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<!DOCTYPE html>
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<title>Controlling knitr output</title>
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<h1>Controlling knitr output</h1>
<p>You can control what is output from code chunks by changing knitr options.
options are described <a href="http://yihui.name/knitr/options">here</a> in detail.</p>
<p>Below some of the options I most use are shown.</p>
<h2>Code evaluation</h2>
<p><code>eval=FALSE</code> will turn the code evaluation off. The code still will be shown.</p>
<p>with <code>eval=TRUE</code></p>
<pre><code class="r">summary(cars)
</code></pre>
<pre><code>## speed dist
## Min. : 4.0 Min. : 2
## 1st Qu.:12.0 1st Qu.: 26
## Median :15.0 Median : 36
## Mean :15.4 Mean : 43
## 3rd Qu.:19.0 3rd Qu.: 56
## Max. :25.0 Max. :120
</code></pre>
<p>with <code>eval=FALSE</code></p>
<pre><code class="r">summary(cars)
</code></pre>
<h2>Supressing warnings</h2>
<p>You can supress warnings in the code output by setting <code>warning=FALSE</code></p>
<p><code>warning=TRUE</code></p>
<pre><code class="r">cor(c(1, 1, 1), c(1, 1, 1))
</code></pre>
<pre><code>## Warning: the standard deviation is zero
</code></pre>
<pre><code>## [1] NA
</code></pre>
<p><code>warning=FALSE</code></p>
<pre><code class="r">cor(c(1, 1, 1), c(1, 1, 1))
</code></pre>
<pre><code>## [1] NA
</code></pre>
<h2>Supressing messages</h2>
<p>You can suppress messages from the code by setting <code>message=FALSE</code></p>
<p><code>message=TRUE</code></p>
<pre><code class="r">library(GenomicRanges)
</code></pre>
<pre><code>## Loading required package: BiocGenerics Loading required package: parallel
##
## Attaching package: 'BiocGenerics'
##
## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport,
## clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply,
## parSapply, parSapplyLB
##
## The following object is masked from 'package:stats':
##
## xtabs
##
## The following objects are masked from 'package:base':
##
## anyDuplicated, as.data.frame, cbind, colnames, duplicated, eval, Filter,
## Find, get, intersect, lapply, Map, mapply, match, mget, order, paste,
## pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int,
## rownames, sapply, setdiff, sort, table, tapply, union, unique, unlist
##
## Loading required package: IRanges
</code></pre>
<p><code>message=FALSE</code> for the R chunk.</p>
<pre><code class="r">library(GenomicRanges)
</code></pre>
<h2>Evaluate code but hide figures</h2>
<pre><code class="r">plot(cars)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-7"/> </p>
<p>You can also execute code but hide the figures with <code>fig.show='hide'</code> option for the R chunk.</p>
<pre><code class="r">plot(cars)
</code></pre>
<pre><code class="r">summary(cars)
</code></pre>
<pre><code>## speed dist
## Min. : 4.0 Min. : 2
## 1st Qu.:12.0 1st Qu.: 26
## Median :15.0 Median : 36
## Mean :15.4 Mean : 43
## 3rd Qu.:19.0 3rd Qu.: 56
## Max. :25.0 Max. :120
</code></pre>
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