Functions Explained
Overview
Teaching: 45 min
Exercises: 15 minQuestions
How can I write a new function in R?
Objectives
Define a function that takes arguments.
Return a value from a function.
Check argument conditions with
stopifnot()
in functions.Test a function.
Set default values for function arguments.
Explain why we should divide programs into small, single-purpose functions.
If we only had one data set to analyze, it would probably be faster to load the file into a spreadsheet and use that to plot simple statistics. However, the gapminder data is updated periodically, and we may want to pull in that new information later and re-run our analysis again. We may also obtain similar data from a different source in the future.
In this lesson, we’ll learn how to write a function so that we can repeat several operations with a single command.
What is a function?
Functions gather a sequence of operations into a whole, preserving it for ongoing use. Functions provide:
- a name we can remember and invoke it by
- relief from the need to remember the individual operations
- a defined set of inputs and expected outputs
- rich connections to the larger programming environment
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.
Defining a function
Let’s open a new R script file in the functions/
directory and call it
functions-lesson.R.
Let’s define a function fahr_to_kelvin()
that converts temperatures from
Fahrenheit to Kelvin:
fahr_to_kelvin <- function(temp) {
kelvin <- ((temp - 32) * (5 / 9)) + 273.15
return(kelvin)
}
We define fahr_to_kelvin()
by assigning it to the output of function
. The
list of argument names are contained within parentheses. Next, the
body of the function–the
statements that are executed when it runs–is contained within curly braces
({}
). The statements in the body are indented by two spaces. This makes the
code easier to read but does not affect how the code operates.
It is useful to think of creating functions like writing a cookbook. First you define the “ingredients” that your function needs. In this case, we only need one ingredient to use our function: “temp”. After we list our ingredients, we then say what we will do with them, in this case, we are taking our ingredient and applying a set of mathmatical operators to it.
When we call the function, the values we pass to it as arguments are assigned to those variables so that we can use them inside the function. Inside the function, we use a return statement to send a result back to whoever asked for it.
Tip
One feature unique to R is that the return statement is not required. R automatically returns whichever variable is on the last line of the body of the function. But for clarity, we will explicitly define the return statement.
Let’s try running our function. Calling our own function is no different from calling any other function:
# freezing point of water
fahr_to_kelvin(32)
[1] 273.15
# boiling point of water
fahr_to_kelvin(212)
[1] 373.15
Challenge 1
Write a function called
kelvin_to_celsius()
that takes a temperature in Kelvin and returns that temperature in Celsius.Hint: To convert from Kelvin to Celsius you subtract 273.15
Solution to challenge 1
Write a function called
kelvin_to_celsius
that takes a temperature in Kelvin and returns that temperature in Celsiuskelvin_to_celsius <- function(temp) { celsius <- temp - 273.15 return(celsius) }
Combining functions
The real power of functions comes from mixing, matching and combining them into ever-larger chunks to get the effect we want.
Let’s define two functions that will convert temperature from Fahrenheit to Kelvin, and Kelvin to Celsius:
fahr_to_kelvin <- function(temp) {
kelvin <- ((temp - 32) * (5 / 9)) + 273.15
return(kelvin)
}
kelvin_to_celsius <- function(temp) {
celsius <- temp - 273.15
return(celsius)
}
Challenge 2
Define the function to convert directly from Fahrenheit to Celsius, by reusing the two functions above (or using your own functions if you prefer).
Solution to challenge 2
Define the function to convert directly from Fahrenheit to Celsius, by reusing these two functions above
fahr_to_celsius <- function(temp) { temp_k <- fahr_to_kelvin(temp) result <- kelvin_to_celsius(temp_k) return(result) }
Interlude: Defensive Programming
Now that we’ve begun to appreciate how writing functions provides an efficient
way to make R code re-usable and modular, we should note that it is important
to ensure that functions only work in their intended use-cases. Checking
function parameters is related to the concept of defensive programming.
Defensive programming encourages us to frequently check conditions and throw an
error if something is wrong. These checks are referred to as assertion
statements because we want to assert some condition is TRUE
before proceeding.
They make it easier to debug because they give us a better idea of where the
errors originate.
Checking conditions with stopifnot()
Let’s start by re-examining fahr_to_kelvin()
, our function for converting
temperatures from Fahrenheit to Kelvin. It was defined like so:
fahr_to_kelvin <- function(temp) {
kelvin <- ((temp - 32) * (5 / 9)) + 273.15
return(kelvin)
}
For this function to work as intended, the argument temp
must be a numeric
value; otherwise, the mathematical procedure for converting between the two
temperature scales will not work. To create an error, we can use the function
stop()
. For example, since the argument temp
must be a numeric
vector, we
could check for this condition with an if
statement and throw an error if the
condition was violated. We could augment our function above like so:
fahr_to_kelvin <- function(temp) {
if (!is.numeric(temp)) {
stop("temp must be a numeric vector.")
}
kelvin <- ((temp - 32) * (5 / 9)) + 273.15
return(kelvin)
}
If we had multiple conditions or arguments to check, it would take many lines
of code to check all of them. Luckily R provides the convenience function
stopifnot()
. We can list as many requirements that should evaluate to TRUE
;
stopifnot()
throws an error if it finds one that is FALSE
. Listing these
conditions also serves a secondary purpose as extra documentation for the
function.
Let’s try out defensive programming with stopifnot()
by adding assertions to
check the input to our function fahr_to_kelvin()
.
We want to assert the following: temp
is a numeric vector. We may do that like
so:
fahr_to_kelvin <- function(temp) {
stopifnot(is.numeric(temp))
kelvin <- ((temp - 32) * (5 / 9)) + 273.15
return(kelvin)
}
It still works when given proper input.
# freezing point of water
fahr_to_kelvin(temp = 32)
[1] 273.15
But fails instantly if given improper input.
# Metric is a factor instead of numeric
fahr_to_kelvin(temp = as.factor(32))
Error in fahr_to_kelvin(temp = as.factor(32)): is.numeric(temp) is not TRUE
Challenge 3
Use defensive programming to ensure that our
fahr_to_celsius()
function throws an error immediately if the argumenttemp
is specified inappropriately.Solution to challenge 3
Extend our previous definition of the function by adding in an explicit call to
stopifnot()
. Sincefahr_to_celsius()
is a composition of two other functions, checking inside here makes adding checks to the two component functions redundant.fahr_to_celsius <- function(temp) { stopifnot(is.numeric(temp)) temp_k <- fahr_to_kelvin(temp) result <- kelvin_to_celsius(temp_k) return(result) }
More on combining functions
Now, we’re going to define a function that calculates the Gross Domestic Product of a nation from the data available in our dataset:
# Takes a dataset and multiplies the population column
# with the GDP per capita column.
calcGDP <- function(dat) {
gdp <- dat$pop * dat$gdpPercap
return(gdp)
}
We define calcGDP()
by assigning it to the output of function
. The list of
argument names are contained within parentheses. Next, the body of the function
– the statements executed when you call the function – is contained within
curly braces ({}
).
We’ve indented the statements in the body by two spaces. This makes the code easier to read but does not affect how it operates.
When we call the function, the values we pass to it are assigned to the arguments, which become variables inside the body of the function.
Inside the function, we use the return()
function to send back the result.
This return()
function is optional: R will automatically return the results of
whatever command is executed on the last line of the function.
calcGDP(head(gapminder))
[1] 6567086330 7585448670 8758855797 9648014150 9678553274 11697659231
That’s not very informative. Let’s add some more arguments so we can extract that per year and country.
# Takes a dataset and multiplies the population column
# with the GDP per capita column.
calcGDP <- function(dat, year=NULL, country=NULL) {
if(!is.null(year)) {
dat <- dat[dat$year %in% year, ]
}
if (!is.null(country)) {
dat <- dat[dat$country %in% country,]
}
gdp <- dat$pop * dat$gdpPercap
new <- cbind(dat, gdp=gdp)
return(new)
}
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")
Ok, so there’s a lot going on in this function now. In plain English, the function now subsets the provided data by year if the year argument isn’t empty, then subsets the result by country if the country argument isn’t empty. Then it calculates the GDP for whatever subset emerges from the previous two steps. The function then adds the GDP as a new column to the subsetted data and returns this as the final result. You can see that the output is much more informative than a vector of numbers.
Let’s take a look at what happens when we specify the year:
head(calcGDP(gapminder, year=2007))
country year pop continent lifeExp gdpPercap gdp
12 Afghanistan 2007 31889923 Asia 43.828 974.5803 31079291949
24 Albania 2007 3600523 Europe 76.423 5937.0295 21376411360
36 Algeria 2007 33333216 Africa 72.301 6223.3675 207444851958
48 Angola 2007 12420476 Africa 42.731 4797.2313 59583895818
60 Argentina 2007 40301927 Americas 75.320 12779.3796 515033625357
72 Australia 2007 20434176 Oceania 81.235 34435.3674 703658358894
Or for a specific country:
calcGDP(gapminder, country="Australia")
country year pop continent lifeExp gdpPercap gdp
61 Australia 1952 8691212 Oceania 69.120 10039.60 87256254102
62 Australia 1957 9712569 Oceania 70.330 10949.65 106349227169
63 Australia 1962 10794968 Oceania 70.930 12217.23 131884573002
64 Australia 1967 11872264 Oceania 71.100 14526.12 172457986742
65 Australia 1972 13177000 Oceania 71.930 16788.63 221223770658
66 Australia 1977 14074100 Oceania 73.490 18334.20 258037329175
67 Australia 1982 15184200 Oceania 74.740 19477.01 295742804309
68 Australia 1987 16257249 Oceania 76.320 21888.89 355853119294
69 Australia 1992 17481977 Oceania 77.560 23424.77 409511234952
70 Australia 1997 18565243 Oceania 78.830 26997.94 501223252921
71 Australia 2002 19546792 Oceania 80.370 30687.75 599847158654
72 Australia 2007 20434176 Oceania 81.235 34435.37 703658358894
Or both:
calcGDP(gapminder, year=2007, country="Australia")
country year pop continent lifeExp gdpPercap gdp
72 Australia 2007 20434176 Oceania 81.235 34435.37 703658358894
Let’s walk through the body of the function:
calcGDP <- function(dat, year=NULL, country=NULL) {
Here we’ve added two arguments, year
, and country
. We’ve set
default arguments for both as NULL
using the =
operator
in the function definition. This means that those arguments will
take on those values unless the user specifies otherwise.
if(!is.null(year)) {
dat <- dat[dat$year %in% year, ]
}
if (!is.null(country)) {
dat <- dat[dat$country %in% country,]
}
Here, we check whether each additional argument is set to null
, and whenever
they’re not null
overwrite the dataset stored in dat
with a subset given by
the non-null
argument.
I did this so that our function is more flexible for later. We can ask it to calculate the GDP for:
- The whole dataset;
- A single year;
- A single country;
- A single combination of year and country.
By using %in%
instead, we can also give multiple years or countries to those
arguments.
Tip: Pass by value
Functions in R almost always make copies of the data to operate on inside of a function body. When we modify
dat
inside the function we are modifying the copy of the gapminder dataset stored indat
, 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.
Tip: 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. When we call
calcGDP()
, the variablesdat
,gdp
andnew
only exist inside the body of the function. 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.
gdp <- dat$pop * dat$gdpPercap
new <- cbind(dat, gdp=gdp)
return(new)
}
Finally, we calculated the GDP on our new subset, and created a new data frame with that column added. This means when we call the function later we can see the context for the returned GDP values, which is much better than in our first attempt where we got a vector of numbers.
Challenge 4
Test out your GDP function by calculating the GDP for New Zealand in 1987. How does this differ from New Zealand’s GDP in 1952?
Solution to challenge 4
calcGDP(gapminder, year = c(1952, 1987), country = "New Zealand")
GDP for New Zealand in 1987: 65050008703
GDP for New Zealand in 1952: 21058193787
Challenge 5
The
paste()
function can be used to combine text together, e.g:best_practice <- c("Write", "programs", "for", "people", "not", "computers") paste(best_practice, collapse=" ")
[1] "Write programs for people not computers"
Write a function called
fence()
that takes two vectors as arguments, calledtext
andwrapper
, and prints out the text wrapped with thewrapper
:fence(text=best_practice, wrapper="***")
Note: the
paste()
function has an argument calledsep
, which specifies the separator between text. The default is a space: “ “. The default forpaste0()
is no space “”.Solution to challenge 5
Write a function called
fence()
that takes two vectors as arguments, calledtext
andwrapper
, and prints out the text wrapped with thewrapper
:fence <- function(text, wrapper){ text <- c(wrapper, text, wrapper) result <- paste(text, collapse = " ") return(result) } best_practice <- c("Write", "programs", "for", "people", "not", "computers") fence(text=best_practice, wrapper="***")
[1] "*** Write programs for people not computers ***"
Tip
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.
Tip: 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:
- Write a function
- Comment parts of the function to document its behaviour
- Load in the source file
- Experiment with it in the console to make sure it behaves as you expect
- Make any necessary bug fixes
- Rinse and repeat.
Formal documentation for functions, written in separate
.Rd
files, gets turned into the documentation you see in help files. The roxygen2 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
Use
function
to define a new function in R.Use parameters to pass values into functions.
Use
stopifnot()
to flexibly check function arguments in R.Load functions into programs using
source()
.