ggplot2 - How to R present two colsums with ggplot stat_summary?

Question

I think R designed tool for the taks is ggplot2 stat_summary so I rejected barplot because of the linked thread in the body.

The problem here is the declaration of R table structure with column headers ECG 1 and ECG 2 for the sums M.1.sum and M.2.sum, respectively, I think. I try to do it with means.long <- melt(M.1.sum, M.2.sum). Each item, M.1.sum and M.2.sum, has corresponding row-wise ids in ids which should also included in the data structure itself, I think. My proposal for its table column and row declarations is with aes(x=ids, y=value) where value is about the sums in ggplot declaration. Code

library('ggplot2')
library('reshape2')

M <- structure(c(-0.21, -0.205, -0.225, -0.49, -0.485, -0.49, 
   -0.295, -0.295, -0.295, -0.56, -0.575, -0.56, -0.69, -0.67, 
   -0.67, -0.08, -0.095, -0.095), .Dim = c(3L, 6L))
M2 <- structure(c(-0.121, -0.1205, -0.1225, -0.149, -0.485, -0.49, 
   -0.295, -0.295, -0.295, -0.56, -0.1575, -0.56, -0.69, -0.67, 
   -0.117, -0.08, -0.1095, -0.1095), .Dim = c(3L, 6L))

ids <- seq(1,6)    
M.1.sum <- colSums(M)
M.2.sum <- colSums(M2)

# http://stackoverflow.com/q/22305023/54964
means.long <- melt(M.1.sum, M.2.sum)
ggplot(means.long, aes(x=ids, y=value ))+ # ,fill=factor(ids))) + 
  stat_summary(fun.y=mean, geom="bar",position=position_dodge(1)) + 
  scale_fill_discrete(name="ECG",
                      breaks=c(1, 2),
                      labels=c("1", "2"))+
  stat_summary(fun.ymin=min,fun.ymax=max,geom="errorbar",
               color="grey80",position=position_dodge(1), width=.2) + 
  xlab("ID")+ylab("Sum potential")

#deprecated because stat_summary designed for the case
#barplot(M.1.sum, ids)
#barplot(M.2.sum, ids)

Output does not look right

Expected output: 6x two columns side by side with legend of two items

Not sure how to use this one fill=factor(ids))) because I did not label any columns in the table. How can you better make the table?

R: 3.3.1
OS: Debian 8.5

Answer 1

With ggplot, it is essential to have a single data frame with everything in it (at least for a single plotting layer, e.g., all the bars in a plot). You create a data frame of the column sums, and then try to use external vectors for the id and the grouping, which makes things difficult.

This is how I would do it:

means = rbind(
    data.frame(mean = colSums(M), source = "M", col = 1:ncol(M)),
    data.frame(mean = colSums(M2), source = "M2", col = 1:ncol(M2))
)

means$col = factor(means$col)
## one nice data frame with everything needed for the plot    
means
#       mean source col
# 1  -0.6400      M   1
# 2  -1.4650      M   2
# 3  -0.8850      M   3
# 4  -1.6950      M   4
# 5  -2.0300      M   5
# 6  -0.2700      M   6
# 7  -0.3640     M2   1
# 8  -1.1240     M2   2
# 9  -0.8850     M2   3
# 10 -1.2775     M2   4
# 11 -1.4770     M2   5
# 12 -0.2990     M2   6

ggplot(means, aes(x = col, y = mean, fill = source)) +
    geom_bar(stat = 'identity', position = 'dodge')

You seem to want error bars too. I have no idea what would define those error bars - if you look at geom_errorbar it expects aesthetics ymin and ymax. If you calculate whatever values you want and add them as column to the data frame above, adding the error bar to the plot should be easy.