gauden

# ------------------------------------------------------------------------------
# PREPARATION
# First download two files from Gapminder, where an extremely rich set of
# data sources are curated in the form of Excel spreadsheets:
#     http://www.gapminder.org/data/
#
# I am here using the WHO alcohol consumption data from:
# http://spreadsheets.google.com/pub?key=0AgogXXPMARyldGJqTDRfNHBWODJMRWlZaVhNclhNZXc&output=xls
# And the World Bank GDP data from:
# http://spreadsheets.google.com/pub?key=0ArfEDsV3bBwCdHh3d1FPOVg1WXM3V2huRWc2cjM3TkE&output=xls

gauden

# snippet to make a tile plot


library("ggplot2")

x <- rnorm(5000, 0, 1.5)
y <- rnorm(5000, 0, 1.5)
df <- data.frame(cbind(x,y))

p <- ggplot(df, 

gauden

#!/usr/bin/env python

#
# Converts any integer into a base [BASE] number. I have chosen 62
# as it is meant to represent the integers using all the alphanumeric
# characters, [no special characters] = {0..9}, {A..Z}, {a..z}
#
# I plan on using this to shorten the representation of possibly long ids,
# a la url shortenters
#

gauden

Preparation for the Data Visualization Course

• Date: 2013-02-24
• Author: Gauden Galea
• URL: https://gist.github.com/gauden/5023638
• License: Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)

Introduction

This will be, to a great extent, a hands-on course. Participants should download and install the applications listed below in order to ensure a common operating environment.

gauden

[
  {
    "op": "core/column-removal",
    "description": "Remove column URL",
    "columnName": "URL"
  },
  {
    "op": "core/column-removal",
    "description": "Remove column Details",
    "columnName": "Details"

gauden

{
 "metadata": {
  "name": "Demo of ioff() and ion()"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

gauden

# https://github.com/matplotlib/matplotlib/issues/881
# Several of the ColorBrewer maps are "qualitative", meaning
# they are just a group of colors that can be used together 
# for categories of data.  So I remapped Accent to segments 
# instead of continuous:

# Actually, these should be used with ListedColormap, and 
# the number of colors should depend on the number of 
# categories in the data, with colors removed from the 
# list in a certain order?

gauden

# Something in lines of http://stackoverflow.com/questions/348630/how-can-i-download-all-emails-with-attachments-from-gmail
# Make sure you have IMAP enabled in your gmail settings.
# Right now it won't download same file name twice even if their contents are different.

import email
import getpass, imaplib
import os
import sys

detach_dir = '.'

gauden

#!/usr/bin/env bash

curl "http://datascienceatthecommandline.com/" > source.html

< source.html scrape -b -e '//div[@class="sect3"]/h3' |
xml2json -t xml2json |
jq '.html.body.h3[]["#text"]' |
sed 's/"//g' > list.txt

command -V $(cat list.txt) | 

gauden

def pad_axis(ax, pct=2.0):
    for dim in ('x', 'y'):
        getter = ax.get_xlim if dim =='x' else ax.get_ylim
        setter = ax.set_xlim if dim =='x' else ax.set_ylim
        
        lo, hi = getter()
        pad = (hi-lo) / 100.0 * pct
        setter(lo-pad, hi+pad)