Multiple histograms in ggplot2

Question

Here is a short part of my data:

dat <-structure(list(sex = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("male", 
"female"), class = "factor"), A = c(1, 2, 0, 2, 1, 2, 2, 0, 2, 
0, 1, 2, 2, 0, 0, 2, 0, 0, 0, 2), B = c(0, 0, 0, 0, 0, 2, 0, 
0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0), C = c(1, 2, 1, 0, 0, 
2, 1, 1, 0, 1, 1, 0, 1, 2, 1, 2, 0, 2, 1, 2), D = c(2, 2, 0, 
2, 2, 2, 1, 0, 1, 1, 1, 0, 1, 2, 0, 0, 1, 1, 1, 0), E = c(0, 
0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 2, 2), F = c(2, 
2, 1, 2, 1, 2, 2, 0, 1, 2, 0, 1, 2, 2, 0, 1, 2, 2, 2, 2)), .Names = c("sex", 
"A", "B", "C", "D", "E", "F"), variable.labels = structure(c("sex", 
"zenuwac", "panieke", "gespann", "rustelo", "angstig", "onzeker"
), .Names = c("sex", "anx01", "anx02", "anx03", "anx04", "anx05", 
"anx06")), codepage = 20127L, row.names = c(NA, 20L), class = "data.frame")

A data frame with scores of males and females on six 3-point variables. Now I want to create a plot which shows the histograms of the scores of each variable of both males and females in a grid. For example, I can do:

layout(matrix(1:12,6,2,byrow=TRUE))
par(mar=c(2,1,2,1))
for (i in 1:6) for (s in c("male","female")) hist(dat[dat$sex==s,i+1],main=paste("item",names(dat)[i+1],s))

which results in:

I could make this look better but I am more interested in learning how to use ggplot2. So my question is, how do I create a pretty version of this using ggplot2? One thing I got working is:

library("ggplot2")
grid.newpage()
pushViewport(viewport(layout = grid.layout(6, 2)))   
for (s in 1:2)
{
    for (i in 1:6)
    {
        p <- qplot(dat[dat$sex==c("male","female")[s],i+1]+0.5, geom="histogram", binwidth=1)
        print(p, vp = viewport(layout.pos.row = i, layout.pos.col = s))
    }
}

But I guess there is a much easier way to do this?

Answer 1

You can try grid.arrange() from the gridExtra package; i.e., store your plots in a list (say qplt), and use

do.call(grid.arrange, qplt)

Other ideas: use facetting within ggplot2 (sex*variable), by considering a data.frame (use melt).

As a sidenote, it would be better to use stacked barchart or Cleveland's dotplot for displaying items response frequencies, IMO. (I gave some ideas on CrossValidated.)

---

For the sake of completeness, here are some implementation ideas:

# simple barchart
ggplot(melt(dat), aes(x=as.factor(value), fill=as.factor(value))) + 
  geom_bar() + facet_grid (variable ~ sex) + xlab("") + coord_flip() + 
  scale_fill_discrete("Response")

my.df <- ddply(melt(dat), c("sex","variable"), summarize, 
               count=table(value))
my.df$resp <- gl(3, 1, length=nrow(my.df), labels=0:2)

# stacked barchart
ggplot(my.df, aes(x=variable, y=count, fill=resp)) + 
  geom_bar() + facet_wrap(~sex) + coord_flip()

# dotplot
ggplot(my.df, aes(x=count, y=resp, colour=sex)) + geom_point() + 
  facet_wrap(~ variable)