One of the best ways to improve your reach as a data scientist is to write functions. Functions allow you to automate common tasks in a more powerful and general way than copy-and-pasting. Writing a function has three big advantages over using copy-and-paste:
Writing good functions is a lifetime journey. Even after using R for many years I still learn new techniques and better ways of approaching old problems. The goal of this chapter is not to teach you every esoteric detail of functions but to get you started with some pragmatic advice that you can apply immediately.
As well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code. Good code style is like correct punctuation. Youcanmanagewithoutit, but it sure makes things easier to read! As with styles of punctuation, there are many possible variations. Here we present the style we use in our code, but the most important thing is to be consistent.
You should consider writing a function whenever you've copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). For example, take a look at this code. What does it do?
You might be able to puzzle out that this rescales each column to have a range from 0 to 1. But did you spot the mistake? I made an error when copying-and-pasting the code for `df$b`: I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because it prevents you from making this type of mistake.
This code only has one input: `df$a`. (If you're surprised that `TRUE` is not an input, you can explore why in the exercise below.) To make the inputs more clear, it's a good idea to rewrite the code using temporary variables with general names. Here this code only requires a single numeric vector, so I'll call it `x`:
Pulling out intermediate calculations into named variables is a good practice because it makes it more clear what the code is doing. Now that I've simplified the code, and checked that it still works, I can turn it into a function:
Note the overall process: I only made the function after I'd figured out how to make it work with a simple input. It's easier to start with working code and turn it into a function; it's harder to create a function and then try to make it work.
At this point it's a good idea to check your function with a few different inputs:
```{r}
rescale01(c(-10, 0, 10))
rescale01(c(1, 2, 3, NA, 5))
```
As you write more and more functions you'll eventually want to convert these informal, interactive tests into formal, automated tests. That process is called unit testing. Unfortunately, it's beyond the scope of this book, but you can learn about it in <http://r-pkgs.had.co.nz/tests.html>.
Compared to the original, this code is easier to understand and we've eliminated one class of copy-and-paste errors. There is still quite a bit of duplication since we're doing the same thing to multiple columns. We'll learn how to eliminate that duplication in [iteration], once you've learned more about R's data structures in [vectors].
Another advantage of functions is that if our requirements change, we only need to make the change in one place. For example, we might discover that some of our variables include infinite values, and `rescale01()` fails:
This is an important part of the "do not repeat yourself" (or DRY) principle. The more repetition you have in your code, the more places you need to remember to update when things change (and they always do!), and the more likely you are to create bugs over time.
It's important to remember that functions are not just for the computer, but are also for humans. R doesn't care what your function is called, or what comments it contains, but these are important for human readers. This section discusses some things that you should bear in mind when writing functions that humans can understand.
The name of a function is important. Ideally, the name of your function will be short, but clearly evoke what the function does. That's hard! But it's better to be clear than short, as RStudio's autocomplete makes it easy to type long names.
Generally, function names should be verbs, and arguments should be nouns. There are some exceptions: nouns are ok if the function computes a very well known noun (i.e. `mean()` is better than `compute_mean()`), or accessing some property of an object (i.e. `coef()` is better than `get_coefficients()`). A good sign that a noun might be a better choice is if you're using a very broad verb like "get", "compute", "calculate", or "determine". Use your best judgement and don't be afraid to rename a function if you figure out a better name later.
If your function name is composed of multiple words, I recommend using "snake\_case", where each lowercase word is separated by an underscore. camelCase is a popular alternative. It doesn't really matter which one you pick, the important thing is to be consistent: pick one or the other and stick with it. R itself is not very consistent, but there's nothing you can do about that. Make sure you don't fall into the same trap by making your code as consistent as possible.
If you have a family of functions that do similar things, make sure they have consistent names and arguments. Use a common prefix to indicate that they are connected. That's better than a common suffix because autocomplete allows you to type the prefix and see all the members of the family.
Where possible, avoid overriding existing functions and variables. It's impossible to do in general because so many good names are already taken by other packages, but avoiding the most common names from base R will avoid confusion.
Use comments, lines starting with `#`, to explain the "why" of your code. You generally should avoid comments that explain the "what" or the "how". If you can't understand what the code does from reading it, you should think about how to rewrite it to be more clear. Do you need to add some intermediate variables with useful names? Do you need to break out a subcomponent of a large function so you can name it? However, your code can never capture the reasoning behind your decisions: why did you choose this approach instead of an alternative? What else did you try that didn't work? It's a great idea to capture that sort of thinking in a comment.
Another important use of comments is to break up your file into easily readable chunks. Use long lines of `-` and `=` to make it easy to spot the breaks.
RStudio provides a keyboard shortcut to create these headers (Cmd/Ctrl + Shift + R), and will display them in the code navigation drop-down at the bottom-left of the editor:
To get help on `if` you need to surround it in backticks: `` ?`if` ``. The help isn't particularly helpful if you're not already an experienced programmer, but at least you know how to get to it!
Here's a simple function that uses an `if` statement. The goal of this function is to return a logical vector describing whether or not each element of a vector is named.
This function takes advantage of the standard return rule: a function returns the last value that it computed. Here that is either one of the two branches of the `if` statement.
The `condition` must evaluate to either `TRUE` or `FALSE`. If it's a vector, you'll get a warning message; if it's an `NA`, you'll get an error. Watch out for these messages in your own code:
You can use `||` (or) and `&&` (and) to combine multiple logical expressions. These operators are "short-circuiting": as soon as `||` sees the first `TRUE` it returns `TRUE` without computing anything else. As soon as `&&` sees the first `FALSE` it returns `FALSE`. You should never use `|` or `&` in an `if` statement: these are vectorised operations that apply to multiple values (that's why you use them in `filter()`). If you do have a logical vector, you can use `any()` or `all()` to collapse it to a single value.
Be careful when testing for equality. `==` is vectorised, which means that it's easy to get more than one output. Either check the length is already 1, collapse with `all()` or `any()`, or use the non-vectorised `identical()`. `identical()` is very strict: it always returns either a single `TRUE` or a single `FALSE`, and doesn't coerce types. This means that you need to be careful when comparing integers and doubles:
But if you end up with a very long series of chained `if` statements, you should consider rewriting. One useful technique is the `switch()` function. It allows you to evaluate selected code based on position or name.
Both `if` and `function` should (almost) always be followed by squiggly brackets (`{}`), and the contents should be indented by two spaces. This makes it easier to see the hierarchy in your code by skimming the left-hand margin.
An opening curly brace should never go on its own line and should always be followed by a new line. A closing curly brace should always go on its own line, unless it's followed by `else`. Always indent the code inside curly braces.
The arguments to a function typically fall into two broad sets: one set supplies the __data__ to compute on, and the other supplies arguments that control the __details__ of the computation. For example:
Generally, data arguments should come first. Detail arguments should go on the end, and usually should have default values. You specify a default value in the same way you call a function with a named argument:
The default value should almost always be the most common value. The few exceptions to this rule are to do with safety. For example, it makes sense for `na.rm` to default to `FALSE` because missing values are important. Even though `na.rm = TRUE` is what you usually put in your code, it's a bad idea to silently ignore missing values by default.
When you call a function, you typically omit the names of the data arguments, because they are used so commonly. If you override the default value of a detail argument, you should use the full name:
You can refer to an argument by its unique prefix (e.g. `mean(x, n = TRUE)`), but this is generally best avoided given the possibilities for confusion.
Notice that when you call a function, you should place a space around `=` in function calls, and always put a space after a comma, not before (just like in regular English). Using whitespace makes it easier to skim the function for the important components.
The names of the arguments are also important. R doesn't care, but the readers of your code (including future-you!) will. Generally you should prefer longer, more descriptive names, but there are a handful of very common, very short names. It's worth memorising these:
As you start to write more functions, you'll eventually get to the point where you don't remember exactly how your function works. At this point it's easy to call your function with invalid inputs. To avoid this problem, it's often useful to make constraints explicit. For example, imagine you've written some functions for computing weighted summary statistics:
Be careful not to take this too far. There's a tradeoff between how much time you spend making your function robust, versus how long you spend writing it. For example, if you also added a `na.rm` argument, I probably wouldn't check it carefully:
```{r}
wt_mean <- function(x, w, na.rm = FALSE) {
if (!is.logical(na.rm)) {
stop("`na.rm` must be logical")
}
if (length(na.rm) != 1) {
stop("`na.rm` must be length 1")
}
if (length(x) != length(w)) {
stop("`x` and `w` must be the same length", call. = FALSE)
This is a lot of extra work for little additional gain. A useful compromise is the built-in `stopifnot()`: it checks that each argument is `TRUE`, and produces a generic error message if not.
How do these functions work? They rely on a special argument: `...` (pronounced dot-dot-dot). This special argument captures any number of arguments that aren't otherwise matched.
It's useful because you can then send those `...` on to another function. This is a useful catch-all if your function primarily wraps another function. For example, I commonly create these helper functions that wrap around `str_c()`:
Here `...` lets me forward on any arguments that I don't want to deal with to `str_c()`. It's a very convenient technique. But it does come at a price: any misspelled arguments will not raise an error. This makes it easy for typos to go unnoticed:
Arguments in R are lazily evaluated: they're not computed until they're needed. That means if they're never used, they're never called. This is an important property of R as a programming language, but is generally not important when you're writing your own functions for data analysis. You can read more about lazy evaluation at <http://adv-r.had.co.nz/Functions.html#lazy-evaluation>.
Figuring out what your function should return is usually straightforward: it's why you created the function in the first place! There are two things you should consider when returning a value:
1. Does returning early make your function easier to read?
The value returned by the function is usually the last statement it evaluates, but you can choose to return early by using `return()`. I think it's best to save the use of `return()` to signal that you can return early with a simpler solution. A common reason to do this is because the inputs are empty:
Another reason is because you have a `if` statement with one complex block and one simple block. For example, you might write an if statement like this:
But if the first block is very long, by the time you get to the `else`, you've forgotten the `condition`. One way to rewrite it is to use an early return for the simple case:
If you want to write your own pipeable functions, it's important to think about the return value. Knowing the return value's object type will mean that your pipeline will "just work". For example, with dplyr and tidyr the object type is the data frame.
There are two basic types of pipeable functions: transformations and side-effects. With __transformations__, an object is passed to the function's first argument and a modified object is returned. With __side-effects__, the passed object is not transformed. Instead, the function performs an action on the object, like drawing a plot or saving a file. Side-effects functions should "invisibly" return the first argument, so that while they're not printed they can still be used in a pipeline. For example, this simple function prints the number of missing values in a data frame:
The last component of a function is its environment. This is not something you need to understand deeply when you first start writing functions. However, it's important to know a little bit about environments because they are crucial to how functions work. The environment of a function controls how R finds the value associated with a name. For example, take this function:
In many programming languages, this would be an error, because `y` is not defined inside the function. In R, this is valid code because R uses rules called __lexical scoping__ to find the value associated with a name. Since `y` is not defined inside the function, R will look in the __environment__ where the function was defined:
This behaviour seems like a recipe for bugs, and indeed you should avoid creating functions like this deliberately, but by and large it doesn't cause too many problems (especially if you regularly restart R to get to a clean slate).
The advantage of this behaviour is that from a language standpoint it allows R to be very consistent. Every name is looked up using the same set of rules. For `f()` that includes the behaviour of two things that you might not expect: `{` and `+`. This allows you to do devious things like:
This is a common phenomenon in R. R places few limits on your power. You can do many things that you can't do in other programming languages. You can do many things that 99% of the time are extremely ill-advised (like overriding how addition works!). But this power and flexibility is what makes tools like ggplot2 and dplyr possible. Learning how to make best use of this flexibility is beyond the scope of this book, but you can read about in [_Advanced R_](http://adv-r.had.co.nz).
