Chapter 7: Introduction to Functions

In this module, we’ll explore three different approaches to using more advanced capabilities in R. After considering a function in an abstract sense, we’ll dive into using built-in R functions, loading R packages, and writing our own functions. Note, you may need to learn a bit about vectors from module-7 to understand some of the functions described below.

Helpful links:

• R Function Cheatsheet
• User Defined R Functions

7.1 What are functions?

In a broad sense, a function is a task that you may want to perform one or more times throughout a program. They provide a way of encapsulating multiple instructions into an aggregate capability that is useful in a variety of different contexts. Depending on the function (and the programming language), functions may have a varying number of inputs and outputs.

Let’s imagine a function (a series of lines of code) that could determine the largest number in a set of numbers. That function’s input would be the set of numbers, and the output would be the largest number. In R, a function only outputs a single-object (i.e., variable), though that object may be of any type (more on this later). R functions are just like other object (variables) in R, and are referenced by name. As in many programming languages, the function is executed by placing function inputs (also referred to as arguments or parameters) inside parentheses following the function name:

# Execute the `min` function to determine the minimum number of 10, 3, and 7/5
min(1, 8/6, 7/5)  # 1

7.2 Built-in R Functions

The R software program comes with a number of functions built into the language. In the example above, we used the min function to determine which number was smallest. Each argument to that function was a number, and the output was the smallest number (1). Here’s a very small table of functions to experiment with:

Function Name	Description	Example
c(a,b, ...)	Concatenate multiple items into a vector	c(1,2) # returns 1,2
length(a)	Determine vector length	length(c(1,2)) # returns 2
paste(a, b, ...)	Concatenate characters into one value	paste("Hi", "there") # returns “hi there”
length(a)	Determine vector length	length(c(1,2)) # returns 2
seq(a, b)	Return a sequence from a to b	seq(1,5) # returns 1, 2, 3, 4, 5
sum(a, b, ...)	Calculates the sum of all input values	sum(1,5) # returns 6
tolower(a)	Returns the characters in lowercase	tolower("Hi there") # returns “hi there”

To learn about function inputs/outputs, see the documentation of that function by entering ?FunctionName into your RStudio console.

7.3 Loading Functions

Packages are additional sets of R functions that are written and published by the R community. Because many R users encounter the same data management / data analysis challenges, programmers are able to benefit from the work of others (this is the amazing thing about the open-source community). R packages do not ship with the R software by default, and need to be downloaded (once) and loaded into your program each time you with to use them. While this may seem cumbersome, the R program would be huge if you had to download all packages to install it.

Luckily, it is quite simple to install and load R packages from within R! To do so, you’ll need to use the built-in R functions install.packages and library. Here’s an example of installing and loading the stringr package.

# Download/install the `stringr` package. Only needs to be done once on your machine
install.packages("stringr")
library(stringr)

When you load a package, you may receive a warning message about the package being built under a previous version of R. In all likelihood, this shouldn’t cause a problem, but you should pay attention to the details of the messages, and keep them in mind (especially if you start getting unexpected errors). After loading the package with the library function, you have access to functions that were written as part of that package.

7.4 Writing functions

Even more exciting than loading other peoples’ functions is writing your own. Any time that you have a task that you may repeat throughout a script, it’s good practice to write a function to perform that task. This will limit repetition and reduce the likelihood of error.

Similarly to creating any other variable, you use the assignment operator (<-) to store a function in a variable. It’s best practice assign functions names in CamelCase without any periods(.) in the name. This helps distinguish functions from other variables. Let’s take a look a simple function to understand it’s anatomy:

# Write a function to add two numbers together
AddNumbers <- function(a, b) {
  # Function body: perform tasks in here
  answer <- a + b

  # Return statement: what you want the function to output
  return (answer)
}

# Execute your AddNumbers function with the values 2 and 6
AddNumbers(3,6)  # 9

Let’s use the function as an example to better understand function anatomy:

Arguments: The values that are put into a function are referred to as arguments or parameters. When you define a function, you specify the arguments that the function accepts within the first set of parentheses (in the case, a and b). Note, we could have called a and b by any names we wanted. The important point is that those are the values that are used throughout the internal body of the function.

Body: The body of the function is the block of code that falls between the curly braces ({ }). The function body specifies all tasks that your function will perform. Within this section, your arguments (a and b), will assume the values passed into the function upon execution.

Return value: The last line of your function body is your return value. In R, functions returns a single value, which you should put in parentheses after the word return.

To execute the function, we simply passed in two arguments: 3 and 6. R then executes each line in the function body, replacing the arguments (a and b) with the values that were put into the function (3 and 6). Finally, R will output the return value at the end of the function.

For practice writing basic functions, see exercise-1.

7.5 Conditional Statements

In R, as in other programming languages, you often want to take different actions given a set of conditions. Conditional statements allow us to isolate chunks of code to execution given different contexts, which is often valuable within functions. In an abstract sense, an conditional statement is saying:

if(SOMETHING IS TRUE) {
  # DO EVERYTHING IN HERE
} else {
  # OTHERWISE, DO EVERYTHING IN HERE
}

In order to execute a conditional statement, the value inside of the if() parentheses must return a boolean value (or must resolve to a boolean value). Note, you can provide multiple different conditions if you use the elseif() syntax throughout this process:

if(SOMETHING IS TRUE) {
  # DO EVERYTHING IN HERE
} elseif(SOMETHING ELSE IS TRUE) {
  # DO EVERYTHING IN HERE
} else {
  # BY DEFAULT, DO EVERYTHING IN HERE
}

For practice writing conditional statements in functions, see exercise-2. Note, you’ll need to be familiar with some of the concepts from Chapter 8.