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.
by Kellie Ottoboni, Rebecca Barter, Ryan Giordano, Sara Stoudt
In 2015, UC Berkeley experienced a series of high-profile sexual harassment incidents, prompting the graduate students in the Statistics Department to hold a roundtable event. While this response was initiated by overt sexual harassment, our discussion extended to other subtler gender biases. This article outlines the events that lead to the roundtable’s inception, the details of organizing and hosting the event, and our thoughts on what did and didn’t work.
Pie charts are terrible Pie charts are notoriously terrible for data visualization. Humans are genuinely bad at comparing angles. Thus, the only reason one should ever use a pie chart is to make beautiful pie-chart-art.
par(mar = c(0, 1, 0, 1)) pie( c(280, 60, 20), c('Sky', 'Sunny side of pyramid', 'Shady side of pyramid'), col = c('#0292D8', '#F7EA39', '#C4B632'), init.angle = -50, border = NA ) Figure 1: Pyramid pie chart art.
