In this week’s #TidyTuesday screencast I explore some cow facts, using the emojifont package for custom symbols as plot points.
Like they say, long journeys start with a single step. This is my first time looking at a #TidyTuesday dataset and my first time doing an R screencast.
This week’s data is on incarceration in the US, and in just a short exploration I find some interesting features.
Lately I’ve been referencing the classic Tufte book The Visual Display of Quantitative Information. I don’t agree with everything tufte has to say, but some of his advice is fantastic.
People who consume my results like bar charts, likely because they’re familiar from Excel. I’ve been working on some ggplot2 settings that generate beautiful barplots.
Here it is:
library(ggplot2) library(viridisLite) library(dplyr) iris %>% group_by(Species) %>% summarize(mean.Sepal.Length = mean(Sepal.Length)) %>% ggplot(aes(x=Species, y=mean.