Monthly Archives: October 2012

Animated map of 2012 US election campaigning, with R and ffmpeg

[vimeo 52312754]

(Video link here, in case the embedded player doesn’t work for you.)

Idea: see if I can mimic the idea behind Ben Schmidt’s lovely video of ocean shipping routes, and apply it to another dataset. But which?
“Hmm… what’s another interesting dataset about some competitors traveling around a mostly-fixed area at the same time?… Hey friends, stop  giving me election news, I need to think of an idea… Oh.” Continue reading

Javascript and D3 for R users, part 2: running off the R server instead of Python

Thank you all for the positive responses to Basics of JavaScript and D3 for R Users! Quick update: last time we had to dabble in a tiny bit of Python to start a local server, in order to actually run JavaScript and D3 examples on our home computer… However, commenter Shankar had the great idea of using the R server instead. He provided some example code, but reported that it didn’t work with all the examples.

Here’s my alternative code, which works with all the D3 examples I’ve tried so far. Unlike Shankar’s approach with lower-level functions, I found it simpler to use Jeffrey Horner’s excellent Rook package.

# Load the Rook library

# Where is your d3 directory located?
myD3dir <- 'C:/Downloads'

# Start the server
s <- Rhttpd$new()

# To view a different D3 example,
# change the directory and .html file names below
# and rerun s$add() and s$browse()
# List all the subdirectories that contain
# any files it will need to access (.js, .css, .html, etc)
urls = c('/d3','/d3/examples','/d3/examples/choropleth'),
root = myD3dir
# browse(1) would load the default RookTest app instead

# When you're done,
# clean up by stopping and removing the server

If I understand the Rook documentation correctly, you just can’t browse directories using R’s local server. So you’ll have to type in the exact directory and HTML file for each example separately. But otherwise, this should be a simple way to play with D3 for anyone who’d rather stick within R instead of installing Python.

Hot Pot recipe, and stages of learning

I use Mark Bittman’s How to Cook Everything all the time, and I can really identify with his “four stages of learning how to teach yourself to cook”:

First, you slavishly follow recipes; this is useful.

In stage two, you synthesize some of the recipes you’ve learned. […] You learn your preferences. You might, if you’re dedicated, consult two, three, four cookbooks before you tackle anything.

The third stage incorporates what you’ve learned with the preferences you’ve developed, what’s become your repertoire, your style, and leads you to search out new things. […] This is the stage at which many people bring cookbooks to bed, looking for links and inspiration; they don’t follow recipes quite as much, but sometimes begin to pull ideas from a variety of sources and simply start cooking.

Stage four is that of the mature cook, a person who consults cookbooks for fun or novelty but for the most part has both a fully developed repertoire and — far, far more importantly — the ability to start cooking with only an idea of what the final dish will look like. There’s a pantry, there’s a refrigerator, and there is a mind capable of combining ingredients from both to Make Dinner.

These phases seem to apply in other areas as well. Consider foreign languages: first, you parrot your phrasebook word-for-word. Next, you learn to plug in new words or conjugations and combine pieces of several phrases. Third, you’ve started to grasp the grammar and the structure of the language; you have enough vocabulary to get by in basic scenarios, though you enjoy learning more. Fourth, you’ve reached fluency and “the ability to start [speaking] with only an idea of what the final [sentence] will look like.”

Anyhow, when you spend most of your time in stage 2 or possibly 3, it’s a pleasure to reach stage 4 sometimes — just coming home and BAM! making something tasty with whatever’s in the fridge + pantry. That happened recently with some shaved beef my fiancée and I found at Trader Joes, combined with memories of a delicious hot pot restaurant in the DC area. I didn’t have any mala spice available (too bad, as it does indeed cause a delicious “neurological confusion”), and I make no claims to authenticity, but it was a seriously tasty recipe-less culinary adventure. Recipe follows, although there are no proportions — everything is “to taste”!

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Basics of JavaScript and D3 for R Users

Hadley Wickham, creator of the ggplot2 R package, has been learning JavaScript and its D3 library for the next iteration of ggplot2 (tentatively titled r2d3?)… so I suspect it’s only a matter of time before he pulls the rest of the R community along.

Below are a few things that weren’t obvious when I first tried reading JavaScript code and the D3 library in particular. (Please comment if you notice any errors.) Then there’s also a quick walkthrough for getting D3 examples running locally on your computer, and finally a list of other tutorials & resources. In a future post, we’ll explore one of the D3 examples and practice tweaking it.

Perhaps these short notes will help other R users get started more quickly than I did. Even if you’re a ways away from writing complex JavaScript from scratch, it can still be useful to take one of the plentiful D3 examples and modify it for your own purposes. Continue reading

Carl Morris Symposium on Large-Scale Data Inference (3/3)

The final summary of last week’s symposium on statistics and data visualization (see part 1 and part 2)… Below I summarize Chris Volinsky’s talk on city planning with mobile data, and the final panel discussion between the speakers plus additional guests.

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Carl Morris Symposium on Large-Scale Data Inference (2/3)

Continuing the summary of last week’s symposium on statistics and data visualization (see part 1 and part 3)… Here I describe Dianne Cook’s discussion of visual inference, and Rob Kass’ talk on statistics in cognitive neuroscience.

[Edit: I’ve added a few more related links throughout the post.]

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Carl Morris Symposium on Large-Scale Data Inference (1/3)

I enjoyed this week’s Symposium on Large-Scale Data Inference, which honored Harvard’s Carl Morris as the keynote speaker. This was the 2nd such symposium; last year’s honoree was Brad Efron (whose new book I also recommend after seeing it at this event).

This year’s focus was the intersection of statistics and data visualization around the question, “Can we believe what we see?” I was seriously impressed by the variety and quality of the speakers & panelists — many thanks to Social & Scientific Systems for organizing! Look for the lecture videos to be posted online in January.

See below for the first two speakers, Carl Morris and Mark Hansen. The next posts will summarize talks by Di Cook and Rob Kass (part 2), and Chris Volinsky and the final panel discussion (part 3).

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USGS mapping suggestions

Geology, to put it bluntly, rocks. Where else can you talk about cleavage, bedding attitudes, discharge, and thrust faults with a straight face?

Anyhow, the United States Geological Survey (USGS) has a nice document of Suggestions To Authors of their technical reports and maps. In particular, the chapter on “Preparing maps and other illustrations” seems to be a good reference on maps for those of us without much formal cartography/geography training. For example, there are good tips on index maps (little inset maps showing the context around the bigger map, or pointing out where your study took place). The overall focus is naturally on geological maps, but much of the advice applies to other kinds of maps and visualizations too.

This is the 7th edition from 1991, so perhaps it’s due for an update, but the advice still seems solid. I’d also love to see the 1st edition from 1909 and see how much the guide has changed.