If you measure the same person twice, you have longitudinal data. We all love longitudinal data because we can understand how their health outcomes change with time and this helps answering many interesting research questions. However, newer R users often face a problem in managing longitudinal data because it often comes in two ‘shapes’: the wide and the long. Some analysis can be easily conducted in wide format (e.g. two-sample t-tests) while the others require a long one (e.g. growth curve models). This article aims to provide you an overview of what long and wide format data are and how you could easily convert between them.

I recently accepted a position at UCSD and had a week off between the last day at my old job and the first day at my new job. I thought this would be a good time to build a shiny application for plotting data with Chernoff Faces.

Follow along with the presentation and recreate all the analysis results for yourself.

I show how to seasonally adjust published electronic card transactions spend in New Zealand using the US Census Bureau’s excellent X-13ARIMA-SEATS software, the Spanish SEATS algorithm and Christoph Sax’s seasonal R package; and how to build a new ‘stat’ for ggplot2 to make it easy to do seasonal adjustment on the fly for a graphic of a time series split by various grouping dimensions.