147 lines
31 KiB
HTML
147 lines
31 KiB
HTML
<!DOCTYPE html>
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<html>
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<!--
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%\VignetteEngine{knitr::knitr_notangle}
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%\VignetteIndexEntry{An R HTML Vignette with knitr}
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-->
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<head>
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<style type="text/css">
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.inline {
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background-color: #f7f7f7;
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border:solid 1px #B0B0B0;
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}
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.error {
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font-weight: bold;
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color: #FF0000;
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}
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.warning {
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font-weight: bold;
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}
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.message {
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font-style: italic;
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}
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.source, .output, .warning, .error, .message {
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padding: 0 1em;
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border:solid 1px #F7F7F7;
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}
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.source {
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background-color: #f5f5f5;
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}
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.left {
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text-align: left;
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}
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.right {
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text-align: right;
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}
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.center {
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text-align: center;
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}
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.hl.num {
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color: #AF0F91;
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}
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.hl.sng {
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color: #317ECC;
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}
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.hl.com {
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color: #AD95AF;
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font-style: italic;
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}
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.hl.opt {
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color: #000000;
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}
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.hl.def {
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color: #585858;
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}
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.hl.kwa {
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color: #295F94;
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font-weight: bold;
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}
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.hl.kwb {
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color: #B05A65;
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}
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.hl.kwc {
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color: #55aa55;
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}
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.hl.kwd {
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color: #BC5A65;
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font-weight: bold;
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}
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</style>
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<meta charset="utf-8">
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<style type="text/css">
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body,td{width:800px;margin:auto;}
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</style>
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<title>An R HTML vignette with knitr</title>
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</head>
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<body>
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<p>This is an R HTML vignette. The file extension is <code>*.Rhtml</code>, and
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it has to include a special comment to tell R that this file needs to be
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compiled by <strong>knitr</strong>:</p>
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<pre><!--
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%\VignetteEngine{knitr::knitr}
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%\VignetteIndexEntry{The Title of Your Vignette}
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-->
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</pre>
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<p>Now you can write R code chunks:</p>
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<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 def">(cars)</span>
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</pre></div>
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<div class="output"><pre class="knitr r">## speed dist
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## Min. : 4.0 Min. : 2
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## 1st Qu.:12.0 1st Qu.: 26
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## Median :15.0 Median : 36
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## Mean :15.4 Mean : 43
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## 3rd Qu.:19.0 3rd Qu.: 56
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## Max. :25.0 Max. :120
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</pre></div>
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<div class="source"><pre class="knitr r"><span class="hl def">fit</span><span class="hl kwb">=</span><span class="hl kwd">lm</span><span class="hl def">(dist</span><span class="hl opt">~</span><span class="hl def">speed,</span> <span class="hl kwc">data</span><span class="hl def">=cars)</span>
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<span class="hl kwd">summary</span><span class="hl def">(fit)</span>
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</pre></div>
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<div class="output"><pre class="knitr r">##
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## Call:
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## lm(formula = dist ~ speed, data = cars)
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##
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## Residuals:
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## Min 1Q Median 3Q Max
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## -29.07 -9.53 -2.27 9.21 43.20
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##
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## Coefficients:
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## Estimate Std. Error t value Pr(>|t|)
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## (Intercept) -17.579 6.758 -2.60 0.012 *
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## speed 3.932 0.416 9.46 1.5e-12 ***
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## ---
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## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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##
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## Residual standard error: 15.4 on 48 degrees of freedom
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## Multiple R-squared: 0.651, Adjusted R-squared: 0.644
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## F-statistic: 89.6 on 1 and 48 DF, p-value: 1.49e-12
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</pre></div>
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</div></div>
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<p>You can also embed plots, for example:</p>
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<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 def">(</span><span class="hl kwc">mar</span><span class="hl def">=</span><span class="hl kwd">c</span><span class="hl def">(</span><span class="hl num">4</span><span class="hl def">,</span><span class="hl num">4</span><span class="hl def">,</span><span class="hl num">.1</span><span class="hl def">,</span><span class="hl num">.1</span><span class="hl def">))</span>
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<span class="hl kwd">plot</span><span class="hl def">(cars,</span> <span class="hl kwc">pch</span><span class="hl def">=</span><span class="hl num">19</span><span class="hl def">)</span>
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</pre></div>
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</div><div class="rimage center"><img 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" alt="plot of chunk cars-plot" class="plot" /></div></div>
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<p>For package vignettes, you need to encode images in base64 strings using the
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<code>knitr::image_uri()</code> function so that the image files are no longer
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needed after the vignette is compiled. For example, you can add this chunk in
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the beginning of a vignette:</p>
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<div class="chunk" id="setup"><div class="rcode"><div class="source"><pre class="knitr r"><span class="hl kwd">library</span><span class="hl def">(knitr)</span>
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<span class="hl com"># to base64 encode images</span>
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<span class="hl def">opts_knit</span><span class="hl opt">$</span><span class="hl kwd">set</span><span class="hl def">(</span><span class="hl kwc">upload.fun</span> <span class="hl def">= image_uri)</span>
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</pre></div>
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</div></div>
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</body>
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