[r] Grouped bar plot in ggplot

First you need to get the counts for each category, i.e. how many Bads and Goods and so on are there for each group (Food, Music, People). This would be done like so:

raw <- read.csv("http://pastebin.com/raw.php?i=L8cEKcxS",sep=",")
raw[,2]<-factor(raw[,2],levels=c("Very Bad","Bad","Good","Very Good"),ordered=FALSE)
raw[,3]<-factor(raw[,3],levels=c("Very Bad","Bad","Good","Very Good"),ordered=FALSE)
raw[,4]<-factor(raw[,4],levels=c("Very Bad","Bad","Good","Very Good"),ordered=FALSE)

raw=raw[,c(2,3,4)] # getting rid of the "people" variable as I see no use for it

freq=table(col(raw), as.matrix(raw)) # get the counts of each factor level

Then you need to create a data frame out of it, melt it and plot it:

Names=c("Food","Music","People")     # create list of names
data=data.frame(cbind(freq),Names)   # combine them into a data frame
data=data[,c(5,3,1,2,4)]             # sort columns

# melt the data frame for plotting
data.m <- melt(data, id.vars='Names')

# plot everything
ggplot(data.m, aes(Names, value)) +   
  geom_bar(aes(fill = variable), position = "dodge", stat="identity")

Is this what you're after?

To clarify a little bit, in ggplot multiple grouping bar you had a data frame that looked like this:

> head(df)
  ID Type Annee X1PCE X2PCE X3PCE X4PCE X5PCE X6PCE
1  1    A  1980   450   338   154    36    13     9
2  2    A  2000   288   407   212    54    16    23
3  3    A  2020   196   434   246    68    19    36
4  4    B  1980   111   326   441    90    21    11
5  5    B  2000    63   298   443   133    42    21
6  6    B  2020    36   257   462   162    55    30

Since you have numerical values in columns 4-9, which would later be plotted on the y axis, this can be easily transformed with reshape and plotted.

For our current data set, we needed something similar, so we used freq=table(col(raw), as.matrix(raw)) to get this:

> data
   Names Very.Bad Bad Good Very.Good
1   Food        7   6    5         2
2  Music        5   5    7         3
3 People        6   3    7         4

Just imagine you have Very.Bad, Bad, Good and so on instead of X1PCE, X2PCE, X3PCE. See the similarity? But we needed to create such structure first. Hence the freq=table(col(raw), as.matrix(raw)).