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kjhealy / dataviz_0122

SH Course Jan 22

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A Three Day Remote Seminar via Code Horizons.

Information for Participants

This page contains information for seminar participants. Please read it and follow the instructions below to install the necessary software and course materials.

Sign-up and Registration

Course Overview

This course will teach you how to think about good data visualization, and how to do it. We begin with some core principles about how we see graphs, what makes some of them better than others, and how to begin cultivating good judgment about visualization. Then, through a series of worked examples, you will learn how to use ggplot to make graphs piece by piece. The emphasis throughout is on acquiring a practical feel for and good judgement about the way ggplot can be used, from the simplest cases to sophisticated, highly customized data visualizations.

Learning objectives

The effective use of graphs and charts is an important way to explore data for yourself and to communicate your ideas and results to others. Being able to produce effective plots from data is also the best way to develop an eye for reading and critically understanding visualizations made by others, whether presented in academia, business, policy, or the media. Learning how to visualize data effectively is more than just knowing how to write code that produces figures from data. This course will teach you how to do that. But it will also teach you how to think about the information you want to show, and how best to present it to your audience—including when the audience is yourself.

Is this course for me?

You should take this workshop if:

  1. You want to understand the basic principles behind effective data visualizations, and how they are implemented in R and ggplot.
  2. You want to better develop your practical sense for why some graphs and figures work well while others do not.
  3. You want to feel more confident and fluent in ggplot, in order to make, refine, and effectively present good data visualizations.

Preparation

We will be working with the most recent stable versions of R and RStudio, as well as with a number of additional packages. You will need to install R, RStudio, and the necessary packages on your own computer.

Installing R and RStudio on your Laptop

Installing the required additional Packages

  • Once the applications are installed, launch RStudio. Either carefully type in or copy-and-paste the following lines of code at R’s command prompt, located in the RStudio window named “Console”, and then hit return. In the code below, the <- arrow is made up of two keystrokes, first < and then the short dash or minus symbol, -.
## Code to run at the RStudio console begins here

my_packages <- c("tidyverse", "broom", "coefplot", "cowplot", 
                  "drat", "gapminder", "GGally", "ggforce", 
                  "ggrepel", "ggraph", "ggridges", "graphlayouts", 
                  "here", "margins", "maps", "mapproj", 
                  "mapdata", "MASS", "naniar", "palmerpenguins", 
                  "patchwork", "prismatic", "quantreg", "remotes", 
                  "scales", "sf", "socviz", "survey", "srvyr", 
                  "tidygraph", "viridis", "viridisLite")

install.packages(my_packages, repos = "http://cran.rstudio.com")

data_packages <- c("covdata", "congress", "kjhnet", "nycdogs")

drat::addRepo("kjhealy")
install.packages(data_packages, repos = "https://kjhealy.github.io/drat/")

## Code to run ends here

If you do not have one already, it will be useful to create a GitHub account and obtain a GitHub Personal Access Token. (This is not required for the course, it will just be helpful to have one.) To get set up, read and carefully follow the instructions in Parts I and II of Happy Git With R, and then also the instructions in Appendix B of the same document.

The Course Packet

This repo is also the course packet. It contains the course material, data, slides, and code for the graphs we will draw. It’s an RStudio project. Clone it with Git or download it as a zip file and decompress it. To launch the project, double-click on dataviz.Rproj and RStudio will open with this project ready to go.

What’s here

RMarkdown files

  • course_notes.Rmd is an RMarkdown document to get you started on taking your own notes for the course, and writing your own code.
  • scratch.Rmdis where I’ll work on examples or queries that come up in class.
  • The other .Rmd files contain all of the code snippets from the slides.

The data/ folder

Datasets we may use during the course.

The slides/ folder

PDF versions of the slides from the course.

Instructor

Kieran Healy is Professor of Sociology at Duke University. He is the author of Data Visualization: A Practical Introduction (Princeton University Press, 2019). Much of his research has been about the social organization of exchange in human blood and organs, cultural goods, software, and ideas. His current work focuses on the moral order of market society, the effect of models and measurement on social classification, and the link between those two topics, especially in the consumer credit market. You can learn more at https://kieranhealy.org

About

SH Course Jan 22