Joanne joannecheng

Visualizing Rails Issues

Live coding demo from talk at Kod.io @ Linz 2014.

This example uses topojson.neighbors to compute neighboring congressional districts (from the 113th congress) from geographic boundaries by detecting shared edges (arcs). The red district is the hovered district, and the orange district is the neighboring districts.

[ Launch: Tributary inlet ] 7502290 by joannecheng

count	date_collected	gem
14314	2013-11-12	paperclip
9417	2013-11-12	carrierwave
367	2013-11-12	clearance
40062	2013-11-12	devise
1142	2013-11-12	poltergeist
2124	2013-11-12	capybara-webkit
5368	2013-11-12	shoulda-matchers
49259	2013-11-12	factory_girl

Setting up Processing for Scala

(on OSX)

Install Processing
Move Processing.app/ to your preferred destination (mine is in /Applications/)
set scala classpath to include your-install-location/Processing.app/Contents/Resources/Java/core/library/core.jar. I created a little runner called run-sketch that looks like this:

export PROCESSING_CLASS_PATH=$CLASSPATH:/Applications/Processing.app/Contents/Resources/Java/core/library/core.jar

Data visualization is an effective method of communication that's been growing in popularity in the past few years. We have been exposed to beautiful infographics, interactive news articles, and stories based around raw numbers and statistics. In this talk, I'll describe the technologies and techniques I use to create visualizations and the things I've learned during my quest to be part of this field. We'll talk about programming, design, statistics, and journalism and how they intersect in data visualization. After the talk, we'll take a look at data sourced from the meetup* and create visualizations of our own.

* Please fill out this form before the meet up date.

[ Launch: Closer to the real thing ] 7148126 by joannecheng[ Launch: Tributary inlet ] 7142407 by trinary

	require 'csv'
	rows = CSV.read("Medicare_Provider_Charge_Inpatient_DRG100_FY2011.csv")[1..-1]
	column_names = %w(drg_definition provider_name state average_total_payments_to_provider)
	puts column_names.to_csv

	rows.each do \|row\|
	puts [row[0], row[2], row[5], row[-1]].to_csv
	end

	inpatient <- read.csv('/Users/joannecheng/dev/viz/healthcarechallenge/Medicare_Provider_Charge_Inpatient_DRG100_FY2011.csv')

	drgs <- unique(inpatient$DRG.Definition)

	for(i in 1:length(drgs)) {
	treatment <- toString(drgs[i])
	dg <- inpatient[inpatient['DRG.Definition']==treatment,]
	boxplot((Average.Covered.Charges - Average.Total.Payments)*Total.Discharged~Provider.State,
	data=dg, las=2, xlab='State', ylab='Total Unpaid',
	main=treatment)