My template for controlling publication quality figures

The following is a template that I usually start with when producing figures for publication. It allows me to control:

1. The overall size of the figure (in inches) (WIDTH, HEIGHT)
2. The layout of figure subplots (using the layout() function) (LO)
3. The resolution of the figure (for a .png file) (RESO)

I define the overall dimensions of the figure in units of measurement (e.g. inches or centimeters) in order to control how the figure will look on the printed page. For example, a typical journal page might have ~8 inches of space for a 2 column figure and ~4 inches for a 1 column figure.

I define margins (mar, oma) in terms of point size (ps), since this relates to the height of text, which allows of control of axis labeling. By defining the outer margins (OMA) and point size (PS) before calling layout, you will have these margins incorporated. Then, by running the x11() device (after the #), you can check your figure layout with layout.show(n):

I learned recently that the layout() function will adjust the character expansion size (par()$cex) depending on how your device is split up. For that reason, I usually include another line of code resetting par(cex=1) before proceeding with individual plots.

Finally, the three different device types included in the template are:

1. x11(), for initial tweaking of the layout and general functionality of the plotting code
2. png(), for producing a compact figure useful in pasting into Word documents, and for cases where the figure contains a lot of information and would be slow to loading as a .pdf
3. pdf(), for a vector-based figure that is fully scalable / zoomable. When not too big, these figures look the best, and can also be embedded in LaTeX documents

I have been able to use this template to successfully control my figures to the formatting requirements of specific journals or other publications (e.g. overall size, point size, resolution, etc.).


Figure template:

Choosing colors visually with 'getcolors'

When plotting, I am constantly defaulting to the "main" colors in R - In other words, the colors that one can quickly call by number (1="black", 2="red", 3="green", 4="blue", ... etc.) . In my opinion, these colors do not lend themselves well to compelling graphics. I imagine this is the reason for the inclusion of the much more pleasing color palettes used by default in the popular graphical package ggplot2. I try and choose better colors for final figure versions for publishing, but it is usually a tedious process of trial and error with functions like rgb(). There are some nice alternate color palettes out there probably more in line with color theory, and one has a lot of flexibility with functions like colorRampPalette(), but I wanted to have a function where I can choose colors visually in order to speed up the process. Below is the function getcolors(), which allows for this selection by using a simplified color swatch to allow selection with a mouse using the locator() function (above, top plot). Following selection, a second plot opens showing how these colors look next to each other and on a background gradient of black to white. The function uses an RGB color model: Red increases on the y-axis, Green increases on the x-axis, and Blue is a repeated sequence of levels across the x-axis).

For the example, I chose 4 colors, which are saved in a vector. These colors were subsequently used to make the following line plot:

the getcolors function:

Lomb-Scargle periodogram for unevenly sampled time series

In the natural sciences, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree of interpolation to fill in gaps. An alternative is the Lomb-Scargle method (or least-squares spectral analysis, LSSA), which estimates a frequency spectrum based on a least squares fit of sinusoid.

The above figure shows a Lomb-Scargle periodogram of a time series of sunspot activity (1749-1997) with 50% of monthly values missing. As expected (link1, link2), the periodogram displays a a highly significant maximum peak at a frequency of ~11 years.

The function comes from a nice set of functions that I found here: http://research.stowers-institute.org/efg/2005/LombScargle/R/index.htm. An accompanying paper focusing on its application to time series of gene expression can be found here.

Below is a comparison to an FFT of the full time series. For another great resource on spectral analysis, and time series-related R methods in general, see the following website: http://zoonek2.free.fr/UNIX/48_R/15.html.

To reproduce the example:

Producing animated GIFs and Videos

It took me a while to figure out how to use the animation package on my Windows OS. In making an animated GIF, the problem seems to have been quite simple in the end (and I should have been more patient in reading the instructions!) - Following installation of the program ImageMagick, one has to define where the program convert.exe is located using 'ani.options()'.

One is also able to make great videos:

To reproduce the example (requires 'spirographR' function):