R for reproducible scientific analysis

Creating 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, data may be 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.

Defining a function

Let’s open a new R script file in the functions/ directory and call it functions-lesson.R.

my_sum <- function(a, b) {
  the_sum <- a + b
  return(the_sum)
}

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.

When we call the function, the values we pass to it 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.

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

Combining functions

The real power of functions comes from mixing, matching and combining them into ever large 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)
}

Applying functions to datasets

We’re going to define a function that calculates the average year of birth in our health dataset:

# Takes a dataset and calculates the average year of birth for a
# specified study group.
calcBirthYearAverage <- function(dat) {
  birthYearAverage <- mean(dat$birthYear)
  return(birthYearAverage)
}

We define calcBirthYearAverage 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.

calcBirthYearAverage(healthData)

[1] 1933.588

That’s not very informative, since the dataset comprises data from two studies that were performed decades apart. Let’s add another argument so we can calculate the average year of birth for a particular study group.

# Takes a dataset and calculates the average year of birth for a
# specified study group.
calcBirthYearAverage <- function(dat, group = "Group 1") {
  birthYearAverage <- mean(dat[dat$HIGroup == group, ]$birthYear)
  return(birthYearAverage)
}

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")

The function now subsets the provided data by group before taking the average year of birth. A default value of 1 is given for group, so that if no value is specified when you call the function, the result of the function will be for group 1. You need to be careful when setting default values; sometimes you can get some unexpected behaviour from functions if you don’t realise that an argument has a default value.

Let’s take a look at what happens when we specify the study group:

calcBirthYearAverage(healthData,"Group 1")

[1] 1910.041

calcBirthYearAverage(healthData,"Group 2")

[1] 1955.426

calcBirthYearAverage(healthData)

[1] 1910.041

What if we want to look at the average year of birth for specific year levels?

best_practice <- c("Write", "programs", "for", "people", "not", "computers")
paste(best_practice, collapse=" ")

[1] "Write programs for people not computers"

fence(text=best_practice, wrapper="***")

Challenge solutions

kelvin_to_celsius <- function(temp) {
 celsius <- temp - 273.15
 return(celsius)
}

fahr_to_celsius <- function(temp) {
  temp_k <- fahr_to_kelvin(temp)
  result <- kelvin_to_celsius(temp_k)
  return(result)
}

calcBirthYearAverage <- function(dat, group, yearLevel) {
  birthYearAverage <- mean(dat[dat$HIGroup == group & dat$education %in% yearLevel, ]$birthYear)
  return(birthYearAverage)
}

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 ***"