Question

I’d like to create a bar chart using factors and more than two variables! My data looks like this:

     Var1 Var2 ... VarN Factor1 Factor2
Obs1  1-5 1-5  ... 1-5     
Obs2  1-5 1-5  ... ...
Obs3  ... ...  ... ...

Each datapoint is a likert item ranging from 1-5

Plotting total sums using a dichotomized version (every item above 4 is a one, else 0)

I converted the data using this

MyDataFrame = dichotomize(MyDataFrame,>=4)
p <- colSums(MyDataFrame)
p <- data.frame(names(p),p)
names(p) <- c("var","value")
ggplot(p,aes(var,value)) + geom_bar() + coord_flip()

Doing this i loose the information provided by factor1 etc, i’d like to use stacking in order to visualize from which group of people the rating came

Is there a elegant solution to this problem? I read about using reshape to melt the data and then applying ggplot?

Answer 1

I would suggest the following: use one of your factor for stacking, the other one for faceting. You can remove position="fill" to geom_bar() to use counts instead of standardized values.

my.df <- data.frame(replicate(10, sample(1:5, 100, rep=TRUE)), 
                    F1=gl(4, 5, 100, labels=letters[1:4]), 
                    F2=gl(2, 50, labels=c("+","-")))
my.df[,1:10] <- apply(my.df[,1:10], 2, function(x) ifelse(x>4, 1, 0))
library(reshape2)
my.df.melt <- melt(my.df)
library(plyr)
res <- ddply(my.df.melt, c("F1","F2","variable"), summarize, sum=sum(value))
library(ggplot2)
ggplot(res, aes(y=sum, x=variable, fill=F1)) +
   geom_bar(stat="identity", position="fill") + 
   coord_flip() +
   facet_grid(. ~ F2) + 
   ylab("Percent") + xlab("Item")

In the above picture, I displayed observed frequencies of ‘1’ (value above 4 on the Likert scale) for each combination of F1 (four levels) and F2 (two levels), where there are either 10 or 15 observations:

> xtabs(~ F1 + F2, data=my.df)
   F2
F1   +  -
  a 15 10
  b 15 10
  c 10 15
  d 10 15

I then computed conditional item sum scores with ddply,^† using a ‘melted’ version of the original data.frame. I believe the remaining graphical commands are highly configurable, depending on what kind of information you want to display.

_{^† In this simplified case, the ddply instruction is equivalent to with(my.df.melt, aggregate(value, list(F1=F1, F2=F2, variable=variable), sum)).}