Data visualisation

In 1997, I wrote a paper that was accepted for the newly fledged Journal of Statistical Software. That article is still available at https://www.jstatsoft.org/article/view/v002i06. It looks nothing like the original published paper. Papers today are only published in pdf format, unlike the original which were delivered as html, with pdf being a secondary format o help readers who preferred a printed copy. My paper was titled “Calibrate your to Recognise High-Dimensional Shapes from their Low-Dimensional Projections”.

Multicollinearity This was one of the comments from a recent review of a paper: As you note in the paper, it seems likely that there are still issues with multi-collinearity Multicollinearity means that the observations are co-linear in some combination of the variables. This has been relaxed in practice to mean substantial association between explanatory variables. When your explanatory variables have substantial association between them, it means that you don’t have a stable base on which to build a model.

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.

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.

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.