R for reproducible scientific analysis

Creating functions

Any operation you will perform more than once can be put into a function. That way, rather than retyping all the commands (and potentially making errors), you can simply call the function, passing it a new dataset or parameters. This may seem cumbersome at first, but writing functions to automate repetitive tasks is incredibly powerful. E.g. each time you call ggplot you are calling a function that someone wrote. Imagine if each time you wanted to make a plot you had to copy and paste or write that code from scratch!

Defining a function

Recall the components of a function. E.g. the log function (see ?log) takes “arguments” x and base and “returns” the base-base logarithm of x. Functions take arguments as input and yield return-values as output. You can define functions to do any number of operations on any number of arguments, but always output a single return value (however there are complex objects into which you can put multiple objects, should you need to).

Let’s start by defining a simple function to add two numbers. This is the basic structure, which you can read as “assign to the variable my_sum a function that takes arguments a and b and returns the_sum.” The body of the function is delimited by the curly-braces. The statements in the body are indented. This makes the code easier to read but does not affect how the code operates.

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

Notice that no numbers were summed when we ran that code, but now the Environment has an object called my_sum that has type function. You can call my_sum just like you would any other function. When you do, the code between the curly-braces of the my_sum definition is run with whatever values you pass to a and b substituted in their place.

my_sum(a = 2, b = 2)

[1] 4

my_sum(3, 4)

[1] 7

Just like log provides a default value of base (exp(1)) so that you don’t have to type it every time, you can provide default values to any arguments of your function. Then if the user doesn’t specify them, the defaults will be used.

my_sum2 <- function(a = 1, b = 2) {
  the_sum <- a + b
  return(the_sum)
}
my_sum2()

[1] 3

my_sum2(b = 7)

[1] 8

Temperature conversion

Let’s define a function F_to_K that converts temperatures from Fahrenheit to Kelvin:

F_to_K <- function(temp) {
  K <- ((temp - 32) * (5 / 9)) + 273.15
  return(K)
}

Calling our own function is no different from calling any other function:

# freezing point of water
F_to_K(32)

[1] 273.15

# boiling point of water
F_to_K(212)

[1] 373.15

source()ing functions

You can load all the functions in your code/functions.R script without even opening the file, via the source function. This allows you to keep your functions separate from the analyses which use them.

source('code/functions.R')

Combining functions

The real power of functions comes from mixing, matching and combining them into ever large chunks to get the effect we want.

A more-useful function

Let’s write a function to calculate countries’ GDPs in their local currency.

• Start with Canada, exchange rate is 1.279098

• Turn that into a function that takes country and exchange rate.

• Here is a table of exchange rates for many countries.

• Call the function with some row

• lapply over rows

Do by joins

And by year

• Get all the exchange rates over time. You can download that here.

• join, filter

Challenge solutions

plotPopGrowth <- function(countrytoplot, dat = gapminder) {
    df <- filter(dat, country == countrytoplot) 
    plot <- ggplot(df, aes(year, pop)) + 
        geom_line()
    return(plot)
}
plotPopGrowth('Canada')