How to draw neat polygons around scatterplot regions in ggplot2 [closed]

Question

How do I add a neat polygon around a group of points on a scatterplot? I am using ggplot2 but am disappointed with the results of geom_polygon.

The dataset is over there, as a tab-delimited text file. The graph below shows two measures of attitudes towards health and unemployment in a bunch of countries:

I would like to switch from geom_density2d to the less fancy but empirically more correct geom_polygon. The result on unsorted data is unhelpful:

How do I draw 'neat' polygons that behave as contour paths around the min-max y-x values? I tried sorting the data to no avail.

Code:

print(fig2 <- ggplot(d, aes(man, eff, colour=issue, fill=issue)) + 
geom_point() + geom_density2d(alpha=.5) + labs(x = "Efficiency", y = "Mandate"))

The d object is obtained with this CSV file.

Solution:

Thanks to Wayne, Andy W and others for their pointers! The data, code and graphs have been posted to GitHub. The result looks like this:

Answer 1

With some googling I came across the website of Gota Morota who has an example of doing this already on her website. Below is that example extended to your data.

library(ggplot2)
library(plyr)
work <- "E:\\Forum_Post_Stuff\\convex_hull_ggplot2"
setwd(work)

#note you have some missing data
mydata <- read.table(file = "emD71JT5.txt",header = TRUE, fill = TRUE)
nomissing <- na.omit(mydata) #chull function does not work with missing data

#getting the convex hull of each unique point set
df <- nomissing
find_hull <- function(df) df[chull(df$eff, df$man), ]
hulls <- ddply(df, "issue", find_hull)

plot <- ggplot(data = nomissing, aes(x = eff, y = man, colour=issue, fill = issue)) +
geom_point() + 
geom_polygon(data = hulls, alpha = 0.5) +
labs(x = "Efficiency", y = "Mandate")
plot

Answer 2

If I understand your problem, you're looking for the convex hull of health and of unemployment. There are probably several packages to do this in R, one of which is package geometry. I'd imagine that the points are sorted in order around the perimeter, but you'd have to check that.

EDIT: Here's an example, which doesn't use ggplot, but I hope it's useful. The example in the chull documentation seems to be wrong, which might be throwing you off:

X <- matrix(rnorm(2000), ncol = 2)
X.chull <- chull (X)
X.chull <- c(X.chull, X.chull[1])
plot (X)
lines (X[X.chull,])

EDIT 2: OK, here is something using ggplot2. We turn X into a data.frame with variables x and y. Then:

library(ggplot2)
X <- as.data.frame(X)
hull <- chull(X)
hull <- c(hull, hull[1])
ggplot(X, aes(x=x, y=y)) + geom_polygon(data=X[hull,], fill="red") + geom_point()

Note that the geom_point is using the data (X) and aes from the ggplot, while I'm overriding it in the geom_polygon.

To get it fully, you'd need to put the x and y for the hull for both issues into bar, using a third column issue to differentiate them.

Answer 3

As of this afternoon, I've wrapped the chull function inside an R package as a geom_convexhull function.

Once the package is loaded, it can be used as any other geom, in your case it should be something like :

ggplot(d, aes(man, eff, colour=issue, fill=issue)) + 
  geom_convexhull(alpha=.5) + 
  geom_point() + 
  labs(x = "Efficiency", y = "Mandate"))

The package is available on github : https://github.com/cmartin/ggConvexHull