R is for Running Running, stats, pretty plots and some R learnin'!!

The first step is to get the library we need— ggplot2 —and load the data:

library(ggplot2)
r<-read.table("file.txt", header=TRUE, sep="|")

Representing dates in R is pretty simple, but representing times is a little trickier.

The next two lines convert the dates in the table into dates that R understands and converts the times to seconds for the sake of the plot.

r$Date<-as.Date(r$Date,format='%m/%d/%Y')
r$Times<-(as.numeric(as.POSIXct(strptime(r$Time, format="%M:%OS"))) -
          as.numeric(as.POSIXct(strptime("0", format="%S")))
         )

The second line is the result of some Google searching and StackExchange finding, but in the end it converts the MM:SS formatted times into seconds and stores it in Times (note the extra s to denote seconds).

We want the y-labels back in our MM:SS format, and it would be nice, for a small amount of data, to label the y-axis of every point.

secs<-c(r$Times,seq(from=18*60, to=max(r$Times)+120, by=60*1))
labels<-paste((as.integer(secs/60)),
              formatC(round((secs/60 - as.integer(secs/60)) * 60),
                      width=2,
                      flag="0"),
              sep=":")

First we make a vector called secs that has my run times, converted to seconds, and then some ``normal'' times (19:00, 20:00, etc) converted to seconds. The line:

seq(from=18*60, to=(max(r$Times)+120, by=60*1))

makes a sequence of numbers starts at eighteen minutes (because I'm confident I'll never run a 18:00 5k) and ends at two minutes more than my slowest time (this leaves room on the plot for labels and frames the times). The labels will be every one minutes (by=60*1). That sequence defines the y-axis points, but would make for non-intuitive labels.

The next line creates a vector called labels that converts the seconds into the format MM:SS by paste-ing together minutes and seconds separated by a colon (sep=":"). To get minutes, we simply take the integer part of secs divided by 60, and that's the first half of our paste. The second half of the paste also needs to be padded with leading zeros if it isn't long enough (otherwise your time might be 20:9 instead of 20:09), so we use the formatC function with the options: our number, width=2 (pad to two characters), and flag="0" (pad with 0s). Our number is the decimal part of (secs divided by 60), multiplied by 60 to get seconds and rounded to the nearest integer.

At this point we have two vectors: secs and labels that match each other—one has seconds and one has MM:SS, each in the same location in the vector.

At this point, we have all the data we need in the R data frame (a data frame is like one sheet in an Excel spreadsheet) called r, some labels in secs and labels and all we have left to do is use ggplot2 to plot it.

ggplot2 builds a plot piece by piece, which is nice for making incremental changes, and also nice for explaining, since each piece stands on its own.

plot = ggplot(r, aes(x=Date,y=Times,label=r$Event)) + geom_step()

This line creates the plot object (although you can call it whatever you want, it's a normal R variable) and starts the ggplot process by telling it ``We're using the r data frame and aesthetically we are going to use Date for the x-data and Times (our Time converted to seconds) for the y-data and we're going to label it with the Event name. To draw a line, we want steps, not a series of slopes, so we add geom_step() to the plot.

Next we add the text for the label= we specified above and set a size (3) and a vertical adjustment so they are above the point (vjust=-0.5):

plot = plot + geom_text(size=3,vjust=-0.5)

The x-axis can be a little too short to leave room for the long race names in the labels, so we add a little on each end, by subtracting from the minimum date (min(r$Date)) and adding to the maximum date (max(r$Date)). The amount added is a guess based on the size of the labels of the first and latest races.

plot = plot + xlim((min(r$Date)-60),max(r$Date)+90)

Then we add points to each race along the step line and also a smoothing range (the gray area in the plot) to get some sort of prediction of the range.

plot = plot + geom_point() + stat_smooth(method="glm")

Lastly, we use the secs and labels from above to make y-axis labels, and set the range of the y-axis to be between 18 minutes (60*18) (since I don't think I'll break an 18-minute 5k) and the slowest time (max(r$Times)); turn of the x-axis label, since the fact they it is dates is pretty evident, and then call plot to draw the plot.

plot = plot + scale_y_continuous(breaks=secs,
                                 labels=labels,
                                 limits=c(60*18,max(r$Times)))
plot = plot + xlab("")
plot