The importance of good data visualization is hard to overstate. However, as soon as you start to get multiple related datasets with many different variables, making useful graphics becomes a major challenge. Excel is of-course a no go for biogeeks and usually we and most of our geeks-colleagues around the world turn to R. However, R’s basic graphics capabilities is not very well suited for organizing and making lots of plots at the same time.

Here is a pharmacology example: Say you want to make 12 dose-response plots of various compounds tested in various cell lines. With basic R this would require writing a for-loop and fidling around a lot with axis and plot labelling and the par()-function to make them fit on one page. With basic R you would have be extremely careful to make the code general and reusable for next time when you have different compounds and different cell lines.

Enter ggplot2 and the grammar of graphics. ggplot2 is a package for implementing the grammar of graphics, which allows you to write extremely succinct and natural languages like code that produces stunning visualizations. Here is some code

p = qplot(Concentration, Percent.of.control, 
               data=screening_data, 
               geom=c("point", "smooth"), colour=Response.type) +
        scale_x_log10() +
        facet_grid(Compound ~ Cell.line) +
        coord_cartesian(ylim=c(-10, 110))
print(p)

And now the plot:

If you compare the code and the plot you will realize that the code contains about the words that you would use if you were told to briefly describe the plot using English. Me like!

If you want to compare differences in potency of the compounds in different cell lines that can easily be accomplished by changing the variables:

q =  qplot(Cell.line, Percent.of.control, 
                 data=screening_data, 
                 geom=c("point"), colour=Response.type) +
        facet_grid(Compound ~ Concentration) +
        coord_cartesian(ylim=c(-10, 110))
print(q)

In many ways it is similar to lattice, but Ggplot2 is more opinionated, e.g. it forces you to organize your data in long data.frames. Conveniently it includes several useful tools for reformating your data structures.

Read more  in the draft ggplot2-book, which freely available from the developer.

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