Code is a means of communication, not just to the computer, but to other people. This is important because every project you undertake is fundamentally collaborative, and even if you're not working with other people you'll definitely be working with future-you.
After solving a data analysis challenge, it's often worth looking at your code and thinking about whether or not it's obvious what you've done. If you spend a little time rewriting your code while the ideas are fresh, you can save a lot of time later trying to recreate what your code did.
To me, this is what mastering R as a programming language is all about: making it easier to express yourself, so that over time your becomes more and more clear, and easier to write. In this chapter, you'll learn some of the most important skills, but to learn more you need to study R as a programming language, not just an interactive environment for data science. We have written two books that will help you do so:
* [Hands on programming with R](http://shop.oreilly.com/product/0636920028574.do),
by Garrett Grolemund. This is an introduction to R as a programming language
and is a great place to start if R is your first programming language.
* [Advanced R](http://adv-r.had.co.nz) by Hadley Wickham. This dives into the
details of R the programming language. This is a great place to start if
you've programmed in other languages and you want to learn what makes R
special, different, and particularly well suited to data analysis.
You get better very slowly if you don't consciously practice, so this chapter brings together a number of ideas that we mention elsewhere into one focussed chapter on code as communication.
* If you're working with functions that don't have a dataframe based API
(i.e. you pass them individual vectors, not a data frame and expressions
to be evaluated in the context of that data frame), you might find `%$%`
useful. It "explodes" out the variables in a data frame so that you can
refer to them explicitly. This is useful when working with many functions
in base R:
```{r}
mtcars %$%
cor(disp, mpg)
```
## When not to use the pipe
The pipe is a powerful tool, but it's not the only tool at your disposal, and it doesn't solve every problem! Generally, you should reach for another tool when:
* Your pipes get longer than five or six lines. It's a good idea to create
intermediate objects with meaningful names. That helps with debugging,
because it's easier to figure out when things went wrong. It also helps
understand the problem, because a good name can be very evocative of the
purpose.
* You have multiple inputs or outputs.
* Instead of creating a linear pipeline where you're primarily transforming
one object, you're starting to create a directed graphs with a complex
dependency structure. Pipes are fundamentally linear and expressing
complex relationships with them does not often yield clear code.
* For assignment. magrittr provides the `%<>%` operator which allows you to
replace code like:
```R
mtcars <- mtcars %>% transform(cyl = cyl * 2)
```
with
```R
mtcars %<>% transform(cyl = cyl * 2)
```
I'm not a fan of this operator because I think assignment is such a
special operation that it should always be clear when it's occuring.
In my opinion, a little bit of duplication (i.e. repeating the
name of the object twice), is fine in return for making assignment
more explicit.
I think it also gives you a better mental model of how assignment works
in R. The above code does not modify `mtcars`: it instead creates a
As you become a better R programmer, you'll learn more techniques for reducing various types of duplication. This allows you to do more with less, and allows you to express yourself more clearly by taking advantage of powerful programming constructs.
Two main tools for reducing duplication are functions and for-loops. You tend to use for-loops less often in R than in other programming languages because R is a functional programming language. That means that you can extract out common patterns of for loops and put them in a function.
### Extracting out a function
Whenever you've copied and pasted code more than twice, you need to take a look at it and see if you can extract out the common components and make a function. 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 0--1. Did you spot the mistake? I made an error when updating the code for `df$b`, and I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because it helps make your code more understandable (because you can name the operation), and it prevents you from making this sort of update error.
Now we can use that to simplify our original example:
```{r}
df$a <- rescale01(df$a)
df$b <- rescale01(df$b)
df$c <- rescale01(df$c)
df$d <- rescale01(df$d)
```
This makes it more clear what we're doing, and avoids one class of copy-and-paste errors. However, we still have quite a bit of duplication: we're doing the same thing to each column.
### Common looping patterns
Before we tackle the problem of rescaling each column, lets start with a simpler case. Imagine we want to summarise each column with its median. One way to do that is to use a for loop. Every for loop has three main components:
Now imagine that you also want to compute the interquartile range of each column? How would you change the function? What if you also wanted to calculate the min and max?
```{r}
col_min <- function(df) {
out <- vector("numeric", ncol(df))
for (i in 1:ncol(df)) {
out[i] <- min(df[[i]])
}
out
}
col_max <- function(df) {
out <- vector("numeric", ncol(df))
for (i in 1:ncol(df)) {
out[i] <- max(df[[i]])
}
out
}
```
I've now copied-and-pasted this function three times, so it's time to think about how to generalise it. If you look at these functions, you'll notice that they are very similar: the only difference is the function that gets called.
I mentioned earlier that R is a functional programming language. Practically, what this means is that you can not only pass vectors and data frames to functions, but you can also pass other functions. So you can generalise these `col_*` functions by adding an additional argument:
```{r}
col_summary <- function(df, fun) {
out <- vector("numeric", ncol(df))
for (i in 1:ncol(df)) {
out[i] <- fun(df[[i]])
}
out
}
col_summary(df, median)
col_summary(df, min)
```
We can take this one step further and use another cool feature of R functions: "`...`". "`...`" just takes any additional arguments and allows you to pass them on to another function:
```{r}
col_summary <- function(df, fun, ...) {
out <- vector("numeric", ncol(df))
for (i in 1:ncol(df)) {
out[i] <- fun(df[[i]], ...)
}
out
}
col_summary(df, median, na.rm = TRUE)
```
If you've used R for a bit, the behaviour of function might seem familiar: it looks like the `lapply()` or `sapply()` functions. Indeed, all of the apply function in R abstract over common looping patterns.
There are two main differences with `lapply()` and `col_summary()`:
* `lapply()` returns a list. This allows it to work with any R function, not
just those that return numeric output.
* `lapply()` is written in C, not R. This gives some very minor performance
improvements.
As you learn more about R, you'll learn more functions that allow you to abstract over common patterns of for loops.
### Exercises
1. Adapt `col_summary()` so that it only applies to numeric inputs.
You might want to start with an `is_numeric()` function that returns
a logical vector that has a TRUE corresponding to each numeric column.
1. How do `sapply()` and `vapply()` differ from `col_summary()`?
(This is an advanced topic. You shouldn't worry too much about it when you first start writing functions. Instead you should focus on getting a function that works right for the easiest 80% of the problem. Then in time, you'll learn how to get to 99% with minimal extra effort. The defaults in this book should steer you in the right direction: we avoid teaching you functions with major suprises.)
In this section you'll learn an important principle that lends itself to reliable and readable code: favour code that can be understood with a minimum of context. On one extreme, take this code:
What does it do? You can glean only a little from the context: `foo()` is a function that takes (at least) two arguments, and it returns a result we store in `baz`. But apart from that, you have no idea. To understand what this function does, you need to read the definitions of `foo()`, `bar`, and `qux`. Using better variable names helps a lot:
It's now much easier to see what's going on! Function and variable names are important because they tell you about (or at least jog your memory of) what the code does. That helps you understand code in isolation, even if you don't completely understand all the details. Unfortunately naming things is hard, and its hard to give concrete advice apart from giving objects short but evocative names. As autocomplete in RStudio has gotten better, I've tended to use longer names that are more descriptive. Short names are faster to type, but you write code relatively infrequently compared to the number of times that you read it.
The idea of minimising the context needed to understand your code goes beyond just good naming. You also want to favour functions with predictable behaviour and few surprises. If a function does radically different things when its inputs differ slightly, you'll need to carefully read the surrounding context in order to predict what it will do. The goal of this section is to educate you about the most common ways R functions can be surprising and to provide you with unsurprising alternatives.
Avoiding these three types of functions helps you to write code that you is easily understand and fails obviously with unexpected input. If this behaviour is so important, why do any functions behave differently? It's because R is not just a programming language, but it's also an environment for interactive data analysis. Some things make sense for interactive use (where you quickly check the output and guessing what you want is ok) but don't make sense for programming (where you want errors to arise as quickly as possible).
You might notice that these issues revolve around data frames. That's unfortunate because data frames are the data structure you'll use most commonly. It's ironic, the most frustrating things about programming in R are features that were originally designed to make your data analysis easier! Data frames try very hard to be helpful:
These features all made sense at the time they were added to R, but computing environments have changed a lot, and these features now tend to cause a lot of problems. dplyr disables them for you:
One of the most frustrating for programming is they way `[` returns a vector if the result has a single column, and returns a data frame otherwise. In other words, if you see code like `df[x, ]` you can't predict what it will return without knowing the value of `x`. This can trip you up in surprising ways. For example, imagine you've written this function to return the last row of a data frame:
Another common cause of problems is the `sapply()` function. If you've never heard of it before, feel free to skip this bit: just remember to avoid it! The problem with `sapply()` is that it tries to guess what the simplest form of output is, and it always succeeds.
In the next chapter, you'll learn about the purrr package which provides a variety of alternatives. In this case, you could use `map_chr()` which always returns a character vector: if it can't, it will throw an error. Another option is the base `vapply()` function which takes a third argument indicating what the output should look like.
This doesn't make `sapply()` bad and `vapply()` and `map_chr()` good. `sapply()` is nice because you can use it interactively without having to think about what `f` will return. 95% of the time it will do the right thing, and if it doesn't you can quickly fix it. `map_chr()` is more important when your programming because a clear error message is more valuable when an operation is buried deep inside a tree of function calls. At this point its worth thinking more about
These are called "non-standard evaluation", or NSE for short, because the usual lookup rules don't apply. In both cases above neither `displ` nor `cty` are present in the global environment. Instead both ggplot2 and dplyr look for them first in a data frame. This is great for interactive use, but can cause problems inside a function because they'll fall back to the global environment if the variable isn't found.
Because dplyr currently has no way to force a name to be interpreted as either a local or parent variable, as I've only just realised that's really you should avoid NSE. In a future version you should be able to do:
The first property is particularly important. If a function has hidden additional inputs, it's very difficult to even know where the important context is!
The biggest breaker of this rule in base R are functions that create data frames. Most of these functions have a `stringsAsFactors` argument that defaults to `getOption("stringsAsFactors")`. This means that a global option affects the operation of a very large number of functions, and you need to be aware that depending on an external state a function might produce either a character vector or a factor. In this book, we steer you away from that problem by recommnding functions like `readr::read_csv()` and `dplyr::data_frame()` that don't rely on this option. But be aware of it! Generally if a function is affected by a global option, you should avoid setting it.
Only use `options()` to control side-effects of a function. The value of an option should never affect the return value of a function. There are only three violations of this rule in base R: `stringsAsFactors`, `encoding`, `na.action`. For example, base R lets you control the number of digits printed in default displays with (e.g.) `options(digits = 3)`. This is a good use of an option because it's something that people frequently want control over, but doesn't affect the computation of a result, just its display. Follow this principle with your own use of options.
Another class of problems is functions that try really really hard to always return a useful result. Unfortunately they try so hard that they never throw error messages so you never find out if the input is really really weird.
