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Sometimes we want to perform a calculation, or a set of calculations, multiple times in our code. We could write out the equation over and over in our code – OR – we could chose to build a function that allows us to repeat several operations with a single command. This tutorial will focus on creating functions in R.

R Skill Level: Beginner - you’re learning R


After completing this tutorial, you will be able to:

  • Explain why we should divide programs into small, single-purpose functions.
  • Use a function that takes parameters (input values).
  • Return a value from a function.
  • Set default values for function parameters.
  • Write, or define, a function.
  • Test and debug a function. (This section in construction).

Things You’ll Need To Complete This Tutorial

You will need the most current version of R and, preferably, RStudio loaded on your computer to complete this tutorial.

Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets. An overview of setting the working directory in R can be found here.

R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.

Creating Functions

Sometimes we want to perform a calculation, or a set of calculations, multiple times in our code. For example, we might need to convert units from Celsius to Kelvin, across multiple datasets and save if for future use.

We could write out the equation over and over in our code – OR – we could chose to build a function that allows us to repeat several operations with a single command. This tutorial will focus on creating functions in R.

Getting Started

Let’s start by defining a function fahr_to_kelvin that converts temperature values from Fahrenheit to Kelvin:

fahr_to_kelvin <- function(temp) {
	    kelvin <- ((temp - 32) * (5/9)) + 273.15

Notice the syntax used to define this function:

FunctionNameHere <- function(Input-variable-here){

The definition begins with the name of your new function. Use a good descriptor of the function you are doing and make sure it isn’t the same as a a commonly used R function!

This is followed by the call to make it a function and a parenthesized list of parameter names. The parameters are the input values that the function will use to perform any calculations. In the case of fahr_to_kelvin, the input will be the temperature value that we wish to convert from fahrenheit to kelvin. You can have as many input parameters as you would like (but too many are poor style).

The body, or implementation, is surrounded by curly braces { }. Leaving the initial curly bracket at the end of the first line and the final one on its own line makes functions easier to read (for the human, the machine doesn’t care). In many languages, the body of the function - the statements that are executed when it runs - must be indented, typically using 4 spaces.

Data Tip: While it is not mandatory in R to indent your code 4 spaces within a function, it is strongly recommended as good practice!

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.

The last line within the function is what R will evaluate as a returning value. Remember that the last line has to be a command that will print to the screen, and not an object definition, otherwise the function will return nothing - it will work, but will provide no output. In our example we print the value of the object Kelvin.

Calling our own function is no different from calling any other built in R function that you are familiar with. Let’s try running our function.

# call function for F=32 degrees

## [1] 273.15

# We could use `paste()` to create a sentence with the answer
paste('The boiling point of water (212 Farenheit) is', fahr_to_kelvin(212),'degrees Kelvin.')

## [1] "The boiling point of water (212 Farenheit) is 373.15 degrees Kelvin."

We’ve successfully called the function that we defined, and we have access to the value that we returned.

Question: What would happen if we instead wrote our function as:

fahr_to_kelvin_test <- function(temp) {
	kelvin <- ((temp - 32) * (5/9)) + 273.15

Try it:


Nothing is returned! This is because we didn’t specify what the output was in the final line of the function.

However, we can see that the fuction still worked by assigning the function to object “a” and calling “a”.

# assign to a
a <- fahr_to_kelvin_test(32)

# value of a

## [1] 273.15

We can see that even though there was no output from the function, the function was still operational.

Challenge: Writing Functions

Now that we’ve seen how to turn Fahrenheit into Kelvin, try your hand at converting Kelvin to Celsius. Remember, for the same temperature Kelvin is 273.15 degrees less than Celsius.

Compound Functions

What about converting Fahrenheit to Celsius? We could write out the formula as a new function or we can combine the two functions we have already created. It might seem a bit silly to do this just for converting from Fahrenheit to Celcius but think about the other applciations where you will use fuctions!

# use two functions (F->K & K->C) to create a new one (F->C)
fahr_to_celsius <- function(temp) {
	temp_k <- fahr_to_kelvin(temp)
	temp_c <- kelvin_to_celsius(temp_k)
paste('freezing point of water (32 Fahrenheit) in Celsius:', fahr_to_celsius(32.0))

## [1] "freezing point of water (32 Fahrenheit) in Celsius: 0"

This is our first taste of how larger programs are built: we define basic operations, then combine them in ever-large chunks to get the effect we want. Real-life functions will usually be larger than the ones shown here—typically half a dozen to a few dozen lines—but they shouldn’t ever be much longer than that, or the next person who reads it won’t be able to understand what’s going on.

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