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<title>A minimal R Markdown example</title>

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</head>

<body>
<h1>A minimal R Markdown example</h1>

<pre><code class="r">library(histry)
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4
</code></pre>

<pre><code class="r">kt = knitr_tracker()
</code></pre>

<pre><code>## Tracing function &quot;evaluate_call&quot; in package
## &quot;namespace:evaluate&quot;
</code></pre>

<pre><code>## Tracking session history. To turn off tracking call trackHistory().
</code></pre>

<pre><code>## Tracing function &quot;evaluate_call&quot; in package
## &quot;namespace:evaluate&quot;
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4
</code></pre>

<pre><code class="r">hist_within_doc()
</code></pre>

<pre><code>## Tracing function &quot;evaluate_call&quot; in package
## &quot;namespace:evaluate&quot;
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4 
## 2 
## 2
</code></pre>

<p>A quote:</p>

<blockquote>
<p>Markdown is not LaTeX.</p>
</blockquote>

<p>To compile me, run this in R:</p>

<pre><code>library(knitr)
knit(&#39;001-minimal.Rmd&#39;)
</code></pre>

<p>See <a href="https://github.com/yihui/knitr-examples/blob/master/001-minimal.md">output here</a>.</p>

<h2>code chunks</h2>

<p>A <em>paragraph</em> here. A code chunk below (remember the three backticks):</p>

<pre><code class="r">1+1
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4 
## [1] 2
</code></pre>

<pre><code class="r">.4-.7+.3 # what? it is not zero!
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4 
## [1] 5.551115e-17
</code></pre>

<h2>graphics</h2>

<p>It is easy.</p>

<pre><code class="r">plot(1:10)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-3"/></p>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4
</code></pre>

<pre><code class="r">hist(rnorm(1000))
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-3"/></p>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4
</code></pre>

<h2>inline code</h2>

<p>Yes I know the value of pi is 3.1415927, and 2 times pi is 6.2831853.</p>

<h2>math</h2>

<p>Sigh. You cannot live without math equations. OK, here we go: \(\alpha+\beta=\gamma\). Note this is not supported by native markdown. You probably want to try RStudio, or at least the R package <strong>markdown</strong>, or the function <code>knitr::knit2html()</code>.</p>

<h2>nested code chunks</h2>

<p>You can write code within other elements, e.g. a list</p>

<ol>
<li><p>foo is good</p>

<pre><code class="r">strsplit(&#39;hello indented world&#39;, &#39; &#39;)[[1]]
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4 
## [1] &quot;hello&quot;    &quot;indented&quot; &quot;world&quot;
</code></pre></li>
<li><p>bar is better</p></li>
</ol>

<p>Or inside blockquotes:</p>

<blockquote>
<p>Here is a quote, followed by a code chunk:</p>

<pre><code class="r">x = 1:10
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4
</code></pre>

<pre><code class="r">rev(x^2)
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4 
##  [1] 100  81  64  49  36  25  16   9   4   1
</code></pre>
</blockquote>

<h2>conclusion</h2>

<pre><code class="r">if(exists(&quot;histropts&quot;))
    histropts$history$exprs
</code></pre>

<pre><code>## Tracing evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,  .... step 23,4,4 
## [[1]]
## hist_within_doc()
## 
## [[2]]
## 1 + 1
## 
## [[3]]
## 0.4 - 0.7 + 0.3
## 
## [[4]]
## plot(1:10)
## 
## [[5]]
## hist(rnorm(1000))
## 
## [[6]]
## strsplit(&quot;hello indented world&quot;, &quot; &quot;)[[1]]
## 
## [[7]]
## x = 1:10
## 
## [[8]]
## rev(x^2)
</code></pre>

<p>Nothing fancy. You are ready to go. When you become picky, go to the <a href="http://yihui.name/knitr/">knitr website</a>.</p>

<p><img src="http://yihui.name/knitr/images/knit-logo.png" alt="knitr logo"/></p>

</body>

</html>
