A simple square binning function

While I like the hexbin package and see the advantage of having more circular bins, I find that square binning allowed me to take advantage of lower level plotting functions like image, which allows for more flexibility when plotting. The following is an efficient implementation within the function sqbin, available in the sinkr package.

Example:

#library(devtools)
#install_github("marchtaylor/sinkr")
library(sinkr)
 
# Synthetic data
set.seed(1)
n <- 1e6
x <- runif(n, min=-3, max=3)
y <- 4*x^2 + rnorm(n, sd=5)
 
sqbin.res <- sqbin(x,y)
 
# Plot
op <- par(mar=c(4,4,1,1))
image(sqbin.res, col=jetPal(20))
par(op)
 
# Plot with legend
op <- par(no.readonly = TRUE)
lo <- matrix(1:2, nrow=1, ncol=2)
layout(lo, widths=c(4,1), heights=c(4), respect=TRUE)
par(cex=1)
par(mar=c(3,3,1,1))
image(sqbin.res, col=jetPal(20))
par(mar=c(3,0,1,4))
imageScale(sqbin.res$z, col=jetPal(20), axis.pos=4)
mtext("Frequency", line=2.5, side=4)
par(op)

Created by Pretty R at inside-R.org

New version of image.scale function

(Note: the most recent version, imageScale can be found in the sinkr package: https://github.com/marchtaylor/sinkr)

Below is an updated version of the image.scale function. In the old version, one had to constantly use additional arguments to suppress axes and their labels. The new version contains the additional arguments axis.pos (1, 2, 3, or 4) for defining the side of the axis, and add.axis (TRUE or FALSE), for defining whether the axis is plotted. Based on the position of the axis, the scale color levels are automatically drawn in a horizontal (axis.pos = 1[bottom] or 3[top]) or vertical (axis.pos = 2[left] or 4[right]) orientation. For the right plot above, the argument add.axis=FALSE so that additional control over axis ticks and labels could be added in an additional step with axis(). The function mtext() can be used to add additional labels to the scale.


The image.scale function:

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:

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):

Finding a pin in a haystack - PCA image filtering

I found the following post regarding the anomalous metal object observed in a Curiosity Rover photo to be fascinating - specifically, the clever ways that some programmers used for filtering the image for the object. The following answer on mathematica.stackexchange.com was especially illuminating for its use of a multivariate distribution to describe the color channels for a test region of "sand". This distribution was subsequently used to assess if the rest of the image colors belonged to the same distribution.

Create polygons from a matrix

The following function matrix.poly allows for the addition of polygons to a plot based on a matrix and defined matrix positions. I have used this function on occasion to highlight specific matrix locations (e.g. in the above figure). You can do the same by overlaying another image (left in above plot) but with this function you will have all other polygon plotting possibilities (e.g. borders etc.).

Adding a transparent image layer to a plot

The following example shows how to add a transparent image-type layer to a plot. The add.alpha function (below) simply adds transparency to a vector of colors which is then introduced in the "col" argument of an image plot.