EVOLUTION-MANAGER
Edit File: knitr-html.html
<!DOCTYPE html> <html> <!-- %\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{An R HTML Vignette with knitr} --> <head> <style type="text/css"> .inline { background-color: #f7f7f7; border:solid 1px #B0B0B0; } .error { font-weight: bold; color: #FF0000; } .warning { font-weight: bold; } .message { font-style: italic; } .source, .output, .warning, .error, .message { padding: 0 1em; border:solid 1px #F7F7F7; } .source { background-color: #f5f5f5; } .left { text-align: left; } .right { text-align: right; } .center { text-align: center; } .hl.num { color: #AF0F91; } .hl.str { color: #317ECC; } .hl.com { color: #AD95AF; font-style: italic; } .hl.opt { color: #000000; } .hl.std { color: #585858; } .hl.kwa { color: #295F94; font-weight: bold; } .hl.kwb { color: #B05A65; } .hl.kwc { color: #55aa55; } .hl.kwd { color: #BC5A65; font-weight: bold; } </style> <meta charset="utf-8"> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <style type="text/css"> body,td{width:800px;margin:auto;} </style> <title>An R HTML vignette with knitr</title> </head> <body> <p>This is an R HTML vignette. The file extension is <code>*.Rhtml</code>, and it has to include a special comment to tell R that this file needs to be compiled by <strong>knitr</strong>:</p> <pre><!-- %\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{The Title of Your Vignette} --> </pre> <p>Now you can write R code chunks:</p> <div class="chunk" id="cars-demo"><div class="rcode"><div class="source"><pre class="knitr r"><span class="hl kwd">summary</span><span class="hl std">(cars)</span> </pre></div> <div class="output"><pre class="knitr r">## 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 </pre></div> <div class="source"><pre class="knitr r"><span class="hl std">fit</span><span class="hl kwb">=</span><span class="hl kwd">lm</span><span class="hl std">(dist</span><span class="hl opt">~</span><span class="hl std">speed,</span> <span class="hl kwc">data</span><span class="hl std">=cars)</span> <span class="hl kwd">summary</span><span class="hl std">(fit)</span> </pre></div> <div class="output"><pre class="knitr r">## ## Call: ## lm(formula = dist ~ speed, data = cars) ## ## Residuals: ## Min 1Q Median 3Q Max ## -29.07 -9.53 -2.27 9.21 43.20 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -17.579 6.758 -2.60 0.012 * ## speed 3.932 0.416 9.46 1.5e-12 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 15.4 on 48 degrees of freedom ## Multiple R-squared: 0.651, Adjusted R-squared: 0.644 ## F-statistic: 89.6 on 1 and 48 DF, p-value: 1.49e-12 </pre></div> </div></div> <p>You can also embed plots, for example:</p> <div class="chunk" id="cars-plot"><div class="rcode"><div class="source"><pre class="knitr r"><span class="hl kwd">par</span><span class="hl std">(</span><span class="hl kwc">mar</span><span class="hl std">=</span><span class="hl kwd">c</span><span class="hl std">(</span><span class="hl num">4</span><span class="hl std">,</span><span class="hl num">4</span><span class="hl std">,</span><span class="hl num">.1</span><span class="hl std">,</span><span class="hl num">.1</span><span class="hl std">))</span> <span class="hl kwd">plot</span><span class="hl std">(cars,</span> <span class="hl kwc">pch</span><span class="hl std">=</span><span class="hl num">19</span><span class="hl std">)</span> </pre></div> </div><div class="rimage center"><img 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" title="plot of chunk cars-plot" alt="plot of chunk cars-plot" class="plot" /></div></div> <p>For package vignettes, you need to encode images in base64 strings using the <code>knitr::image_uri()</code> function so that the image files are no longer needed after the vignette is compiled. For example, you can add this chunk in the beginning of a vignette:</p> <div class="chunk" id="setup"><div class="rcode"><div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl std">(knitr)</span> <span class="hl com"># to base64 encode images</span> <span class="hl std">opts_knit</span><span class="hl opt">$</span><span class="hl kwd">set</span><span class="hl std">(</span><span class="hl kwc">upload.fun</span> <span class="hl std">= image_uri)</span> </pre></div> </div></div> </body> </html>