DataViz TidyTuesday ggplot2

Hello, TidyTuesday!

Monday • May 6, 2019

Header image by Richard Strozynski

During my after-thesis-vacation, I managed to tick off two long-term todo’s on my list - set up a homepage and to participate in the #TidyTuesday challenge to strengthen my data science and visualization skills and share my work and code tricks with the amazing Rstats community on Twitter. The #TidyTuesday challenge is a excellent opportunity to do so!

What is it about?

The R4DS (“R for Data Science”) online learning community founded by Jesse Maegan is a great opportunity to train skills related to data wrangling and visualization and forms a network of mentors and learners. One of the first projects born out of this collaboration is #TidyTuesday - a weekly social data project that focusses on understanding how to summarize and arrange data to make meaningful charts with {ggplot2}, {tidyr}, {dplyr} and other tools in the {tidyverse} ecosystem founded by Thomas Mock. The intent is to provide a safe and supportive forum to practice their wrangling and data visualization skills.

Every Tuesday, R enthusiasts from all over the world clean, wrangle, tidy and visualize a new data set and share it on Twitter using the hashtag #TidyTuesday. I follow the challenges quite some time and I am amazed by the high-quality visualizations some contributors share on their Twitter (check for example the work of David Smale, Harro Cyranka and Jake Kaupp to name a few) but I during the PhD Thesis I always felt bad starting such a fun project. Since I submitted my thesis a few weeks ago and since I was anyway running deeper and deeper down the {tidyverse} hole during the PhD project, using their packages for all nearly all steps from data processing to statistical modelling and plotting, I finally gave it a try!

First Contribution: Week 17

Highly motivated I opened the challenge of the current week (Week 17) - and what a bummer, my first contribution had to focus on anime data, one of the least favorite genres in my life. So I decided to subset the data to the only anime I know by heart: the lovely crafted Studio Ghibli movies which my partner is a big fan of (I mean, we even have a Totoro drawing hanging in our flat…)!

Catching Up: Week 14 and 16

From this moment on, I was addicted and picked two other datasets, one on bike traffic in Seattle (Week 14):

… and several data sets in that were part of the inspiring article “Mistakes, we’ve drawn a few - Learning from our errors in data visualisation” by The Economist (Week 16):

I decided to stick to the overall theme as a collection of data viz which I may put together as a poster of my highlights - or maybe not. At some point, however, I likely going to use a different theme or a theme which fits the data more each week.

Latest Contribution: Week 18

And finally my contribution to the challenge last week with a data set from a study of Winger et al. (2019) on bird collisions in Chicago (Week 18):

You can find all these and upcoming #TidyTuesday contributions in the Visualization section and the corresponding codes on my GitHub page! In future blog posts, I am going to share some thoughts on why I picked a given chart type, which color palettes I use and some tips and tricks of styling your ggplots - stay tuned 🤓


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