101 lines
44 KiB
HTML
101 lines
44 KiB
HTML
<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
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<meta name="generator" content="litedown 0.4">
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<title>Not An Introduction to knitr</title>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@xiee/utils@1.13.44/css/prism-xcode.min.css">
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css">
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@xiee/utils@1.13.44/css/default.min.css">
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</head>
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<body>
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<div class="frontmatter">
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<div class="title"><h1>Not An Introduction to knitr</h1></div>
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<div class="author"><h2>Yihui Xie</h2></div>
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<div class="date"><h3>2024-11-06</h3></div>
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</div>
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<div class="body">
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<p>The <strong>knitr</strong> package is an alternative tool to Sweave based on a different
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design with more features. This document is not an introduction, but only serves
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as a placeholder to guide you to the real manuals, which are available on the
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package website <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a> (e.g. the <a href="https://yihui.org/knitr/demo/manual/">main
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manual</a> and the <a href="https://yihui.org/knitr/demo/graphics/">graphics
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manual</a> ), and remember to read the help
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pages of functions in this package. There is a book “Dynamic Docuemnts with R
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and knitr” for this package, too.</p>
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<p>Anyway, here is a code chunk that shows you can compile vignettes with <strong>knitr</strong>
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as well using R 3.0.x, which supports non-Sweave vignettes:</p>
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<pre><code class="language-r">options(digits = 4)
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rnorm(20)
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</code></pre>
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<pre><code>#> [1] -0.53125 -1.10327 0.77924 -1.64279 2.12976 0.14292 -0.61442 -0.83602
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#> [9] 0.55669 -3.08241 -1.60739 -0.51008 1.40616 0.10845 2.56358 -0.66988
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#> [17] -1.49418 -0.19985 -0.07476 -0.43768
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</code></pre>
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<pre><code class="language-r">fit = lm(dist ~ speed, data = cars)
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b = coef(fit)
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</code></pre>
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<table>
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<thead>
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<tr>
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<th></th>
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<th align="right">Estimate</th>
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<th align="right">Std. Error</th>
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<th align="right">t value</th>
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<th align="right">Pr(>|t|)</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>(Intercept)</td>
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<td align="right">-17.579</td>
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<td align="right">6.758</td>
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<td align="right">-2.601</td>
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<td align="right">0.012</td>
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</tr>
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<tr>
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<td>speed</td>
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<td align="right">3.932</td>
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<td align="right">0.416</td>
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<td align="right">9.464</td>
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<td align="right">0.000</td>
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</tr>
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</tbody>
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</table>
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<p>The fitted regression equation is \(Y=-17.6+3.93x\).</p>
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<pre><code class="language-r">par(mar=c(4, 4, 1, .1))
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plot(cars, pch = 20)
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abline(fit, col = 'red')
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</code></pre>
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<div id="fig:graphics" class="figure">
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<p><img src="data:image/png;base64,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" alt="A scatterplot with a regression line." /></p>
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<div class="fig-caption">
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<p><span class="ref-number-fig">1</span>
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A scatterplot with a regression line.</p>
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</div>
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</div>
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<h2 id="sec:references">References</h2>
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<p>Xie Y (2024).
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|
<em>knitr: A General-Purpose Package for Dynamic Report Generation in R</em>.
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R package version 1.49, <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
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</p>
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<p>Xie Y (2015).
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<em>Dynamic Documents with R and knitr</em>, 2nd edition.
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Chapman and Hall/CRC, Boca Raton, Florida.
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ISBN 978-1498716963, <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
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</p>
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<p>Xie Y (2014).
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“knitr: A Comprehensive Tool for Reproducible Research in R.”
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In Stodden V, Leisch F, Peng RD (eds.), <em>Implementing Reproducible Computational Research</em>.
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Chapman and Hall/CRC.
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ISBN 978-1466561595.
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</p>
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</div>
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