Plotting tables with DrRacket has been teaching me a lot. It felt wrong to go out of DrRacket and into the shell just to montage (ImageMagick) the row-based and column-based plots together. Racket features plot-pict so it seemed like the right time to start learning the pict library.(fn:1) With picts I was able to vc-append a gray separator between the two plots, and to add pip-arrows-lines to point out connections between the two views.

The plots I work with can become long images. Long picts are awkward to work with in DrRacket. I could only scale to the top, and could not check the lower part of the plot. Enclosing the pict code withpict->bitmap makes iterative development smoother. A bitmap is much easier to scroll in DrRacket.

It took a few attempts to learn how to save the pict->bitmap images to file. I was looking for a basic Scheme-like approach with open-output-file or with-output-to-file but nothing worked. The task of saving plot-pict bitmaps as png files has become my introduction to objects in Racket. You send an object a method and arguments. It didn't make sense to me until reading the ambiguous sentence I just typed. With send you don't send the very next expression somehwere, you send the following expressions to the next expression. So send's second argument (if you see send as a typical Racketfunction) is what gets the rest of the arguments.

The visualizations might be ready to print out and think about.

After putting a table of data into R-style, “long form” lists, Racket's group-by and sort allows for creative labeling and ordering. The hours spent with R/ggplot2 got me ready for “pilfering” ideas into Racket. (fn:2)

Working with this table of fisheries data made it easy to appreciate functional programming with sort. You are free to create a function that will sort, or order, the data. It's simple to adhere to Howard Wainer's rules for “table construction.”(fn:1)

データ表の視覚化で何を気づくかな？「 一本釣漁業」と「囲刺網漁業」はなぜ増えたのかな？ カニも54年に増えたみたい(fn:2)。それは生態系の理由か経済的な理由か？ カニの天敵が減たやろうか、悪影響が出るまで餌が増えたか、 それか、 遠いところから人がカニを水俣に持って来ただろうか？

元の表は原田正純「水俣病」(岩波新書)12ページにあった。

Working with a table full of data worthy of attention is great for keeping focus. Getting data into “long form” as is done with R might not be necessary with Racket, at least not with the simple data I work with for visualizations. The code for this visualization was getting too complex for a few days, I had to go back and get the data into a convenient form. Racket's ->plot-label *number* *digits* makes it easy to arrange for helpful labels.

(“1954” (“mullet” 54453.75 90.75625000000001)

The figures provide only a slight hint for the imagination to get started in an attempt to guess at the pain and turmoil involved in living with methyl mercury poisoning and then applying for relief from corporate and government bureaucracy.

Code and Data for the visualization are below.