This tutorial provides the basics of installing and working with packages in R.
After completing this tutorial, you will be able to:
- Describe the basics of an R package
- Install a package in R
- Call (use) an installed R package
- Update a package in R
- View the packages installed on your computer
Things You’ll Need To Complete This Tutorial
You will need the most current version of R and, preferably,
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.
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.
- More on packages from Quick-R.
- Article on R-bloggers about installing packages in R.
About Packages in R
Packages are collections of R functions, data, and compiled code in a well-defined format. When you install a package it gives you access to a set of commands that are not available in the base R set of functions. The directory where packages are stored is called the library. R comes with a standard set of packages. Others are available for download and installation. Once installed, they have to be loaded into the session to be used.
Installing Packages in R
To install a package you have to know where to get the package. Most established packages are available from "CRAN" or the Comprehensive R Archive Network.
Packages download from specific CRAN "mirrors"" where the packages are saved
(assuming that a binary, or set of installation files, is available for your
operating system). If you have not set a preferred CRAN mirror in your
options(), then a menu will pop up asking you to choose a location from which
you'd like to install your packages.
To install any package from CRAN, you use
install.packages(). You only need to
install packages the first time you use R (or after updating to a new version).
# install the ggplot2 package install.packages("ggplot2")
Use a Package
Once a package is installed (basically the functions are downloaded to your computer), you need to "call" the package into the current session of R. This is essentially like saying, "Hey R, I will be using these functions now, please have them ready to go". You have to do this ever time you start a new R session, so this should be at the top of your script.
When you want to call a package, use
library(PackageNameHere). You may also
see some people using
require() -- while that works in most cases, it does
function slightly differently and best practice is to use
# load the package library(ggplot2)
What Packages are Installed Now?
If you want to use a package, but aren't sure if you've installed it before,
you can check! In code you, can use
# check installed packages installed.packages()
If you are using RStudio, you can also check out the Packages tab. It will list all the currently installed packages and have a check mark next to them if they are currently loaded and ready to use. You can also update and install packages from this tab. While you can "call" a package from here too by checking the box I wouldn't recommend this as calling the package isn't in your script and you if you run the script again this could trip you up!
Sometimes packages are updated by the users who created them. Updating packages can sometimes make changes to both the package and also to how your code runs. ** If you already have a lot of code using a package, be cautious about updating packages as some functionality may change or disappear.**
Otherwise, go ahead and update old packages so things are up to date.
In code you, can use
old.packages() to check to see what packages are out of
update.packages() will update all packages in the known libraries
interactively. This can take a while if you haven't done it recently! To update
everything without any user intervention, use the
ask = FALSE argument.
If you only want to update a single package, the best way to do it is using
# list all packages where an update is available old.packages() # update all available packages update.packages() # update, without prompts for permission/clarification update.packages(ask = FALSE) # update only a specific package use install.packages() install.packages("plotly")
In RStudio, you can also manage packages using Tools -> Install Packages.
Challenge: Installing Packages
Check to see if you can install the
dplyr package or a package of interest to
- Check to see if the
dplyrpackage is installed on your computer.
- If it is not installed, install the "dplyr" package in R.
- If installed, is it up to date?
Get Lesson Code
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