What is an elegant way of visualising two time series with many data points? I need to draw two time-series plots on a graph in R. The problem I am facing is as follows. Each plot has 200 data points, and therefore, the graph that I have produced looks clumsy on my two-column pdf file. I am trying to use different pch values, but still the graph doesn't look good to me. Could someone help me to visualize this data in an elegant way, please?  Please see the graph below. The most important requirement is that the graph should be clear on printing.
The R Code that I have used is as follows:
plot(temp11, type="b", col="blue", ylim=c(0.5,1), lwd=0.8, lty=4, pch=1, xlab="Time", ylab="price", cex.lab=1.2,cex.axis=1.2)
> lines(temp22, type="b", col="red", lwd=1.5,lty=1, pch=18)
> legend("top",legend=c("temp11","temp22"),lty=c(4,1),pch=c(1,18), col=c("blue", "red"),text.col=c("blue", "red"))


 A: Partial transparency ("alpha") might help you here, e.g.:
> temp11=runif(100)
> temp22=runif(100)
> plot(temp11, type="p", col=rgb(0.2, 0.2, 1, 0.6), pch=19, xlab="Time", ylab="price")
> lines(temp11, lwd=3, col=rgb(0.2, 0.2, 1, 0.3))
> points(temp22, pch=19, col=rgb(1, 0.2, 0.2, 0.6))
> lines(temp22, lwd=3, col=rgb(1, 0.2, 0.2, 0.3))

Here is an example of the image:

It doesn't look that great with runif data, but on your data I think it would work a bit better.  Alpha support is device-dependent, but PDF does support it.
A: Here's what I was trying to say in the comment, with a little bit more detail (though this might not be the answer to your question).  First, get some data.
x <- arima.sim(200, model = list(ar = 0.6)) + 3
y <- arima.sim(200, model = list(ar = -0.7)) - 3

A plot with two columns would look something like this:
par(mfrow = c(1,2))
plot(x)
plot(y)

with a graph like so:
 
while a plot with two rows would look something like this:
par(mfrow = c(2,1))
plot(x)
plot(y)

with a graph like so:

And if you'd like to keep both series on the same axes then you could do like this:
par(mfrow = c(1,1))
ts.plot(x, y)

which would look something like this:

I was trying to communicate that if you make the plot wider rather than taller then it helps to see what's happening inside the lengthy time series better.  You can manually resize the graphics device to get the aspect you like, or if you use RStudio, when you export the image you can set whatever dimensions desired.
