Data visualisation

Getting past the little hiccups to getting plotly animations into slides

Goal I just gave a short talk at ISCB-ASC 2018 about visualising high-dimensional data, which involves showing dynamic graphics. In the past, I have run the tour, captured the window and saved to a movie, and embedded this into the Rmarkdown xaringan slides. It seems a bit discombobulated to make the slides this way, and a better way to work would be to make a tour animation using plotly. This turned out to take me two days to get it working, through little mistakes that were not easy to debug by googling the problem.

Analysing my energy usage

Download your data You can get access to your own electricity and gas usage data from https://www.citipower.com.au/our-services/myenergy. You will need a copy of your power bill, which has your smart meter number and meter id, to register for an account. Reading the data The data structure is described here. The data is not especially nicely formatted (surprise). The main components are: The time resolution is half-hourly. And values for each day are spread across the columns.

Rookie mistakes and how to fix them when making plots of data

In this assignment, the focus was to practice data cleaning. Students suggested questions to build a class survey, to get to know the interests of other class members, and then completed the composed survey. After cleaning the data, a few summary plots of interesting aspects of the data were made. There are some common mistakes that rookies often make when constructing data plots: packing too much into a single graphic, leaving categorical variables unordered, reversing norms for response and explanatory variables, conditioning in wrong order, plotting counts when proportions should be the focus, not normalizing by counts, using a boxplot for small sample size.