A Tutorial for ggplot2

Here is a quick ggplot2 tutorial from Isomorphismes from which I've completed the plots below

These two lines of code produce the same plot but show the differences between qplot and ggplot.

> qplot(clarity, data=diamonds, fill=cut, geom="bar")
> ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar()

Data displayed with a continuous scale (top) and discrete scale (bottom) 

> qplot(wt, mpg, data=mtcars, colour=cyl)

> qplot(wt, mpg, data=mtcars, colour=factor(cyl))

I think it works better with different colours for the factors but you can change shapes too. 

>qplot(wt, mpg, data=mtcars, shape=factor(cyl))

Dodge is probably better for comparing data but lets face it fill is prettier

> qplot(clarity, data=diamonds, geom="bar", fill=cut, position="dodge")

>qplot(clarity, data=diamonds, geom="bar", fill=cut, position="fill")

Not with this data but this is the great plot to use for comparing over a time series.

> qplot(clarity, data=diamonds, geom="freqpoly", group=cut, colour=cut, position="identity")

Changed this one to get better smoothers. More info on that here.

> qplot(wt, mpg, data=mtcars, colour=factor(cyl), geom=c("smooth", "point"), method=glm)

When dealing with lots of data points overplotting is a common problem as you can see from the first plot above. 


> t.df <- data.frame(x=rnorm(4000), y=rnorm(4000))
> p.norm <- ggplot(t.df, aes(x,y))
> p.norm + geom_point()


There are 3 easy ways to deal with it. Make the points more transparent. Reduce the size of the points. Make the points hollow.

> p.norm + geom_point(alpha=.15)

> p.norm + geom_point(shape=".")

> p.norm + geom_point(shape=1)

This is also helpful for saving plots

> jpeg('rplot.jpg')
> plot(x,y)
> dev.off()

#Don't forget to turn it back on again

> dev.new()