Books and lessons about ggplot2

February 19, 2014
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(This article was first published on There is grandeur in this view of life » R, and kindly contributed to R-bloggers)

I recently got an email from a person at Packt publishing, who suggested I write a book for them about ggplot2. My answer, which is perfectly true, is that I don’t have the time, nor the expertise to do that. What I didn’t say is that 1) a quick web search suggests that Packt doesn’t have the best reputation and 2) there are already two books about ggplot2 that I think covers the entire field: the indispensible ggplot2 book, written by Hadley Wickham, the author of the package, and the R Graphics Cookbok by Wincent Chang. There are too many decent but not great R books on the market already and there is no reason for me to spend time to create another one.

However, there are a few things I’d like to tell the novice, in general, about ggplot2:

1. ggplot2 is not necessarily superior to lattice or base graphics. It’s largely a matter of taste, you can make very nice plots with either plotting system and in some situations everybody will turn away from their favourite system and use another. For instance, a lot of built-in diagnostic plots come preprogrammed in base graphics. The great thing about ggplot2 is how it allows the user to think about plots as layers of mappings between variables and geometric objects. Base graphics and lattice are organised in a different way, which is not necessarily worse; it depends on the way you’re accustomed to thinking about statistical graphics.

2. There are two ways to start a plot in ggplot2: qplot( ) or ggplot( ). qplot is the quicker way and probably the one you should learn first. But don’t be afraid of the ggplot function! All it does is set up an empty plot and connect a data frame to it. After that you can add your layers and map your variables almost the same way you would do with qplot. After you’ve become comfortable with qplot, just try building plots with ggplot a few times, and you’ll see how similar it is.

3. The magic is not in plotting the data but in tidying and rearranging the data for plotting. Make sure to put all your labels, indicators and multiple series of data into the same data frame (most of the time: just cbind or merge them together), melt the data frame and pass it to ggplot2. If you want to layer predictions on top of your data, put output from your model in another data frame. It is perfectly possible, often advisable, to write functions that generate ggplot2 plots, but make sure to always create the data frame to be plotted first and then pass it on. I suggest don’t trying to create the mappings, that is x= and y= and the like, on the fly. There is always the risk of messing up the order of the vectors, and also, because ggplot2 uses metaprogramming techniques for the aesthetics, you might see unexpected behaviours when putting function calls into the mapping.

4. Worry about mapping variables and facetting first and then change the formatting. Because of how plots as well as settings in ggplot2 are objects that you can store and pass around, you can first create a raw version of the plot, and then just add (yes add, with the ”+” operator) the formatting options you want. So taken together, the workflow for making any ggplot2 plot goes something like this: 1) put your data frame in order; 2) set up a basic plot with the qplot or ggplot function; 3) add one extra geom at at time (optional if you make a simple plot with qplot, since qplot sets up the first geometry for you); 4) add the settings needed to make the plot look good.

Happy ggplotting!


Postat i:computer stuff, data analysis Tagged: ggplot2, R

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