Lab 06 - Take a sad plot and make it better

Given below are two data visualizations that violate many data visualization best practices. Improve these visualizations using R and the tips for effective visualizations that we introduced in class. You should produce one visualization per dataset. Your visualization should be accompanied by a brief paragraph describing the choices you made in your improvement, specifically discussing what you didn’t like in the original plots and why, and how you addressed them in the visualization you created.

On the due date you will give a brief presentation describing one of your improved visualizations and the reasoning for the choices you made.

Learning goals

Getting started

Go to the course GitHub organization and locate your homework repo, clone it in RStudio and open the R Markdown document. Knit the document to make sure it compiles without errors.

Warm up

Before we introduce the data, let’s warm up with some simple exercises. Update the YAML of your R Markdown file with your information, knit, commit, and push your changes. Make sure to commit with a meaningful commit message. Then, go to your repo on GitHub and confirm that your changes are visible in your Rmd and md files. If anything is missing, commit and push again.


We’ll use the tidyverse package for much of the data wrangling and visualisation and the data lives in the dsbox package. These packages are already installed for you. You can load them by running the following in your Console:



The datasets we’ll use are called instructors and fisheries from the dsbox package. Since the datasets are distributed with the package, we don’t need to load them separately; they become available to us when we load the package. You can find out more about the datasets by inspecting their documentation, which you can access by running ?instructors and ?fisheries in the Console or using the Help menu in RStudio to search for instructors or fisheries. You can also find this information here and here.