Graphics

Brandon LeBeau

5 minute read

I recently had an occasion while working on a three variable interaction plot for a paper where I wanted to remove the leading 0’s in the x-axis text labels using ggplot2. This was primarily due to some space concerns I had for the x-axis labels. Unfortunately, I did not find an obvious way to do this in my first go around. After tickering a bit, I’ve found a workaround. The process is walked through below.

Brandon LeBeau

3 minute read

I was emailed by a friend that was looking into their google location data and had asked if I had ever used a json file before in R. I said I had not, but I knew there were packages to do such things. The things I sent were things he had already tried, so what did I decide to do? I went ahead and downloaded my own google location data.

Brandon LeBeau

8 minute read

I saw a post recently about the likelihood of a baseball team winning based on how many runs, hits, and other baseball statistics. I liked the idea and thought of applying that to college football. Particularly, I’m interested in knowing whether scoring more points or having a stout defense improves the likelihood of becoming bowl eligible. Using some data scraped from the cfbDatawarehouse to figure out how likely a team would be bowl eligible based on the number of points they score.

Brandon LeBeau

3 minute read

I often see graphs that are poorly implemented in that they do not achieve their goal. One such type of graph that I see are dodged bar charts. Here is an example of a dodged bar chart summarizing the number of all star players by team (focusing specifically on the AL central division) and year from the Lahman r package: library(Lahman) library(dplyr) library(ggplot2) library(RColorBrewer) AllstarFull$selected <- 1 numAS <- AllstarFull %>% filter(yearID > 2006, lgID == 'AL', teamID %in% c('MIN', 'CLE', 'DET', 'CHA', 'KCA')) %>% group_by(teamID, yearID) %>% summarise(number = sum(selected)) b <- ggplot(numAS, aes(x = teamID, y = number, fill = factor(yearID))) + theme_bw() b + geom_bar(stat = "identity", position = "dodge") + scale_fill_brewer("Year", palette = "Dark2") Note: If you are curious from the above graph, there appears to be two typos in the teamIDs, where CHA should be CHW (Chicago White Sox) and KCA should be KCR (Kansas City Royals).