EDIT: Many years later
For a pure ggplot2 + utils::stack() solution, see the answer by @markus!
A somewhat verbose tidyverse solution, with all non-base packages explicitly stated so that you know where each function comes from:
library(magrittr) # needed for %>% if dplyr is not attached
"http://pastebin.com/raw.php?i=L8cEKcxS" %>%
utils::read.csv(sep = ",") %>%
tidyr::pivot_longer(cols = c(Food, Music, People.1),
names_to = "variable",
values_to = "value") %>%
dplyr::group_by(variable, value) %>%
dplyr::summarise(n = dplyr::n()) %>%
dplyr::mutate(value = factor(
value,
levels = c("Very Bad", "Bad", "Good", "Very Good"))
) %>%
ggplot2::ggplot(ggplot2::aes(variable, n)) +
ggplot2::geom_bar(ggplot2::aes(fill = value),
position = "dodge",
stat = "identity")
The original answer:
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)).
