Reputation: 1045
I thought stacking columns was the default action under ggplot2 but that does not seem to be happening for my plot. I am trying to take two vectors (may or may not have the same length) and graph them in the same plot as stacked bars. Here is a simple example:
z1<-c(500, 300, 200, 100)
z2<-c(800, 100, 50)
names(z1)<-c("a", "b", "c", "d")
names(z2)<-c("a", "c", "e")
z1<-as.data.frame(z1)
z2<-as.data.frame(z2)
colnames(z1)<-"total"
colnames(z2)<-"total"
ggplot()+
labs(x="", y="") +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
scale_y_continuous(labels=format_si()) +
ggtitle("Test") +
geom_bar(data=z1, aes(x=rownames(z1), y=total),position="identity",stat="identity",
fill=rgb(red=200, green=0, blue=50, maxColorValue = 255)) +
geom_bar(data=z2, aes(x=rownames(z2), y=total),position="identity",stat="identity",
fill=rgb(red=0, green=200, blue=50, maxColorValue = 255))
Gives me:
As you can see, the a and c elements are in front of each other instead of stacked.
Upvotes: 1
Views: 551
Reputation: 23109
Just try this:
df <- rbind(cbind(z1, type=rownames(z1), data='z1'), cbind(z2, type=rownames(z2), data='z2'))
ggplot(df, aes(type, total, fill=data)) +
geom_bar(stat="identity") +
scale_fill_manual(values=c(rgb(red=200, green=0, blue=50, maxColorValue = 255), rgb(red=0, green=200, blue=50, maxColorValue = 255)))
Upvotes: 1
Reputation: 4416
This type of data organization would work better:
z1<-c(500, 300, 200, 100)
z2<-c(800, 100, 50)
names(z1)<-c("a", "b", "c", "d")
names(z2)<-c("a", "c", "e")
z1<-as.data.frame(z1)
z2<-as.data.frame(z2)
colnames(z1)<-"total"
colnames(z2)<-"total"
Add group (z1, z2) to the data
z1$Group <- "z1"
z2$Group <- "z2"
Add the rownames as a variable column
z1$rnm <- rownames(z1)
z2$rnm <- rownames(z2)
Bind these together
zt <- rbind(z1, z2)
A much simplified plot
ggplot(zt, aes(x=rnm, y=total, fill=Group)) +
geom_bar(stat="identity")
At its core here, you need to understand aesthetics and what type of data is most efficient with ggplot2. Having separate calls for each group/data ignores the power of a factor variable with several levels. For example, experiment with swapping Group
for rnm
in the example.
Upvotes: 1