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<!DOCTYPE html>
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<title>Software Carpentry: Programming with R</title>
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<a href="index.html"><h1 class="title">Programming with R</h1></a>
<h2 class="subtitle">Instructor’s Guide</h2>
<h2 id="legend">Legend</h2>
<p>We are using a dataset with records on inflammation from patients following an arthritis treatment. With it we explain <code>R</code> data structure, basic data manipulation and plotting, writing functions and loops.</p>
<h2 id="overall">Overall</h2>
<p>This lesson is written as an introduction to R, but its real purpose is to introduce the single most important idea in programming: how to solve problems by building functions, each of which can fit in a programmer’s working memory. In order to teach that, we must teach people a little about the mechanics of manipulating data with lists and file I/O so that their functions can do things they actually care about. Our teaching order tries to show practical uses of every idea as soon as it is introduced; instructors should resist the temptation to explain the “other 90%” of the language as well.</p>
<p>The secondary goal of this lesson is to give them a usable mental model of how programs run (what computer science educators call a <a href="reference.html#notional-machine">notional machine</a> so that they can debug things when they go wrong. In particular, they must understand how function call stacks work.</p>
<p>The final example asks them to build a command-line tool that works with the Unix pipe-and-filter model. We do this because it is a useful skill and because it helps learners see that the software they use isn’t magical. Tools like <code>grep</code> might be more sophisticated than the programs our learners can write at this point in their careers, but it’s crucial they realize this is a difference of scale rather than kind.</p>
<p>The <code>R</code> novice inflammation contains a lot of material to cover. Remember this lesson does not spend a lot of time on data types, data structure, etc. It is also on par with the similar lesson on Python. The objective is to explain modular programming with the concepts of functions, loops, flow control, and defensive programming (i.e. SWC best practices). Supplementary material is available for R specifics (<a href="01-supp-addressing-data.html">Addressing Data</a>, <a href="01-supp-data-structures.html">Data Types and Structure</a>, <a href="01-supp-factors.html">Understanding Factors</a>, <a href="01-supp-intro-rstudio.html">Introduction to RStudio</a>, <a href="01-supp-read-write-csv.html">Reading and Writing .csv</a>, <a href="03-supp-loops-in-depth.html">Loops in R</a>, <a href="06-best-practices-R.html">Best Practices for Using R and Designing Programs</a>, <a href="07-knitr-R.html">Dynamic Reports with knitr</a>, <a href="08-making-packages-R.html">Making Packages in R</a>).</p>
<p>A typical, half-day, lesson would use the first three lessons:</p>
<ol style="list-style-type: decimal">
<li><a href="01-starting-with-data.html">Analyzing Patient Data</a></li>
<li><a href="02-func-R.html">Creating Functions</a></li>
<li><a href="03-loops-R.html">Analyzing Multiple Data Sets</a></li>
</ol>
<p>An additional half-day could add the next two lessons:</p>
<ol start="4" style="list-style-type: decimal">
<li><a href="04-cond.html">Making choices</a></li>
<li><a href="05-cmdline.html">Command-Line Programs</a></li>
</ol>
<p>Time-permitting, you can fit in one of these shorter lessons that cover bigger picture ideas like best practices for organizing code, reproducible research, and creating packages:</p>
<ol start="6" style="list-style-type: decimal">
<li><a href="06-best-practices-R.html">Best practices for using R and designing programs</a></li>
<li><a href="07-knitr-R.html">Dynamic reports with knitr</a></li>
<li><a href="08-making-packages-R.html">Making packages in R</a></li>
</ol>
<h2 id="analyzing-patient-data"><a href="01-starting-with-data.html">Analyzing Patient Data</a></h2>
<ul>
<li><p>Check learners are reading files from the correct location (set working directory); remind them of the shell lesson</p></li>
<li><p>Provide shortcut for the assignment operator (<code><-</code>) (RStudio: Alt+- on Windows/Linux; Option+- on Mac)</p></li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">dat <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"data/inflammation-01.csv"</span>, <span class="dt">header =</span> <span class="ot">FALSE</span>)
animal <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"m"</span>, <span class="st">"o"</span>, <span class="st">"n"</span>, <span class="st">"k"</span>, <span class="st">"e"</span>, <span class="st">"y"</span>)
<span class="co"># Challenge - Slicing (subsetting data)</span>
animal[<span class="dv">4</span>:<span class="dv">1</span>] <span class="co"># first 4 characters in reverse order</span></code></pre>
<pre class="output"><code>[1] "k" "n" "o" "m"
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">animal[-<span class="dv">1</span>] <span class="co"># remove first character</span></code></pre>
<pre class="output"><code>[1] "o" "n" "k" "e" "y"
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">animal[-<span class="dv">4</span>] <span class="co"># remove fourth character</span></code></pre>
<pre class="output"><code>[1] "m" "o" "n" "e" "y"
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">animal[-<span class="dv">1</span>:-<span class="dv">4</span>] <span class="co"># remove first to fourth characters</span></code></pre>
<pre class="output"><code>[1] "e" "y"
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">animal[<span class="kw">c</span>(<span class="dv">5</span>, <span class="dv">2</span>, <span class="dv">3</span>)] <span class="co"># new character vector</span></code></pre>
<pre class="output"><code>[1] "e" "o" "n"
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - Subsetting data</span>
<span class="kw">max</span>(dat[<span class="dv">5</span>, <span class="dv">3</span>:<span class="dv">7</span>])</code></pre>
<pre class="output"><code>[1] 3
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">sd_day_inflammation <-<span class="st"> </span><span class="kw">apply</span>(dat, <span class="dv">2</span>, sd)
<span class="kw">plot</span>(sd_day_inflammation)</code></pre>
<h2 id="addressing-data"><a href="01-supp-addressing-data.html">Addressing Data</a></h2>
<ul>
<li>Note that the data frame <code>dat</code> is not the same set of data as in other lessons</li>
</ul>
<h2 id="data-types-and-structure"><a href="01-supp-data-structures.html">Data Types and Structure</a></h2>
<ul>
<li>Lesson on data types and structures</li>
</ul>
<h2 id="understanding-factors"><a href="01-supp-factors.html">Understanding Factors</a></h2>
<h2 id="introduction-to-rstudio"><a href="01-supp-intro-rstudio.html">Introduction to RStudio</a></h2>
<h2 id="reading-and-writing-.csv"><a href="01-supp-read-write-csv.html">Reading and Writing .csv</a></h2>
<h2 id="creating-functions"><a href="02-func-R.html">Creating Functions</a></h2>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - Create a function</span>
fence <-<span class="st"> </span>function(original, wrapper) {
answer <-<span class="st"> </span><span class="kw">c</span>(wrapper, original, wrapper)
<span class="kw">return</span>(answer)
}</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - A more advanced function</span>
analyze <-<span class="st"> </span>function(filename) {
<span class="co"># Plots the average, min, and max inflammation over time.</span>
<span class="co"># Input is character string of a csv file.</span>
dat <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file =</span> filename, <span class="dt">header =</span> <span class="ot">FALSE</span>)
avg_day_inflammation <-<span class="st"> </span><span class="kw">apply</span>(dat, <span class="dv">2</span>, mean)
<span class="kw">plot</span>(avg_day_inflammation)
max_day_inflammation <-<span class="st"> </span><span class="kw">apply</span>(dat, <span class="dv">2</span>, max)
<span class="kw">plot</span>(max_day_inflammation)
min_day_inflammation <-<span class="st"> </span><span class="kw">apply</span>(dat, <span class="dv">2</span>, min)
<span class="kw">plot</span>(min_day_inflammation)
}
<span class="co"># Challenge - rescale</span>
rescale <-<span class="st"> </span>function(v) {
<span class="co"># Rescales a vector, v, to lie in the range 0 to 1.</span>
L <-<span class="st"> </span><span class="kw">min</span>(v)
H <-<span class="st"> </span><span class="kw">max</span>(v)
result <-<span class="st"> </span>(v -<span class="st"> </span>L) /<span class="st"> </span>(H -<span class="st"> </span>L)
<span class="kw">return</span>(result)
}</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - A function with default argument values</span>
rescale <-<span class="st"> </span>function(v, <span class="dt">lower =</span> <span class="dv">0</span>, <span class="dt">upper =</span> <span class="dv">1</span>) {
<span class="co"># Rescales a vector, v, to lie in the range lower to upper.</span>
L <-<span class="st"> </span><span class="kw">min</span>(v)
H <-<span class="st"> </span><span class="kw">max</span>(v)
result <-<span class="st"> </span>(v -<span class="st"> </span>L) /<span class="st"> </span>(H -<span class="st"> </span>L) *<span class="st"> </span>(upper -<span class="st"> </span>lower) +<span class="st"> </span>lower
<span class="kw">return</span>(result)
}
answer <-<span class="st"> </span><span class="kw">rescale</span>(dat[, <span class="dv">4</span>], <span class="dt">lower =</span> <span class="dv">2</span>, <span class="dt">upper =</span> <span class="dv">5</span>)
<span class="kw">min</span>(answer)</code></pre>
<pre class="output"><code>[1] 2
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">max</span>(answer)</code></pre>
<pre class="output"><code>[1] 5
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">answer <-<span class="st"> </span><span class="kw">rescale</span>(dat[, <span class="dv">4</span>], <span class="dt">lower =</span> -<span class="dv">5</span>, <span class="dt">upper =</span> -<span class="dv">2</span>)
<span class="kw">min</span>(answer)</code></pre>
<pre class="output"><code>[1] -5
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">max</span>(answer)</code></pre>
<pre class="output"><code>[1] -2
</code></pre>
<h2 id="analyzing-multiple-data-sets"><a href="03-loops-R.html">Analyzing Multiple Data Sets</a></h2>
<ul>
<li>The transition from the previous lesson to this one might be challenging for a very novice audience. Do not rush through the challenges, maybe drop some.</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - Using loops</span>
print_N <-<span class="st"> </span>function(N) {
nseq <-<span class="st"> </span><span class="kw">seq</span>(N)
for (num in nseq) {
<span class="kw">print</span>(num)
}
}
<span class="kw">print_N</span>(<span class="dv">3</span>)</code></pre>
<pre class="output"><code>[1] 1
[1] 2
[1] 3
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">total <-<span class="st"> </span>function(vec) {
<span class="co">#calculates the sum of the values in a vector</span>
vec_sum <-<span class="st"> </span><span class="dv">0</span>
for (num in vec) {
vec_sum <-<span class="st"> </span>vec_sum +<span class="st"> </span>num
}
<span class="kw">return</span>(vec_sum)
}
ex_vec <-<span class="st"> </span><span class="kw">c</span>(<span class="dv">4</span>, <span class="dv">8</span>, <span class="dv">15</span>, <span class="dv">16</span>, <span class="dv">23</span>, <span class="dv">42</span>)
<span class="kw">total</span>(ex_vec)</code></pre>
<pre class="output"><code>[1] 108
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">expo <-<span class="st"> </span>function(base, power) {
result <-<span class="st"> </span><span class="dv">1</span>
for (i in <span class="kw">seq</span>(power)) {
result <-<span class="st"> </span>result *<span class="st"> </span>base
}
<span class="kw">return</span>(result)
}
<span class="kw">expo</span>(<span class="dv">2</span>, <span class="dv">4</span>)</code></pre>
<pre class="output"><code>[1] 16
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - Using loops to analyze multiple files</span>
analyze_all <-<span class="st"> </span>function(pattern) {
<span class="co"># Runs the function analyze for each file in the current working directory</span>
<span class="co"># that contains the given pattern.</span>
filenames <-<span class="st"> </span><span class="kw">list.files</span>(<span class="dt">path =</span> <span class="st">"data"</span>, <span class="dt">pattern =</span> pattern, <span class="dt">full.names =</span> <span class="ot">TRUE</span>)
for (f in filenames) {
<span class="kw">analyze</span>(f)
}
}</code></pre>
<h2 id="loops-in-r"><a href="03-supp-loops-in-depth.html">Loops in R</a></h2>
<h2 id="making-choices"><a href="04-cond-colors-R.html">Making Choices</a></h2>
<h2 id="making-choices-1"><a href="04-cond.html">Making Choices</a></h2>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - Using conditions to change behaviour</span>
plot_dist <-<span class="st"> </span>function(x, threshold) {
if (<span class="kw">length</span>(x) ><span class="st"> </span>threshold) {
<span class="kw">boxplot</span>(x)
} else {
<span class="kw">stripchart</span>(x)
}
}
plot_dist <-<span class="st"> </span>function(x, threshold, <span class="dt">use_boxplot =</span> <span class="ot">TRUE</span>) {
if (<span class="kw">length</span>(x) ><span class="st"> </span>threshold &<span class="st"> </span>use_boxplot) {
<span class="kw">boxplot</span>(x)
} else if (<span class="kw">length</span>(x) ><span class="st"> </span>threshold &<span class="st"> </span>!use_boxplot) {
<span class="kw">hist</span>(x)
} else {
<span class="kw">stripchart</span>(x)
}
}
<span class="co"># Challenge - Changing behaviour of the plot command</span>
analyze <-<span class="st"> </span>function(filename, <span class="dt">output =</span> <span class="ot">NULL</span>) {
<span class="co"># Plots the average, min, and max inflammation over time.</span>
<span class="co"># Input:</span>
<span class="co"># filename: character string of a csv file</span>
<span class="co"># output: character string of pdf file for saving</span>
if (!<span class="kw">is.null</span>(output)) {
<span class="kw">pdf</span>(output)
}
dat <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file =</span> filename, <span class="dt">header =</span> <span class="ot">FALSE</span>)
avg_day_inflammation <-<span class="st"> </span><span class="kw">apply</span>(dat, <span class="dv">2</span>, mean)
<span class="kw">plot</span>(avg_day_inflammation, <span class="dt">type =</span> <span class="st">"l"</span>)
max_day_inflammation <-<span class="st"> </span><span class="kw">apply</span>(dat, <span class="dv">2</span>, max)
<span class="kw">plot</span>(max_day_inflammation, <span class="dt">type =</span> <span class="st">"l"</span>)
min_day_inflammation <-<span class="st"> </span><span class="kw">apply</span>(dat, <span class="dv">2</span>, min)
<span class="kw">plot</span>(min_day_inflammation, <span class="dt">type =</span> <span class="st">"l"</span>)
if (!<span class="kw">is.null</span>(output)) {
<span class="kw">dev.off</span>()
}
}</code></pre>
<h2 id="best-practices-for-using-r-and-designing-programs"><a href="06-best-practices-R.html">Best Practices for Using R and Designing Programs</a></h2>
<h2 id="command-line-programs"><a href="05-cmdline.html">Command-Line Programs</a></h2>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - A simple command line program</span>
cat arith.R</code></pre>
<pre class="output"><code>main <- function() {
# Performs addition or subtraction from the command line.
#
# Takes three arguments:
# The first and third are the numbers.
# The second is either + for addition or - for subtraction.
#
# Ex. usage:
# Rscript arith.R 1 + 2
# Rscript arith.R 3 - 4
#
args <- commandArgs(trailingOnly = TRUE)
num1 <- as.numeric(args[1])
operation <- args[2]
num2 <- as.numeric(args[3])
if (operation == "+") {
answer <- num1 + num2
cat(answer)
} else if (operation == "-") {
answer <- num1 - num2
cat(answer)
} else {
stop("Invalid input. Use + for addition or - for subtraction.")
}
}
main()
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">cat find-pattern.R</code></pre>
<pre class="output"><code>main <- function() {
# Finds all files in the current directory that contain a given pattern.
#
# Takes one argument: the pattern to be searched.
#
# Ex. usage:
# Rscript find-pattern.R csv
#
args <- commandArgs(trailingOnly = TRUE)
pattern <- args[1]
files <- list.files(pattern = pattern)
cat(files, sep = "\n")
}
main()
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">## Challenge - A command line program with arguments
cat check.R</code></pre>
<pre class="output"><code>main <- function() {
# Checks that all csv files have the same number of rows and columns.
#
# Takes multiple arguments: the names of the files to be checked.
#
# Ex. usage:
# Rscript check.R inflammation-*
#
args <- commandArgs(trailingOnly = TRUE)
first_file <- read.csv(args[1], header = FALSE)
first_dim <- dim(first_file)
# num_rows <- dim(args[1])[1] # nrow(args[1])
# num_cols <- dim(args[1])[2] # ncol(args[1])
for (filename in args[-1]) {
new_file <- read.csv(filename, header = FALSE)
new_dim <- dim(new_file)
if (new_dim[1] != first_dim[1] | new_dim[2] != first_dim[2]) {
cat("Not all the data files have the same dimensions.")
}
}
}
main()
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - Shorter command line arguments</span>
cat readings-usage.R</code></pre>
<pre class="output"><code>main <- function() {
args <- commandArgs(trailingOnly = TRUE)
action <- args[1]
filenames <- args[-1]
if (!(action %in% c("--min", "--mean", "--max"))) {
usage()
} else if (length(filenames) == 0) {
process(file("stdin"), action)
} else {
for (f in filenames) {
process(f, action)
}
}
}
process <- function(filename, action) {
dat <- read.csv(file = filename, header = FALSE)
if (action == "--min") {
values <- apply(dat, 1, min)
} else if (action == "--mean") {
values <- apply(dat, 1, mean)
} else if (action == "--max") {
values <- apply(dat, 1, max)
}
cat(values, sep = "\n")
}
usage <- function() {
cat("usage: Rscript readings-usage.R [--min, --mean, --max] filenames", sep = "\n")
}
main()
</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Challenge - Implementing wc in R</span>
cat line-count.R</code></pre>
<pre class="output"><code>main <- function() {
args <- commandArgs(trailingOnly = TRUE)
if (length(args) > 0) {
for (filename in args) {
input <- readLines(filename)
num_lines <- length(input)
cat(filename)
cat(" ")
cat(num_lines, sep = "\n")
}
} else {
input <- readLines(file("stdin"))
num_lines <- length(input)
cat(num_lines, sep = "\n")
}
}
main()
</code></pre>
<h2 id="dynamic-reports-with-knitr"><a href="07-knitr-R.html">Dynamic Reports with knitr</a></h2>
<h2 id="making-packages-in-r"><a href="08-making-packages-R.html">Making Packages in R</a></h2>
<h2 id="using-git-in-rstudio">Using Git in RStudio</h2>
<p>Some instructors will demo RStudio’s git integration at some point during the workshop. This often goes over very well, but there can be a few snags with the setup. First, RStudio may not know where to find git. You can specify where git is located in <em>Tools > Global Options > Git/SVN</em>; on Mac/Linux git is often in <code>usr/bin/git</code> or <code>usr/local/bin/git</code> and on Windows it is often in <code>C:/Program Files (x86)/Git/bin/git.exe</code>. If you don’t know where git is installed on someone’s computer, open a terminal and try <code>which git</code> on Mac/Linux, or <code>where git</code> or <code>whereis git.exe</code> on Windows. See <a href="http://stat545-ubc.github.io/git03_rstudio-meet-git.html">Jenny Bryan’s instructions</a> for more detail.</p>
<p>If Windows users select the option “Run Git from the Windows command prompt” while setting up Git Bash, RStudio will automatically find the git executable. If you plan to demo git in RStudio during your workshop, you should edit the workshop setup instructions to have the Windows users choose this option during setup.</p>
<p>Another common gotcha is that the push/pull buttons in RStudio are grayed out, even after you have added a remote and pushed to it from the command line. You need to add an upstream tracking reference before you can push and pull directly from RStudio; have your learners do <code>git push -u origin master</code> from the command line and this should resolve the issue.</p>
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