2018

One of my major goals at the start of they year was to start a blog / website as a way to encourage me to work on things I personally find interesting, and have a place to share them. I suceeded in getting this site up and running in September, and have mainly used it to share visualisations produced for community projects like #MakeoverMonday and #TidyTuesday.

I was happy that I managed to post at least one thing each week, but I feel I didn’t spend as much time as I wanted on things I am really interested in. I got much more out of working on #TidyTuesday pieces than #MakeoverMonday so I will probably focus more on the former next year when I do decide to participate.

I was really pleased / surprised by the amount of likes and views I got on my post which tried to recreate one of Nathan Yau’s visualisations, and I learnt quite a lot from it so I’ll try to do more of these sorts of posts next year.

2019 Goals

I imagine a lot of these goals will change, and new things will come up that I decide are more important. For example, at the start of 2018 I thought I’d be spending a lot more time playing with Deep Learning techniques and Machine Learning competitions but realised I was currently more interested in the communication of data, which lead to a much greater focus on data visualisation.

But here are a number of things I plan to do over the next 12 months:

Books:

• Read “Advanced R” and “The Art of R Programming”
• Work through “React+D3v4” by Swizec Teller.

R:

• Gain a better understanding of the internals of the R language, for example how do packages work and the object orientated elements of the language.
• Think about making a package.
• Learn all the keyboard shortcuts / other key features of RStudio.

Courses:

• I loved the first part of “Programming Languages” by the University of Washington on Coursera, I’d really like to finish the other two parts this year.
• Work through the Wes Bos Advanced React / Node.js courses to build on my existing Javascript knowledge.
• Keep working through interesting courses on DataCamp.
• If time, work through at least one online graphic design course.

Data Viz:

• Focus less on things like #MakeoverMonday and #TidyTuesday and do more posts / visualisations which are on areas I am particularly interested in or that are currently topical.
• Finish my post on streamgraphs I’ve been working on forever.
• Build my own ggplot2 themes for use in both my personal projects, and at work.
• Focus more on how to tell stories within multiple visualisations (either static or scrollers), particularly focussing on good annotation and relevant text.
• Share original work on /r/informationisbeautiful/.
• Do more visualisations which use Javascript (e.g. D3 / React).

Statistics:

• Try and get really comfortable with Bayesian approaches to modelling so that I’m happy to reach for them in the same way I would currently use GLMs, tree based models or neural network approaches.