Last week I gave an SGSA seminar on interactive visualizations in R. Here is a long-form version of the talk. Why be interactive? Interactivity allows the viewer to engage with your data in ways impossible by static graphs. With an interactive plot, the viewer can zoom into the areas the care about, highlight the data points that are relevant to them and hide the information that isn’t. Above all of that, making simple interactive plots are a sure-fire way to impress your coworkers!
This week is the Docathon at BIDS (a.k.a. that wonderful place that I spend all my time). A docathon is like a hackathon but is focused on developing material and tools for documentation. We have loads of projects signed up to receive some documentation-love and an impressive number of excited participants! We kicked off the event with a series of tutorials for writing “good” documentation. I gave an R-specific tutorial where I discussed using devtools to both develop and document your package.
Last week, practical statistics met to discuss all things ANOVA. Below you will find the slides from my talk, but read on if you would like to learn all about ANOVA. When ANOVA is used and who uses it? ANOVA is used far and wide by the scientific community and beyond. Unfortunately, scientists also frequently misuse ANOVA. A study by Wu et al. (2011) showed that from a survey of 10 leading Chinese medical journals in 2008, 446 articles used ANOVA, and of those articles, 59% of them used ANOVA incorrectly.