Data Visualisation: Hex Codes, Pantone Colours and Accessibility

One of the things I find hardest about data visualisation is colouring. I’m not a natural artist, much preferring everything in gentle shades of monochrome. Possibly beige. Obviously for any kind of data visualisation, this limited .Quite frankly this is the kind of comfort zone that needs setting on fire.

I’ve found this site really helpful: it’s a listing of the Pantone colours with both Hex and RGB codes for inserting straight into your visualisations. It’s a really useful correspondence if I’m working with someone (they can give me the Pantone colour numbers of their website or report palette- I just search the page).

One thing I’ve found, however, is that a surprising (to me) number of people have some kind of colour-based visual impairment. A palette that looks great to me may be largely meaningless to someone I’m working with. I found this out in one of those forehead slapping moments when I couldn’t understand why a team member wasn’t seeing the implications of my charts. That’s because, to him, those charts were worse than useless. They were a complete waste of his time.

Some resources I’ve found helpful in making my visualisations more accessible are the colourblind-friendly palettes discussed here and this discussion on R-Bloggers. The latter made me realise that up until now I’ve been building visualisations that were obscuring vital information for many users.

The things I think are important for building an accessible visualisation are:

  • Yes, compared to more subtle palettes, colour-blind friendly palettes look like particularly lurid unicorn vomit. They don’t have to look bad if you’re careful about combinations, but I’m of the opinion that prioritising accessibility for my users is more important than “pretty”.
  • Redundant encoding (discussed in the R-bloggers link above) is a great way ensuring users can make out the information you’re trying to get across. To make sure this is apparent in your scale, use a combination of scale_colour_manual() and scale_linetype_manual(). The latter works the same as scale_colour_manual() but is not as well covered in the literature.
  • Consider reducing the information you’re putting into each chart, or using a combination of facets and multiple panels. The less there is to differentiate, the easier it can be on your users. This is a good general point and not limited to those with colourblindness.

Describing simple statistics

I’m a huge believer in the usefulness of learning by doing. That makes me a huge believer in Shiny, which allows me to create and deploy simple apps that allow students to do just that.

This latest app is a simple one that allows you to manipulate either the mean or the variance of a normal distribution and see how that changes the shape of the distribution.

If you want to try out making Shiny apps, but need a place to start, check out Oliver Keyes’ excellent start up guide.

application view1

application view 2

Yes, you can: learn data science

Douglas Adams had it right in Dirk Gently’s Holistic Detective Agency. Discussing the mathematical complexity of the natural world, he writes:

… the mind is capable of understanding these matters in all their complexity and in all their simplicity. A ball flying through the air is responding to the force and direction with which it was thrown, the action of gravity, the friction of the air which it must expend its energy on overcoming, the turbulence of the air around its surface, and the rate and direction of the ball’s spin. And yet, someone who might have difficulty consciously trying to work out what 3 x 4 x 5 comes to would have no trouble in doing differential calculus and a whole host of related calculations so astoundingly fast that they can actually catch a flying ball.

If you can catch a ball, you are performing complex calculus instinctively. All we are doing in formal mathematics and data science is putting symbols and a syntax around the same processes you use to catch that ball.

Maybe you’ve spent a lot of your life believing you “can’t” or are “not good at” mathematics, statistics or whatever bugbear of the computational arts is getting to you. These are concepts we begin to internalise at a very early age and often carry them through our lives.

The good news is yes you can. If you can catch that ball (occasionally at least!) then there is a way for you to learn data science and all the things that go with it. It’s just a matter of finding the one that works for you.

Yes you can.

Cheat Sheets: The New Programmer’s Friend

Cheat sheets are brilliant: whether you’re learning to program for the first time or you’re picking up a new language. Most data scientists are probably programming regularly in multiple languages at any given time: cheat sheets are a handy reference guide that saves you from googling how to “do that thing you know I did it in python yesterday but how does it go in stata?”

This post is an ongoing curation of cheat sheets in the languages I use. In other words, it’s a cheat sheet for cheat sheets. Because a blog post is more efficient than googling “that cheatsheet, with the orange bit and the boxes.” You can find my list of the tutorials and how-to guides I enjoyed here.

R cheat sheets + tutorials

Python cheat sheets

Stata cheat sheets

  • There is a whole list of them here, organised by category.
  • Stata cheat sheet, I could have used this five years ago. Also very useful when it’s been awhile since you last played in the stata sandpit.
  • This isn’t a cheat sheet, but it’s an exhaustive list of commands that makes it easy to find what you want to do- as long as you already have a good idea.

SPSS cheat sheets

  • “For Dummies” has one for SPSS too.
  • This isn’t so much a cheat sheet but a very basic click-by-click guide to trying out SPSS for the first time. If you’re new to this, it’s a good start. Since SPSS is often the gateway program for many people, it’s a useful resource.

General cheat sheets + discusions

  • Comparisons between R, Stata, SPSS, SAS.
  • This post from KD Nuggets has lots of cheat sheets for R, Python, SQL and a bunch of others.

I’ll add to this list as I find things.