Two articles ( -1 -2) about Corona Virus taught be about Infection Reproduction Numbers and Case Fatality Rates. But the graphic for one reminded me of Edward Tufte's Visual Explanations and its criticism of “pop journalism.” ( -3) The offending graphic ( -4) made me think of Howard Wainer's advice about ordering data also. Racket's sort lets us implement the advice with one-line of code.
The reproduction numbers for these seven diseases are simple enough to provide good visualization practice. This might be a good case study to ease into Racket Plot coding and Data Visualization.
In Understanding Graphs and Tables Howard Wainer advises:
Order the rows and columns in a way that makes sense. We are almost never interested in “Austria First.” Two useful ways to order the data are:
a. Size places-Put the largest first. Often we look most carefully at what is on top and less carefully further down. Put the biggest thing first!
Also, ordering by some aspect of the data often reflects ordering by some hidden variable that can be inferred.
b. Naturally – Time is ordered from the past to the future. Showing data in that order melds well with what the viewer might expect. This is always a good idea.
— Howard Wainer (fn:1)
Howard Wainer's advice came to mind while working with data from a book about Minamata Disease (and pollution or Damage to the Commons in general).
The code to visualize Jun Ui's table of employment data is an attempt to implement Howard Wainer's advice (and general ink-to-data ratio advice for data visualization) with free software: DrRacket and Racket Plot.
Getting information from a table is like extracting sunlight from a cucumber. (Farquhar & Farquhar, 1891) (fn:3)
Working with data from Minamata Disease materials seems like a worthy way to learn Racket and Data Visualization. I see graphs of chemical production from the factory that I would like to merge with the fishery depletion data. The units are Kan貫: 3.75kg or 8.6lb. If there is a need, I'll have to translate the fish names and units for an English version.
Hopefully the “Reproducible Research” approach will become common practice everywhere. It would be nice to have tables of data for every visualization we see, it seems like a responsible approach.
It takes a certain sort of focus and patience to create a visualization, but I think the work makes the data tables more meaningful. It's hard to keep your attention on this sort of reading. Maybe visualizations could help. Now I have to print these visualizations out and write essays for them. If the plots help create decent, useful writing they will have served a purpose.
As always the code and data are below, Reproducible Research!!
Getting information from a table is like extracting sunlight from a cucumber. (Farquhar & Farquhar, 1891) (fn:3)
With repetition I'll be able to abstract and simplify the code to produce an alternative for histograms(bar-charts). With that in mind, I re-visited some data from Mike Davis's Planet of Slums to make this Percent-Scale-Labeled-Line plot of data.
The countries are ordered by millions of residents in slums, but the lines show what percentage of the urban population is taken up by those millions. The USA has a million more people in slums than Egypt, but twelve point eight million people is smaller percentage of its urban population. Later it might be interestingto compare my too-complex gnuplot code with my getting-simpler Racket code for this data and visualization.
While working with the PercentScale-LineLabel code, some advice came to mind. Visualizations get better with higher ink-to-information ratios so we should avoid labels and any other “presentation bureacracy” when possible. I also felt that it was disorienting to label the percent-scaled lines with the figure for millions of people. With an Howard Wainer article (fn:3) coming to mind, it seemed better to make the visualization simpler, more table-like, but still with the aid to understanding provide by lines showing how the figure for millions relates to a particular country's total urban population. I keep the lines because of a few paragraphs from Solomon Messing's blog post(fn:4):
> ... judgements about position relative to a baseline are dramatically more accurate than judgements about angles, area, or length (with no baseline).
I'm hoping the lines and position of the percentage figures will be helpful since they all share the same baseline. And I thing the figures in millions needs some sort of context for each country.
I suppose the plot above could help with the book's table. With more time I'd like to work in a line that shows the percentage of the entire world's slum population in each country. Or maybe a line that shows each country's population in proportion to the country with the greatest population. But I suppose it's easy enough to answer certain questions with this simple visualization. It's not too hard to mentally calculate that Ethiopia and Tanzania, while having a high proportion of their urban populations in slums, have less slum populations that are less than ten percent as large as China's.
The code is below.
