The Evolution of a ggplot (Ep. 1)

In this series of blog posts, I aim to show you how to turn a default ggplot into a plot that visualizes information in an appealing and easily understandable way. The goal of each blog post is to provide a step-by-step tutorial explaining how my visualization have evolved from a typical basic ggplot. All plots are going to be created with 100% {ggplot2} and 0% Inkscape.

In the first episode, I transform a basic boxplot into a colorful and self-explanatory combination of a jittered dot strip plot and a lollipop plot. I am going to use data provided by the UNESCO on global student to teacher ratios that was selected as data for the #TidyTuesday challenge 19 of 2019.

I have prepared the data in the first way to map each countrys most recently reported student-teacher ratio in primary education as a tile map. I used the tile-based world data provided by Maarten Lambrechts to create this map as the first visualization for my weekly contribution:

For the second chart next to the tile map, I wanted to highlight the difference of the mean student ratio per continent but without discarding the raw data on the country-level. Therefore, I transformed the information on the region to represent the six continents excluding Antarctica (hm, do penguins not go to school?! Seems so… 🐧) and merged both datasets. If you would like to run the code yourself, you find the data preparation steps here. This is how the relevant columns of the merged and cleaned dataset looks like, showing two examples per continent:

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