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A plot of co-authorships in my little corner of science

author year count image

 
Here’s a mostly useless visualization of the collection of journal articles that sits in my reference database in Endnote. I deal mostly in marine biology, physiology, biomechanics, and climate change papers, with a few molecular/genetics papers thrown in here and there. The database has 3325 entries, 2 of which have ambiguous publication years and aren’t represented above. This is by no means an exhaustive survey of the literature in my field, it’s just an exhaustive survey of the literature on my computer.

To make this figure, I first had Endnote export the database to … Continue Reading

Part 2: Make your R figures legible in Powerpoint/Keynote presentations

In the previous post, I outlined some tips for increasing the size of figure labels for figures that are meant to be displayed on a projector. The previous post used the base R plot() function, but the procedure when plotting with ggplot2 is different and usually quite a bit simpler than the stock R plotting functions. As before, I’m outputting the figures here as 1024×768 PNG image files, since they’re sure to work in whatever version of PowerPoint or Keynote you’re stuck using.

I’ll begin by generating some random data and dates to use in the plots. … Continue Reading

Generating polygon boundaries for plotting simple time series data with missing data

Every so often I want to plot some data with pretty upper and lower error bounds, such as temperature data through time, perhaps with the maximum and minimum temperature range or standard error bounds for averaged data. The polygon( ) function can make those sorts of pretty plots. However, I’ll often have chunks of missing data for periods of time, so I have to break up the polygons that go with the plotted data. I could swear I wrote a function to do this several months ago, but it’s lost in a pile of other scripts, so I re-wrote a … Continue Reading

Modifying basic plots in R

Below is a walk-through of some of the basics of customizing plots in R. These are all based on the graphics package that comes in the base installation of R.

Let’s start by making a basic plot in R. In the code snippets below, green text behind a # sign is considered comments by R, so everything after a # sign on a line will be ignored by R. We’ll call the plot command and supply it with two vectors of numbers representing the x and y values:

plot(c(1,2,3,4,5,6),c(4,3,6,2,1,1)) #x and y data for our example plot

which produces this plot:

[caption … Continue Reading