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R Data Skills for Bioinformatics

Writing and Applying Functions to Data

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Overview

Teaching: 20 min
Exercises: 10 min
Questions

  • What are apply functions in R?

  • How can I write a new function in R?

Objectives

  • Define a function that takes arguments.

  • Return a value from a function.

  • Test a function.

  • Set default values for function arguments.

  • Explain why we should divide programs into small, single-purpose functions.

Writing and Applying Functions to Data

In this section, we’ll cover one of the cornerstones of R: how to write and apply functions to data. This approach of applying functions to data rather than writing explicit loops follows from a functional-programming style that R inherited from one of its language influences, Scheme. Specifically, our focus will be on applying functions to R’s lists using lapply() and sapply(), but the same ideas extend to other R data structures through similar “apply” functions. Solving common data analysis problems with apply functions is tricky at first, but mastering it will serve you well in R.

Using lapply() with lists

Suppose you have a list of numeric values (here, generated at random with rnorm()):

ll <- list(a=rnorm(6, mean=1), b=rnorm(6, mean=4), c=rnorm(6, mean=6))
ll

$a
[1] 1.18878269 1.70679099 0.52000990 1.22904283 1.54318797 0.01839539

$b
[1] 4.083775 3.679889 5.255608 3.005899 2.792800 5.726441

$c
[1] 6.442686 6.473601 4.913205 5.188414 7.045060 5.489694

How might we calculate the mean of each vector stored in this list? If you’re familiar with for loops in other languages, you may approach this problem using R’s for loops:

for (i in 1:length(ll)) {
  print(mean(ll[[i]]))
}

[1] 1.034368
[1] 4.090735
[1] 5.925443

However, this is not idiomatic R; a better approach is to use an apply function that applies another function to each list element.

Apply functions

Function Description Example
apply apply a function to a matrix apply(x, margin, function)
lapply apply a function to a list; return a list lapply(list, sum)
sapply same as lapply, but returns a vector sapply(list, sum)
tapply apply a function to a vector split based on factor levels tapply(mtcars$wt, mtcars$cyl, mean)
vapply same as sapply but you need to specify the type of return value vapply(list, sum, FUN.VALUE=double(1))
mapply a multivariate version of sapply mapply(rep, 1:4, 4:1)

margin = 1 indicates rows, margin = 2 indicates columns

For example, to calculate the mean of each list element, we’d want to apply the function mean() to each element. To do so, we can use the function lapply() (the l is for list):

lapply(ll, mean)

$a
[1] 1.034368

$b
[1] 4.090735

$c
[1] 5.925443

lapply() has several advantages over the loop shown above:

We can also use lapply() in parallel (see p. 233 of the textbook for details)

Tips for apply functions

1) Don’t call the function you pass to lapply()—for example, don’t do lapply(ll, mean(x)) or `lapply(ll, mean()).

2) In some cases you will need to specify additional arguments to the function you’re passing to lapply(). For example, mean() returns NA if some data are missing. We ignore NA values by calling mean() with the argument na.rm=TRUE:

ll[[c(3,2)]] <- NA
lapply(ll, mean)

$a
[1] 1.034368

$b
[1] 4.090735

$c
[1] NA

ll[[c(3,2)]] <- NA
lapply(ll, mean, na.rm=TRUE)

$a
[1] 1.034368

$b
[1] 4.090735

$c
[1] 5.815812

Writing functions.

Sometimes, we don’t have an exact function that we want to apply to our data. But we can just write it :) R makes writing functions very easy, both because you should be writing lots of them to organize your code and applying functions is such a common operation in R.

What is a function?

Functions gather a sequence of operations into a whole, preserving it for ongoing use. Functions provide:

As the basic building block of most programming languages, user-defined functions constitute “programming” as much as any single abstraction can. If you have written a function, you are a computer programmer.

For example, we could write a simple version of R’s mean() function with na.rm=TRUE and apply it to the list:

meanRemoveNA <- function(x) mean(x, na.rm=TRUE)
lapply(ll, meanRemoveNA)

$a
[1] 1.034368

$b
[1] 4.090735

$c
[1] 5.815812

The syntax for meanRemoveNA() is a common shortened version of the general syntax for R functions:

fun_name <- function(args) {
  # body, containing R expressions  
  return(value)
}

Function definitions consist of arguments, a body, and a return value. Functions that contain only one line in their body can omit the braces (as the meanRemoveNA() function does). Similarly, using return() to specify the return value is optional; R’s functions will automatically return the last evaluated expression in the body. These syntactic shortcuts are commonly used in R, as we often need to quickly write functions to apply to data.

Alternatively, we could forgo creating a function named meanRemoveNA() in our global environment altogether and instead use an anonymous function. Anonymous functions are useful when we only need a function once for a specific task:

lapply(ll, function(x) mean(x, na.rm=TRUE))

$a
[1] 1.034368

$b
[1] 4.090735

$c
[1] 5.815812

meanRemoveNAVerbose <- function(x, warn=TRUE) {
# A function that removes missing values when calculating the mean # and warns us about it.
if (any(is.na(x)) && warn) { warning("removing some missing values!")
}
mean(x, na.rm=TRUE)
}
lapply(ll, meanRemoveNAVerbose)

Warning in FUN(X[[i]], ...): removing some missing values!

$a
[1] 1.034368

$b
[1] 4.090735

$c
[1] 5.815812

Note, that functions over one line should be kept in a file and sent to the interpreter. RStudio conveniently allows you to send a whole function definition at once with Command-Option-f on a Mac and Control-Alt-f on Windows.

If you’ve been writing these functions down into a separate R script (a good idea!), you can load in the functions into our R session by using the source function:

source("functions/functions-lesson.R")

Function tips

Pass by value

Functions in R almost always make copies of the data to operate on inside of a function body. When we modify these data inside the function we are modifying the copy of them, not the original variable we gave as the first argument.

This is called “pass-by-value” and it makes writing code much safer: you can always be sure that whatever changes you make within the body of the function, stay inside the body of the function.

Function scope

Another important concept is scoping: any variables (or functions!) you create or modify inside the body of a function only exist for the lifetime of the function’s execution. Even if we have variables of the same name in our interactive R session, they are not modified in any way when executing a function.

Additional resources

R has some unique aspects that can be exploited when performing more complicated operations. We will not be writing anything that requires knowledge of these more advanced concepts. In the future when you are comfortable writing functions in R, you can learn more by reading the R Language Manual or this chapter from Advanced R Programming by Hadley Wickham. For context, R uses the terminology “environments” instead of frames.

Tip4: Testing and documenting

It’s important to both test functions and document them: Documentation helps you, and others, understand what the purpose of your function is, and how to use it, and its important to make sure that your function actually does what you think.

When you first start out, your workflow will probably look a lot like this:

  1. Write a function
  2. Comment parts of the function to document its behaviour
  3. Load in the source file
  4. Experiment with it in the console to make sure it behaves as you expect
  5. Make any necessary bug fixes
  6. Rinse and repeat.

Formal documentation for functions, written in separate .Rd files, gets turned into the documentation you see in help files. The [roxygen2][roxygen] package allows R coders to write documentation alongside the function code and then process it into the appropriate .Rd files. You will want to switch to this more formal method of writing documentation when you start writing more complicated R projects.

Formal automated tests can be written using the testthat package.

Key Points

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