EntryLevelR EntryLevelR - 24 days ago 6
R Question

Adding two y-axis titles on the same axis

I am going to use a dataset and plot that came from a previous problem (Here):

dat <- read.table(text = " Division Year OperatingIncome
1 A 2012 11460
2 B 2012 7431
3 C 2012 -8121
4 D 2012 15719
5 E 2012 364
6 A 2011 12211
7 B 2011 6290
8 C 2011 -2657
9 D 2011 14657
10 E 2011 1257
11 A 2010 12895
12 B 2010 5381
13 C 2010 -2408
14 D 2010 11849
15 E 2010 517",header = TRUE,sep = "",row.names = 1)

dat1 <- subset(dat,OperatingIncome >= 0)
dat2 <- subset(dat,OperatingIncome < 0)
ggplot() +
geom_bar(data = dat1, aes(x=Year, y=OperatingIncome, fill=Division),stat = "identity") +
geom_bar(data = dat2, aes(x=Year, y=OperatingIncome, fill=Division),stat = "identity") +
scale_fill_brewer(type = "seq", palette = 1)


It includes the following plot, which is where my question comes in:

Plot of interest

My question: Is it possible for me to change the y-axis label to two different labels on the same side? One would say "Negative Income" and be on the bottom portion of the y-axis. The other would say "Positive Income" and be on the upper portion of the SAME y-axis.

I have seen this question asked in terms of dual y-axis for different scales (on opposite sides), but I specifically want this on the same y-axis. Appreciate any help - I also would prefer to use ggplot2 for this problem, if possible.

Answer

You can use annotate to add labels for negative and positive income. To add text outside the plot panel, you'll need to turn off clipping. Below are examples of adding text both inside and outside the plot panel:

# Plot
p = ggplot() + 
  geom_bar(data = dat1, aes(x=Year, y=OperatingIncome, fill=Division),stat = "identity") +
  geom_bar(data = dat2, aes(x=Year, y=OperatingIncome, fill=Division),stat = "identity") +
  scale_fill_brewer(type = "seq", palette = 1) +
  geom_hline(yintercept=0, lwd=0.3, colour="grey20") +
  scale_x_continuous(breaks=sort(unique(dat$Year))) +
  theme_bw()

# Annotate inside plot area
p +  coord_cartesian(xlim=range(dat$Year) + c(-0.45,0.4)) + 
  annotate(min(dat$Year) - 0.53 , y=c(-5000,5000), label=c("Negative Income","Positive Income"), 
           geom="text", angle=90, hjust=0.5, size=3, colour=c("red","blue"))

enter image description here

# Annotate outside plot area by turning off clipping
pp = p + coord_cartesian(xlim=range(dat$Year) + c(-0.4,0.4)) + 
  annotate(min(dat$Year) - 0.9, y=c(-6000,10000), label=c("Negative Income","Positive Income"), 
           geom="text", angle=90, hjust=0.5, size=4, colour=c("red","blue")) +
  labs(y="")

pp <- ggplot_gtable(ggplot_build(pp))
pp$layout$clip <- "off"
grid.draw(pp)

enter image description here

You can also use cowplot as suggested by @Gregor. I haven't tried this before, so maybe there's a better approach than what I've done below, but it looks like you have to use viewport coordinates, rather than data coordinates, to place the annotations.

# Use cowplot
library(cowplot)

ggdraw() +
  draw_plot(p + labs(y=""), 0,0,1,1) +
  draw_label("Positive Income", x=0.01, y = 0.5, col="blue", size = 10, angle=90) +
  draw_label("Negative Income", x=0.01, y = 0.15, col="red", size = 10, angle=90) 

enter image description here

I realize the data in the question is just for illustration, but for data like this, a line plot might prove easier to understand:

library(dplyr)

ggplot(dat, aes(x=Year, y=OperatingIncome, color=Division)) + 
  geom_hline(yintercept=0, lwd=0.3, colour="grey50") +
  geom_line(position=position_dodge(0.2), alpha=0.5) +
  geom_text(aes(label=Division), position=position_dodge(0.2), show.legend=FALSE) +
  scale_x_continuous(breaks=sort(unique(dat$Year))) +
  theme_bw() +
  guides(colour=FALSE) +
  geom_line(data=dat %>% group_by(Year) %>% summarise(Net=sum(OperatingIncome), Division=NA),
            aes(x=Year, y=Net), alpha=0.4) +
  geom_text(data=dat %>% group_by(Year) %>% summarise(Net=sum(OperatingIncome), Division=NA),
            aes(x=Year, y=Net, label="Net"), colour="black") 

enter image description here

Or, if a bar plot is required, maybe something like this:

ggplot() + 
  geom_bar(data = dat %>% arrange(OperatingIncome) %>% 
             mutate(Division=factor(Division,levels=unique(Division))), 
           aes(x=Year, y=OperatingIncome, fill=Division), 
           stat="identity", position="dodge") +
  geom_hline(yintercept=0, lwd=0.3, colour="grey20") +
  theme_bw()

enter image description here