Data is everywhere, and we typically make sense of it in the form of data visualisation. But how do we know what we see is the same as everyone else? It turns out not everyone is the same, and colour vision deficiencies (such as colourblindness) are not uncommon. This talk unpacks some of the physiology of the human visual system, so that we can understand how to better visualise data. Specifically, in this talk I explain how:

• Colourblindness actually works
• We can evaluate existing colour palettes or images
• Create better ones that are accessible to all

• Colour choice matters
• Choosing colours is hard
• We can use Hue / Chroma / Luminance to describe colour
• See established palettes: colorspace / viridis / scico
• Assess colours with colorspace::specplot()
• Assess colourblindness with colorspace::cvd_emulator()
• Evaluate your own colour palettes at hclwizard.com
• Choose colour palettes with
  • colorspace::choose_palette()
  • colorspace::choose_color()
  • colorspace::hcl_color_picker()
  • colorspace::hcl_wizard()

• Adam Sparks
• Lisa Charlotte Rost
• Philip Grove
• Miles McBain
• Di Cook
• Stuart Lee
• Achim Zeileis
• Emil Hvitfeldt

• Colorspace Package by Zeileis et al
• Colorspace Paper by Zeileis et al
• Colorspace talk by Achim Zeileis
• http://hclwizard.org/ website for assessing colour
• How your colorblind and colorweak readers see your colors (Part 1 of 3) by Lisa Charlotte Rost
• What to consider when visualizing data for colorblind readers (Part 2 of 3) by Lisa Charlotte Rost
• The Science of Color Perception by Calder Hanson
• Radiolab episode on colour vision

• Slides made using xaringan
• Extended with xaringanthemer
• Colours taken + modified from lorikeet theme from ochRe
• Header font is Josefin Sans
• Body text font is Montserrat
• Code font is Fira Mono

These slides are generated using drake - you will need the packages installed below, and once you have done that, you can generate the slides into the slides/ folder with:

library(drake)
r_make()

The following packages are required:

colorspace
conflicted
dotenv
drake
ggspectra
knitr
magick
photobiology
photobiologyWavebands
rmarkdown
scales
tidyverse
rmapshaper
rnaturalearth
raster
glue
here
xaringanthemer
xaringan
patchwork
prismatic
pals
gplots

And the following from github:

• swish-climate-impact-assessment/awaptools
• ropenscilabs/ochRe
• ropenscilabs/icon
• clauswilke/colorblindr
• hadley/emo

Dr. Nicholas Tierney (PhD. Statistics, BPsySci (Honours)) is a Lecturer in Business Analytics and Statistics at Monash University, working with Professors Dianne Cook and Rob Hyndman. His research aims to improve data analysis workflow, and make data analysis more accessible. Crucial to this work is producing high quality software to accompany each research idea. Mostly recently, Nick's work is focussing on exploring longitudinal data (brolgar), and improving how we share data alongside research ( ddd). Other work has focussed on exploring data with the R package visdat, and on creating analysis principles and tools to simplify working with, exploring, and modelling missing data with the package naniar. Nick has experience working with decision trees (treezy), optimisation (maxcovr), Bayesian Data Analysis, and MCMC diagnostics (mmcc.

Nick is a member of the rOpenSci collective, which works to make science open using R, has been the lead organiser for the rOpenSci ozunconf events from 2016-2018 (2016, 2017, 2018), and co-hosts the rstats podcast "Credibly Curious" with Dr. Saskia Freytag. Outside of research, Nick likes to hike, rockclimb, make coffee, bake sourdough, (eventually) knit a hat, take photos, and explore new hobbies.