R

The background to useR! 2018

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.

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.

nullabor

R package containing tools for doing statistical inference with data plots

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.

foRwards

Leading the R community forwards in widening the participation of women and other under-represented groups.

useR! 2018

The conference useR! 2018 will be held in Australia July 10-13, at the Brisbane Convention and Exhibition Centre. This is the first time that it has been held outside Europe or North America.

SCOTUS over the years

In the days since the death of Justice Antonin Scalia, there has been a lot of discussion on what is going to happen now - whether President Obama should or should not nominate a candidate to fill the vacancy in the supreme court. As I write this, FoxNews reports that Americans are almost 2:1 in favor of a nomination by President Obama, politifact has rated the claim from the Republican rumour mill of an `80 year old tradition to not nominate a supreme court candidate during an election’ as half right (which could also be read as half wrong, just to indicate my side of things).

Better cricket plots

I’m sitting watching cricket tonight, the first day of the Australia vs West Indies Boxing Day test. Just now video of retired batsman Chris Rogers being honored was played, along with a plot of his batting record, shown on screen similar to this one below: Howzat? What are they trying to show? What’s the data in this plot? Is it a bar chart? A histogram? What does color mean?

Statistical Sciences, Cornell University

This week I have been visiting the Department of Statistical Sciences at Cornell University. This is the home of many venerable statisticians. At first sight it appears that statisticians are spread all over the university, and technically they are because funding comes from many directions, but almost all are actually located in a suite in Comstock Hall. Professor Paul Velleman is one of the pioneers of data-centrist thinking about statistics. He produced the software called DataDesk in the early 90s that some saw as rivaling LispStat and particularly JMP for introductory statistics classes.