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Tutorial8_AdvancedTopics.html
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Tutorial8_AdvancedTopics.html
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<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
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<meta http-equiv="Content-Style-Type" content="text/css" />
<meta name="generator" content="pandoc" />
<meta name="author" content="DPI R Bootcamp" />
<title>Tutorial 8: Advanced Topics</title>
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<body>
<div class="slide titlepage">
<h1 class="title">Tutorial 8: Advanced Topics</h1>
<p class="author">
DPI R Bootcamp
</p>
<p class="date">Jared Knowles</p>
</div>
<div class="section slide level1" id="overview">
<h1>Overview</h1>
<p>In this lesson we hope to learn: - Coding Style - Write a `for' loop - Write a basic function - Optimize R with parallelization - Mixed effect models - Data mining with R - Animations - Git, GitHub, and add-on packages</p>
<p align="center">
<img src="data:image/png;base64,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" height="81" width="138">
</p>
</div>
<div class="section slide level1" id="basic-principles">
<h1>Basic Principles</h1>
<ul class="incremental">
<li>Good professional data analysis is computer programming</li>
<li>An analysis project is computer code</li>
<li>Computer code has bugs and to fight bugs the code has to be readable</li>
</ul>
<p>-</p>
</div>
<div class="section slide level1" id="coding-style">
<h1>Coding Style</h1>
<ul class="incremental">
<li>An important part of making work reproducible is making your code readable and understandable</li>
<li>Much of this involves looking into where to break lines and how to simplify expressions without making them obscure</li>
<li>You can also take some cues about how to make your code consistent and clean from reading code of others on GitHub</li>
<li>A few tips help though</li>
</ul>
</div>
<div class="section slide level1" id="for-looping">
<h1>For Looping</h1>
<ul class="incremental">
<li>Don't do it for reasons we have discussed, R is very slow at these</li>
<li>Sometimes it is inevitable, so here is how to do it:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Loop to calculate number of students per grade</span>
nstudents <- <span class="kw">rep</span>(<span class="ot">NA</span>, <span class="dv">6</span>)
for (i in <span class="kw">unique</span>(df$grade)) {
nstudents[[i - <span class="dv">2</span>]] <- <span class="kw">length</span>(df$stuid[df$grade == i])
}
nstudents</code></pre>
<pre><code>## [1] 500 400 500 400 500 400</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(<span class="kw">factor</span>(df$grade))</code></pre>
<pre><code>## 3 4 5 6 7 8
## 500 400 500 400 500 400</code></pre>
</div>
<div class="section slide level1" id="why-is-a-loop-slow">
<h1>Why is a loop slow?</h1>
<ul class="incremental">
<li>Run a simple test <a href="http://stackoverflow.com/questions/7142767/why-are-loops-slow-in-r">from StackOverflow</a></li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">A = <span class="kw">matrix</span>(<span class="kw">as.numeric</span>(<span class="dv">1</span>:<span class="fl">1e+05</span>))
<span class="kw">system.time</span>({
Sum = <span class="dv">0</span>
for (i in <span class="kw">seq_along</span>(A)) {
Sum = Sum + A[[i]]
}
Sum
})</code></pre>
<pre><code>## user system elapsed
## 0.33 0.00 0.40</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">
<span class="kw">system.time</span>({
<span class="kw">sum</span>(A)
})</code></pre>
<pre><code>## user system elapsed
## 0 0 0</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rm</span>(A)</code></pre>
</div>
<div class="section slide level1" id="write-a-simple-function">
<h1>Write a simple function</h1>
<ul class="incremental">
<li>Functions are easy</li>
<li>To view any function in R just type <code>print(myfunction)</code></li>
<li>For speed, some functions are not viewable because they are bytecompiled</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(mean) <span class="co">#bytecode, we can't see it</span></code></pre>
<pre><code>## function (x, ...)
## standardGeneric("mean")
## <environment: 0x000000001178e870>
## attr(,"generic")
## [1] "mean"
## attr(,"generic")attr(,"package")
## [1] "base"
## attr(,"package")
## [1] "base"
## attr(,"group")
## list()
## attr(,"valueClass")
## character(0)
## attr(,"signature")
## [1] "x"
## attr(,"default")
## Method Definition (Class "derivedDefaultMethod"):
##
## function (x, ...)
## UseMethod("mean")
## <bytecode: 0x000000000920dd98>
## <environment: namespace:base>
##
## Signatures:
## x
## target "ANY"
## defined "ANY"
## attr(,"skeleton")
## function (x, ...)
## UseMethod("mean")(x, ...)
## attr(,"class")
## [1] "standardGeneric"
## attr(,"class")attr(,"package")
## [1] "methods"</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(order)</code></pre>
<pre><code>## function (..., na.last = TRUE, decreasing = FALSE)
## {
## z <- list(...)
## if (any(unlist(lapply(z, is.object)))) {
## z <- lapply(z, function(x) if (is.object(x))
## xtfrm(x)
## else x)
## if (!is.na(na.last))
## return(do.call("order", c(z, na.last = na.last, decreasing = decreasing)))
## }
## else if (!is.na(na.last))
## return(.Internal(order(na.last, decreasing, ...)))
## if (any(diff(l.z <- vapply(z, length, 1L)) != 0L))
## stop("argument lengths differ")
## ans <- vapply(z, is.na, rep.int(NA, l.z[1L]))
## ok <- if (is.matrix(ans))
## !apply(ans, 1, any)
## else !any(ans)
## if (all(!ok))
## return(integer())
## z[[1L]][!ok] <- NA
## ans <- do.call("order", c(z, decreasing = decreasing))
## keep <- seq_along(ok)[ok]
## ans[ans %in% keep]
## }
## <bytecode: 0x000000000892ff20>
## <environment: namespace:base></code></pre>
</div>
<div class="section slide level1" id="still-we-can-write-a-number-of-simple-functions-very-quickly">
<h1>Still, we can write a number of simple functions very quickly</h1>
<ul class="incremental">
<li>Let's write a function to turn factors into characters</li>
<li>Because typing <code>x<-as.character(x)</code> is really obnoxious</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">defac <- function(x) {
<span class="co"># assign function a name, and list its arguments</span>
x <- <span class="kw">as.character</span>(x) <span class="co"># what does function do?</span>
x <span class="co"># last line is output of function</span>
}
a <- <span class="kw">factor</span>(letters)
<span class="kw">summary</span>(a)
<span class="kw">summary</span>(<span class="kw">defac</span>(a))
<span class="kw">summary</span>(<span class="kw">as.character</span>(a))</code></pre>
</div>
<div class="section slide level1" id="simple-data-cleaning-function">
<h1>Simple Data Cleaning Function</h1>
<ul class="incremental">
<li>What if we want to extract the numeric elements out of <code>foo</code> which has both a character and a missing value?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">extractN <- function(x) {
x <- <span class="kw">suppressWarnings</span>(<span class="kw">as.numeric</span>(x))
<span class="co"># ignore warnings because we don't care</span>
x <- x[!<span class="kw">is.na</span>(x)]
x
}
foo <- <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">4</span>, <span class="dv">3</span>, <span class="ot">NA</span>, <span class="dv">5</span>, <span class="dv">60</span>, <span class="ot">NA</span>)
<span class="kw">extractN</span>(foo)</code></pre>
<pre><code>## [1] 1 4 3 5 60</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">A <- <span class="kw">extractN</span>(foo)</code></pre>
</div>
<div class="section slide level1" id="complicating-functions">
<h1>Complicating Functions</h1>
<ul class="incremental">
<li>Functions can have many arguments and be much more complex</li>
<li>They can also be incredibly specialized--remember we are writing code for us!</li>
</ul>
</div>
<div class="section slide level1" id="mixed-effect-models">
<h1>Mixed Effect Models</h1>
<ul class="incremental">
<li>Mixed effect models, or random effect models, are a staple for analyzing data that exists in groups (like students in classrooms)</li>
<li>The underlying belief is that observations in the same group, to some degree, look more like one another than would be randomly expected (or than members of the other group)</li>
<li>Random effects help us measure and parse out this similarity to avoid it biasing our statistical model (fixed effects is another alternative)</li>
<li>In R we use the <code>lme4</code> package to do this work, or in a Bayesian framework (<em>fancy</em>) we can use winBUGS or JAGS</li>
<li>We'll stick with <code>lme4</code> by Doug Bates (a retired UW-Madison Professor of Statistics)</li>
</ul>
</div>
<div class="section slide level1" id="the-basics-of-lme4">
<h1>The Basics of lme4</h1>
<p><code>mymod_me<-lmer(readSS~factor(grade)+factor(race)+female+disab+ell+(1|dist)+(1|stuid),data=df)</code></p>
<ul class="incremental">
<li>items in <code>(1|dist)</code> denote a random effect. In this case we are measuring a random effect for the variable <code>dist</code> in our data.</li>
<li>Otherwise the formula is the same as a regular <code>lm</code> formula (by design</li>
<li>The output is tricky to interpret, but a basic mixed model is more accurate than the non-mixed version of the model (at least in nested school/district student test data)</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(lme4)
mymod_me <- <span class="kw">lmer</span>(readSS ~ <span class="kw">factor</span>(grade) + <span class="kw">factor</span>(race) + female + disab + ell +
(<span class="dv">1</span> | dist) + (<span class="dv">1</span> | stuid), <span class="dt">data =</span> df)
<span class="kw">print</span>(mymod_me, <span class="dt">correlation =</span> <span class="ot">FALSE</span>)</code></pre>
<pre><code>## Linear mixed model fit by REML
## Formula: readSS ~ factor(grade) + factor(race) + female + disab + ell + (1 | dist) + (1 | stuid)
## Data: df
## AIC BIC logLik deviance REMLdev
## 30731 30826 -15350 30774 30699
## Random effects:
## Groups Name Variance Std.Dev.
## stuid (Intercept) 10626 103.1
## dist (Intercept) 487 22.1
## Residual 1564 39.5
## Number of obs: 2700, groups: stuid, 1200; dist, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 378.24 31.03 12.2
## factor(grade)4 62.72 3.05 20.6
## factor(grade)5 124.67 3.28 38.0
## factor(grade)6 182.67 3.99 45.8
## factor(grade)7 236.64 4.20 56.4
## factor(grade)8 301.41 4.74 63.5
## factor(race)B -61.87 28.05 -2.2
## factor(race)H -34.29 27.54 -1.2
## factor(race)I -6.09 43.93 -0.1
## factor(race)W 10.37 28.00 0.4
## female 8.76 6.21 1.4
## disab -5.26 8.50 -0.6
## ell -18.81 13.10 -1.4</code></pre>
</div>
<div class="section slide level1" id="lmer-vs.-lm">
<h1>LMER vs. LM</h1>
<pre class="sourceCode r"><code class="sourceCode r">mymod <- <span class="kw">lm</span>(readSS ~ <span class="kw">factor</span>(grade) + <span class="kw">factor</span>(race) + female + disab + ell, <span class="dt">data =</span> df)
<span class="kw">qplot</span>(readSS, <span class="kw">predict</span>(mymod), <span class="dt">data =</span> df, <span class="dt">alpha =</span> <span class="kw">I</span>(<span class="fl">0.3</span>), <span class="dt">color =</span> <span class="kw">I</span>(<span class="st">"blue"</span>)) +
<span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">x =</span> df$readSS, <span class="dt">y =</span> <span class="kw">fitted</span>(mymod_me)), <span class="dt">alpha =</span> <span class="kw">I</span>(<span class="fl">0.4</span>), <span class="dt">color =</span> <span class="st">"dark green"</span>) +
<span class="kw">theme_dpi</span>() + <span class="kw">xlab</span>(<span class="st">"Observed"</span>) + <span class="kw">ylab</span>(<span class="st">"Predicted"</span>) + <span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">x =</span> <span class="dv">370</span>,
<span class="dt">y =</span> <span class="dv">700</span>), <span class="dt">label =</span> <span class="st">"Green is Results of the </span><span class="ch">\n</span><span class="st"> Mixed Model"</span>)</code></pre>
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" title="plot of chunk modelcomp" alt="plot of chunk modelcomp" width="700px" height="500px" />
</div>
<div class="section slide level1" id="data-mining-with-r">
<h1>Data Mining with R</h1>
<ul class="incremental">
<li>When prediction is all that matters and we do not need to understand the <em>what</em> of prediction, just to accurately classify groups, then <code>caret</code> provides best in class data mining tools to us</li>
<li>More and more data miners in competitions, industry, and high stakes settings are using algorithms programmed and developed in R to do their work</li>
<li>We don't have time to fully implement such an analysis here, but just show what a typical workflow for the <code>caret</code> package looks like</li>
</ul>
</div>
<div class="section slide level1" id="using-the-caret">
<h1>Using the caret</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(caret)
<span class="co"># Set aside test set</span>
testset <- <span class="kw">sample</span>(<span class="kw">unique</span>(df$stuid), <span class="dv">500</span>)
df$case <- <span class="dv">0</span>
df$case[df$stuid %in% testset] <- <span class="dv">1</span>
<span class="co"># Draw a training set of data (random subset of students)</span>
training <- <span class="kw">subset</span>(df, case == <span class="dv">0</span>)
testing <- <span class="kw">subset</span>(df, case == <span class="dv">1</span>)
training <- training[, <span class="kw">c</span>(<span class="dv">3</span>, <span class="dv">6</span>:<span class="dv">16</span>, <span class="dv">21</span>, <span class="dv">22</span>, <span class="dv">28</span>, <span class="dv">29</span>, <span class="dv">30</span>)] <span class="co"># subset vars</span>
trainX <- training[, <span class="dv">1</span>:<span class="dv">15</span>]
refac <- function(x) <span class="kw">as.factor</span>(<span class="kw">as.character</span>(x))
trainX$stuid <- <span class="kw">refac</span>(trainX$stuid)
trainX$dist <- <span class="kw">refac</span>(trainX$dist)
trainX$year <- <span class="kw">refac</span>(trainX$year)
<span class="co"># Parameters</span>
ctrl <- <span class="kw">trainControl</span>(<span class="dt">method =</span> <span class="st">"repeatedcv"</span>, <span class="dt">number =</span> <span class="dv">7</span>, <span class="dt">repeats =</span> <span class="dv">3</span>, <span class="dt">summaryFunction =</span> defaultSummary)
<span class="co"># Search grid</span>
grid <- <span class="kw">expand.grid</span>(<span class="dt">.interaction.depth =</span> <span class="kw">seq</span>(<span class="dv">2</span>, <span class="dv">6</span>, <span class="dt">by =</span> <span class="dv">1</span>), <span class="dt">.n.trees =</span> <span class="kw">seq</span>(<span class="dv">200</span>,
<span class="dv">700</span>, <span class="dt">by =</span> <span class="dv">100</span>), <span class="dt">.shrinkage =</span> <span class="kw">c</span>(<span class="fl">0.05</span>, <span class="fl">0.1</span>))
<span class="co"># Boosted tree search</span>
gbmTune <- <span class="kw">train</span>(<span class="dt">x =</span> trainX, <span class="dt">y =</span> training$mathSS, <span class="dt">method =</span> <span class="st">"gbm"</span>, <span class="dt">metric =</span> <span class="st">"RMSE"</span>,
<span class="dt">trControl =</span> ctrl, <span class="dt">tuneGrid =</span> grid, <span class="dt">verbose =</span> <span class="ot">FALSE</span>)
<span class="co"># gbmPred<-predict(gbmTune,testing[,names(trainX)])</span></code></pre>
</div>
<div class="section slide level1" id="plot-gbm">
<h1>Plot GBM</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(gbmTune)</code></pre>
<img 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" title="plot of chunk gbmplot" alt="plot of chunk gbmplot" width="700px" height="500px" />
</div>
<div class="section slide level1" id="optimizing-r">
<h1>Optimizing R</h1>
<ul class="incremental">
<li>For <strong>big</strong> data problems, sometimes R can be a little slow</li>
<li>There are two ways to speed R up--compiling code on the fly and running code in parallel</li>
<li>Compiling code turns our function into <strong>bytecode</strong> which can be executed by the computer faster</li>
<li>Running parallel uses the multiple <em>processor cores</em> in just about every modern PC to simultaneously calculate parts of a problem</li>
<li>Unfortunately, when you write your code in parallel it really depends on what operating system you are using since different functions to do parallel work are set up differently on each OS</li>
</ul>
</div>
<div class="section slide level1" id="a-quick-windows-parallel-example">
<h1>A Quick Windows Parallel Example</h1>
<pre class="sourceCode r"><code class="sourceCode r">n <- <span class="dv">10000</span>
rep <- <span class="dv">5</span>
<span class="co"># tLoop <- replicate(rep, system.time( integLoop(func, xint, yint, n) ))</span>
<span class="co"># summary(tLoop[3,])</span>
tVec <- <span class="kw">replicate</span>(rep, <span class="kw">system.time</span>(<span class="kw">integVec</span>(func, xint, yint, n)))
<span class="kw">summary</span>(tVec[<span class="dv">3</span>, ])</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.008 0.020 0.020</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">tApply <- <span class="kw">replicate</span>(rep, <span class="kw">system.time</span>(<span class="kw">integApply</span>(func, xint, yint, n)))
<span class="kw">summary</span>(tApply[<span class="dv">3</span>, ])</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22.3 23.8 24.3 24.6 25.8 26.8</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">
<span class="co"># 2 Core Cluster</span>
<span class="kw">library</span>(snow)
c1 <- <span class="kw">makeCluster</span>(<span class="kw">c</span>(<span class="st">"localhost"</span>, <span class="st">"localhost"</span>), <span class="dt">type =</span> <span class="st">"SOCK"</span>)
tSnow1 <- <span class="kw">replicate</span>(rep, <span class="kw">system.time</span>(<span class="kw">integSnow</span>(c1, func, xint, yint, n)))
<span class="kw">summary</span>(tSnow1[<span class="dv">3</span>])</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 25.7 25.7 25.7 25.7 25.7 25.7</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">stopCluster</span>(c1)</code></pre>
</div>
<div class="section slide level1" id="animations">
<h1>Animations</h1>
<ul class="incremental">
<li>R can produce animated plots as well</li>
<li>Though animations get a bad rap in the data visualization community, they can be effective at illustrating things to users</li>
<li>Requires GMCONVERT and FFMPEG and potentially other tools</li>
</ul>
</div>
<div class="section slide level1" id="animating-a-random-normal-distribution">
<h1>Animating a Random Normal Distribution</h1>
<embed width="700px" height="500px" name="plugin" src="figure/slides8-animationtest.swf" type="application/x-shockwave-flash">
</div>
<div class="section slide level1" id="git-and-github">
<h1>Git and GitHub</h1>
<ul class="incremental">
<li>Working alone or in a group, <code>git</code> and GitHub can help</li>
<li><a href="http://try.github.com/">Learn Git and GitHub</a></li>
<li>Just try GitHub for a project, it is a great way to organize code</li>
<li>Of course, you can find all these tutorials <a href="http://www.github.com/jknowles/r_tutorial_ed">on GitHub</a></li>
</ul>
</div>
<div class="section slide level1" id="exercises">
<h1>Exercises</h1>
<ol class="incremental" style="list-style-type: decimal">
<li>Enjoy R!!!!</li>
</ol>
</div>
<div class="section slide level1" id="references">
<h1>References</h1>
<ol class="incremental" style="list-style-type: decimal">
<li><a href="http://dirk.eddelbuettel.com/papers/parallelR_techRep.pdf">Technical Report on Parallel Computing in R</a></li>
</ol>
</div>
<div class="section slide level1" id="session-info">
<h1>Session Info</h1>
<p>It is good to include the session info, e.g. this document is produced with <strong>knitr</strong> version 0.9.6. Here is my session info:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(<span class="kw">sessionInfo</span>(), <span class="dt">locale =</span> <span class="ot">FALSE</span>)</code></pre>
<pre><code>## R version 2.15.2 (2012-10-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
##
## attached base packages:
## [1] splines grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] R2SWF_0.4 snow_0.3-10 gbm_1.6-3.2 reshape_0.8.4
## [5] caret_5.15-048 foreach_1.4.0 cluster_1.14.3 reshape2_1.2.2
## [9] lme4_0.999999-0 Matrix_1.0-10 lattice_0.20-10 xtable_1.7-0
## [13] vcd_1.2-13 colorspace_1.2-0 MASS_7.3-22 Hmisc_3.10-1
## [17] survival_2.37-2 sandwich_2.2-9 quantreg_4.94 SparseM_0.96
## [21] gridExtra_0.9.1 mgcv_1.7-22 eeptools_0.1 mapproj_1.2-0
## [25] maps_2.3-0 proto_0.3-10 plyr_1.8 stringr_0.6.2
## [29] ggplot2_0.9.3 lmtest_0.9-30 zoo_1.7-9 knitr_0.9.6
##
## loaded via a namespace (and not attached):
## [1] codetools_0.2-8 compiler_2.15.2 dichromat_1.2-4
## [4] digest_0.6.0 evaluate_0.4.3 formatR_0.7
## [7] gtable_0.1.2 iterators_1.0.6 labeling_0.1
## [10] munsell_0.4 nlme_3.1-106 RColorBrewer_1.0-5
## [13] scales_0.2.3 stats4_2.15.2 tools_2.15.2</code></pre>
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<div class="section slide level1" id="attribution-and-license">
<h1>Attribution and License</h1>
<p xmlns:dct="http://purl.org/dc/terms/">
<a rel="license" href="http://creativecommons.org/publicdomain/mark/1.0/"> <img src="data:image/png;base64,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" style="border-style: none;" alt="Public Domain Mark" /> </a> <br /> This work (<span property="dct:title">R Tutorial for Education</span>, by <a href="www.jaredknowles.com" rel="dct:creator"><span property="dct:title">Jared E. Knowles</span></a>), in service of the <a href="http://www.dpi.wi.gov" rel="dct:publisher"><span property="dct:title">Wisconsin Department of Public Instruction</span></a>, is free of known copyright restrictions.
</p>
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