Data visualisation

useR! 2018 was held for the first time in the southern hemisphere, and the feedback from participants has been very positive. I have been asked to write about the organisation and this is a good way to get some of the planning and decisions and operations into print, so that it might be useful for others charged with conference organisation. There are a lot of people who made the conference a success, and their contributions need to be acknowledged.

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.

Non-Uniform Monash Business Analytics Team: This is a research working group consisting of facutly, post-docs and students in Econometrics and Business Statistics at Monash University. The group meets regularly to discuss current research and host visitors talking to the group. If you would like to visit and present your work to the group, send email numbats@googlegroups.com.