D-Lab's R Data Visualization Workshop

This repository contains the materials for D-Lab's R Data Visualization workshop.

Prerequisites

Prior experience with R Fundamentals is assumed.

Check D-Lab's Learning Pathways to figure out which of our workshops to take!

Workshop Goals

In this workshop, we provide an introduction to data visualization in R. First, we'll cover some basics of visualization theory. Then, we'll explore how to plot data in R using ggplot2. The learning objectives are:

1. Identify instances when visualization helps with analysis and understanding
2. Understand the general principles and common pitfalls of data visualization
3. Learn the ggplot functions for creating data visualizations

We will cover the following types of plots:

• histograms
• bar charts
• box plots
• scatter plots

We will also explore the basic grammar of ggplot2, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or paneling by groups. Throughout the workshop, we'll discuss the plot types best suited for particular types of data.

Basic familiarity with R is assumed. If you are not familiar with material in R Fundamentals, we recommend attending that workshop first.

Installation Instructions

We will use RStudio to go through the workshop materials, which requires installation of both the R language and the RStudio software. Complete the following steps:

1. Download R: Follow the links according to the operating system that you are running. Download the package, and install R onto your compute. You should install the most recent version (at least version 4.0).
2. Download RStudio: Install RStudio Desktop. This should be free. Do this after you have already installed R. The D-Lab strongly recommends an RStudio edition of 2022.02.0+443 "Prairie Trillium" or higher.
3. Download these workshop materials:

• Click the green "Code" button in the top right of the repository information.
• Click "Download Zip".
• Extract this file to a folder on your computer where you can easily access it (we recommend Desktop).

4. Optional: if you're familiar with git, you can instead clone this repository by opening a terminal and entering git clone git@github.com:dlab-berkeley/R-Data-Visualization.git.

Run the code

Now that you have all the required software and materials, you need to run the code:

1. Launch the RStudio software.

2. Use the file navigator to find the R-Data-Visualization folder that you downloaded from Github.

3. Double click on the R-Data-Visualization.Rproj file, and click "yes" when RStudio asks you to confirm whether you want to open up the project.

4. Open up the R-Data-Visualization.Rmd file, located in the lessons folder.

5. If you do not have the tidyverse package installed (which includes ggplot2 ), be sure to install it using the install.packages() function in the first code block of the R-Data-Visualization.Rmd file.

6. Run a chunk of code by clicking the green "play" button in the upper right hand corner of each code chunk. Alternatively, place your cursor on a given line and press "Command + Enter" (Mac) or "Control + Enter" (PC) to run an individual line of code.

7. Theere are challenges throughout the workshop. The file solutions/solutions.R contains the (you guessed it) solutions to these challenges.

Is R not working on your laptop?

If you do not have R installed and the materials loaded on your workshop by the time it starts, we strongly recommend using the UC Berkeley Datahub to run the materials for these lessons. You can access the DataHub by clicking this link.

The DataHub downloads this repository, along with any necessary packages, and allows you to run the materials in an RStudio instance on UC Berkeley's servers. No installation is necessary from your end - you only need an internet browser and a CalNet ID to log in. By using the DataHub, you can save your work and come back to it at any time. When you want to return to your saved work, just go straight to DataHub, sign in, and you click on the R-Data-Visualization folder.

Additional Resources

Check out the following resources to learn more about data visualization and R:

• R Markdown: The Definitive Guide
• The tidyverse style guide
• Software Carpentry
• Quick-R
• UCLA idre
• R-bloggers
• Stack Overflow - R

About the UC Berkeley D-Lab

D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.

Visit the D-Lab homepage to learn more about us. You can view our calendar for upcoming events, learn about how to utilize our consulting and data services, and check out upcoming workshops.

Other D-Lab R Workshops

Here are other R workshops offered by the D-Lab:

Basic Competency

• Fast-R
• R Data Wrangling
• R Functional Programming
• Geospatial Fundamentals in R with sf
• Census Data in R

Intermediate/Advanced Competency

• Advanced Data Wrangling in R
• Unsupervised Learning in R
• R Machine Learning with tidymodels
• Introduction to Deep Learning in R
• R Package Development

Contributors

• Alexander Stephenson
• Pratik Sachdeva
• Evan Muzzall
• Lawrence Yin Tello
• Josh Quan
• Hero Ashman

Thanks to Software Carpentry, Chris Paciorek, Rochelle Terman, and the R-bootcamp for inspiration.