Skip to main content

Using R at Grattan Institute

5 Using packages

5.1 What are packages?

R comes with a lot of functions - commands - built in to do a broad range of tasks. You could, if you really wanted, import a dataset, clean it up, estimate a model, and make a plot just using the functions that come with R - known as ‘base R’21. But using packages will make your life easier.

Like R itself, packages are free and open source. You can install them from within RStudio using the methods described below.

5.2 How to install packages

You’ll typically install packages using the console in RStudio. That’s the part of the window that, by default, sits in the bottom-left corner of the screen.

In our work at Grattan, we use packages from two different source: the Comprehensive R Archive Network (CRAN) and Github. The main difference you need to know about is that we use different commands to install packages from these two sources.

To install a package from CRAN, we use the command install.packages().

For example, this code will install the ggplot2 package from CRAN:

install.packages("ggplot2")

The easiest way to install a package from Github is to use the function install_github(). Unfortunately, this function doesn’t come with base R. The install_github() function is part of the remotes package. To use it, we first need to install remotes from CRAN:

install.packages("remotes")

Now we can install packages from Github using the install_github() function from the remotes package. For example, here’s how we would install the Grattan ggplot2 theme, which we’ll discuss later in this website:

remotes::install_github("grattan/grattantheme", dependencies = TRUE, upgrade = "always")

5.3 Get set up: install packages for Grattan

Just starting out or setting up a new machine? Run this block of code to get yourself all set up:

cran_packages <- c("devtools", "tidyverse", "readabs", "janitor", 
                   "rio", "sf")

install.packages(cran_packages)

github_packages <- c("grattan/grattantheme", "grattan/grattandata",
                     "runapp-aus/strayr",  "grattan/grattanReporter")

remotes::install_github(github_packages,
                        dependencies = TRUE,
                        upgrade = "always")

5.4 Using packages

Before using a function that comes from a package, you need to tell R where to look for the function. There are two main ways to do that.

We can either load (aka ‘attach’) the package by using the library() function. We typically do this at the top of a script.

library(remotes)

# Now that the `remotes` package is loaded, we can use its `install_github()` function:

install_github("grattan/grattantheme")

Or, we can use two colons :: to tell R to use an individual function from a package without loading it:

remotes::install_github("grattan/grattantheme")

It usually makes sense to load a package with library(), unless you only need to use one of its function once or twice. There’s no harm to using the :: operator even if you have already loaded a package with library(). This can remove ambiguity both for R and for humans reading your code, particularly if you’re using an obscure function - it makes it clearer where the function comes from.

5.5 Package versions

5.5.1 Updating packages

It’s generally a good idea to keep your packages up-to-date. The easiest way to do this is to run this code:

devtools::update_packages()

This will upgrade all your packages - including those you’ve installed from CRAN and Github.

When you run the above command, it will prompt you to ask which packages you want to update - press 1 for ‘All’.

If it asks you ‘Do you want to install from sources the package which needs compilation?’ type ‘no’ and press enter.22

5.5.2 Downgrading packages

Sometimes, when packages change, their functions evolve. The arguments to a function might change, or a function might be phased out (‘deprecated’) in favour of another. You can usually just adapt your workflow to the package’s new version without much fuss. If you find this isn’t the case, and you want to downgrade to an earlier version of a package, it’s straightforward. Just use the install_version() function, like this:

devtools::install_version("devtools", "1.13.3")

It’s rare that you’d need to downgrade. Better to stay up to date, and adapt your code when necessary to changes in packages.

5.6 Packages commonly used at Grattan

Some packages we use at Grattan - like the tidyverse collection of packages - are very popular among R users. Some - like the grattantheme package - are specific to Grattan Institute. Others - like the readabs package - are made by Grattan people, useful at Grattan, but also used outside of the Institute. To install a core set of packages we use at Grattan, click here and run the code chunk.

5.6.1 Using the tidyverse

The main packages in the tidyverse include:

  • ggplot2 for making beautiful, customisable graphs
  • dplyr for manipulating data frames
  • tidyr for tidying your data
  • readr for importing data from a broad range of formats
  • purrr for functional programming
  • stringr for manipulating strings of text

All these packages (and more!) will automatically be loaded for you when you run the command23:

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.6     ✔ dplyr   1.0.8
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

A range of other packages are installed on your machine as part of the tidyverse. These include:

  • readxl for importing Excel spreadsheets into R
  • haven for importing Stata, SAS and SPSS data
  • lubridate for working with dates
  • rvest for scraping websites

Although these packages are installed as part of the tidyverse, they aren’t loaded automatically when you run library(tidyverse). You’ll need to load them individually, like:

5.6.2 Grattan-specific packages

A range of Grattan people have written packages that come in handy at Grattan. * grattantheme The grattantheme package, by Matt Cowgill and Will Mackey, helps to make your ggplot2 charts Grattan-y. We cover the package extensively in the data visualisation chapter. Find it on Github.

  • grattandata The grattandata package, by Matt Cowgill and Jonathan Nolan, is used to load microdata from the Grattan microdata repository. We cover this in the reading data chapter. Find it on Github.

  • grattan The grattan package, created by Hugh Parsonage, contains two broad sets of functions. One set of functions (sometimes known by the nickname “Grattax”) is used for modelling the personal income tax system. Another set of functions (“Grattools”) are useful for a lot of our work, like converting dates to financial years (grattan::date2fy()) or a version of dplyr::ntile() that uses weights (grattan::weighted_ntile()). Find it on Github.

  • grattanReporter The grattanReporter package, created by Hugh Parsonage (and currently maintained by Will Mackey), runs a series of checks on Grattan’s LaTeX reports to ensure consistent style. Find it on Github.

5.6.3 Other commonly-used, useful packages

There are other packages we commonly use at Grattan, including some developed by Grattan staff. These include:

  • strayr This package, by Will Mackey and others, is very handy for working with Australian classifications (eg ANZSCO, ANZSIC, ASCED), and for using Australian spatial data. You’ll want it if you’re going to be making maps. Find it on Github.

  • readabs The readabs package, by Matt Cowgill, provides an easy way to download, tidy, and import ABS time series data in R. Find it on Github.