9011","title":"tibble 3.2.1.9011","text":"Merge pull request #1565 tidyverse/snapshot-main-rcc-smoke-null.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219010","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9010","title":"tibble 3.2.1.9010","text":"Merge pull request #1562 tidyverse/snapshot-main-rcc-smoke-null. Merge pull request #1561 tidyverse/snapshot-main-rcc-full-config-os-macos-latest-r-release.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219009","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9009","title":"tibble 3.2.1.9009","text":"Merge pull request #1560 tidyverse/snapshot-main-R-CMD-check-base-null.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-2-1-9008","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.2.1.9008","text":"Fix rendering suggested packages missing.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"testing-3-2-1-9007","dir":"Changelog","previous_headings":"","what":"Testing","title":"tibble 3.2.1.9007","text":"Override :: avoid failures tests without suggested packages.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219006","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9006","title":"tibble 3.2.1.9006","text":"Merge branch ‘docs’.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"chore-3-2-1-9005","dir":"Changelog","previous_headings":"","what":"Chore","title":"tibble 3.2.1.9005","text":"Snapshot updates rcc-smoke (null) (#1548).","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-2-1-9004","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.2.1.9004","text":"Alt text (@maelle, #1528, #1539).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219003","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9003","title":"tibble 3.2.1.9003","text":"Merged cran-3.2.1 main.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219002","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9002","title":"tibble 3.2.1.9002","text":"Internal changes .","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219001","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9001","title":"tibble 3.2.1.9001","text":"Merge pull request #1527 tidyverse/snapshot-main-rcc-smoke-null.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-3219000","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1.9000","title":"tibble 3.2.1.9000","text":"Internal changes .","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-321","dir":"Changelog","previous_headings":"","what":"tibble 3.2.1","title":"tibble 3.2.1","text":"CRAN release: 2023-03-20","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-2-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.2.1","text":"Use symbol instead string .Call().","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-320","dir":"Changelog","previous_headings":"","what":"tibble 3.2.0","title":"tibble 3.2.0","text":"CRAN release: 2023-03-08","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-2-0","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.2.0","text":"Accurate location source error error messages (#1379, #1065, #1508). as_data_frame() now also refers .data.frame() deprecation message (#1149, #1506).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-3-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 3.2.0","text":"Deprecated functions arguments detect usage CRAN packages (#1515): data_frame_(), lst_(), frame_data() as_tibble(validate = ), as_tibble(NULL), new_tibble(subclass = ) add_row() add_column() non-data-frame input add_column() input non-unique names corner cases tbl[[x]] Breaking change: Remove knit_print.trunc_mat() method (#1516). Forward trunc_mat() new-style pillar methods (#1517).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.2.0","text":"Allow glue() classed characters subassignment (#1150, #1503).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-2-0","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.2.0","text":"Reduce overhead single-column subset assignment (#1363).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-2-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.2.0","text":"New vignette(\"extending\") (#275, #1512). Minor updates (#1151, #1070, #1512, #1485). Update example nrow argument new_tibble() (@heavywatal, #1394). Fix display mermaid diagrams vignette(\"formats\") (@maelle, #1497, #1498). 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Mention median() Recovery section vignette(\"numbers\") (#1197).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-317","dir":"Changelog","previous_headings":"","what":"tibble 3.1.7","title":"tibble 3.1.7","text":"CRAN release: 2022-05-03","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-change-3-1-7","dir":"Changelog","previous_headings":"","what":"Breaking change","title":"tibble 3.1.7","text":"trunc_mat() now returns value different structure. considered implementation detail can change future, rely . guarantee calling print() display input like tibble (#1059).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-1-7","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.1.7","text":"Avoid listing dim_desc() reexports. Add examples data frame matrix columns (#978, #1012).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-7","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.7","text":"Require rlang 1.0.1 pillar 1.7.0 (#1063). Prefer class .subclass rlang::error_cnd() (#1015, #1060).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-316","dir":"Changelog","previous_headings":"","what":"tibble 3.1.6","title":"tibble 3.1.6","text":"CRAN release: 2021-11-07 set_num_opts() set_char_opts() reexported pillar (#959). view() uses rlang::expr_deparse(width = Inf) avoid errors long |> pipes (#957). new_tibble() checks nrow argument nonnegative less 2^31 (#916). tbl_sum.tbl_df() ellipsis formals extensibility.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-315","dir":"Changelog","previous_headings":"","what":"tibble 3.1.5","title":"tibble 3.1.5","text":"CRAN release: 2021-09-30 Avoid necessity set \"tibble.view_max\" option lazy tables (#954). Avoid blanket import lifecycle package compatibility upcoming rlang (#955, @romainfrancois).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-314","dir":"Changelog","previous_headings":"","what":"tibble 3.1.4","title":"tibble 3.1.4","text":"CRAN release: 2021-08-25","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-1-4","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.1.4","text":".data.frame.tbl_df() strips inner column names (#837). new_tibble() allows omitting nrow argument (#781).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-3-1-4","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 3.1.4","text":"Move vignette(\"digits\"), vignette(\"numbers\"), ?num ?char pillar package (#913). Replace iris trees (#943). Various documentation improvements. New ?tibble_options help page (#912).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-1-4","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.1.4","text":"x[, j] <- one_row_value avoids explicit recycling right-hand side, recycling happens implicitly vctrs::vec_assign() performance (#922).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-4","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.4","text":"Vignettes now tested snapshot test (#919). new_tibble() uses vctrs::new_data_frame() internally (#726, @DavisVaughan). Adapt pillar 1.6.2. Fix tests compatibility pillar 1.6.2.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-313","dir":"Changelog","previous_headings":"","what":"tibble 3.1.3","title":"tibble 3.1.3","text":"CRAN release: 2021-07-23","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-1-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.1.3","text":"tbl[row, col] <- rhs treats -NA logical vector missing value existing data (#773) right-hand side value (#868). means column initialized NA (type logical) change type row updated value different type. [[<-() supports symbols (#893).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-1-3","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.1.3","text":"as_tibble_row() supports arbitrary vectors (#797). enframe() deframe() support arbitrary vectors (#730). tibble() tibble_row() ignore columns evaluate NULL, verbatim NULL passed (#895, #900). new_tibble() now faster (#901, @mgirlich).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-3","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.3","text":"Establish compatibility rlang > 0.4.11 (#908). Use pillar::dim_desc() (#859). Establish compatibility testthat > 3.0.3 (#896, @lionel-). Bump required versions ellipsis vctrs avoid warning package load.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-312","dir":"Changelog","previous_headings":"","what":"tibble 3.1.2","title":"tibble 3.1.2","text":"CRAN release: 2021-05-16 Bump required versions ellipsis vctrs avoid warning package load.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-311","dir":"Changelog","previous_headings":"","what":"tibble 3.1.1","title":"tibble 3.1.1","text":"CRAN release: 2021-04-18 num() char() reexported pillar (#880). tribble() frame_matrix() give error values named (#871, @lorenzwalthert). Document cli.num_colors option (#410). Fix new_tibble() examples compatibility pillar 1.6.0.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-310","dir":"Changelog","previous_headings":"","what":"tibble 3.1.0","title":"tibble 3.1.0","text":"CRAN release: 2021-02-25","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.1.0","text":"has_rownames() now works correctly data frames \"row.names\" attribute malformed due problem structure() (#852). tbl[FALSE, \"column\"] <- x adds new column (#846).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-1-0","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.1.0","text":"Importing pillar 1.5.0, cli crayon now suggested packages (#475). size_sum() now reexported pillar (#850, @topepo). as_tibble() hints often use .name_repair argument column names invalid (#855). as_tibble.table() mentions .name_repair argument error message (#839).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-1-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.1.0","text":"Remove compatibility code pillar < 1.5.0 (#861). Moved functions “stable” lifecycle (#860).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-306","dir":"Changelog","previous_headings":"","what":"tibble 3.0.6","title":"tibble 3.0.6","text":"CRAN release: 2021-01-29 vec_ptype_abbr.tbl_df() type_sum.tbl_df() now uses name topmost class subclasses \"tbl_df\" (#843). Ignore errors formats.Rmd vignette. Avoid tidy evaluation pillar compatibility code.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-305","dir":"Changelog","previous_headings":"","what":"tibble 3.0.5","title":"tibble 3.0.5","text":"CRAN release: 2021-01-15 Use testthat edition 3, compatible testthat 3.0.1 (#827, #832).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-304","dir":"Changelog","previous_headings":"","what":"tibble 3.0.4","title":"tibble 3.0.4","text":"CRAN release: 2020-10-12","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"compatibility-3-0-4","dir":"Changelog","previous_headings":"","what":"Compatibility","title":"tibble 3.0.4","text":"Establish compatibility upcoming pillar 1.5.0 (#818). tbl_sum() shows “data frame” instead “tibble” objects inheriting \"tbl\" \"tbl_df\" (#818). Register format.tbl() print.tbl() methods pillar doesn’t (#816). Use vctrs::num_as_location() internally subset assignment rows columns better error messages (#746). Adapt tests development version testthat.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-0-4","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.0.4","text":"Fix documentation link base::Extract. add_row(df) adds empty row (#809, @DavisVaughan).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-303","dir":"Changelog","previous_headings":"","what":"tibble 3.0.3","title":"tibble 3.0.3","text":"CRAN release: 2020-07-10 Fix test compatibility rlang 0.4.7. Fix warning needs_dots arguments pillar >= 1.4.5 (#798).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-302","dir":"Changelog","previous_headings":"","what":"tibble 3.0.2","title":"tibble 3.0.2","text":"CRAN release: 2020-07-07","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-0-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.0.2","text":"[[ works classed indexes , e.g. created glue::glue() (#778). add_column() works without warning 0-column data frames (#786). tribble() now better handles named inputs (#775) objects non-vtrs classes like lubridate::Period (#784) formattable::formattable (#785).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-0-2","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.0.2","text":"Subsetting subassignment faster (#780, #790, #794). .null() preferred is_null() speed. Implement continuous benchmarking (#793).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"compatibility-3-0-2","dir":"Changelog","previous_headings":"","what":"Compatibility","title":"tibble 3.0.2","text":"is_vector_s3() longer reexported pillar (#789).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-301","dir":"Changelog","previous_headings":"","what":"tibble 3.0.1","title":"tibble 3.0.1","text":"CRAN release: 2020-04-20","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"compatibility-fixes-3-0-1","dir":"Changelog","previous_headings":"","what":"Compatibility fixes","title":"tibble 3.0.1","text":"[<-.tbl_df() coerces matrices data frames (#762). Use delayed import cli work around unload problems downstream packages (#754).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-3-0-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 3.0.1","text":"soft-deprecation warnings actually visible. .name_repair function, repair messages shown (#763). Remove superseded signal as_tibble.list(), as_tibble_row() works size 1.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"enhancements-3-0-1","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"tibble 3.0.1","text":"as_tibble(validate = ) now always triggers deprecation warning. Subsetting subassignment rows one-column matrices work , deprecation warning (#760). Attempts update tibble row atomic vector give clearer error message. Recycling message subassignment appears target size != 1. Tweak title “Invariants” vignette.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-300","dir":"Changelog","previous_headings":"","what":"tibble 3.0.0","title":"tibble 3.0.0","text":"CRAN release: 2020-03-30","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"major-breaking-changes-3-0-0","dir":"Changelog","previous_headings":"","what":"Major breaking changes","title":"tibble 3.0.0","text":"Subset assignment (“subassignment”) also subsetting become stricter. Symptoms: Error: common type … Error: Assigned data ... must compatible … must one dimension, 2 Error: Lossy cast … … “invariants” article https://tibble.tidyverse.org/dev/articles/invariants.html describes invariants operations follow tibble, important differences data frames. tried make subsetting subassignment safe possible, errors caught early , introducing little friction possible. List classes longer automatically treated vectors. Symptoms: Error: columns tibble must vectors Error: Expected vector, ... object implement class wraps list S3 vector, need include \"list\" class: Alternatively, implement vec_proxy() method described https://vctrs.r-lib.org/reference/vec_data.html, construct class list_of(). Added experimental support inner names columns, form tibble(= c(b = 1)). Inner names longer stripped creating tibble. maintained slicing operations yet updated assigning row subscript. change may break existing comparison tests don’t expect names columns (#630). Symptoms: “names target current” comparing","code":"structure(x, class = c(\"your_s3_class\", \"list\"))"},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-3-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 3.0.0","text":"tibble() now splices anonymous data frames, tibble(tibble(= 1), b = ) equivalent tibble(= 1, b = ). means tibble(trees) now three columns, use tibble(trees = trees) intention create packed data frame (#581). name-repair help topic gone, refer ?vctrs::vec_as_names instead. expression() columns converted lists workaround lacking support vctrs (#657). tribble() now stricter combining values. values column must compatible, otherwise error occurs (#204). criteria wrapping list column now based vctrs principles: non-vectors vectors vctrs::vec_size() unequal 1 wrapped lists. $ warns unconditionally column found, [[ doesn’t warn. add_row() now uses vctrs::vec_rbind() hood, means columns combined vctrs::vec_c(). particular, factor columns converted character one columns character column.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"soft-deprecations-3-0-0","dir":"Changelog","previous_headings":"","what":"Soft deprecations","title":"tibble 3.0.0","text":"Soft-deprecate subclass argument new_tibble(). Soft-deprecate as_tibble() without arguments (#683). Preparing move glimpse() tbl_sum() pillar package. package implements methods, please import generics pillar soon become available .","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-3-0-0","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 3.0.0","text":"Internals now make heavy use vctrs package, following invariants defined . Name repair responsibility vctrs now (#464). errors emitted directly package inherit \"tibble_error\" \"rlang_error\" classes. cases, \"vctrs_error\" errors may passed . exact subclass subject change. Example: tibble(= quote(b)) raises error inherits \"tibble_error_column_must_be_vector\", \"tibble_error\" \"rlang_error\", \"error\" \"condition\" like errors. rely wording \"tibble_error_column_must_be_vector\", likely change. Use following pattern catch errors emitted tibble: New tibble_row() constructs tibbles exactly one row, fails. Non-vector objects automatically wrapped list, vectors (including lists) must length one (#205). New as_tibble_row() as_tibble_col() convert bare vector one-row one-column tibble, respectively. as_tibble_col() also works non-bare vectors. Using as_tibble() bare vectors superseded (#447). as_tibble.data.frame() uses implicit row names asked create column row names. allows lossless direct conversion matrices row names tibbles (#567, @stufield). Implement str.tbl_df() (#480). tribble() now returns columns \"unspecified\" type 0-row tibbles. add_row() add_column() now restore attributes avoid errors appending sf objects tibble subclasses (#662). add_column() gains .name_repair argument. given, .data must unique columns, deprecation message. Allow POSIXlt columns, now better supported dplyr tools thanks vctrs (#626). tibble() ignores NULL arguments, named unnamed (#580). view() works remote data sources applying strategy print() glimpse(). maximum number rows case can specified using new n argument, default taken new \"tibble.view_max\" option (#695).","code":"tryCatch(   your_code(),   tibble_error = function(cnd) {   } )"},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"output-3-0-0","dir":"Changelog","previous_headings":"","what":"Output","title":"tibble 3.0.0","text":"Formatting dimensions never uses scientific notation. glimpse() uses “Rows” “Columns” instead “Variables” “Observations”, ’re sure data tidy (#614). view() now uses created (passed) title argument (#610, @xvrdm).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-3-0-0","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 3.0.0","text":"Construction subsetting large data frames faster now (#717, @romainfrancois).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-3-0-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 3.0.0","text":"Import lifecycle package (#669). new_tibble() removes redundant subclasses \"class\" attribute. Using classed conditions. classes start \"tibble_error_\" also contain \"tibble_error\" (#659). magrittr pipe %>% reexported.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-213","dir":"Changelog","previous_headings":"","what":"tibble 2.1.3","title":"tibble 2.1.3","text":"CRAN release: 2019-06-06 Fix compatibility R 3.5 earlier, regression introduced tibble 2.1.2.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-212","dir":"Changelog","previous_headings":"","what":"tibble 2.1.2","title":"tibble 2.1.2","text":"CRAN release: 2019-05-29 Relax version requirements. Fix test failing pillar upgrade.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-211","dir":"Changelog","previous_headings":"","what":"tibble 2.1.1","title":"tibble 2.1.1","text":"CRAN release: 2019-03-16 Three dots used even \"unique\" name repair (#566). add_row(), add_case() add_column() now signal warning per session input data frame (#575). Fix view() case object named x exists global environment (#579).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-201","dir":"Changelog","previous_headings":"","what":"tibble 2.0.1","title":"tibble 2.0.1","text":"CRAN release: 2019-01-12 tibble names can set NULL within RStudio, R routines within RStudio relied behaviour (#563, @kevinushey). as_tibble.matrix(validate = TRUE) works , lifecycle warning (#558). Replace new_list_along() rep_along() support rlang 0.3.1 (#557, @lionel-).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-200","dir":"Changelog","previous_headings":"","what":"tibble 2.0.0","title":"tibble 2.0.0","text":"CRAN release: 2019-01-04","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-2-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 2.0.0","text":"tibble() as_tibble() functions, low-level new_tibble() constructor, undergone major overhaul improve consistency. suspect package code affected analysis code. improve compatibility existing code, breaking changes reduced minimum cases replaced warning appears per session. Call tibble:::scoped_lifecycle_errors() updating packages scripts new semantics API turn warnings errors. compatibility code removed tibble 3.0.0. optional arguments moved past ellipsis, must specified named arguments. affects mostly n argument as_tibble.table(), passing n unnamed still works (warning). new_tibble() optimized performance, function longer strips dimensions 1d arrays longer checks correctness names column lengths. (still checks object named, except zero-length input.) Use new validate_tibble() need checks (#471). nrow argument new_tibble() now mandatory. class argument replaces now deprecated subclass argument, latter supported quietly time (#518). Setting names tibble via names(df) <- ... now also requires minimal names, otherwise warning issued per session (#466). as_tibble(), checking names also enabled default, even tibbles, matrices matrix-like objects: names must exist, NA names allowed. Coercing matrix without column names trigger warning per session. (corresponds \"minimal\" checks described .). validate argument as_tibble() deprecated, see alternatives. (as_tibble.tbl_df() method removed, as_tibble.data.frame() method used tibbles.) as_tibble() always checks columns 1D 2D vectors type POSIXlt, even validate = FALSE (now deprecated). Calling as_tibble() vector now warns per session. Use enframe(name = NULL) converting vector one-column tibble, enframe() converting named vector two-column tibble. data_frame() frame_data() soft-deprecated, please use tibble() tribble() (#111). tibble_(), data_frame_(), lst_() soft-deprecated. Please use tibble() lst() (#111, #509). .tibble() as_data_frame() officially deprecated generic anymore, please use/implement as_tibble() (#111). as_tibble.data.frame() (also as_tibble.matrix()) strip row names default. Code relies tibbles keeping row names now see: different result calling rownames() row.names(), rows full NA values subsetting rows character vector, e.g. as_tibble(mtcars)[\"Mazda RX4\", ]. Call pkgconfig::set_config(\"tibble::rownames\", NA) revert old behavior keeping row names. Packages import tibble can call set_config() .onLoad() function (#114). as_tibble() drops extra classes, particular as_tibble.grouped_df() now removes grouping (#535). column_to_rownames() now always coerces data frame, row names longer supported tibbles (#114). *_rownames() functions, first argument renamed .data consistency (#412). Subsetting one row [..., , drop = TRUE] returns tibble (#442). print.tbl_df() method removed, print.tbl() method handles printing (#519).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-features-2-0-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tibble 2.0.0","text":"tibble() supports columns matrices data frames (#416). new .rows argument tibble() as_tibble() allows specifying expected number rows explicitly, even ’s evident data. allows writing defensive code. Column name repair direct support, via new .name_repair argument tibble() as_tibble(). takes following values: \"minimal\": name repair checks, beyond basic existence. \"unique\": Make sure names unique empty. \"check_unique\": (default value), name repair, check unique. \"universal\": Make names unique syntactic. function: apply custom name repair (e.g., .name_repair = make.names .name_repair = ~make.names(., unique = TRUE) names style base R). validate argument as_tibble() deprecated supported (emits message per session). Use .name_repair = \"minimal\" instead validate = FALSE, .name_repair = \"check_unique\" instead validate = TRUE. need support older versions tibble, pass .name_repair validate arguments consistent way, message emitted case (#469, @jennybc). Row name handling stricter. Row names never (never ) supported tibble() new_tibble(), now stripped default as_tibble(). rownames argument as_tibble() supports: NULL: remove row names (default), NA: keep row names, string: name new column contain existing row names, longer present result. old default can restored calling pkgconfig::set_config(\"tibble::rownames\", NA), also works packages import tibble. new_tibble() as_tibble() now also strip \"dim\" attribute columns one-dimensional arrays. (tibble() already .) Internally, as_tibble() implementation forward extra arguments ... as_tibble.list() handled. means common .rows .name_repair can used inputs. suggest implementations method . enframe() (name = NULL) deframe() now support one-column tibbles (#449). Improved S4 support adding exportClass(tbl_df) NAMESPACE (#436, @jeffreyhanson @javierfajnolla). New validate_tibble() checks tibble internal consistency (#471). Bring error message invalid column type line allowed matrix/df cols (#465, @maxheld83).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-functions-2-0-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"tibble 2.0.0","text":"Added experimental view() function always returns input invisibly calls utils::View() interactive mode (#373).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"output-2-0-0","dir":"Changelog","previous_headings":"","what":"Output","title":"tibble 2.0.0","text":"set_tidy_names() tidy_names() helpers list new names using bullet list six items (#406). one-character ellipse (cli::symbol$ellipsis) printed instead \"...\" available, affects glimpse() output truncated lists (#403). Column names types now formatted identically glimpse() print.tbl_df(). tidy_names() quotes variable names reporting repair (#407). error messages now follow tidyverse style guide (#223). as_tibble() prints informative error message using rownames argument input data frame matrix row names (#388, @anhqle). column_to_rownames() uses real variable name error message (#399, @alexwhan). Lazy tibbles exactly 10 rows longer show “…rows” (#371). glimpse() shows information obtained tbl_sum(), e.g. grouping information grouped_df dplyr (#550).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-2-0-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 2.0.0","text":"glimpse() takes coloring account computing column width, output longer truncated prematurely coloring enabled. glimpse() disambiguates outputs factors levels contain commas (#384, @anhqle). print.tbl_df() negative value n behaves n omitted (#371). Fixed output extra column names contain spaces.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-2-0-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 2.0.0","text":"Skip dplyr tests unavailable (#420, @QuLogic). Skip mockr tests unavailable (#454, @Enchufa2). Use fansi::strwrap_ctl() instead string wrapping routine. tibble() uses recycled values construction unrecycled values validation. tibble() now faster wide tibbles. Subsetting [ operator faster (#544). Avoid use stop() examples packages installed (#453, @Enchufa2). Fix as_tibble() examples using correct argument names requireNamespace() call (#424, @michaelweylandt). as_tibble() checks column length (#365, @anhqle). Using rlang::list2() (#391, @lionel-).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-142","dir":"Changelog","previous_headings":"","what":"tibble 1.4.2","title":"tibble 1.4.2","text":"CRAN release: 2018-01-22","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-4-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.4.2","text":"Fix OS X builds. tibble.width option honored (#369). tbl[1, , drop = TRUE] now behaves identically data frames (#367). Fix error message accessing columns using logical index vector (#337, @mundl). glimpse() returns input zero-column data frames.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-1-4-2","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 1.4.2","text":"enframe(NULL) now returns enframe(logical()) (#352). tribble() now ignores trailing commas (#342, @anhqle). Updated vignettes website documentation.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"performance-1-4-2","dir":"Changelog","previous_headings":"","what":"Performance","title":"tibble 1.4.2","text":"Faster printing wide tibbles (#360). Faster construction subsetting tibbles (#353). call nrow() head() glimpse(), ncol().","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-141","dir":"Changelog","previous_headings":"","what":"tibble 1.4.1","title":"tibble 1.4.1","text":"CRAN release: 2017-12-25","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-formatting-1-4-1","dir":"Changelog","previous_headings":"","what":"New formatting","title":"tibble 1.4.1","text":"new pillar package now responsible formatting tibbles. Pillar try display many columns possible, necessary truncating shortening output. Colored output highlights important information guides eye. vignette tibble package describes adapt custom data types optimal display tibble.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-features-1-4-1","dir":"Changelog","previous_headings":"","what":"New features","title":"tibble 1.4.1","text":"Make add_case() alias add_row() (#324, @LaDilettante). as_tibble() gains rownames argument (#288, #289). as_tibble.matrix() repairs column names. Tibbles now support character subsetting (#312). `[.tbl_df`() supports drop = TRUE omits warning j passed. calls df[, j, drop = TRUE] df[j, drop = TRUE] now compatible data frames (#307, #311).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-4-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.4.1","text":"Improved compatibility remote data sources glimpse() (#328). Logical indexes supported, warning raised length match number rows 1 (#318). Fixed width word wrapping extra information (#301). Prevent add_column() dropping classes attributes removing use cbind(). Additionally ensures add_column() can used grouped data frames (#303, @DavisVaughan). add_column() empty zero-row tibble variable nonzero length now produces correct error message (#319).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-changes-1-4-1","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"tibble 1.4.1","text":"Reexporting has_name() rlang, instead forwarding, avoid warning importing rlang tibble. Compatible R 3.1 (#323). Remove Rcpp dependency (#313, @patperry).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-134","dir":"Changelog","previous_headings":"","what":"tibble 1.3.4","title":"tibble 1.3.4","text":"CRAN release: 2017-08-22","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-4","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.4","text":"Values length 1 tibble() call recycled prior evaluating subsequent arguments, improving consistency mutate() (#213). Recycling values length 1 tibble() call maintains class (#284). add_row() now always preserves column data types input data frame way rbind() (#296). lst() now handles duplicate names, value defined last used case clash. Adding columns zero-row data frames now also works mixing lengths 1 0 new columns (#167). validate argument now also supported as_tibble.tbl_df(), default FALSE (#278). must passed named argument, as_tibble(validate = TRUE).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"formatting-1-3-4","dir":"Changelog","previous_headings":"","what":"Formatting","title":"tibble 1.3.4","text":"format_v() now always surrounds lists [] brackets, even length one. affects glimpse() output list columns (#106). Factor levels escaped printing (#277). Non-syntactic names now also escaped glimpse() (#280). tibble() gives consistent error message case duplicate column names (#291).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-133","dir":"Changelog","previous_headings":"","what":"tibble 1.3.3","title":"tibble 1.3.3","text":"CRAN release: 2017-05-28","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.3","text":"Added format() print() methods tbl tbl_df classes, protect malformed tibbles inherit \"tbl_df\" \"tbl\", created e.g. ungroup() dplyr 0.5.0 earlier (#256, #263). column width non-syntactic columns computed correctly (#258). Printing tibble doesn’t apply quote escaping list columns. Fix error tidy_names(syntactic = TRUE, quiet = FALSE) names fixed (#260, @imanuelcostigan). Remove unused import declaration assertthat.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-131","dir":"Changelog","previous_headings":"","what":"tibble 1.3.1","title":"tibble 1.3.1","text":"CRAN release: 2017-05-17","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.1","text":"Subsetting zero columns longer returns wrong number rows (#241, @echasnovski).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"interface-changes-1-3-1","dir":"Changelog","previous_headings":"","what":"Interface changes","title":"tibble 1.3.1","text":"New set_tidy_names() tidy_names(), simpler version repair_names() works unchanged now (#217). New rowid_to_column() adds rowid column first column removes row names (#243, @barnettjacob). .equal.tbl_df() method removed, calling .equal() now forwards base::.equal.data.frame(). compare tibbles ignoring row column order, please use dplyr::all_equal() (#247).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"formatting-1-3-1","dir":"Changelog","previous_headings":"","what":"Formatting","title":"tibble 1.3.1","text":"Printing now uses x instead Unicode multiplication sign, avoid encoding issues (#216). String values now quoted printing contain non-printable characters quotes (#253). print(), format(), tbl_sum() methods now implemented class \"tbl\" \"tbl_df\". allows subclasses use tibble’s formatting facilities. formatting header can tweaked implementing tbl_sum() subclass, expected return named character vector. print.tbl_df() method still implemented compatibility downstream packages, calls NextMethod(). printing routine, relying print.data.frame() anymore. Now providing format.tbl_df() full support Unicode characters names data, also glimpse() (#235).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"misc-1-3-1","dir":"Changelog","previous_headings":"","what":"Misc","title":"tibble 1.3.1","text":"Improve formatting error messages (#223). Using rlang instead lazyeval (#225, @lionel-), rlang functions (#244). tribble() now handles values class (#237, @NikNakk). Minor efficiency gains replacing (.na()) anyNA() (#229, @csgillespie). microbenchmark package now used conditionally (#245). pkgdown website.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-130","dir":"Changelog","previous_headings":"","what":"tibble 1.3.0","title":"tibble 1.3.0","text":"CRAN release: 2017-04-01","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.3.0","text":"Time series matrices (objects class mts ts) now supported as_tibble() (#184). all_equal() function (called .equal.tbl_df()) now forwards dplyr fails helpful message installed. Data frames list columns compared anymore, differences declared class (data.frame vs. tbl_df) ignored. .equal.tbl_df() method gives warning forwards NextMethod() dplyr installed; call .equal(.data.frame(...), ...) avoid warning. ensures consistent behavior function, regardless dplyr loaded (#198).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"interface-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Interface changes","title":"tibble 1.3.0","text":"Now requiring R 3.1.0 instead R 3.1.3 (#189). Add .tibble() alias as_tibble() (#160, @LaDilettante). New frame_matrix(), similar frame_data() matrices (#140, #168, @LaDilettante). New deframe() reverse operation enframe() (#146, #214). Removed unused dependency assertthat.","code":""},{"path":[]},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"general-1-3-0","dir":"Changelog","previous_headings":"Features","what":"General","title":"tibble 1.3.0","text":"Keep column classes adding row empty tibble (#171, #177, @LaDilettante). Singular plural variants error messages mention list objects (#116, #138, @LaDilettante). add_column() can add columns length 1 (#162, #164, @LaDilettante).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"input-validation-1-3-0","dir":"Changelog","previous_headings":"Features","what":"Input validation","title":"tibble 1.3.0","text":"attempt read update missing column now throws clearer warning (#199). attempt call add_row() grouped data frame results helpful error message (#179).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"printing-1-3-0","dir":"Changelog","previous_headings":"Features","what":"Printing","title":"tibble 1.3.0","text":"Render Unicode multiplication sign x represented current locale (#192, @ncarchedi). Backtick NA names printing (#206, #207, @jennybc). glimpse() now uses type_sum() also S3 objects (#185, #186, @holstius). max.print option ignored printing tibble (#194, #195, @t-kalinowski).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-1-3-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 1.3.0","text":"Fix typo obj_sum documentation (#193, @etiennebr). Reword documentation tribble() (#191, @kwstat). Now explicitly stating minimum Rcpp version 0.12.3.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-1-3-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 1.3.0","text":"Using registration native routines.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-12","dir":"Changelog","previous_headings":"","what":"tibble 1.2","title":"tibble 1.2","text":"CRAN release: 2016-08-26","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.2","text":"tibble.width option used glimpse() finite (#153, @kwstat). New as_tibble.poly() support conversion poly object tibble (#110). add_row() now correctly handles existing columns type list updated (#148). .equal() doesn’t throw error anymore one columns named na.last, decreasing method (#107, @BillDunlap).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"interface-changes-1-2","dir":"Changelog","previous_headings":"","what":"Interface changes","title":"tibble 1.2","text":"New add_column(), analogously add_row() (#99). print.tbl_df() gains n_extra method interface trunc_mat() now . add_row() add_column() gain ..arguments indicate row (number) column (number name) new data inserted. Updated added columns named ..(#99). Rename frame_data() tribble(), stands “transposed tibble”. former still available alias (#132, #143).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"features-1-2","dir":"Changelog","previous_headings":"","what":"Features","title":"tibble 1.2","text":"add_row() now can add multiple rows, recycling (#142, @jennybc). Use multiply character × instead x printing dimensions (#126). Output tests disabled Windows. Back-tick non-semantic column names output (#131). Use dttm instead time POSIXt values (#133), now used columns difftime class. Better output 0-row results total number rows unknown (e.g., SQL data sources).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-1-2","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 1.2","text":"New object summary vignette shows methods define custom vector classes used tibble columns (#151). Added examples print.tbl_df(), now using data nycflights13 instead Lahman (#121), guidance install nycflights13 package necessary (#152). Minor changes vignette (#115, @helix123).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"tibble-11","dir":"Changelog","previous_headings":"","what":"tibble 1.1","title":"tibble 1.1","text":"CRAN release: 2016-07-04 Follow-release.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-1","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tibble 1.1","text":"tibble() longer alias frame_data() (#82). Remove tbl_df() (#57). $ returns NULL column found, without partial matching. warning given (#109). [[ returns NULL column found (#109).","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"output-1-1","dir":"Changelog","previous_headings":"","what":"Output","title":"tibble 1.1","text":"Reworked output: concise summary (begins hash # contains text (#95)), removed empty line, showing number hidden rows columns (#51). trailing metadata also begins hash # (#101). Presence row names indicated star printed output (#72). Format NA values character columns <NA>, like print.data.frame() (#69). number printed extra cols now option (#68, @lionel-). Computation column width properly handles wide (e.g., Chinese) characters, tests still fail Windows (#100). glimpse() shows nesting structure lists uses angle brackets type (#98). Tibbles POSIXlt columns can printed now, text <POSIXlt> shown placeholder encourage usage POSIXct (#86). type_sum() shows topmost class S3 objects.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"error-reporting-1-1","dir":"Changelog","previous_headings":"","what":"Error reporting","title":"tibble 1.1","text":"Strict checking integer logical column indexes. integers, passing non-integer index --bounds index raises error. logicals, vectors length 1 ncol supported. Passing matrix array now raises error case (#83). Warn setting non-NULL row names (#75). Consistently surround variable names single quotes error messages. Use “Unknown column ‘x’” error message column found, like base R (#94). stop() warning() now always called call. = FALSE.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"coercion-1-1","dir":"Changelog","previous_headings":"","what":"Coercion","title":"tibble 1.1","text":".Dim attribute silently stripped columns 1d matrices (#84). Converting tibble without row names regular data frame add explicit row names. as_tibble.data.frame() preserves attributes, uses as_tibble.list() calling overriden methods may lead endless recursion.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"new-features-1-1","dir":"Changelog","previous_headings":"","what":"New features","title":"tibble 1.1","text":"New has_name() (#102). Prefer tibble() as_tibble() data_frame() as_data_frame() code documentation (#82). New .tibble() is_tibble() (#79). New enframe() converts vectors two-column tibbles (#31, #74). obj_sum() type_sum() show \"tibble\" instead \"tbl_df\" tibbles (#82). as_tibble.data.frame() gains validate argument (as_tibble.list()), TRUE input validated. Implement as_tibble.default() (#71, hadley/dplyr#1752). has_rownames() supports arguments data frames.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"bug-fixes-1-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"tibble 1.1","text":"Two-dimensional indexing [[ works (#58, #63). Subsetting empty index (e.g., x[]) also removes row names.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"documentation-1-1","dir":"Changelog","previous_headings":"","what":"Documentation","title":"tibble 1.1","text":"Document behavior as_tibble.tbl_df() subclasses (#60). Document test subsetting removes row names.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"internal-1-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"tibble 1.1","text":"Don’t rely knitr internals testing (#78). Fix compatibility knitr 1.13 (#76). Enhance knit_print() tests. Provide default implementation tbl_sum.tbl_sql() tbl_sum.tbl_grouped_df() allow dplyr release tibble release. Explicit tests format_v() (#98). Test output NULL value tbl_sum(). Test subsetting variants (#62). Add missing test dplyr. Use new expect_output_file() testthat.","code":""},{"path":"https://tibble.tidyverse.org/dev/news/index.html","id":"version-10","dir":"Changelog","previous_headings":"","what":"Version 1.0","title":"Version 1.0","text":"CRAN release: 2016-03-23 Initial CRAN release Extracted dplyr 0.4.3 Exported functions: tbl_df() as_data_frame() data_frame(), data_frame_() frame_data(), tibble() glimpse() trunc_mat(), knit_print.trunc_mat() type_sum() New lst() lst_() create lists way data_frame() data_frame_() create data frames (hadley/dplyr#1290). lst(NULL) doesn’t raise error (#17, @jennybc), always uses deparsed expression name (even NULL). New add_row() makes easy add new row data frame (hadley/dplyr#1021). New rownames_to_column() column_to_rownames() (#11, @zhilongjia). New has_rownames() remove_rownames() (#44). New repair_names() fixes missing duplicate names (#10, #15, @r2evans). New is_vector_s3(). Features New as_data_frame.table() argument n control name count column (#22, #23). Use tibble prefix options (#13, #36). glimpse() now (invisibly) returns argument (hadley/dplyr#1570). now generic, default method dispatches str() (hadley/dplyr#1325). default width obtained tibble.width option (#35, #56). as_data_frame() now S3 generic methods lists (old as_data_frame()), data frames (trivial), matrices (efficient C++ implementation) (hadley/dplyr#876), NULL (returns 0-row 0-column data frame) (#17, @jennybc). Non-scalar input frame_data() tibble() (including lists) creates list-valued columns (#7). functions return 0-row n-col data frame data. Bug fixes frame_data() properly constructs rectangular tables (hadley/dplyr#1377, @kevinushey). Minor modifications Uses setOldClass(c(\"tbl_df\", \"tbl\", \"data.frame\")) help S4 (hadley/dplyr#969). tbl_df() automatically generates column names (hadley/dplyr#1606). tbl_dfs gain $ [[ methods ~5x faster defaults, never partial matching (hadley/dplyr#1504), throw error variable exist. [[.tbl_df() falls back regular subsetting used anything single string (#29). base::getElement() now works tibbles (#9). all_equal() allows compare data frames ignoring row column order, optionally ignoring minor differences type (e.g. int vs. double) (hadley/dplyr#821). Used .equal() tibbles. (package contains pure R implementation all_equal(), dplyr code identical behavior written C++ thus faster.) internals data_frame() as_data_frame() aligned, as_data_frame() now automatically recycle length-1 vectors. functions give informative error messages attempting create invalid data frame. can longer create data frame duplicated names (hadley/dplyr#820). functions now check don’t POSIXlt columns, tell use POSIXct (hadley/dplyr#813). data_frame(NULL) raises error “must 1d atomic vector list”. trunc_mat() print.tbl_df() considerably faster wide data frames. now also list first 100 additional variables already screen - control new n_extra parameter print() (hadley/dplyr#1161). type list columns printed correctly (hadley/dplyr#1379). width argument used also 0-row 0-column data frames (#18). used list-columns, S4 objects print class name rather full class hierarchy (#33). Add test [.tbl_df() change class (#41, @jennybc). Improve [.tbl_df() error message. Documentation Update README, edits (#52, @bhive01) enhancements (#54, @jennybc). vignette(\"tibble\") describes difference tbl_dfs regular data frames (hadley/dplyr#1468). Code quality Test using new-style Travis-CI AppVeyor. Full test coverage (#24, #53). Regression tests load known output file (#49). Renamed obj_type() obj_sum(), improvements, better integration type_sum(). Internal cleanup.","code":""}]