1  RStudio Server Setup

Learning Objectives

  • Practice creating an R Project
  • Organize an R Project for effective project management
  • Understand how to move in an R Project using paths and working directories

1.1 Logon to the RStudio Server

To prevent us from spending most of this lesson troubleshooting the myriad of issues that can arise when setting up the R, RStudio, and git environments, we have chosen to have everyone work on a remote server with all of the software you need installed. We will be using a special kind of RStudio just for servers called RStudio Server. If you have never worked on a remote server before, you can think of it like working on a different computer via the internet. Note that the server has no knowledge of the files on your local filesystem, but it is easy to transfer files from the server to your local computer, and vice-versa, using the RStudio server interface.

Server Setup

You should have received an email prompting you to change your password for your server account. If you did not, please put up a post-it and someone will help you.

After you have successfully changed your password log in at: https://included-crab.nceas.ucsb.edu/

1.2 Create an R Project

In this course, we are going to be using an R project to organize our work. An R project is tied to a directory on your local computer, and makes organizing your work and collaborating with others easier.

The Big Idea: using an R project is a reproducible research best practice because it bundles all your work within a working directory. Consider your current data analysis workflow. Where do you import you data? Where do you clean and wrangle it? Where do you create graphs, and ultimately, a final report? Are you going back and forth between multiple software tools like Microsoft Excel, JMP, and Google Docs? An R project and the tools in R that we will talk about today will consolidate this process because it can all be done (and updated) in using one software tool, RStudio, and within one R project.

R Project Setup
  1. In the “File” menu, select “New Project”
  2. Click “New Directory”
  3. Click “New Project”
  4. Under “Directory name” type: training_{USERNAME} (i.e. training_do-linh)
  5. Leave “Create Project as subdirectory of:” set to ~
  6. Click “Create Project”

RStudio should open your new project automatically after creating it. One way to check this is by looking at the top right corner and checking for the project name.

1.3 Organizing an R Project

When starting a new research project, one of the first things I do is create an R Project for it (just like we have here!). The next step is to then populate that project with relevant directories. There are many tools out there that can do this automatically. Some examples are rrtools or usethis::create_package(). The goal is to organize your project so that it is a compendium of your research. This means that the project has all of the digital parts needed to replicate your analysis, like code, figures, the manuscript, and data access.

Some common directories are:

  • data: where we store our data (often contains subdirectories for raw, processed, and metadata data)
  • R: contains scripts for cleaning or wrangling, etc. (some find this name misleading if their work has other scripts beyond the R programming language, in which case they call this directory scripts)
  • plots or figs: generated plots, graphs, and figures
  • docs: summaries or reports of analysis or other relevant project information

Directory organization will vary from project to project, but the ultimate goal is to create a well organized project for both reproducibility and collaboration.

1.4 Moving in an R Project using Paths & Working Directories

Artwork by Allison Horst. A cartoon of a cracked glass cube looking frustrated with casts on its arm and leg, with bandaids on it, containing “setwd,” looks on at a metal riveted cube labeled “R Proj” holding a skateboard looking sympathetic, and a smaller cube with a helmet on labeled “here” doing a trick on a skateboard.

Now that we have your project created (and notice we know it’s an R Project because we see a .Rproj file in our Files pane), let’s learn how to move in a project. We do this using paths.

There are two types of paths in computing: absolute paths and relative paths.

  • An absolute path always starts with the root of your file system and locates files from there. The absolute path to my project directory is: /home/do-linh/training_do-linh

  • Relative paths start from some location in your file system that is below the root. Relative paths are combined with the path of that location to locate files on your system. R (and some other languages like MATLAB) refer to the location where the relative path starts as our working directory.

RStudio projects automatically set the working directory to the directory of the project. This means that you can reference files from within the project without worrying about where the project directory itself is. If I want to read in a file from the data directory within my project, I can simply type read.csv("data/samples.csv") as opposed to read.csv("/home/do-linh/training_do-linh/data/samples.csv").

This is not only convenient for you, but also when working collaboratively. We will talk more about this later, but if Matt makes a copy of my R project that I have published on GitHub, and I am using relative paths, he can run my code exactly as I have written it, without going back and changing /home/do-linh/training_do-linh/data/samples.csv to /home/jones/training_jones/data/samples.csv.

Note that once you start working in projects you should basically never need to run the setwd() command. If you are in the habit of doing this, stop and take a look at where and why you do it. Could leveraging the working directory concept of R projects eliminate this need? Almost definitely!

Similarly, think about how you work with absolute paths. Could you leverage the working directory of your R project to replace these with relative paths and make your code more portable? Probably!

1.5 Setting up R and RStudio on your Computer

1.5.1 Check your R Version

For this course, we used the R Version 4.2.2. To check the R Version of your personal computer run this in the Console: R.version$version.string. If you need to download R onto your personal computer or update it, you can do so from CRAN (The Comprehensive R Archive Network).

1.5.2 Check your RStudio Version

For this course, we used the RStudio Version released in December 2022. To check the RStudio Version of your personal computer run this in the Console: RStudio.Version()$version. If you need to download RStudio onto your personal computer or update it, you can do so from Posit. Note you may need to update your computer’s software to download the newest version of RStudio.

Another way to check for RStudio updates is to click “Help” and then “Check for Updates”. Follow the prompts.

1.5.3 Install Packages

In the code chunk below, we have listed out the packages we used for this course. You’re welcome to install each package one-by-one by calling the install.packages("package_name") function. Or you can copy and paste this code chunk and run it which includes a for loop that will check if a package is installed on your personal computer and if not, it will install it. We have also included a line of code that will update our packages. Running this code chunk may take a few minutes.

course_packages <- c("dplyr",

for (course_packages in course_packages) {
    if (!(course_packages %in% installed.packages())) { install.packages(course_packages) }

rm(course_packages) # remove object from Global Environment

# update any out-of-date packages