How do I vertically stack two graphs with the same x scale, but a different y scale in R?

Question

Greetings,

Currently I'm doing the following in R:

require(zoo)
data <- read.csv(file="summary.csv",sep=",",head=TRUE)
cum  = zoo(data$dcomp, as.Date(data$date))
data = zoo(data$compressed, as.Date(data$date))
data <- aggregate(data, identity, tail, 1)
cum  <- aggregate(cum, identity, sum, 1)
days = seq(start(data), end(data), "day")
data2 = na.locf(merge(data, zoo(,days)))

plot(data2,xlab='',ylab='compressed bytes',col=rgb(0.18,0.34,0.55))
lines(cum,type="h",col=rgb(0,0.5,0))

Snip of summary.csv:

date,revision,file,lines,nclass,nattr,nrel,bytes,compressed,diff,dcomp
2007-07-25,16,model.xml,96,11,22,5,4035,991,0,0
2007-07-27,17,model.xml,115,16,26,6,4740,1056,53,777
2007-08-09,18,model.xml,106,16,26,7,4966,1136,47,761
2007-08-10,19,model.xml,106,16,26,7,4968,1150,4,202
2007-09-06,81,model.xml,111,16,26,7,5110,1167,13,258
...

The last two lines plot the information I need, and the result resembles the following: Blue line is the entropy in bytes of the artifact I'm interested. Green lines represent the entropy of the changes.

Now, in this graph, it works well because there isn't a huge difference in scales. But I have other graphs where the green lines become so small one cannot see.

The solution I was looking for, involved two things:

1. To move the green vertical lines to a second graph, just below the first one, with its own y axis, but shared x axis.
2. To provide it a logarithmic scale, since I'm more interested in the "magnitude", than in the specific values.

Thanks in advance!

P.S. If someone can also tell me how could I put "minor ticks" in the x scale referring to the months, I appreciate :-) If these are too much questions for a single post, I can divide them further.

Answer 1

You can use par(new=T) to plot into the same graph using two different y-axes! Thsi should also solve your problem.

Next you will find a simple example that plots two random normal variables, one on mean 0 the other one on mean 100 (both sd s = 1) in the same plot. The first one in red on the left y-axis, the second one in blue on the right y-axis. Then, axis labels are added.

Here you go:

x <- 1:10
y1 <- rnorm(10)
y2 <- rnorm(10)+100

plot(x,y1,pch=0,type="b",col="red",yaxt="n",ylim=c(-8,2))
par(new=T)
plot(x,y2,pch=1,type="b",col="blue",yaxt="n",ylim=c(98,105))

axis(side=2)
axis(side=4)

looks like this then (remember red on left axis, blue on right axis):

UPDATE:
Based on comments I produced an updated version of my graph. Now I dig a little deeper into base graph functionality using par(mar=c(a,b,c,d)) to create a bigger margin around the graph (needed for right axis label), mtext to show the axis labels and and advanced use of the axis function:

x <- 1:100
y1 <- rnorm(100)
y2 <- rnorm(100)+100

par(mar=c(5,5,5,5))

plot(x,y1,pch=0,type="b",col="red",yaxt="n",ylim=c(-8,2),ylab="")
axis(side=2, at=c(-2,0,2))
mtext("red line", side = 2, line=2.5, at=0)

par(new=T)
plot(x,y2,pch=1,type="b",col="blue",yaxt="n",ylim=c(98,108), ylab="")
axis(side=4, at=c(98,100,102), labels=c("98%","100%","102%"))
mtext("blue line", side=4, line=2.5, at=100)

As you see it is pretty straight forward. You can define the position of your data with ylim in the plot function, then use at in the axis function to select which axis ticks you wanna see. Furthermore, you can even provide the labels for the axis ticks (pretty useful for nominal x-axis) via labels in the axis function (done here on the right axis). To add axis labels, use mtext with at for vertical positioning (line for horizontal positioning).

Make sure to check ?plot, ?par, ?axis, and ?mtext for further info.
Great web resources are: Quick-R for Graphs: 1, 2, and 3.

Answer 2

I think you can get what you want using ggplot2. Using the code below, I can produce:

Obviously things like line colours can be changed to what ever you want. On the x-axis I specified major lines on years and minor lines on months.

require(ggplot2)
t = as.Date(0:1000, origin="2008-01-01")  
y1 = rexp(1001)
y2 = cumsum(y1)
df = data.frame(t=t, values=c(y2,y1), type=rep(c("Bytes", "Changes"), each=1001))

g = ggplot(data=df, aes(x=t, y=values)) +
  geom_line() +
  facet_grid(type ~ ., scales="free") +
  scale_y_continuous(trans="log10") +
  scale_x_date(major="years", minor="months") +
  ylab("Log values")
g