Enhancing visualizations (part I)

A fuzzy monster in a beret and scarf, critiquing their own column graph on a canvas in front of them while other assistant monsters (also in berets) carry over boxes full of elements that can be used to customize a graph (like themes and geometric shapes). In the background is a wall with framed data visualizations. Stylized text reads 'ggplot2: build a data masterpiece.'

Pre-class Prep

A. Install required packages

install.packages("paletteer") # a comprehensive collection of color palettes in R using a common interface
install.packages("viridis") # Colorblind-Friendly Color Maps for R
install.packages("RColorBrewer") # ColorBrewer Palettes
install.packages("tigris") # for downloading and using Census TIGER/Line shapefiles in R

B. Download data

County-level precipitation data (Feb 2019 - Jan 2024) for the contiguous U.S, made available by NOAA National Centers for Environmental Information (NCEI) (the download buttons are located just beneath the rendered map – choose the CSV option)
- rename the data file as county-feb19-jan24-precip.csv and save it to your class repo at the file path: EDS-240-class-examples/week5/data/county-feb19-jan24-precip.csv

C. Download .qmd templates & review data wrangling code

Download all necessary templates (links in the table below) and save them to a EDS-240-data-viz-examples/week5/ folder.
Review the data wrangling code under the ## Setup section of ¹each template file and make note of any questions you might have. Due to time constraints, we won’t be live-coding our way through the data wrangling code in class. However, we’ll reserve some time to answer any questions before we jump into data viz .

¹We will walk through the 5.3 template setup / wrangling code together in class, but it will still benefit you to review it a bit beforehand!

D. (OPTIONAL, but highly recommended) Install Google Chrome extensions

I’ll be using both of these (really wonderful and easy-to-use) tools during lecture and encourage you to install them!

Let’s get color blind: simulates different color deficiencies in the browser
Grayscale the Web: turn specific sites or tabs grayscale
ColorPick Eyedropper: a browser-based color chooser tool (I recommended that you install this for week 2 discussion, so you may already have it!)

Lecture Materials

Lecture slides	Code-along template	Code-along key
Lecture 5.1: good data viz	NA	NA
Lecture 5.2: colors	5.2 template	5.2 key
Lecture 5.3: choropleth	5.3 template	5.3 key

Pre-discussion Prep

A. (OPTIONAL) Bring a cool data viz!

This week’s discussion section will be a time to explore, share, and discuss “non-standard” chart types, as well as those that present data using the fundamental chart types we’ve already discussed in different and exciting ways. You’ll have time to browse some data viz during section, but it would be great to bring any inspiring examples you might have come across already.

B. (OPTIONAL) Get yourself a Bluesky account

Bluesky is quickly becoming the Twitter replacement for data science community members to connect, share, and learn together. You can stay up to date on #TidyTuesday submissions and follow many of the great data viz folks in the field. Check out the resources page for more information on how to get started.

Discussion Materials

Discussion Slides	Exercise instructions	Exercise solutions
D5 slides: “non-standard” chart types	NA	NA

Assignment Reminders

Assignment Type	Assignment Title	Date Assigned	Date Due
EOC	EOC (week 5)	Mon 02/03/2025	Mon 02/03/2025, 11:55pm PT
SR	Mid-course reflection (SR #2)	Mon 02/03/2025	Sat 02/08/2025, 11:59pm PT