I have proportion response data for 4 experimental groups, with 2 different statistics computed for each group. I want the following figure (which I can achieve):
I obtain this figure with the following code:
Group<-c('a','b','c','d','a','b','c','d')
Statistic<-c('Mean','Mean','Mean','Mean','d','d','d','d')
Val<-c(.75,.83,.79,.69,.5,.02,.1,.3)
dfm2<-data.frame(cbind(Group,Statistic,Val))
ggplot(dfm2,aes(x = Group,y = Val)) +
geom_bar(aes(fill = Statistic),position = dodge',stat='identity')
However, when I change the limits of the y-axis (to [0,1] since I have proportions) by adding the line of code:
+ scale_y_continuous(limits=c(0, 1))
I get
Error: Discrete value supplied to continuous scale
So I understand this means I have a non-continuous variable. I have tried converting my Statistic variable by using as.numeric()
and countless other options but to no avail. If anyone can assist me in solving this problem and/or explain what causes it, I would be most appreciative.
The problem is the unnecessary use of cbind
inside data.frame
. cbind
creates a matrix. A matrix must have all values of the same mode (numeric, character, etc.). Since at least one of variables (two in this case) is character mode, cbind
coerces Val
to character as well. data.frame
converts the three character variables to factor. Either way, Val
is a discrete (categorical) value rather than numeric, resulting in an error when you use scale_y_continuous
.
Change to dfm2 <- data.frame(Group,Statistic,Val)
and the error will go away.
You can check the effect of cbind
and data.frame
on data types as follows:
cbind(Group, Statistic, Val)
Group Statistic Val
[1,] "a" "Mean" "0.75"
[2,] "b" "Mean" "0.83"
...
[7,] "c" "d" "0.1"
[8,] "d" "d" "0.3"
dfm2<-data.frame(cbind(Group,Statistic,Val))
str(dfm2)
'data.frame': 8 obs. of 3 variables:
$ Group : Factor w/ 4 levels "a","b","c","d": 1 2 3 4 1 2 3 4
$ Statistic: Factor w/ 2 levels "d","Mean": 2 2 2 2 1 1 1 1
$ Val : Factor w/ 8 levels "0.02","0.1","0.3",..: 6 8 7 5 4 1 2 3
dfm2 <- data.frame(Group,Statistic,Val)
str(dfm2)
'data.frame': 8 obs. of 3 variables:
$ Group : Factor w/ 4 levels "a","b","c","d": 1 2 3 4 1 2 3 4
$ Statistic: Factor w/ 2 levels "d","Mean": 2 2 2 2 1 1 1 1
$ Val : num 0.75 0.83 0.79 0.69 0.5 0.02 0.1 0.3
If you don't want data.frame
to convert strings to factors, add the argument stringsAsFactors=FALSE
.