Scatterplot matrices (pair plots) with cdata and ggplot2

In myprevious post, I showed how to use cdata package along with ggplot2 ‘s faceting facility to compactly plot two related graphs from the same data. This got me thinking: can I use cdata to produce a ggplot2 version of a scatterplot matrix, or pairs plot?

A pairs plot compactly plots every (numeric) variable in a dataset against every other one. In base plot, you would use the pairs() function. Here is the base version of the pairs plot of the iris dataset:

pairs(iris[1:4], 
      main = "Anderson's Iris Data -- 3 species",
      pch = 21, 
      bg = c("#1b9e77", "#d95f02", "#7570b3")[unclass(iris$Species)])

There are other ways to do this, too:

# not run

library(ggplot2)
library(GGally)
ggpairs(iris, columns=1:4, aes(color=Species)) + 
  ggtitle("Anderson's Iris Data -- 3 species")

library(lattice)
splom(iris[1:4], 
      groups=iris$Species, 
      main="Anderson's Iris Data -- 3 species")

But I wanted to see if cdata was up to the task. So here we go….

First, load the packages:

library(ggplot2)
library(cdata)

To create the pairs plot in ggplot2 , I need to reshape the data appropriately. For cdata , I need to specify what shape I want the data to be in, using a control table. Seethe last post for how the control table works. For this task, creating the control table is slightly more involved.

Here, I specify the variables I want to plot.

meas_vars <- colnames(iris)[1:4]

The function expand_grid() returns a data frame of all combinations of its arguments; in this case, I want all pairs of variables.

# the data.frame() call strips the attributes from
# the frame returned by expand.grid()
controlTable <- data.frame(expand.grid(meas_vars, meas_vars, 
                                       stringsAsFactors = FALSE))
# rename the columns
colnames(controlTable) <- c("x", "y")

# add the key column
controlTable <- cbind(
  data.frame(pair_key = paste(controlTable[[1]], controlTable[[2]]),
             stringsAsFactors = FALSE),
  controlTable)

controlTable

##                     pair_key            x            y
## 1  Sepal.Length Sepal.Length Sepal.Length Sepal.Length
## 2   Sepal.Width Sepal.Length  Sepal.Width Sepal.Length
## 3  Petal.Length Sepal.Length Petal.Length Sepal.Length
## 4   Petal.Width Sepal.Length  Petal.Width Sepal.Length
## 5   Sepal.Length Sepal.Width Sepal.Length  Sepal.Width
## 6    Sepal.Width Sepal.Width  Sepal.Width  Sepal.Width
## 7   Petal.Length Sepal.Width Petal.Length  Sepal.Width
## 8    Petal.Width Sepal.Width  Petal.Width  Sepal.Width
## 9  Sepal.Length Petal.Length Sepal.Length Petal.Length
## 10  Sepal.Width Petal.Length  Sepal.Width Petal.Length
## 11 Petal.Length Petal.Length Petal.Length Petal.Length
## 12  Petal.Width Petal.Length  Petal.Width Petal.Length
## 13  Sepal.Length Petal.Width Sepal.Length  Petal.Width
## 14   Sepal.Width Petal.Width  Sepal.Width  Petal.Width
## 15  Petal.Length Petal.Width Petal.Length  Petal.Width
## 16   Petal.Width Petal.Width  Petal.Width  Petal.Width

The control table specifies that for every row of iris , sixteen new rows get produced, one for each possible pair of variables. The column pair_key will be the key column in the new data frame; there’s one key level for every possible pair of variables. The columns x and y will be the value columns in the new data frame — these will be the columns that we plot.

Now I can create the new data frame, using rowrecs_to_blocks() . I’ll also carry along the Species column to color the points in the plot.

iris_aug = rowrecs_to_blocks(
  iris,
  controlTable,
  columnsToCopy = "Species")

head(iris_aug)

##   Species                  pair_key   x   y
## 1  setosa Sepal.Length Sepal.Length 5.1 5.1
## 2  setosa  Sepal.Width Sepal.Length 3.5 5.1
## 3  setosa Petal.Length Sepal.Length 1.4 5.1
## 4  setosa  Petal.Width Sepal.Length 0.2 5.1
## 5  setosa  Sepal.Length Sepal.Width 5.1 3.5
## 6  setosa   Sepal.Width Sepal.Width 3.5 3.5

Note that the data is now sixteen times larger, which I admit is perverse.

If I didn’t care about how the individual subplots were arranged, I’d be done: I’d plot y vs x , and facet_wrap on pair_key . But I want the subplots arranged in a grid. To do this I use facet_grid , which will require two key columns:

splt <- strsplit(iris_aug$pair_key, split = " ", fixed = TRUE)
iris_aug$xv <- vapply(splt, function(si) si[[1]], character(1))
iris_aug$yv <- vapply(splt, function(si) si[[2]], character(1))
head(iris_aug)

##   Species                  pair_key   x   y           xv           yv
## 1  setosa Sepal.Length Sepal.Length 5.1 5.1 Sepal.Length Sepal.Length
## 2  setosa  Sepal.Width Sepal.Length 3.5 5.1  Sepal.Width Sepal.Length
## 3  setosa Petal.Length Sepal.Length 1.4 5.1 Petal.Length Sepal.Length
## 4  setosa  Petal.Width Sepal.Length 0.2 5.1  Petal.Width Sepal.Length
## 5  setosa  Sepal.Length Sepal.Width 5.1 3.5 Sepal.Length  Sepal.Width
## 6  setosa   Sepal.Width Sepal.Width 3.5 3.5  Sepal.Width  Sepal.Width

And now I can produce the graph, using facet_grid .

# reorder the key columns to be the same order
# as the base version above
iris_aug$xv <- factor(as.character(iris_aug$xv),
                           meas_vars)
iris_aug$yv <- factor(as.character(iris_aug$yv),
                           meas_vars)


ggplot(iris_aug, aes(x=x, y=y)) +
  geom_point(aes(color=Species, shape=Species)) + 
  facet_grid(yv~xv, labeller = label_both, scale = "free") +
  ggtitle("Anderson's Iris Data -- 3 species") +
  scale_color_brewer(palette = "Dark2") +
  ylab(NULL) + 
  xlab(NULL)

This pair plot has x = y plots on the diagonals instead of the names of the variables, but you can confirm that it is otherwise the same as the pair plot produced by pairs() .

Of course, calling pairs() (or ggpairs() , or splom() ) is a lot easier than all this, but now I’ve proven to myself that cdata with ggplot2 can do the job. This version does have a few advantages. It comes with a legend by default, which is nice. And it’s not obvious how to change the color palette in ggpairs() — I prefer the Brewer Dark2 palette, myself.

Luckily, this code is straightforward to wrap as a function, so if you like the cdata version, I’ve now added the PairPlot() function to WVPlots . Now it’s a one-liner, too.

library(WVPlots) 

PairPlot(iris, 
         colnames(iris)[1:4], 
         "Anderson's Iris Data -- 3 species", 
         group_var = "Species")