Nomograms everywhere!
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
At useR!, Jonty Rougier talked about nomograms, a once popular visualisation that has fallen by the wayside with the rise of computers. I’d seen a few before, but hadn’t understood how they worked or why you’d want to use them. Anyway, since that talk I’ve been digging around in biology books from the 60s and 70s, and it seems they are full of them. So for those of you who haven’t seen the talk, here’s how they work.
A basic nomogram consists of three scales. By reading off known values from two of the scales, you can estimate a third one. Here’s an example I found in the ICRP‘s reference manual.
It’s difficult to measure people’s skin surface area, but height and bodyweight are very straightforward. To use the nomogram, you place a ruler (or other straight edge) on the height* and weight scales and read of the point where the ruler crosses the surface area scale. I’m 177cm tall and weigh 72kg, so according to this, my estimated skin surface area is 1.89m2.
Of course nowadays, the standard way to solve this sort of problem is to write a function. Jonty suggested that the main modern use of nomograms is in fieldwork situations, where computers aren’t handily available. (His case study was Kenyan vets trying to estimate the weight of donkeys form there height and girth.)
Altman and Dittmer’s Respiration and Circulation has many more pretty nomograms. I was particularly impressed by those on blood pH, reproduced below for your pleasure.
Your homework is to dig out a pre-1980 textbook and hunt for more nomograms.
*Gruesomely, the fact that the scale is labelled “length” rather than “height” makes me suspect that the bodies that provided the data were in a permanent lying down position – that is, they were corpses.
Tagged: dataviz, nomograms, user2011
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.