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Last active December 22, 2015 13:59
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World economic growth

A simple representation of the yearly increase and descrease of real per person GDP from 1960 to 2011. The visualisation shows whether a country had positive of negative growth in a particular year. Dark blue means positive growth while light blue means negative. White indicates that no observation was available for that year.

The data source is the Penn World Tables. Each country's GDP is converted into US Dollar terms (2005 constant prices) using Purchasing Power Parity weighting. The conversion to real terms allows us to see changes in volumes over time, while the conversion to USD using PPP allows for a common unit of expression. This common unit of expression, although underpinned with many assumptions and measurement errors, makes it more realistic to compare living standards between different countries.

Note how 2009, the year after the global financial crisis, is coloured with light blue in the majority of countries - the world had negative growth almost all over.

To reproduce the data and save it to a file as JSON, run the python script included in the gist and pipe standard output to a file called >> data.json

{"daterange": ["1960", "1961", "1962", "1963", "1964", "1965", "1966", "1967", "1968", "1969", "1970", "1971", "1972", "1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011"], "data": [{"AGO": ["null", "null", "null", "null", "null", "null", "null", "null", "null", "null", "null", 5.970262669029158, 0.220170136070319, 8.159084123503302, 3.3422246679578604, -5.512268260810071, -5.225157210185638, 1.2528858921992514, -5.298150078197339, 1.2645791479165558, -1.4888233321739053, -1.0519608512124412, 0.837784091219751, 2.9421042478971815, 11.017360832654166, -1.943560493555374, 0.8078586812525268, 14.923536457729591, -2.7405593448335264, 2.3725291880007715, 5.389822136718925, 10.16665054670549, 9.63070046015102, -27.354410236277882, 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<!DOCTYPE html>
<html>
<head>
<title>Economic growth in the world since 1960</title>
<script src="http://d3js.org/d3.v3.min.js"></script>
<style type="text/css">
.title {
font-weight: bold;
}
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fill: none;
stroke: none;
stroke-opacity:.8;
shape-rendering: crispEdges;
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.x-axis path {
stroke: white;
fill: none;
}
.x-axis text {
font-size: 10px;
}
.labels {
font-size: 5px;
}
#na {
fill: white;
}
#zero {
fill: #6b6ecf;
}
#positive {
fill: #393b79;
}
#negative {
fill: #17becf;
}
</style>
</head>
<body>
<script type="text/javascript">
(function(){
var margin = {top: 70, bottom: 30, left: 100, right: 50},
width = 900 - margin.left - margin.right,
height = 450 - margin.top - margin.bottom;
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var parseDate = d3.time.format("%Y").parse;
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var yScale = d3.scale.ordinal().rangeRoundBands([0, height], 1, 0);
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.attr("class", "bar-svg");
var x = graphSvg.append("g")
.attr("class", "x-axis");
var dataUrl = "data.json";
d3.json(dataUrl, function(data) {
data.daterange.forEach(function(d) {
d = parseDate(d);
});
xScale.domain(d3.extent(data.daterange, function(d) { return parseDate(d); }));
yScale.domain(data.data.map(function(d) { return d.id; }));
d3.select(".base-svg").append("text")
.attr("x", margin.left)
.attr("y", (margin.top)/2)
.attr("text-anchor", "start")
.text("Real per person GDP growth in the world (PPP): 1960 to 2011")
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.attr("id", function(d) { return d.id; });
data.data.forEach(function(activity) {
individualEconomy = activity[activity.id];
graphSvg.append("g")
.attr("class", function(d) { return activity.id; })
.selectAll("rect")
.data(individualEconomy)
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.attr("x", function(d,i) { return xScale(parseDate(data.daterange[i])); })
.attr("y", function() { return yScale(activity.id)})
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.attr("width", width/individualEconomy.length)
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.attr("class", function() { return activity.id; })
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labels.on("mouseover", function() {
var country = d3.select(this).attr("id");
d3.selectAll("#" + country).style("font-size", "12px");
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d3.selectAll("#" + country).style("font-size", "5px");
d3.selectAll("." + country).attr("stroke", "none");
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</body>
</html>
"""Getting and reshaping some Penn World Table data.
Data about real per capita GDP growth throughout the world
form 1950 to 2011, at chained Purchasing Power Parity
in USD millions (2005 price level). Phew, a mouthful.
Penn World Table download URL:
http://citaotest01.housing.rug.nl/FebPwt/Dmn/AggregateXs.mvc/PivotShow
Dropbox URL:
https://dl.dropboxusercontent.com/u/104325750/pwt_data/FebPwtExport952013.csv
"""
import pandas as pd
import json as json
def get_data(url):
data = pd.read_csv(url)
return data
def main():
url = "https://dl.dropboxusercontent.com/u/104325750/pwt_data/FebPwtExport952013.csv"
data = get_data(url)
pivoted_data = data.pivot('year', 'country', 'gdp').pct_change()*100
pivoted_data.replace(to_replace='NaN', value='null', inplace=True)
pivoted_data = pivoted_data[pivoted_data.index >= 1960]
countries = [ str(pivoted_data.columns.values[i])
for i in range(len(pivoted_data.columns.values)) ]
years = [ str(pivoted_data.index.values[i])
for i in range(len(pivoted_data.index.values)) ]
data = []
for country in countries:
data.append({
country: list(pivoted_data[country]),
'id': country})
all_data = {'daterange': years, 'data': data}
print json.dumps(all_data)
if __name__ == '__main__':
main()
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