A Chart of Recent Comrades Marathon Winners

Continuing on my quest to document the Comrades Marathon results, today I have put together a chart showing the winners of both the men and ladies races since 1980. Click on the image below to see a larger version.

Winners of both men's and ladies' races at Comrades Marathon showing the year and the number of times that the race has been won by each athlete.

The analysis started off with the same data set that I was working with before, from which I extracted only the records for the winners.

winners = subset(results, gender.position == 1, select = c(year, name, gender, race.time))
head(winners)
     year               name gender race.time
1    1980          Alan Robb   Male  05:38:25
428  1980 Isavel Roche-Kelly Female  07:18:00
3981 1981      Bruce Fordyce   Male  05:37:28
4055 1981 Isavel Roche-Kelly Female  06:44:35
7643 1982      Bruce Fordyce   Male  05:34:22
7873 1982        Cheryl Winn Female  07:04:59

I then added in a field which gives a count of the number of times each person won the race.

library(plyr)
winners = ddply(winners, .(name), function(df) {
     df = df[order(df$year),]
     df$count = 1:nrow(df)
     return(df)
})
subset(winners, name == "Bruce Fordyce")
   year          name gender race.time count
7  1981 Bruce Fordyce   Male  05:37:28     1
8  1982 Bruce Fordyce   Male  05:34:22     2
9  1983 Bruce Fordyce   Male  05:30:12     3
10 1984 Bruce Fordyce   Male  05:27:18     4
11 1985 Bruce Fordyce   Male  05:37:01     5
12 1986 Bruce Fordyce   Male  05:24:07     6
13 1987 Bruce Fordyce   Male  05:37:01     7
14 1988 Bruce Fordyce   Male  05:27:42     8
15 1990 Bruce Fordyce   Male  05:40:25     9

The chart was generated as a scatter plot using ggplot2. The size of the points relates to the number of times each person won the race. The colour scale is as you might imagine: pink for the ladies and blue for the men.

library(ggplot2)

ggplot(winners, aes(x = year, y = name, color = gender)) +
  geom_point(aes(size = count), shape = 19, alpha = 0.75) +
  scale_size_continuous(range = c(5, 15)) +
  ylab("") + xlab("") +
  scale_x_discrete(expand = c(0, 1)) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, colour = "black"),
    axis.text.y = element_text(colour = "black"),
    legend.position = "none",
    panel.background = element_blank(),
    panel.grid.major = element_line(linetype = "dotted", colour = "grey"),
    panel.grid.major.x = element_blank()
  )

Two of the key aspects of getting this to look just right were:

  • the call to scale_size_continuous() which ensured that a reasonable range of point sizes was used and
  • the call to scale_x_discrete() which expanded the plot very slightly so that the points near the borders were not cropped.