If you’re using
R for the first time you may have looked at
?plot (2 page help file) or
?par (12 page help file) to figure out what’s going on. It’s overwhelming.
This document explains the
parameters I always bother to set. That way you can get decent
plots without reading every
(If you are just using
R for the very first time and need some data, type
data(pima) to load some interesting pre-cleaned data sets. Then do
plot(faithful) to see how the
base::plot functions. Type
??pima if you can’t find the dataset.)
> plot(faithful, pch=20, col=rgb(.1,.1,.1,.5), cex=.6)
Firstly: what is
par? When you type
par( lwd=3, col="#333333", yaxt="n" ), it will open an empty box that will hold your next
plot( dnorm, -3, 3). You can run different plots in the box and as long as you don’t close it, the line-width will be 3 times bigger than default, the y-axis won’t have labels, and the colour will be dark-grey.
There are a lot of plotting options. Here are the ones I use regularly:
cex = .8. Decreases the size of type or plotted points by 20%.
par(new=TRUE). Use this to plot two things on top of each other. Beware, the labels will overprint over each other too (but this doesn’t matter for quick, casual plots).
col = "red",
col = "#333333". I think
#333333is the best default colour and I use
redif a point or line needs to stand out.
col=rgb(.1,.1,.1,.5). This is another decent grey for overplotting. I used this in the Old Faithful plot at the top. The first three numbers are Red, Green, Blue and the fourth is Transparency.
lwd = 3. This is a good line width, I think, especially with the dark grey
pch = 20. Plots points with a small circle.
pch=19is a slightly larger dot and
pch=15is a square. Read after the second group of bullets for more info.
png("name of the plot.png"). Then do
plot(z), and remember to finish it off with
dev.off()means device off; the
par()window and the
png()file are considered “graphic devices”.]
Here are the ones I use less regularly, but still more than weird stuff like
lend = butt. Line ending is square rather than mitred. I use this before I make a histogram.
xlog=TRUE. “Hubble made this significantly worse chart before it was discovered that all data look like straight lines on log-log plots.” —Lawrence Krauss
las=1. If you want all of your axis labels to be printed horizontally.
mfrow=c(2,2). If you want to juxtapose four plots next to each other.
mfrowand they will write like a typewriter, left-to-right and starting over on a new line after 2 spots have been filled in.
mfcol=c(3,3)and they will fill in vertically. (Try it if what I said doesn’t make sense.)
yaxs="n". This suppresses printing the vertical axis labels. I do this when plotting a distribution because those vertical numbers aren’t meaningful.
main="It's a plot about nothing. Don't you get it? People _love_ nothing!". This is the title of the plot.
legend( "top right", legend=c("control", "placebo", "test group"), fill = c("black", "#333333", "red"), border="white", bty="n"). This is how I find legends look good. You should only need to change the placement,
fillto make it work for your plot.
plotmultiple figures in the same picture do
mfcol=c(3,2). Then the next six = three × two
plots you run will go in left-to-right or up-to-down order, filling in six spots.
Don’t forget to do
par(mfrow=c(1,1))after you’re done, to go back to one
- If you want to save your plot to a file rather than “print” it to the screen, type
png("a plot about nothing.png"); plot( stuff ); dev.off(). The
dev.off()tells the system to go back to normal (printing to the screen—PNG device off).
- One more awesome advice from the StackOverflow
Rcommunity: how to get some sweet, sweet log-axis tickmarks. Read all about it.
Most of these can be done inside of
plot( dpois, 0, 15, lwd = 3) or beforehand in a
par(lwd=3); plot( dpois, 0, 15). With
par(new=TRUE) and par(mfrow=c(2,2)), though, you need to do them in a
If you forget what the colours or the pch shapes are, do this:
plot( 1:25, pch=1:25, col=1:25 ). You’ll get this:
So basically, you only want
pch=20 and sometimes
pch=15, like I said.
One more thing you might like to learn is how to colour important data points red and normal ones grey. I’ll explain that another time.