Before we Start
Last updated on 2023-09-08 | Edit this page
- How to find your way around RStudio?
- How to interact with R?
- How to manage your environment?
- How to install packages?
- Install latest version of R.
- Install latest version of RStudio.
- Navigate the RStudio GUI.
- Install additional packages using the packages tab.
- Install additional packages using R code.
The term “
R” is used to refer to both the programming
language and the software that interprets the scripts written using
RStudio is currently a very popular way to not only write your R scripts but also to interact with the R software. To function correctly, RStudio needs R and therefore both need to be installed on your computer.
To make it easier to interact with R, we will use RStudio. RStudio is the most popular IDE (Integrated Development Environment) for R. An IDE is a piece of software that provides tools to make programming easier.
You can also use the R Presentations feature to present your work in an HTML5 presentation mixing Markdown and R code. You can display these within R Studio or your browser. There are many options for customising your presentation slides, including an option for showing LaTeX equations. This can help you collaborate with others and also has an application in teaching and classroom use.
The learning curve might be steeper than with other software but with R, the results of your analysis do not rely on remembering a succession of pointing and clicking, but instead on a series of written commands, and that’s a good thing! So, if you want to redo your analysis because you collected more data, you don’t have to remember which button you clicked in which order to obtain your results; you just have to run your script again.
Working with scripts makes the steps you used in your analysis clear, and the code you write can be inspected by someone else who can give you feedback and spot mistakes.
Working with scripts forces you to have a deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.
Reproducibility is when someone else (including your future self) can obtain the same results from the same dataset when using the same analysis.
R integrates with other tools to generate manuscripts from your code. If you collect more data, or fix a mistake in your dataset, the figures and the statistical tests in your manuscript are updated automatically.
An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with these requirements.
To further support reproducibility and transparency, there are also packages that help you with dependency management: keeping track of which packages we are loading and how they depend on the package version you are using. This helps you make sure existing workflows work consistently and continue doing what they did before.
Packages like renv let you “save” and “load” the state of your project library, also keeping track of the package version you use and the source it can be retrieved from.
With 10,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine statistical approaches from many scientific disciplines to best suit the analytical framework you need to analyze your data. For instance, R has packages for image analysis, GIS, time series, population genetics, and a lot more.
The skills you learn with R scale easily with the size of your dataset. Whether your dataset has hundreds or millions of lines, it won’t make much difference to you.
R is designed for data analysis. It comes with special data structures and data types that make handling of missing data and statistical factors convenient.
R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.
The plotting functionalities in R are endless, and allow you to adjust any aspect of your graph to convey most effectively the message from your data.
Thousands of people use R daily. Many of them are willing to help you through mailing lists and websites such as Stack Overflow, or on the RStudio community. Questions which are backed up with short, reproducible code snippets are more likely to attract knowledgeable responses.
Anyone can inspect the source code to see how R works. Because of this transparency, there is less chance for mistakes, and if you (or someone else) find some, you can report and fix bugs.
Because R is open source and is supported by a large community of developers and users, there is a very large selection of third-party add-on packages which are freely available to extend R’s native capabilities.
Let’s start by learning about RStudio, which is an Integrated Development Environment (IDE) for working with R.
The RStudio IDE open-source product is free under the Affero General Public License (AGPL) v3. The RStudio IDE is also available with a commercial license and priority email support from RStudio, Inc.
We will use the RStudio IDE to write code, navigate the files on our computer, inspect the variables we create, and visualize the plots we generate. RStudio can also be used for other things (e.g., version control, developing packages, writing Shiny apps) that we will not cover during the workshop.
One of the advantages of using RStudio is that all the information you need to write code is available in a single window. Additionally, RStudio provides many shortcuts, autocompletion, and highlighting for the major file types you use while developing in R. RStudio makes typing easier and less error-prone.
It is good practice to keep a set of related data, analyses, and text self-contained in a single folder called the working directory. All of the scripts within this folder can then use relative paths to files. Relative paths indicate where inside the project a file is located (as opposed to absolute paths, which point to where a file is on a specific computer). Working this way makes it a lot easier to move your project around on your computer and share it with others without having to directly modify file paths in the individual scripts.
RStudio provides a helpful set of tools to do this through its “Projects” interface, which not only creates a working directory for you but also remembers its location (allowing you to quickly navigate to it). The interface also (optionally) preserves custom settings and open files to make it easier to resume work after a break.
- Under the
Filemenu, click on
New project, choose
New directory, then
- Enter a name for this new folder (or “directory”) and choose a
convenient location for it. This will be your working
directory for the rest of the day (e.g.,
- Click on
- Create a new file where we will type our scripts. Go to File >
New File > R script. Click the save icon on your toolbar and save
your script as “
The simplest way to open an RStudio project once it has been created
is to navigate through your files to where the project was saved and
double click on the
.Rproj (blue cube) file. This will open
RStudio and start your R session in the same directory
.Rproj file. All your data, plots and scripts will
now be relative to the project directory. RStudio projects have the
added benefit of allowing you to open multiple projects at the same time
each open to its own project directory. This allows you to keep multiple
projects open without them interfering with each other.
Let’s take a quick tour of RStudio.
RStudio is divided into four “panes”. The placement of these panes and their content can be customized (see menu, Tools -> Global Options -> Pane Layout).
The Default Layout is:
- Top Left - Source: your scripts and documents
- Bottom Left - Console: what R would look and be like without RStudio
- Top Right - Environment/History: look here to see what you have done
- Bottom Right - Files and more: see the contents of the project/working directory here, like your Script.R file
Using a consistent folder structure across your projects will help keep things organized and make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, you might create directories (folders) for scripts, data, and documents. Here are some examples of suggested directories:
data/Use this folder to store your raw data and intermediate datasets. For the sake of transparency and provenance, you should always keep a copy of your raw data accessible and do as much of your data cleanup and preprocessing programmatically (i.e., with scripts, rather than manually) as possible.
data_output/When you need to modify your raw data, it might be useful to store the modified versions of the datasets in a different folder.
documents/Used for outlines, drafts, and other text.
fig_output/This folder can store the graphics that are generated by your scripts.
scripts/A place to keep your R scripts for different analyses or plotting.
You may want additional directories or subdirectories depending on your project needs, but these should form the backbone of your working directory.
The working directory is an important concept to understand. It is the place where R will look for and save files. When you write code for your project, your scripts should refer to files in relation to the root of your working directory and only to files within this structure.
Using RStudio projects makes this easy and ensures that your working
directory is set up properly. If you need to check it, you can use
getwd(). If for some reason your working directory is not
the same as the location of your RStudio project, it is likely that you
opened an R script or RMarkdown file not your
.Rproj file. You should close out of RStudio and open the
.Rproj file by double clicking on the blue cube! If you
ever need to modify your working directory in a script,
setwd('my/path') changes the working directory. This should
be used with caution since it makes analyses hard to share across
devices and with other users.
For this lesson we will use the following folders in our working
fig_output/. Let’s write them all in
lowercase to be consistent. We can create them using the RStudio
interface by clicking on the “New Folder” button in the file pane
(bottom right), or directly from R by typing at console:
dir.create("data") dir.create("data_output") dir.create("fig_output")
You can either download the data used for this lesson from GitHub or
with R. You can copy the data from this GitHub
link and paste it into a file called
data/ directory you just created. Or you can do this
directly from R by copying and pasting this in your terminal (your
instructor can place this chunk of code in the Etherpad):
download.file( "https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/SAFI_clean.csv", "data/SAFI_clean.csv", mode = "wb" )
The basis of programming is that we write down instructions for the computer to follow, and then we tell the computer to follow those instructions. We write, or code, instructions in R because it is a common language that both the computer and we can understand. We call the instructions commands and we tell the computer to follow the instructions by executing (also called running) those commands.
There are two main ways of interacting with R: by using the console or by using script files (plain text files that contain your code). The console pane (in RStudio, the bottom left panel) is the place where commands written in the R language can be typed and executed immediately by the computer. It is also where the results will be shown for commands that have been executed. You can type commands directly into the console and press Enter to execute those commands, but they will be forgotten when you close the session.
Because we want our code and workflow to be reproducible, it is better to type the commands we want in the script editor and save the script. This way, there is a complete record of what we did, and anyone (including our future selves!) can easily replicate the results on their computer.
RStudio allows you to execute commands directly from the script editor by using the Ctrl + Enter shortcut (on Mac, Cmd + Return will work). The command on the current line in the script (indicated by the cursor) or all of the commands in selected text will be sent to the console and executed when you press Ctrl + Enter. If there is information in the console you do not need anymore, you can clear it with Ctrl + L. You can find other keyboard shortcuts in this RStudio cheatsheet about the RStudio IDE.
At some point in your analysis, you may want to check the content of a variable or the structure of an object without necessarily keeping a record of it in your script. You can type these commands and execute them directly in the console. RStudio provides the Ctrl + 1 and Ctrl + 2 shortcuts allow you to jump between the script and the console panes.
If R is ready to accept commands, the R console shows a
> prompt. If R receives a command (by typing,
copy-pasting, or sent from the script editor using Ctrl +
Enter), R will try to execute it and, when ready, will show
the results and come back with a new
> prompt to wait
for new commands.
If R is still waiting for you to enter more text, the console will
+ prompt. It means that you haven’t finished
entering a complete command. This is likely because you have not
‘closed’ a parenthesis or quotation, i.e. you don’t have the same number
of left-parentheses as right-parentheses or the same number of opening
and closing quotation marks. When this happens, and you thought you
finished typing your command, click inside the console window and press
Esc; this will cancel the incomplete command and return you
> prompt. You can then proofread the command(s)
you entered and correct the error.
In addition to the core R installation, there are in excess of 10,000 additional packages which can be used to extend the functionality of R. Many of these have been written by R users and have been made available in central repositories, like the one hosted at CRAN, for anyone to download and install into their own R environment. You should have already installed the packages ‘ggplot2’ and ’dplyr. If you have not, please do so now using these instructions.
You can see if you have a package installed by looking in the
packages tab (on the lower-right by default). You can also
type the command
installed.packages() into the console and
examine the output.
Additional packages can be installed from the ‘packages’ tab. On the packages tab, click the ‘Install’ icon and start typing the name of the package you want in the text box. As you type, packages matching your starting characters will be displayed in a drop-down list so that you can select them.
At the bottom of the Install Packages window is a check box to ‘Install’ dependencies. This is ticked by default, which is usually what you want. Packages can (and do) make use of functionality built into other packages, so for the functionality contained in the package you are installing to work properly, there may be other packages which have to be installed with them. The ‘Install dependencies’ option makes sure that this happens.
Scroll through packages tab down to ‘tidyverse’. You can also type a few characters into the searchbox. The ‘tidyverse’ package is really a package of packages, including ‘ggplot2’ and ‘dplyr’, both of which require other packages to run correctly. All of these packages will be installed automatically. Depending on what packages have previously been installed in your R environment, the install of ‘tidyverse’ could be very quick or could take several minutes. As the install proceeds, messages relating to its progress will be written to the console. You will be able to see all of the packages which are actually being installed.
Because the install process accesses the CRAN repository, you will need an Internet connection to install packages.
It is also possible to install packages from other repositories, as well as Github or the local file system, but we won’t be looking at these options in this lesson.
If you were watching the console window when you started the install of ‘tidyverse’, you may have noticed that the line
was written to the console before the start of the installation messages.
You could also have installed the
tidyverse packages by running this command
directly at the R terminal.
We will be using another package called
here throughout the workshop to manage
paths and directories. We will discuss it more detail in a later
episode, but we will install it now in the console: