For #TidyTuesday week 17 I got a little too ambitious. I had decided to focus on my favourite anime, Cowboy Bebop. My original goal was to use gganimate to plot the top 20 anime for each year, but to also include any anime for previous years back to 1998. This was to show a rolling top 20 from the year that Cowboy Bebop was released. The Data The data for week 17 comes from MyAnimeList.

Continue reading

Week 17 of #MakeoverMonday looked at Stephen Curry’s ranking of stadium popcorn. Here’s the original viz: What works well: A heat chart is perfect for this data. The labels and colours work well There’s a clear title The scoring is simple What could be improved: The colours aren’t great What I focused on: Although I do like the original chart, I decided to try something different.

Continue reading

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:

Continue reading

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.

Continue reading

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).

Continue reading

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.

Continue reading

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.

Continue reading

Author's picture

Jared Braggins

Data Visualisation and Analysis