This project is maintained by amzoss
Welcome to “Visualization in Data Science using R,” a two-day course on using R for visualization.
This course assumes basic familiarity with R — e.g., R syntax, data structures, development environments. Participants with no knowledge of R should consider taking an introductory R short course. A significant portion of the course will use ggplot2 and other tidyverse packages to create visualizations, but prior experience with those packages is not required.
In order to participate in class exercises, participants will need a computer where you have installed current versions of R, RStudio, and the following packages:
You can use this line of code to install these packages:
install.packages(c("tidyverse", "markdown", "knitr", "readxl", "plotly",
"colorspace", "RColorBrewer", "DT", "crosstalk", "flexdashboard", "here"))
This helpful guide to Installing Packages in RStudio describes the process for installing packages once you have installed R and RStudio. Just copy each of the package names listed above into the install packages dialog box, either one at a time or in a long list.
Having appropriate user permissions to install packages on the fly would be useful.
Mac users without Xcode already installed may see a prompt to install Xcode or the “command line tools” to compile some packages from source. You should go ahead and do that – at least the command line tools. This is often a large installation, though, so it may take a bit of time.
I will be sharing datasets and code using this course’s GitHub repository, but you will not have to have a GitHub account or a git installation on your laptop to participate in the course. We will walk through downloading the files from the GitHub website and opening them in RStudio.
For this course I will be using RMarkdown files* rather than simple R scripts. RMarkdown is a way of interweaving R code with other kinds of descriptive content – text, links, images. When you only use R scripts in RStudio, the plots show up in the plot window, which only shows one plot at a time. RMarkdown allows us to create separate code “chunks” for different plots, and each plot will appear immediately below its code chunk.
If you’re not familiar with RMarkdown, I will give a brief overview, but you can also explore it on your own in advance.
Finally, because this is a course where you and I will be walking through exercises together, you may find it especially helpful to have a second monitor attached to your computer, so you can have your RStudio window on one monitor and Zoom on the other. Alternatively, if you have two devices, you could use one to display the Zoom content and the other to take part in the exercises.
*-If you’re wondering what the difference is between R Markdown files and an R Notebook, these details about R Notebooks should help. Also, while Quarto is gaining momentum, R Markdown is still being supported and has a mature ecosystem. Quarto is also an additional installation step. For our purposes, R Markdown files will look almost idential to Quarto documents, so we will continue to use R Markdown for the time being.