This repository contains files for a two-day course on Visualization for Data Science in R, offered during Data Matters in the summer of 2024. The course description and activities are listed below.
See additional details on the pre-work page to prepare for the course.
Course exercises and slides are available from the GitHub repository.
This course is designed for two audiences: experienced visualization designers looking to apply open data science techniques to their work and data science professionals who have limited experience with visualization. Participants will develop skills in visualization design using R, a tool commonly used for data science. Basic familiarity with R is required.
Data science skills are increasingly important for research and industry projects. With complex data science projects, however, come complex needs for understanding and communicating analysis processes and results. Ultimately, an analyst's data science toolbox is incomplete without visualization skills. Incorporating effective visualizations directly into the analysis tool you are using can facilitate quick data exploration, streamline your research process, and improve the reproducibility of your research.
The course will take a project-based approach to learning best practices for visualization for data science. Participants will be guided through several sample analysis and visualization projects that will highlight different types of visualization, different features of R and its visualization capabilities, and different challenges that arise when trying to apply an open data science philosophy to visualization.
- Introduction to visualization in R
- Using ggplot2 for publication-ready graphics
- Applying common graphic design principles to ggplot2 visualizations
- Adding interactivity to visualizations through R Markdown and HTML widgets
As indicated above, 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.
We will use RStudio to interact with R, and all exercises will be distributed in R Markdown files (rather than simple R script files). This allows us to combine R code with non-code elements and promotes a literate programming approach to research.
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 should have installed current versions of R, RStudio, and the following packages: tidyverse, markdown, knitr, readxl, plotly, colorspace, dt, crosstalk, flexdashboard, and here. Permissions to install packages on the fly will be useful.
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ggplot2 Resources