This is an R package of datasets, functions, and course materials to go along with the book Data Visualization: A Practical Introduction (Princeton University Press, 2019).
What's in this Package
socviz package contains about twenty five datasets and a number of utility and convenience functions. The datasets range in size from just a few rows to over 120,000 observations. Most of them are used in Data Visualization: A Practical Introduction (
http://socviz.co), and there are also a few others as well for self-learners and students to practice their skills on.
A course packet is also included. This is a zipped file containing an R Studio project consisting of a nine R Markdown documents that parallel the chapters in the book. They contain the code for almost all the figures in the book (and a few more besides). Some support files are also included, to help demonstrate things like reading in your own data locally in R.
To install the package, you can follow the instructions in the Preface to the book. Alternatively, first download and install R for MacOS, Windows or Linux, as appropriate. Then download and install RStudio. Launch RStudio and then type the following code at the Console prompt (
> ), hitting return at the end of each line:
my_packages <- c("tidyverse", "fs", "devtools") install.packages(my_packages) devtools::install_github("kjhealy/socviz")
Once everything has downloaded and been installed (which may take a little while), load the
The Course Packet
The supporting materials are contained in a compressed
.zip file. To extract them to your Desktop, make sure the
socviz package is loaded as described above. Then do this:
This will copy the
dataviz_course_notes.zip file to your Desktop, and uncompress it into a folder called
dataviz_course_notes. Double-click the file named
dataviz.Rproj to launch the project as a new RStudio session. If you want to uncompress the file somewhere other than your Desktop, e.g. your Documents folder, you can do this:
setup_course_notes(folder = "~/Documents")
More about the Datasets and Functions
The included datasets and functions are documented at http://kjhealy.github.io/socviz/reference/.