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<h1 class="title">Write your own R functions, part 1</h1>
</div>
<div id="TOC">
<ul>
<li><a href="#what-and-why">What and why?</a></li>
<li><a href="#load-the-gapminder-data">Load the Gapminder data</a></li>
<li><a href="#max---min">Max - min</a></li>
<li><a href="#get-something-that-works">Get something that works</a></li>
<li><a href="#turn-the-working-interactive-code-into-a-function">Turn the working interactive code into a function</a></li>
<li><a href="#test-your-function">Test your function</a></li>
<li><a href="#check-the-validity-of-arguments">Check the validity of arguments</a></li>
<li><a href="#packages-for-formal-checks-at-run-time">Packages for formal checks at run time</a></li>
<li><a href="#wrap-up-and-whats-next">Wrap-up and what’s next?</a></li>
<li><a href="#resources">Resources</a></li>
</ul>
</div>
<div id="what-and-why" class="section level3">
<h3>What and why?</h3>
<p>My goal here is to reveal the <strong>process</strong> one long-time useR employs for writing functions. I also want to illustrate why the process is the way it is. Merely looking at the finished product, e.g. source code for R packages, can be extremely deceiving. Reality is generally much uglier … but more interesting!</p>
<p>Why are we covering this now, smack in the middle of data aggregation? Powerful machines like <code>dplyr</code>, <code>plyr</code>, and even the built-in <code>apply</code> family of functions, are ready and waiting to apply your purpose-built functions to various bits of your data. If you can express your analytical wishes in a function, these tools will give you great power.</p>
</div>
<div id="load-the-gapminder-data" class="section level3">
<h3>Load the Gapminder data</h3>
<p>As usual, load the Gapminder excerpt.</p>
<pre class="r"><code>gDat <- read.delim("gapminderDataFiveYear.txt")
str(gDat)
## 'data.frame': 1704 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ gdpPercap: num 779 821 853 836 740 ...
## or do this if the file isn't lying around already
## gd_url <- "http://tiny.cc/gapminder"
## gDat <- read.delim(gd_url)</code></pre>
</div>
<div id="max---min" class="section level3">
<h3>Max - min</h3>
<p>Say you’ve got a numeric vector. Compute the difference between its max and min. <code>lifeExp</code> or <code>pop</code> or <code>gdpPercap</code> are great examples of a typical input. You can imagine wanting to get this statistic after we slice up the Gapminder data by year, country, continent, or combinations thereof.</p>
</div>
<div id="get-something-that-works" class="section level3">
<h3>Get something that works</h3>
<p>First, develop some working code for interactive use, using a representative input. I’ll use Gapminder’s life expectancy variable.</p>
<p>R functions that will be useful: <code>min()</code>, <code>max()</code>, <code>range()</code>. <strong>Read their documentation.</strong></p>
<pre class="r"><code>## get to know the functions mentioned above
min(gDat$lifeExp)
## [1] 23.599
max(gDat$lifeExp)
## [1] 82.603
range(gDat$lifeExp)
## [1] 23.599 82.603
## some natural solutions
max(gDat$lifeExp) - min(gDat$lifeExp)
## [1] 59.004
with(gDat, max(lifeExp) - min(lifeExp))
## [1] 59.004
range(gDat$lifeExp)[2] - range(gDat$lifeExp)[1]
## [1] 59.004
with(gDat, range(lifeExp)[2] - range(lifeExp)[1])
## [1] 59.004
diff(range(gDat$lifeExp))
## [1] 59.004</code></pre>
<p>Internalize this “answer” because our informal testing relies on you noticing departures from this.</p>
<div id="skateboard-perfectly-formed-rear-view-mirror" class="section level4">
<h4>Skateboard >> perfectly formed rear-view mirror</h4>
<p>This image <a href="http://blog.fastmonkeys.com/?utm_content=bufferc2d6e&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer">widely attributed to the Spotify development team</a> conveys an important point.</p>
<p><img src="img/spotify-howtobuildmvp.gif" alt="alt text" /></p>
<p>Build that skateboard before you build the car or some fancy car part. A limited-but-functioning thing is very useful. It also keeps the spirits high.</p>
<p>This is related to the valuable <a href="http://c2.com/cgi/wiki?TelescopeRule">Telescope Rule</a>:</p>
<blockquote>
<p>It is faster to make a four-inch mirror then a six-inch mirror than to make a six-inch mirror.</p>
</blockquote>
</div>
</div>
<div id="turn-the-working-interactive-code-into-a-function" class="section level3">
<h3>Turn the working interactive code into a function</h3>
<p>Add NO new functionality! Just write your very first R function.</p>
<pre class="r"><code>max_minus_min <- function(x) max(x) - min(x)
max_minus_min(gDat$lifeExp)
## [1] 59.004</code></pre>
<p>Check that you’re getting the same answer as you did with your interactive code. Test it eyeball-o-metrically at this point.</p>
</div>
<div id="test-your-function" class="section level3">
<h3>Test your function</h3>
<div id="test-on-new-inputs" class="section level4">
<h4>Test on new inputs</h4>
<p>Pick some new articial inputs where you know (at least approximately) what your function should return.</p>
<pre class="r"><code>max_minus_min(1:10)
## [1] 9
max_minus_min(runif(1000))
## [1] 0.9947266</code></pre>
<p>I know that 10 minus 1 is 9. I know that random uniform [0, 1] variates will be between 0 and 1. Therefore max - min should be less than 1. If I take LOTS of them, max - min should be pretty close to 1.</p>
<p>It is intentional that I tested on integer input as well as floating point. Likewise, I like to use valid-but-random data for this sort of check.</p>
</div>
<div id="test-on-real-data-but-different-real-data" class="section level4">
<h4>Test on real data but <em>different</em> real data</h4>
<p>Back to the real world now. Two other quantitative variables are lying around: <code>gdpPercap</code> and <code>pop</code>. Let’s have a go.</p>
<pre class="r"><code>max_minus_min(gDat$gdpPercap)
## [1] 113282
max_minus_min(gDat$pop)
## [1] 1318623085</code></pre>
<p>Either check these results “by hand” or apply the “does that even make sense?” test.</p>
</div>
<div id="test-on-weird-stuff" class="section level4">
<h4>Test on weird stuff</h4>
<p>Now we try to break our function. Don’t get truly diabolical (yet). Just make the kind of mistakes you can imagine making at 2am when, 3 years from now, you rediscover this useful function you wrote. Give you function inputs it’s not expecting.</p>
<pre class="r"><code>max_minus_min(gDat) ## hey sometimes things "just work" on data.frames!
## Error: only defined on a data frame with all numeric variables
max_minus_min(gDat$country) ## factors are kind of like integer vectors, no?
## Error: max not meaningful for factors
max_minus_min("eggplants are purple") ## i have no excuse for this one
## Error: non-numeric argument to binary operator</code></pre>
<p>How happy are you with those error messages? You must imagine that some entire <strong>script</strong> has failed and that you were hoping to just source it without re-reading it. If a colleague or future you encountered these errors, do you run screaming from the room? How hard is it to pinpoint the usage problem?</p>
</div>
<div id="i-will-scare-you-now" class="section level4">
<h4>I will scare you now</h4>
<p>Here are some great examples STAT545 students devised during class where the function <strong>should break but it does not.</strong></p>
<pre class="r"><code>max_minus_min(gDat[c('lifeExp', 'gdpPercap', 'pop')])
## [1] 1318683072
max_minus_min(c(TRUE, TRUE, FALSE, TRUE, TRUE))
## [1] 1</code></pre>
<p>In both cases, R’s eagerness to make sense of our requests is unfortunately successful. In the first case, a data.frame containing just the quantitative variables is eventually coerced into numeric vector. We can compute max minus min, even though it makes absolutely no sense at all. In the second case, a logical vector is converted to zeroes and ones, which might merit an error or at least a warning.</p>
</div>
</div>
<div id="check-the-validity-of-arguments" class="section level3">
<h3>Check the validity of arguments</h3>
<p>For functions that will be used again – which is not all of them! – it is good to check the validity of arguments. This implements a rule from <a href="http://www.faqs.org/docs/artu/ch01s06.html">the Unix philosophy</a>:</p>
<blockquote>
<p>Rule of Repair: When you must fail, fail noisily and as soon as possible.</p>
</blockquote>
<div id="stopifnot" class="section level4">
<h4>stopifnot</h4>
<p><code>stopifnot()</code> is the entry level solution. I use it here to make sure the input <code>x</code> is a numeric vector.</p>
<pre class="r"><code>mmm <- function(x) {
stopifnot(is.numeric(x))
max(x) - min(x)
}
mmm(gDat)
## Error: is.numeric(x) is not TRUE
mmm(gDat$country)
## Error: is.numeric(x) is not TRUE
mmm("eggplants are purple")
## Error: is.numeric(x) is not TRUE
mmm(gDat[c('lifeExp', 'gdpPercap', 'pop')])
## Error: is.numeric(x) is not TRUE
mmm(c(TRUE, TRUE, FALSE, TRUE, TRUE))
## Error: is.numeric(x) is not TRUE</code></pre>
<p>And we see that it catches all of the self-inflicted damage we would like to avoid.</p>
</div>
<div id="if-then-stop" class="section level4">
<h4>if then stop</h4>
<p><code>stopifnot()</code> doesn’t provide a very good error message. The next approach is very widely used. Put your validity check inside an <code>if()</code> statement and call <code>stop()</code> yourself, with a custom error message, in the body.</p>
<pre class="r"><code>mmm2 <- function(x) {
if(!is.numeric(x)) {
stop('I am so sorry, but this function only works for numeric input!')
}
max(x) - min(x)
}
mmm2(gDat)
## Error: I am so sorry, but this function only works for numeric input!</code></pre>
<p>In addition to offering an apology, note the error raised also contains helpful info on <em>which</em> function threw the error. Nice touch.</p>
<p><em>Note: the above is true when run interactively but currently not true in the rendered document. That is a glitch in <code>knitr</code> that is getting straightened out.</em></p>
</div>
</div>
<div id="packages-for-formal-checks-at-run-time" class="section level3">
<h3>Packages for formal checks at run time</h3>
<p>The <a href="https://github.com/hadley/assertthat"><code>assertthat</code> package</a> “provides a drop in replacement for <code>stopifnot()</code>.” That is quite literally true. The function <code>mmm3</code> differs from <code>mmm2</code> only in the replacement of <code>stopifnot()</code> by <code>assert_that()</code>.</p>
<pre class="r"><code>## install if you do not already have!
## install.packages(assertthat)
library(assertthat)
mmm3 <- function(x) {
assert_that(is.numeric(x))
max(x) - min(x)
}
mmm3(gDat)
## Error: x is not a numeric or integer vector</code></pre>
<p>The <a href="https://github.com/smbache/ensurer"><code>ensurer</code> package</a> is another, newer package with some similar goals, so you may want to check that out as well.</p>
<div id="sidebar-other-uses-for-assertthat-or-ensurer" class="section level4">
<h4>Sidebar: other uses for <code>assertthat</code> or <code>ensurer</code></h4>
<p>Another good use of these packages is to leave checks behind in data analytical scripts. Consider our repetitive use of Gapminder. Every time we load this data, we inspect it, e.g., with <code>str()</code>. Informally, we’re checking that is still has 1704 rows. But we could, and probably should, formalize that with a call like <code>assert_that(nrow(gDat) == 1704)</code>. This would tell us if the data suddenly changed, alerting us to a problem with the data file or the import. This can be a useful wake-up call in scripts that you re-run alot as you build a pipeline, where it’s easy to zone out and stop paying attention.</p>
</div>
</div>
<div id="wrap-up-and-whats-next" class="section level3">
<h3>Wrap-up and what’s next?</h3>
<p>Here’s the function we’ve written so far:</p>
<pre class="r"><code>mmm3
## function(x) {
## assert_that(is.numeric(x))
## max(x) - min(x)
## }</code></pre>
<p>What we’ve accomplished:</p>
<ul>
<li>we’re written our first function</li>
<li>we are checking the validity of its input, argument <code>x</code></li>
<li>we’ve done a good amount of informal testing</li>
</ul>
<p>Where to next? In <a href="block011_write-your-own-function-02.html">part 2</a>, we generalize this function to take differences in other quantiles and learn how to set default values for arguments.</p>
</div>
<div id="resources" class="section level3">
<h3>Resources</h3>
<p>Packages</p>
<ul>
<li><a href="https://github.com/hadley/assertthat"><code>assertthat</code> package</a></li>
<li><a href="https://github.com/smbache/ensurer"><code>ensurer</code> package</a></li>
<li><a href="https://github.com/hadley/testthat"><code>testthat</code> package</a></li>
</ul>
<p>Hadley Wickham’s forthcoming book <a href="http://adv-r.had.co.nz">Advanced R</a></p>
<ul>
<li>Section on <a href="http://adv-r.had.co.nz/Exceptions-Debugging.html#defensive-programming">defensive programming</a></li>
</ul>
<p>Hadley Wickham’s forthcoming book <a href="http://r-pkgs.had.co.nz">R packages</a></p>
<ul>
<li><a href="http://r-pkgs.had.co.nz/tests.html">Testing chapter</a></li>
</ul>
</div>
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