Carlos Scheidegger cscheid

This choropleth encodes unemployment rates from 2008 with a quantize scale ranging from 0 to 15%. A threshold scale is a useful alternative for coloring arbitrary ranges.

You need to get to the bit representation of the float, and then:

inline unsigned int float2fint(unsigned int f) {
    return f ^ ((-(f >> 31)) | 0x80000000);
}

Callahan et al. used this trick in this paper.

Caveats:

A demo of TopoJSON on a U.S. counties shapefile from the census bureau using d3.geo.albersUsa. The same TopoJSON file can also be used to show counties.

The scatterplot matrix visualizations pairwise correlations for multi-dimensional data; each cell in the matrix is a scatterplot. This example uses Anderson's data of iris flowers on the Gaspé Peninsula. Scatterplot matrix design invented by J. A. Hartigan; see also R and GGobi. Data on Iris flowers collected by Edgar Anderson and published by Ronald Fisher.

Keybase proof

I hereby claim:

I am cscheid on github.
I am cscheid (https://keybase.io/cscheid) on keybase.
I have a public key ASCbHGh8DWbPaMRsAqX3jKu2t6uySLoIHP3RpjNWw7XvNQo

To claim this, I am signing this object:

	setwd("~/Downloads")
	obs <- XML2Obs("out.xml", url.map=TRUE)
	unique(names(obs)) #you might want to re_name or add_key before collapsing
	tmp = add_key(obs, parent="url1//osm//way", key.name="way_id")
	tmp = add_key(tmp, parent="url1//osm//node", key.name="node_id")
	tmp = add_key(tmp, parent="url1//osm//relation", key.name="relation_id")
	tables <- collapse(obs) # holy crap this takes a long-ass time
	write.table(tables$`url1//osm//way//nd`, "ways.csv", sep=",")
	write.table(tables$`url1//osm//way//tag`, "way_tags.csv", sep=",")
	write.table(tables$`url1//osm//node`, "nodes.csv", sep=",")

	# You should totally ignore this because it's the first piece of Julia I've ever written.
	# 2-clause BSD, blah.

	function make_cycle(n)
	result = Dict{Int32, Array{Int32}}()
	for i = 1:n
	ii = i - 1
	push!(result, i, [1 + ((i+n-2) % n), 1 + i % n])
	end
	result

	import math
	from pylab import *

	def fact(x):
	return math.gamma(x+1)

	def choose(n,k):
	return fact(n) / (fact(k) * fact(n-k))

	def hypergeom_mass_at_k(N, K, n, k):