Try to fix the feed

_freeze/posts/2024-05-30-seal_of_approval-dtplyr/index/execute-results/html.json

@@ -1,7 +1,7 @@
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-    "markdown": "---\ntitle: \"Seal of Approval: dtplyr\"\nauthor: \"Kelly Bodwin\"\ndate: \"May 07, 2024\"\ncategories: [seal of approval, bridge package]\nimage: \"hex.png\"\ndraft: true\n---\n\n\n## [`dtplyr`](https://github.com/tidyverse/dtplyr)\n\n*Author(s):*\tHadley Wickham, Maximilian Girlich, Mark Fairbanks, Ryan Dickerson, Posit Software PBC\n\n*Maintainer:*\tHadley Wickham (hadley\\@posit.co)\n\n[Seal of Approval](<link to approval>)\n\n![dtplyr hex sticker](hex.png)\n\nProvides a `data.table` backend for `dplyr`. The goal of `dtplyr` is to allow you to write `dplyr` code that is automatically translated to the equivalent, but usually much faster, `data.table` code.\n\n## Relationship with `data.table`\n\n`dtplyr` is a bridge for users who are more comfortable with the `dplyr` syntax, but who want to take advantage of the speed and efficiency benefits of `data.table`.  This package exactly duplicates the core functions of `dplyr`, but replaces the back-end source code (originally in Base R) with `data.table` operations.\n\n## Overview\n\n*Excerpted from the [`dtplyr` vignette](https://dtplyr.tidyverse.org/)*\n\nTo use `dtplyr`, you must at least load `dtplyr` and `dplyr`. You may also want to load `data.table` so you can access the other goodies that it provides:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(data.table)\nlibrary(dtplyr)\nlibrary(dplyr, warn.conflicts = FALSE)\n```\n:::\n\n\n\nThen use `lazy_dt()` to create a “lazy” `data.table` object that tracks the operations performed on it.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars2 <- lazy_dt(mtcars)\n```\n:::\n\n\nYou can preview the transformation (including the generated `data.table` code) by printing the result:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars2 %>% \n  filter(wt < 5) %>% \n  mutate(l100k = 235.21 / mpg) %>% # liters / 100 km\n  group_by(cyl) %>% \n  summarise(l100k = mean(l100k))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nSource: local data table [3 x 2]\nCall:   `_DT1`[wt < 5][, `:=`(l100k = 235.21/mpg)][, .(l100k = mean(l100k)), \n    keyby = .(cyl)]\n\n    cyl l100k\n  <dbl> <dbl>\n1     4  9.05\n2     6 12.0 \n3     8 14.9 \n\n# Use as.data.table()/as.data.frame()/as_tibble() to access results\n```\n:::\n:::\n\n\nBut generally you should reserve this only for debugging, and use `as.data.table()`, `as.data.frame()`, or `as_tibble()` to indicate that you’re done with the transformation and want to access the results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars2 %>% \n  filter(wt < 5) %>% \n  mutate(l100k = 235.21 / mpg) %>% # liters / 100 km\n  group_by(cyl) %>% \n  summarise(l100k = mean(l100k)) %>% \n  as_tibble()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 3 × 2\n    cyl l100k\n  <dbl> <dbl>\n1     4  9.05\n2     6 12.0 \n3     8 14.9 \n```\n:::\n:::",
+    "markdown": "---\ntitle: \"Seal of Approval: dtplyr\"\nauthor: \"Kelly Bodwin\"\ndate: \"May 07, 2024\"\ncategories: [seal of approval, bridge package]\nimage: \"hex.png\"\ndraft: true\n---\n\n\n## [`dtplyr`](https://github.com/tidyverse/dtplyr)\n\n::::{.columns}\n\n:::{.column width=\"40%\"}\n\n![dtplyr hex sticker](hex.png)\n:::\n\n\n:::{.column width=\"60%\"}\n\n*Author(s):*\tHadley Wickham, Maximilian Girlich, Mark Fairbanks, Ryan Dickerson, Posit Software PBC\n\n*Maintainer:*\tHadley Wickham (hadley\\@posit.co)\n\n[Seal of Approval](<link to approval>)\n\n\nProvides a `data.table` backend for `dplyr`. The goal of `dtplyr` is to allow you to write `dplyr` code that is automatically translated to the equivalent, but usually much faster, `data.table` code.\n\n:::\n\n::::\n\n## Relationship with `data.table`\n\n`dtplyr` is a bridge for users who are more comfortable with the `dplyr` syntax, but who want to take advantage of the speed and efficiency benefits of `data.table`.  This package exactly duplicates the core functions of `dplyr`, but replaces the back-end source code (originally in Base R) with `data.table` operations.\n\n## Overview\n\n*Excerpted from the [`dtplyr` vignette](https://dtplyr.tidyverse.org/)*\n\nTo use `dtplyr`, you must at least load `dtplyr` and `dplyr`. You may also want to load `data.table` so you can access the other goodies that it provides:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(data.table)\nlibrary(dtplyr)\nlibrary(dplyr, warn.conflicts = FALSE)\n```\n:::\n\n\n\nThen use `lazy_dt()` to create a “lazy” `data.table` object that tracks the operations performed on it.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars2 <- lazy_dt(mtcars)\n```\n:::\n\n\nYou can preview the transformation (including the generated `data.table` code) by printing the result:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars2 %>% \n  filter(wt < 5) %>% \n  mutate(l100k = 235.21 / mpg) %>% # liters / 100 km\n  group_by(cyl) %>% \n  summarise(l100k = mean(l100k))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nSource: local data table [3 x 2]\nCall:   `_DT1`[wt < 5][, `:=`(l100k = 235.21/mpg)][, .(l100k = mean(l100k)), \n    keyby = .(cyl)]\n\n    cyl l100k\n  <dbl> <dbl>\n1     4  9.05\n2     6 12.0 \n3     8 14.9 \n\n# Use as.data.table()/as.data.frame()/as_tibble() to access results\n```\n:::\n:::\n\n\nBut generally you should reserve this only for debugging, and use `as.data.table()`, `as.data.frame()`, or `as_tibble()` to indicate that you’re done with the transformation and want to access the results:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmtcars2 %>% \n  filter(wt < 5) %>% \n  mutate(l100k = 235.21 / mpg) %>% # liters / 100 km\n  group_by(cyl) %>% \n  summarise(l100k = mean(l100k)) %>% \n  as_tibble()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 3 × 2\n    cyl l100k\n  <dbl> <dbl>\n1     4  9.05\n2     6 12.0 \n3     8 14.9 \n```\n:::\n:::",
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_quarto.yml

@@ -34,17 +34,7 @@ listing:
   contents: posts
   sort: "date desc"
   type: default
-  feed:
-    categories:
-      - documentation
-      - announcements
-      - funding opportunity
-      - grant
-      - guest post
-      - tutorials
-      - tips
-      - testing
-      - developer
+  feed: true
   sort-ui: false
   filter-ui: false