Jas Sohi j450h1

## README.md

      
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                j450h1
                / README.md
            
            
              Last active
              August 29, 2015 14:19
            
              
                Liberal Party of Canada - Misleading Chart - Revision
              
          
    #Original Visualization

This visualization comes from Canada's Liberal Party Website. The message is:
Between 1981 and 2011, Canada’s Real GDP grew by 115%. At the same time the real median income of Canadian families only grew by 15%.
However, Canada's Real GDP Chart's scale is exaggerated as it appears to be 150% growth vs the 15% median income growth. You can see that the Real GDP arrow goes up to the 3rd line (each line is approximately 45-50% since you can fit 3 of those smaller 15% arrows in it (15*3 = 45).

  
## index.html
<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <title>Comparing North American Major Sports Leagues by Revenue &amp; Attendance</title>
  <link href='http://fonts.googleapis.com/css?family=Lora' rel='stylesheet' type='text/css'>
  <script src="http://d3js.org/d3.v3.min.js"></script>
  <script src="http://dimplejs.org/dist/dimple.v2.0.0.min.js"></script>
    <script type="text/javascript">
      function draw(data,filter_year) {

## index.html
<!DOCTYPE html>
<html>
  <head>
    <!-- Load D3 from site -->
    <script src="http://d3js.org/d3.v3.min.js" charset="utf-8"></script>
  </head>

    <!-- CSS (Styling) -->
    <style type="text/css">
            /* Format X and Y Axis */

## .Rhistory

      
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                j450h1
                / .Rhistory
            
            
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              September 20, 2015 11:57
                — forked from nikolay-shenkov/create_vis.js
            
              
                Comparing Revenue & Attendance Across Leagues (NHL, NBA, MLB, NFL)
              
          
              We couldn’t find that file to show.
              
            
## gist:8acfb5e941b4427ebe67
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fire up graphlab create"
   ]
  },
  {

## 0_reuse_code.js
// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

## apple_health_load_analysis_R.r
library(dplyr)
library(ggplot2)
library(lubridate)
library(XML)

#load apple health export.xml file
xml <- xmlParse("C:\\Users\\praskry\\Desktop\\apple_health_data\\export.xml")

#transform xml file to data frame - select the Record rows from the xml file
df <- XML:::xmlAttrsToDataFrame(xml["//Record"])

## date_loop.sh
#!/bin/bash

now=`date +"%Y-%m-%d" -d "05/06/2012"`
end=`date +"%Y-%m-%d" -d "05/23/2012"`

while [ "$now" != "$end" ] ;
do
        now=`date +"%Y-%m-%d" -d "$now + 1 day"`;
        echo $now
done

## catfacts.R
httr::GET("http://catfacts-api.appspot.com/api/facts",
          query = list(number = 5)) %>%
  httr::content(as = "text", encoding = "UTF-8") %>%
  jsonlite::fromJSON()

## active-issues.sql
-- Active issues
-- Count of total active issues in the specified time frame
-- Source: githubarchive public data set via Google BigQuery http://githubarchive.org/

SELECT
	COUNT(DISTINCT JSON_EXTRACT_SCALAR(events.payload, '$.issue.id')) AS events_issue_count
FROM (SELECT * FROM TABLE_DATE_RANGE([githubarchive:day.],TIMESTAMP('2015-09-01'),TIMESTAMP('2016-08-31')))
 AS events

-- 10,723,492 active issues
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8">
	<title>Comparing North American Major Sports Leagues by Revenue & Attendance</title>
	<link href='http://fonts.googleapis.com/css?family=Lora' rel='stylesheet' type='text/css'>
	<script src="http://d3js.org/d3.v3.min.js"></script>
	<script src="http://dimplejs.org/dist/dimple.v2.0.0.min.js"></script>
	<script type="text/javascript">
	function draw(data,filter_year) {
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# Fire up graphlab create"
	]
	},
	{
	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console
	library(dplyr)
	library(ggplot2)
	library(lubridate)
	library(XML)

	#load apple health export.xml file
	xml <- xmlParse("C:\\Users\\praskry\\Desktop\\apple_health_data\\export.xml")

	#transform xml file to data frame - select the Record rows from the xml file
	df <- XML:::xmlAttrsToDataFrame(xml["//Record"])
	#!/bin/bash

	now=`date +"%Y-%m-%d" -d "05/06/2012"`
	end=`date +"%Y-%m-%d" -d "05/23/2012"`

	while [ "$now" != "$end" ] ;
	do
	now=`date +"%Y-%m-%d" -d "$now + 1 day"`;
	echo $now
	done
	httr::GET("http://catfacts-api.appspot.com/api/facts",
	query = list(number = 5)) %>%
	httr::content(as = "text", encoding = "UTF-8") %>%
	jsonlite::fromJSON()
	-- Active issues
	-- Count of total active issues in the specified time frame
	-- Source: githubarchive public data set via Google BigQuery http://githubarchive.org/

	SELECT
	COUNT(DISTINCT JSON_EXTRACT_SCALAR(events.payload, '$.issue.id')) AS events_issue_count
	FROM (SELECT * FROM TABLE_DATE_RANGE([githubarchive:day.],TIMESTAMP('2015-09-01'),TIMESTAMP('2016-08-31')))
	AS events

	-- 10,723,492 active issues