skimr

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font-weight: bold; } code > span.fu { color: #900; font-weight: bold; } code > span.er { color: #a61717; background-color: #e3d2d2; } </style> </head> <body> <h1 class="title toc-ignore">Not An Introduction to knitr</h1> <h4 class="author">Yihui Xie</h4> <h4 class="date">2023-10-26</h4> <p>The <strong>knitr</strong> package <span class="citation">(Xie 2016)</span> is an alternative tool to Sweave based on a different design with more features. This document is not an introduction, but only serves as a placeholder to guide you to the real manuals, which are available on the package website <a href="https://yihui.org/knitr/" class="uri">https://yihui.org/knitr/</a><a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>, and remember to read the help pages of functions in this package. There is a book <span class="citation">(Xie 2015)</span> for this package, but it may not be useful to those who prefer digging out information on the web.</p> <p>Anyway, here is a code chunk that shows you can compile vignettes with <strong>knitr</strong> as well using R 3.0.x, which supports non-Sweave vignettes:</p> <div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">options</span>(<span class="at">digits =</span> <span class="dv">4</span>)</span> <span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">rnorm</span>(<span class="dv">20</span>)</span></code></pre></div> <pre><code>## [1] 1.388985 0.503036 -0.775036 1.806629 -0.555110 0.972180 -0.052065 ## [8] 0.248322 -0.637656 -0.505279 1.075536 -0.005766 0.300989 -1.102360 ## [15] 1.261733 -0.614662 0.660885 0.271355 0.714454 0.923132</code></pre> <div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>fit <span class="ot">=</span> <span class="fu">lm</span>(dist <span class="sc">~</span> speed, <span class="at">data =</span> cars)</span> <span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a>b <span class="ot">=</span> <span class="fu">coef</span>(fit)</span></code></pre></div> <table> <caption>Regression coefficients.</caption> <thead> <tr class="header"> <th align="left"></th> <th align="right">Estimate</th> <th align="right">Std. Error</th> <th align="right">t value</th> <th align="right">Pr(>|t|)</th> </tr> </thead> <tbody> <tr class="odd"> <td align="left">(Intercept)</td> <td align="right">-17.579</td> <td align="right">6.7584</td> <td align="right">-2.601</td> <td align="right">0.0123</td> </tr> <tr class="even"> <td align="left">speed</td> <td align="right">3.932</td> <td align="right">0.4155</td> <td align="right">9.464</td> <td align="right">0.0000</td> </tr> </tbody> </table> <p>The fitted regression equation is <span class="math inline">$Y=-17.5791+3.9324x$</span>.</p> <div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mar=</span><span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">4</span>, <span class="dv">1</span>, .<span class="dv">1</span>))</span> <span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">plot</span>(cars, <span class="at">pch =</span> <span class="dv">20</span>)</span> <span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="fu">abline</span>(fit, <span class="at">col =</span> <span class="st">'red'</span>)</span></code></pre></div> <div class="figure"> <img 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