4  Packages and Functions

4.1 Functions

Functions are a set of instructions that can be called by name. They are useful for automating repetitive tasks and for encapsulating complex tasks.

Functions are defined using the function() function. An example of a function is the dataframe function which we used above to create a dataframe.

A function takes in one or more arguments and returns a value. The arguments are specified in the function definition and the value is returned using the return() function.

To see the arguments of a function, use the ? before the function name e.g. ?dataframe. To see the code of a function, just type the function name without the parentheses.

For example, to see the code of the dataframe function, type dataframe without the parentheses. Other examples are head , str and summary functions.

4.1.1 Creating a function

The basic syntax for defining a function is as follows:

# Define a function
function_name <- function(arg1, arg2, ...) {
    # Function body
    # ...
    # Return value
    return(return_value)
}

For example, let us create a function called add that takes in two arguments and returns the sum of the two arguments.

# Define a function
add <- function(x, y) {
    # Return the sum of the two arguments
    return(x + y)
}

# Call the function
add(5, 3)

[1] 8

4.2 Packages

A package is a collection of functions, data, and documentation that extends the functionality of R. There are thousands of packages available for R.

To use a package, you first need to install it using the install.packages() function. Once installed, you can load the package using the library() function.

For example, to install the dplyr package, you would type install.packages("dplyr").

To load the dplyr package, you would type library(dplyr). To see the functions in a package, type help(package = "package_name") e.g. help(package = "dplyr").

To see the code of a function in a package, type package_name::function_name e.g. dplyr::mutate. You can also use the ? before the function name e.g. ?dplyr::mutate.

4.2.1 Package sources

There are three main sources of packages for R:

• CRAN - The Comprehensive R Archive Network: https://cran.r-project.org/. This is the main source of packages for R. It contains over 15,000 packages. To install a package from CRAN, you can use the install.packages() function.
• GitHub: Most developers store their packages on GitHub. To install a package from GitHub, you can use the install_github() function from the devtools package. e.g. devtools::install_github("dzvoti/hcesNutR").

Tip

Notice how we used package_name::function_name to call the install_github() function.

4.2.2 Loading packages

Once installed a package need to be ‘loaded’ for its function to be available in R. This is done using the library() function.

For example, to load the dplyr package, you would type require(dplyr). The difference between the two functions is that library() will throw an error if the package is not installed, while require() will throw a warning. You can also use the :: operator to call a function from a package without loading the package.

Also,to call the mutate() function from the dplyr package without loading the package, you would type dplyr::mutate(). This is useful when you want to use a function from a package without loading the package.

4.2.3 Removing packages

To remove a package, you can use the remove.packages() function. For example, to remove the dplyr package, you would type remove.packages("dplyr").

4.2.4 Updating packages

To update a package, you can use the update.packages() function. For example, to update the dplyr package, you would type update.packages("dplyr").

4.2.5 Listing installed packages

To list all installed packages, you can use the installed.packages() function. For example, to list all installed packages, you would type installed.packages().

4.2.6 Recomended packages

There are thousands of packages available for R. However, there are some packages that are recommended for beginners. These include:

Recommended Packages {.striped .hover}

Package	Description
tidyverse	A collection of packages designed for data science. It includes the: dplyr, ggplot2, tidyr, readr, purrr, tibble, stringr, forcats and haven packages.
here	A package for managing file paths.

Now that we know what packages and functions are, let us look at some of the functions we can use to manipulate vectors and dataframes in the next section on Data Import,Wrangling and Export.