Andrew McGill arm5077

## potholes.r
library(ggplot2)

potholes <- read.csv(file="potholes.csv", head=TRUE, sep=",", stringsAsFactors=T)
potholes$COMPOSEDATE <- as.POSIXlt(strptime(potholes$COMPOSEDATE, "%m/%d/%Y %H:%M"))
potholes$RESOLVEDDATE <- as.POSIXlt(strptime(potholes$RESOLVEDDATE, "%m/%d/%Y %H:%M"))
potholes$COMPLETED_DATE <- as.POSIXlt(strptime(potholes$COMPLETED_DATE, "%m/%d/%Y %H:%M"))
potholes$DAYS_UNTIL_COMPLETED <- difftime(potholes$COMPLETED_DATE,potholes$COMPOSEDATE, units="days")

# Make subset of dataset that excludes potholes that weren't filled
completedPotholes <- potholes[is.na(potholes$DAYS_UNTIL_COMPLETED) != TRUE,]

## bill-peduto-bot-tweet.py
import twitter
import json
import urllib2
from datetime import datetime
import re
from config import *

# Parser for tranforming Peduto schedule-speak into the lyric timbre of BillPedutoBot!
def writeTweet( event ):
	keywords = {

## doctors.sql
SELECT medicare.*,
       peerAverage.average       AS charge_average,
       ( medicare.average_submitted_chrg_amt - peerAverage.average ) /
       peerAverage.average * 100 AS difference_from_average,
       totalAverage.total_difference_average
FROM   medicare
       JOIN (SELECT Avg(average_submitted_chrg_amt) AS average,
                    hcpcs_code,
                    place_of_service
             FROM   medicare

## README.md

      
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                arm5077
                / README.md
            
            
              Last active
              August 29, 2015 14:01
            
              
                Pennsylvania counties treemap by population
              
          
    It's election season again, and I considered using a treemap to show vote totals for Pennsylvania's gubernatorial primary, which will pit four Democrats against each other. Using an abstract visualization like a treemap, which sizes each county by vote totals, cancels out the visual advantage enjoyed by large-but-unpeopled counties of Central Pennsylvania.
I decided to go with something else, but I thought this demo (sized by population, organized by region, colored by Obama/Romney vote in 2012) was still neat.

  
## index.php
<?PHP

function getURL($url)
{
	// create curl resource
	$ch = curl_init();

	// set url
	curl_setopt($ch, CURLOPT_URL, $url);


## index.html
<!doctype html>
<html>
    <head>
 		<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.3.8/angular.min.js"></script>
		<script src="swatches.json"></script>
		<script type="text/javascript">
			angular.module("swatchApp", [])
				.controller('swatchController', ['$scope', function($scope){
					$scope.colors = swatches;
					$scope.height = window.innerHeight / swatches.length;

## index.html
<!doctype html>
<html ng-app="formatApp">
<head>

	<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.3.11/angular.min.js"></script>

	<style type="text/css">

		body {
			font-family: Helvetica, sans-serif;

## scripts.js
$(window).load(function(){
  $(".magWYSIWYG h1").css("font-size", "20pt");
});

## index.js
var fs  = require("fs");
var http = require('http');

fs.readFile("data.json", 'utf8', function(err, data){
	data = JSON.parse(data);

	data.Results.forEach(function(email, i){
		console.log("http://foia.state.gov/searchapp/"+email.pdfLink);

		// use a timeout so State's server doesn't get mad

## compare_candidates.sql
ALTER TABLE contributions_individual ADD COLUMN contbr_nm_short varchar(200);

UPDATE contributions_individual set contbr_nm_short = SUBSTRING_INDEX(contributions_individual.contbr_nm, ' ', 2);

SELECT contributions_individual.cand_nm,
	contributions_individual.contbr_nm_short,
	contributions_individual.contbr_zip,
	SUM(contributions_individual.contb_receipt_amt) as total,
	b.cand_nm,
	b.contbr_nm_short,
	library(ggplot2)

	potholes <- read.csv(file="potholes.csv", head=TRUE, sep=",", stringsAsFactors=T)
	potholes$COMPOSEDATE <- as.POSIXlt(strptime(potholes$COMPOSEDATE, "%m/%d/%Y %H:%M"))
	potholes$RESOLVEDDATE <- as.POSIXlt(strptime(potholes$RESOLVEDDATE, "%m/%d/%Y %H:%M"))
	potholes$COMPLETED_DATE <- as.POSIXlt(strptime(potholes$COMPLETED_DATE, "%m/%d/%Y %H:%M"))
	potholes$DAYS_UNTIL_COMPLETED <- difftime(potholes$COMPLETED_DATE,potholes$COMPOSEDATE, units="days")

	# Make subset of dataset that excludes potholes that weren't filled
	completedPotholes <- potholes[is.na(potholes$DAYS_UNTIL_COMPLETED) != TRUE,]
	import twitter
	import json
	import urllib2
	from datetime import datetime
	import re
	from config import *

	# Parser for tranforming Peduto schedule-speak into the lyric timbre of BillPedutoBot!
	def writeTweet( event ):
	keywords = {
	SELECT medicare.*,
	peerAverage.average AS charge_average,
	( medicare.average_submitted_chrg_amt - peerAverage.average ) /
	peerAverage.average * 100 AS difference_from_average,
	totalAverage.total_difference_average
	FROM medicare
	JOIN (SELECT Avg(average_submitted_chrg_amt) AS average,
	hcpcs_code,
	place_of_service
	FROM medicare
	<?PHP

	function getURL($url)
	{
	// create curl resource
	$ch = curl_init();

	// set url
	curl_setopt($ch, CURLOPT_URL, $url);
	<!doctype html>
	<html>
	<head>
	<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.3.8/angular.min.js"></script>
	<script src="swatches.json"></script>
	<script type="text/javascript">
	angular.module("swatchApp", [])
	.controller('swatchController', ['$scope', function($scope){
	$scope.colors = swatches;
	$scope.height = window.innerHeight / swatches.length;
	<!doctype html>
	<html ng-app="formatApp">
	<head>

	<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.3.11/angular.min.js"></script>

	<style type="text/css">

	body {
	font-family: Helvetica, sans-serif;
	$(window).load(function(){
	$(".magWYSIWYG h1").css("font-size", "20pt");
	});
	var fs = require("fs");
	var http = require('http');

	fs.readFile("data.json", 'utf8', function(err, data){
	data = JSON.parse(data);

	data.Results.forEach(function(email, i){
	console.log("http://foia.state.gov/searchapp/"+email.pdfLink);

	// use a timeout so State's server doesn't get mad
	ALTER TABLE contributions_individual ADD COLUMN contbr_nm_short varchar(200);

	UPDATE contributions_individual set contbr_nm_short = SUBSTRING_INDEX(contributions_individual.contbr_nm, ' ', 2);

	SELECT contributions_individual.cand_nm,
	contributions_individual.contbr_nm_short,
	contributions_individual.contbr_zip,
	SUM(contributions_individual.contb_receipt_amt) as total,
	b.cand_nm,
	b.contbr_nm_short,