Slopegraphs in R with ggplot2 Information density is up and to the right

I obviously need the ggplot2 library, otherwise I would be lying in the title. I also load the scales library so I can label the points nicely by putting commas in the numbers.

library(ggplot2)
library(scales)

Likely you'll load the data from a file into a data frame, and it doesn't matter too much what it looks like, as long as each row has the start value, the end value, and the label somewhere in it. Or it could be three different variables in other places.

You'll also need a variable for the x-axis. In my case, this is 24 months (see: months<-24)

months<-24
year1<-c(1338229205,5212325386,31725112511)
year3<-c(1372425378,8836570075,49574919628)
group<-c("Group C", "Group B", "Group A")
a<-data.frame(year1,year3,group)

This makes the strings we'll use for the labels by combining the group name with the value at each end of our slope line.

I also use the comma_format()() function from the scales library here to make slightly cleaner looking labels.

If you prefered a more Tufte-esque label, you can omit the newline from the sep= option and flip the order of the second set of labels.

l11<-paste(a$group,comma_format()(round(a$year1/(3600*24*30.5))),sep="\n")
l13<-paste(a$group,comma_format()(round(a$year3/(3600*24*30.5))),sep="\n")

This line is pretty simple but draws the slopelines using the geom_segment where the x-range is 0 to months and the y-range is from the a data frame values.

p<-ggplot(a) + geom_segment(aes(x=0,xend=months,y=year1,yend=year3),size=.75)

These settings turn off all of the default ggplot2 decorations (chartjunk?).

p<-p + theme(panel.background = element_blank())
p<-p + theme(panel.grid=element_blank())
p<-p + theme(axis.ticks=element_blank())
p<-p + theme(axis.text=element_blank())
p<-p + theme(panel.border=element_blank())

The x label is empty because we'll use the column headings to denote the time span (or whatever x-range you have), the y label is still useful, though, although the theme_text(vjust=X) moves the label a little closer—you'll want to play with this for your own plot until it looks correct to you.

Also, I make the plot area a little bigger by setting xlim and ylim to be bigger than the data would imply. This is also a bit of art, so you'll want to look at this for your own chart, too.

p<-p + xlab("") + ylab("Amount Used")
p<-p + theme(axis.title.y=theme_text(vjust=3))
p<-p + xlim((0-12),(months+12))
p<-p + ylim(0,(1.2*(max(a$year3,a$year1))))

Here we use the labels we created above in the geom_text() function, once for each side of the slopeline. The first line below is for the right side (year3) of the chart and the second is for the left side (year1).

The rep.int command that sets x just repeats the number—either 0 or 24—to make the same number of elements in x as there are in y.

Again, the hjust and size parameters will need some attention to get your slopegraph to look just right.

p<-p + geom_text(label=l13, y=a$year3, x=rep.int(months,length(a)),hjust=-0.2,size=3.5)
p<-p + geom_text(label=l11, y=a$year1, x=rep.int( 0,length(a)),hjust=1.2,size=3.5)

The columns titles, or labels at the top of each side of the slopegraph, are set here using geom_text() and setting y to just a bit above the maximum value from the two lists of values (year1 and year2). Don't forget to pay attention to hjust and size again.

p<-p + geom_text(label="Year 1", x=0,     y=(1.1*(max(a$year3,a$year1))),hjust= 1.2,size=5)
p<-p + geom_text(label="Year 3", x=months,y=(1.1*(max(a$year3,a$year1))),hjust=-0.1,size=5)

In the end, this R code produces this figure: