My focus for #TidyTuesday Week 16 was on trying to replicate a chart. For context, the idea was to explore the data behind some before and after charts from the Economist. Here are the charts I was looking at: Instead of trying to make my own version of the better chart, I decided to try and figure out how to replicate some of the elements in it. Specifically: Highlighting points in a scatterplot Label the highlighted points, but to also add custom formatting to some of the labels only Here’s the code:
Week 16 of #MakeoverMonday was a collaboration with RJ Andrews, and looked at the most common words in his book Info We Trust. What works well: The most common word, data, stands out well People are used to seeing and interpreting word clouds What could be improved: Lack of a title or description Having words on different angles makes them hard to read Too many colours that don’t seem to serve a purpose What I focused on: I decided to show the top ten words per section in the book, which highlighted how common the word ‘data’ is.
My focus for #TidyTuesday Week 15 was on getting better at exploratory analysis. This is a bit of a contrast to previous weeks where most of my effort has gone into data visualisation. The Data Week 15’s data comes from Wikipedia. Inspiration for this week came from John Burn-Murdoch’s amazing article in the Financial Times. The Analysis I decided to look at the top Grand Slam winners, but I broke this down by court type (grass, clay or hard).
Week 15 of #MakeoverMonday looked at ranking US states by fiscal condition. Here’s the original visualisation: What works well: Tidy design Good choice of colours Clear question in the title The top/bottom states are clearly stated What could be improved: It isn’t clear if things are improving or getting worse. Is this any different to the previous year? More information could be added in the form of text to help explain what is being shown here What I focused on: I struggled with this one.
This month’s #SWDChallenge was to find a visualisation you like and emulate it. I went with the ‘Live Long’ chart by David McCandless of Information is Beautiful. I’m a big fan of David’s work. His visualisations are stunning. I even own two of his books - Information is Beautiful and Knowledge is Beautiful - which I love to flick through when I’m struggling for inspiration. I decided to emulate the ‘Live Long’ chart because a) I wanted to make an interactive version in Tableau, and b) I saw how I could apply my own style to it.
#TidyTuesday Week 14 was a good challenge for me. I wanted to focus on getting better at creating line charts with ggplot2, and I feel like I achieved that goal. The Data Week 14’s data comes from Seattle’s open data portal and contains information about bike traffic in Seattle. The Analysis Since I’m still trying to get back up to speed with using R, I decided to keep it simple and focus on a) showing traffic over time, and b) showing which crossing registered the most traffic each year.
Week 14 of #MakeoverMonday looked at the different types of waste found on UK beaches per 100m. The data comes from a 2017 by the Marine Conservation Society. Here’s the original visualisation: What works well: Clean layout with minimal colours Clear title and subtitle The icons help improve the design What could be improved: The scattered circles make it hard to compare each category What’s the bigger story behind this viz?