Aesthetically pleasing, publication quality plots in R


I spend a lot of my time making graphs. For a long time I used a Unix package called Grace. This had several advantages, including the ability to create grids of plots very easily. However it also had plenty of limitations, and because it is GUI-based, one had to create each plot from scratch. Although I use Matlab for most data analysis, I’ve always found its plotting capabilities disappointing, so a couple of years ago I bit the bullet and started learning R, using the RStudio interface.

There are several plotting packages for R, including things like ggplot2, which can automate the creation of some plots. Provided your data are in the correct format, this can make plotting really quick, and tends to produce decent results. However, for publication purposes I usually want to have more control over the precise appearance of a graph. So, I’ve found it most useful to construct graphs using the ‘plot’ command, but customising almost every aspect of the graph. There were several things that took me a while to work out, as many functions aren’t as well documented as they could be. So I thought it would be helpful to share my efforts. Below is some code (which you can also download here) that demonstrates several useful techniques for plotting, and should create something resembling the following plot when executed.

Example plot created by the script.

Example plot created by the script.

My intention is to use this script myself as a reminder of how to do different things (at the moment I always have to search through dozens of old scripts to find the last time I did something), and copy and paste chunks of code into new scripts each time I need to make a graph. Please feel free to use parts of it yourself, to help make the world a more beautiful place!

# this script contains examples of the following:
# outputting plots as pdf and eps files
# creating plots with custom tick mark positioning
# drawing points, bars, lines, errorbars, polygons, legends and text (including symbols)
# colour ramps, transparency, random numbers and density plots

# Code to output figures as either an eps or pdf file. Note that R’s eps files appear not to cope well with transparency, whereas pdfs are fine
outputplot <- 0
if(outputplot==1){postscript(“filename.eps”, horizontal = FALSE, onefile = FALSE, paper = “special”, height = 4.5, width = 4.5)}
if(outputplot==2){pdf(“filename.pdf”, bg=”transparent”, height = 5.5, width = 5.5)}
# all the code to create the plot goes here
if(outputplot>0){}  # this line goes after you’ve finished plotting (to output the example below, move it to the bottom of the script)

# set up an empty plot with user-specified axis labels and tick marks
plotlims <- c(0,1,0,1)  # define the x and y limits of the plot (minx,maxx,miny,maxy)
ticklocsx <- (0:4)/4    # locations of tick marks on x axis
ticklocsy <- (0:5)/5    # locations of tick marks on y axis
ticklabelsx <- c(“0″,”0.25″,”0.5″,”0.75″,”1”)        # set labels for x ticks
ticklabelsy <- c(“0″,”0.2″,”0.4″,”0.6″,”0.8″,”1”)    # set labels for y ticks

par(pty=”s”)  # make axis square
plot(x=NULL,y=NULL,axes=FALSE, ann=FALSE, xlim=plotlims[1:2], ylim=plotlims[3:4])   # create an empty axis of the correct dimensions
axis(1, at=ticklocsx, tck=0.01, lab=F, lwd=2)     # plot tick marks (no labels)
axis(2, at=ticklocsy, tck=0.01, lab=F, lwd=2)
axis(3, at=ticklocsx, tck=0.01, lab=F, lwd=2)
axis(4, at=ticklocsy, tck=0.01, lab=F, lwd=2)
mtext(text = ticklabelsx, side = 1, at=ticklocsx)     # add the tick labels
mtext(text = ticklabelsy, side = 2, at=ticklocsy, line=0.2, las=1)  # the ‘line’ command moves away from the axis, the ‘las’ command rotates to vertical
box(lwd=2)      # draw a box around the graph
title(xlab=”X axis title”, col.lab=rgb(0,0,0), line=1.2, cex.lab=1.5)    # titles for axes
title(ylab=”Y axis title”, col.lab=rgb(0,0,0), line=1.5, cex.lab=1.5)

# create some synthetic data to plot as points and lines
datax <- sort(runif(10,min=0,max=1))
datay <- sort(runif(10,min=0.2,max=0.8))
SEdata <- runif(10,min=0,max=0.1)
lines(datax,datay, col=’red’, lwd=3, cex=0.5)     # draw a line connecting the points
arrows(datax,datay,x1=datax, y1=datay-SEdata, length=0.015, angle=90, lwd=2, col=’black’)  # add lower error bar
arrows(datax,datay,x1=datax, y1=datay+SEdata, length=0.015, angle=90, lwd=2, col=’black’)  # add upper error bar
points(datax,datay, pch = 21, col=’black’, bg=’cornflowerblue’, cex=1.6, lwd=3)   # draw the data points themselves

# create some more synthetic data to plot as bars
datax <- 0.1*(1:10)
datay <- runif(10,min=0,max=0.2)
SEdata <- runif(10,min=0,max=0.05)
ramp <- colorRamp(c(“indianred2”, “cornflowerblue”))  # create a ramp from one colour to another
colmatrix <- rgb(ramp(seq(0, 1, length = 10)), max = 255)   # index the ramp at ten points
barplot(datay, width=0.1, col=colmatrix, space=0, xlim=1, add=TRUE, axes=FALSE, ann=FALSE)  # add some bars to an existing plot
arrows(datax-0.05,datay,x1=datax-0.05, y1=datay-SEdata, length=0.015, angle=90, lwd=2, col=’black’)  # add lower error bar
arrows(datax-0.05,datay,x1=datax-0.05, y1=datay+SEdata, length=0.015, angle=90, lwd=2, col=’black’)  # add upper error bar

coltrans=rgb(1,0.5,0,alpha=0.3)             # create a semi-transparent colour (transparency is the alpha parameter, from 0-1)
a <- density(rnorm(100,mean=0.75,sd=0.1))   # make a density distribution from some random numbers
a$y <- 0.2*(a$y/max(a$y))                   # rescale the y values for plotting
polygon(a$x, 1-a$y, col=coltrans,border=NA) # plot upside down hanging from the top axis with our transparent colour

# create a legend that can contain lines, points, or both
legend(0, 1, c(“Lines”,”Points”,”Both”), cex=1, col=c(“darkgrey”,”black”,”black”), pt.cex=c(0,1.8,1.8),“black”,”violet”,”darkgreen”),lty=c(1,0,1), lwd=c(5,3,3), pch=21, pt.lwd=3, box.lwd=2)
# add text somewhere, featuring symbols and formatting
text(0.8,0.95,substitute(paste(italic(alpha), ” = 1″ )),cex=1.2,adj=0)


In Rainbows


Our department recently obtained a custom-made Psychology logo rainbow flag to celebrate pride month.


This matches the rainbow seating in our staff room!


Vision in amblyopia


In recent years there have been a number of treatments proposed for amblyopia in adults. These treatments (reviewed in this paper) often involve balancing the inputs to the two eyes – down-weighting the input to the stronger eye to allow the weaker eye to contribute. The treatments improve visual function in the amblyopic eye, and some patients have even recovered stereo vision (e.g. see this TEDx video by ‘stereo’ Sue Barry). However we know very little about the mechanisms by which these improvements occur, or indeed what the nature of the neural deficits in amblyopia actually are.

Today a new paper came out online in the journal Investigative Ophthalmology & Vision Science. In it, we show that neural responses in amblyopia are reduced in the affected eye. We used a steady-state visual evoked potential technique to measure responses in each eye. The reductions are large enough that they could potentially be used to monitor improvements in visual function during treatment.

These data served as the pilot results for a grant proposal that was recently funded by the Centre for Chronic Diseases and Disorders (and part-funded by the Wellcome Trust). The plan is to use both fMRI and EEG to understand the architecture of the amblyopic binocular visual system, and to monitor improvements in visual function during a course of therapy. A postdoc job is available for 18 months to work on this project, and I’d be very interested in hearing from qualified applicants before the deadline (27th February 2015).

Welcome to new PhD students


Our term started last week. Returning to the lab is Greta Vilidaitė. Greta completed a summer project last year, and now returns as a PhD student. Her project will investigate abnormalities of neural noise in autism spectrum disorders. She is pictured here drinking some very strong mead from her native Lithuania.


Another new addition is Dave Coggan. Dave completed our Cognitive Neuroscience masters course last year, and now returns to start a PhD supervised by Tim Andrews and myself. He will study processes of mid-level vision using fMRI. He is pictured here singing at the annual ECR karaoke night.


Conferences and various things


I’ve just finished a very busy term. It was my first term of proper teaching, which took up a lot more time than I was expecting. It seemed to go well though – I got some very positive feedback from the students, as well as some hilarious comments (“Iron your shirts!!!” being my favourite…).

I’ve also been planning two conferences that we’re holding at York this year. The AVA meeting is in a few weeks time on the 11th April. We’ve got some excellent talks lined up, and have just finalised the program. Also, the BACN meeting is taking place in September, so still plenty of time to submit abstracts for that.

Last week I was up in Glasgow, where I gave a talk in the optometry department at Glasgow Caledonian. I also went to an excellent gig at King Tut’s Wah Wah Hut, where my old band played a few times about 15 years ago. We saw two bands from the 90s: Republica and Space. It looked like this:

Space at King Tut's

Space at King Tut’s

Other than that, I’ve had a couple of papers out this year, and am working on some more. I’m also anxiously awaiting news of a BBSRC grant proposal I put in back in September. I got some very positive reviews in January, and should hear next month whether it’s been funded. Fingers crossed!

Arduino sound to TTL trigger for EEG


A recent query on the Psychtoolbox mailing list about triggers for EEG prompted me to write an explanation of the system we developed at York to do this. I use the method below for steady state EEG, but it should work just as well for ERP designs. The arduino sound trigger was first constructed by my colleague Becky Gilbert, and she has kindly allowed me to share the design and her code.

It uses an Arduino UNO, to which we attached an audio jack socket to the analog input pin A0 (and a ground pin), and a BNC socket to the digital output pin 13. The board was mounted in a standard box. We had our technician drill holes in the box and mount the ports in it for stability. The whole thing is small (11×6.5cm), and powered from USB. Here is a picture with the lid off:


Arduino trigger

We then used the arduino software to upload a script to the board (appended below). It’s a very simple bit of code that just monitors the analog input for changes in voltage above a specific threshold. When a voltage change happens it sends a brief trigger on the digital output, at the appropriate voltage for a TTL pulse. Note that on some systems this pulse is too brief to be detected. If this occurs, inserting a brief pause (e.g. uncomment the //delay(100);) after the digitalWrite(outPin, HIGH) line will extend the pulse.

I connect the jack socket to the headphone socket of the stimulus computer, and the BNC to the trigger socket on the EEG/MEG amplifier system. I use the PsychPortAudio commands in Psychtoolbox to produce a 50ms binary pulse:

PsychPortAudio(‘FillBuffer’, tr, ones(1,220));

which I play at stimulus onset (e.g. directly after a Flip command) using:

PsychPortAudio(‘Start’, tr);

I’ve tested the arduino with an oscilloscope, and it responds within a millisecond of receiving the sound pulse. Of course, the timing accuracy of the sound pulse will depend on your computer hardware – see the PTB documentation and use PsychPortAudioTimingTest to check this. On most systems I’ve tried, the latency is very small indeed, so triggers should be completed well within one screen refresh.

I hope people find this useful, and particular credit to Becky (@BeckyAGilbert) for designing the system. If you have any problems, please leave comments below. Of course producing the box requires a some soldering, and we take no responsibility for any resulting injury, death etc. 😉



#define DEBUG 0 // change to 1 to debug 
 #define THRESHOLD 0.1 // adjust this to control sensitivity in detection of voltage increase
 const int outPin = 13; // change output (D) pin number here
 int prev_voltage = 0; // must be initialized to something
 void setup() {
  if(DEBUG) {
   // initialize serial communication at 9600 bits per second (to write debugging info back to terminal)
  // set output pin
  pinMode(outPin, OUTPUT);
 void loop() {
   // read input on analog pin 0
  int sensorValue = analogRead(A0); // change input (A) pin number here
  // convert analog reading (from 0-1023) to digital
  float voltage = sensorValue * (5.0 / 1023.0);
 if(DEBUG) {
    // print value to serial port (view in Tools > Serial Port Monitor)
    Serial.println(voltage); // This delays the trigger by
                             // about 6ms so remove before using
                             // during experiments
    // Simplest algorithm ever.  
    if (voltage > prev_voltage + THRESHOLD)
      digitalWrite(outPin, HIGH);
     digitalWrite(outPin, LOW); 
   prev_voltage = voltage;

Videos of ECVP noise symposium


A few weeks ago, at ECVP in Bremen, we held a symposium entitled “Visual noise: new insights”. The talks were varied and interesting, and have been recorded for the benefit of those who were unable to attend the symposium.

The videos are available via the following YouTube links:

Remy Allard
Stan Klein [Slides (with additional notes)]
Josh Solomon
Peter Neri
Keith May
Daniel Baker [Slides]

Details of the talks (including abstracts) are available here.

Many thanks to Udo Ernst and his team for hosting and filming the event, and Keith May for editing and uploading the videos.