superheat 0.1.0

First version of superheat now available on CRAN.

Rebecca Barter

1 minute read

superheat 0.1.0 is now available on CRAN. Superheat makes it easy to create extendable, cutomizable, and most importantly, beautiful heatmaps. It has increased flexibility and user-friendliness when compared to alternatives such as heatmap() and pheatmap(). For usage options see the vignette and for examples see the accompanying paper by Barter and Yu (2017). You can install the latest version with: install.packages("superheat") Stay tuned for new versions with added features and minor usability tweaks.

How to Present Good

This week I was asked to give a presentation on presenting. Here lies a summary of my efforts.

Rebecca Barter

11 minute read

This week I was asked to give a presentation on giving presentations. So meta. Anyway, my lack of desire to spend time writing something that may have already been written by someone else led me to find a number of existing slides written by others with the same goal: to present on presenting. The only problem was that although these slides all contained excellent tips on making and subsequently presenting talks, the slides themselves were perfect examples of what not to do when making slides; they were insurmountable walls of text.

Superheat: a simple example

A simple example of using superheat to create beautiful heatmaps.

Rebecca Barter

1 minute read

Making beautiful and customizable heatmaps just got way easier… Introducing the superheat R package! Using superheat, it is now extremely easy to produce plots like the example below describing 10 randomly selected cars from the famous mtcars dataset. library(superheat) set.seed(1347983) selected.rows <- sample(1:nrow(mtcars), 10) X.col <- matrix("black", ncol = ncol(mtcars), nrow = 10) X.col[scale(mtcars[selected.rows, ]) < 0] <- "white" superheat(mtcars[selected.rows,], # add text X.text = round(as.matrix(mtcars[selected.rows, ])), X.text.col = X.col, # scale columns scale = T, # label aesthetics left.