In preparation for this course, you should have several software applications installed onto your computer and ready to use them.
Install the latest operating system for your computer1, and then the latest versions of R
(free,
open-source)2, RStudio
(free, open-source)3, and several R
packages. To install the R
packages, open
RStudio
and run the following command:
install.packages(
c(
"bayesplot", "beeswarm", "bookdown", "broom", "colorspace",
"crosstalk", "d3Network", "dagitty", "diagram", "directlabels",
"distill", "eulerr", "extrafont", "extrafontdb", "flexdashboard",
"flextable", "geojsonio", "GGally", "ggalt", "ggdendro", "ggforce",
"ggfx", "ggiraph", "ggmap", "ggnetwork", "ggnewscale",
"ggplotlyExtra", "ggraph", "ggrastr", "ggrepel", "ggridges",
"ggtext", "ggthemes", "glue", "htmlwidgets", "igraph", "kableExtra",
"knitr", "latex2exp", "magick", "margins", "Matrix", "purrr",
"networkD3", "packcircles", "particles", "patchwork", "polyclip",
"r2d3", "ragg", "reticulate", "rgdal", "rgl", "rmarkdown", "rolldown",
"rstanarm", "sf", "stringi", "stringr", "svglite", "threejs",
"tidygraph", "tidymodels", "tidytext", "tidyverse", "tinytex",
"transformr", "upsetjs"
)
)
We will be using R Markdown files to write and create reports. One good resource to become familiar with what we’ll use is on the “Get Started” menu at https://rmarkdown.rstudio.com/index.html.
For organizing and citing source material, install Zotero (free, open-source), which integrates with RStudio to automatically organize and properly cite your sources (this course website is even linked to my Zotero library to keep everything up-to-date with my reference information).
To get you started — whoop, whoop — you can import into your library
all the references I’ve cited for your course by downloading this bibliography file,
opening Zotero and in its menu, selecting
File | Import...
Open a free github account, and install gitub desktop. This will help with version control of your projects, and allow collaboration with me and other students.
Early in the course, we will focus on best practices in reproducible communications, which involve coding. But businesses also sometimes use more limited, point-and-click style software like Tableau for those who do not code. For comparisons with other visualization tools, I’ve included a table to our selected topics. We will discuss use cases when covering interactive communications; obtain a free, one-year student license beforehand4 .
In the first class, we will discuss a one-to-one comparison in
writing code in R
with packages versus Python with packages
for data transformation and visualization. While course demonstrations
and homeworks focus on R
, I encourage you to also translate
your work to Python as demonstrated for practice. Of
note: I do not generally recommend using Anaconda or
Miniconda for package management in Python as it tends to be fragile and
can break other software. Instead, I install the latest version of Python5 and then manage packages using
pip
, though the setup is beyond the scope of this
course.
I’m running MacOS Monterey 12.2.1, but you may use Windows or Linux too.↩︎
I’m running R version 4.1.3 (2022-03-10).↩︎
I’m running RStudio 2022.02.0-preview+392 “Prairie Trillium” Preview (9b625ecd4777b148ffb1c322c2184e18bbf49b0c, 2022-01-21) for macOS.↩︎
It’s otherwise proprietary and expensive.↩︎
I’m currently running Python version 3.10.1.↩︎
If you see mistakes or want to suggest changes, please create an issue on the source repository.