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junkwhinger/index.html

Last active October 20, 2016 03:30
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Population Structure Changes over time in Korea and Japan
Raw
index.html
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<script charset="utf-8" src="https://d3js.org/d3.v3.min.js">
</script>
<style>
.title {
font: 20px helvetica;
fill: #404040;
text-transform: capitalize;
}
.chart_label,
.axis {
font: 12px helvetica;
fill: rgb(99,99,99);
}
.expl_header {
font: 15px helvetica;
}
.expl {
font: 12px helvetica;
}
.axis path,
.axis line {
color: red;
fill: none;
stroke: #000;
stroke-width: 1px;
}
.left.bar,
.legend_male {
fill: #6b8891;
}
.right.bar,
.legend_female {
fill: #b27b88;
}
.legend_text {
font: 13px helvetica;
}
div.years_buttons {
display: flex;
justify-content: space-between;
width: 900px;
}
div.years_buttons div {
font: 12px helvetica;
padding: 3px;
margin: 7px;
width: 55px;
text-align: center;
}
</style>
<script type="text/javascript">
function draw(data) {
// setting the margin and space
var margin = {top: 60, right: 30, bottom: 24, left: 30, middle: 28},
width = 450 - margin.left - margin.right,
height = 450 - margin.top - margin.bottom;
var regionWidth = width/2 - margin.middle;
var pointA = regionWidth,
pointB = width - regionWidth;
var formatPercent = d3.format(".0%");
// simple two levels: 1) country 2) year
var year_data = d3.nest()
.key(function(d) {return d.year;})
.key(function(d) {return d.country})
.entries(data);
// to reverse the axes for female
function unique(x) {
return x.reverse().filter(function (e, i, x) {return x.indexOf(e, i+1) === -1;}).reverse();
}
// to make translate easier
function translation(x,y) {
return 'translate(' + x + ',' + y + ')';
}
// use the very first year data for the chart
var countries = year_data[0].values;
var i = 0;
// plot charts for each country
countries.forEach(function(c) {
c.totalPopulation = d3.sum(c.values, function(d) {return d.population; });
var percentage = function(d) {return d/ c.totalPopulation;};
array_by_age = d3.nest()
.key(function(d) {return d.age_bin;})
.entries(c.values)
// find the max x value to set the scale of the plots
c.maxValue = Math.max(
d3.max(array_by_age, function(d) {
var male_val = d.values.filter(function(e) {return e.sex === 'male'})[0].population;
return percentage(male_val);
}),
d3.max(array_by_age, function(d) {
var female_val = d.values.filter(function(e) {return e.sex === 'female'})[0].population;
return percentage(female_val);
})
);
// set svg
var svg = d3.select("div.chart").append('svg')
.attr("class", "population_chart")
.attr("id", c.key)
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
// add titles
svg.append("text")
.attr("class", "title")
.attr("x", width / 2)
.attr("y", 0 - (margin.top / 3))
.attr("text-anchor", "middle")
.text(function(d) {
return c.key + "-" + c.values[0].year;
});
// draw legend
if (c.key == 'korea') {
svg.append("rect")
.attr("class", "legend_male")
.attr("x", 0)
.attr("y", 0)
.attr("width", 15)
.attr("height", 15);
svg.append("rect")
.attr("class", "legend_female")
.attr("x", 0)
.attr("y", 20)
.attr("width", 15)
.attr("height", 15);
svg.append("text")
.attr("class", "legend_text")
.attr("x", 18)
.attr("y", 12)
.text("male");
svg.append("text")
.attr("class", "legend_text")
.attr("x", 18)
.attr("y", 32)
.text("female");
}
// set xScale
var xScale = d3.scale.linear()
.domain([0, c.maxValue])
.range([0, regionWidth])
.nice();
// set xScale for male
var xScaleLeft = d3.scale.linear()
.domain([0, c.maxValue])
.range([regionWidth, 0]);
// set xScale for female
var xScaleRight = d3.scale.linear()
.domain([0, c.maxValue])
.range([0, regionWidth]);
// set yScale
var yScale = d3.scale.ordinal()
.domain(unique(c.values.map(function(d) {return d.age_bin})))
.rangeRoundBands([height, 0], 0.1);
// set yAxis for male
var yAxisLeft = d3.svg.axis()
.scale(yScale)
.orient('right')
.tickSize(4,0)
.tickPadding(margin.middle - 4);
// set yAxis for female
var yAxisRight = d3.svg.axis()
.scale(yScale)
.orient('left')
.tickSize(4,0)
.tickFormat('');
// set xAxis for female
var xAxisRight = d3.svg.axis()
.scale(xScale)
.orient('bottom')
.tickFormat(d3.format('%'))
.ticks(5);
// set xAxis for male
var xAxisLeft = d3.svg.axis()
.scale(xScale.copy().range([pointA, 0]))
.orient('bottom')
.tickFormat(d3.format('%'))
.ticks(5);
var leftBarGroup = svg.append('g')
.attr('class', 'lbg')
.attr('transform', translation(pointA, 0) + 'scale(-1,1)');
var rightBarGroup = svg.append('g')
.attr('class', 'rbg')
.attr('transform', translation(pointB, 0));
// left y axis group
svg.append('g')
.attr('class', 'axis y left')
.attr('transform', translation(pointA, 0))
.call(yAxisLeft)
.selectAll('text')
.style('text-anchor', 'middle');
// right y axis group
svg.append('g')
.attr('class', 'axis y right')
.attr('transform', translation(pointB, 0))
.call(yAxisRight);
// left x axis group
svg.append('g')
.attr('class', 'axis x left')
.attr('transform', translation(0, height))
.call(xAxisLeft);
// right x axis group
svg.append('g')
.attr('class', 'axis x right')
.attr('transform', translation(pointB, height))
.call(xAxisRight);
// data that contains 0 for nice animation when the page starts
var null_data = [{
"group": "0-4",
"male": 0,
"female": 0
}, {
"group": "5-9",
"male": 0,
"female": 0
}, {
"group": "10-14",
"male": 0,
"female": 0
}, {
"group": "15-19",
"male": 0,
"female": 0
}, {
"group": "20-24",
"male": 0,
"female": 0
}, {
"group": "25-29",
"male": 0,
"female": 0
}, {
"group": "30-34",
"male": 0,
"female": 0
}, {
"group": "35-39",
"male": 0,
"female": 0
}, {
"group": "40-44",
"male": 0,
"female": 0
}, {
"group": "45-49",
"male": 0,
"female": 0
}, {
"group": "50-54",
"male": 0,
"female": 0
}, {
"group": "55-59",
"male": 0,
"female": 0
}, {
"group": "60-64",
"male": 0,
"female": 0
}, {
"group": "65-69",
"male": 0,
"female": 0
}, {
"group": "70-74",
"male": 0,
"female": 0
}, {
"group": "75-79",
"male": 0,
"female": 0
}, {
"group": "80-84",
"male": 0,
"female": 0
}, {
"group": "85+",
"male": 0,
"female": 0
}];
// initiate the leftBarGroup
leftBarGroup.selectAll('.bar.left')
.data(null_data)
.enter().append('rect')
.attr('class', 'bar left')
.attr('x', 0)
.attr('y', function(d) { return yScale(d.group); })
.attr('width', function(d) { return xScale(percentage(d.male)); })
.attr('height', yScale.rangeBand());
// initiate the rightBarGroup
rightBarGroup.selectAll('.bar.right')
.data(null_data)
.enter().append('rect')
.attr('class', 'bar right')
.attr('x', 0)
.attr('y', function(d) { return yScale(d.group); })
.attr('width', function(d) { return xScale(percentage(d.female)); })
.attr('height', yScale.rangeBand());
var c_data = d3.nest().key(function(d){return d.sex}).entries(c.values);
var male_data = c_data[0].values;
var female_data = c_data[1].values;
// update the data for male
leftBarGroup.selectAll('.bar.left')
.data(male_data)
.transition()
.duration(1000)
.attr('width', function(d) {
return xScale(percentage(d.population)); })
.attr('height', yScale.rangeBand());
// update the data for female
rightBarGroup.selectAll('.bar.right')
.data(female_data)
.transition()
.duration(1000)
.attr('width', function(d) { return xScale(percentage(d.population)); })
.attr('height', yScale.rangeBand());
// to the next year group
i += 1;
});
// update function
// bring new year_data
// find new max value for axis scaling
// reset axes and update data
function update(year_idx) {
var countries = year_data[year_idx].values;
countries.forEach(function(c) {
var target_country = c.key;
c.totalPopulation = d3.sum(c.values, function(d) {return d.population; });
var percentage = function(d) {return d/ c.totalPopulation;};
array_by_age = d3.nest()
.key(function(d) {return d.age_bin;})
// .key(function(d) {return d.sex})
.entries(c.values)
c.maxValue = Math.max(
d3.max(array_by_age, function(d) {
var male_val = d.values.filter(function(e) {return e.sex === 'male'})[0].population;
return percentage(male_val);
}),
d3.max(array_by_age, function(d) {
var female_val = d.values.filter(function(e) {return e.sex === 'female'})[0].population;
return percentage(female_val);
})
);
var xScale = d3.scale.linear()
.domain([0, c.maxValue])
.range([0, regionWidth])
.nice();
var yScale = d3.scale.ordinal()
.domain(unique(c.values.map(function(d) {return d.age_bin})))
.rangeRoundBands([height, 0], 0.1);
var xAxisRight = d3.svg.axis()
.scale(xScale)
.orient('bottom')
.tickFormat(d3.format('%'))
.ticks(5);
var xAxisLeft = d3.svg.axis()
.scale(xScale.copy().range([pointA, 0]))
.orient('bottom')
.tickFormat(d3.format('%'))
.ticks(5);
var n_svg = d3.select("svg#" +c.key)
.data([c.values])
n_svg.select(".title")
.transition()
.duration(1000)
.text(function(c) {
return c[0].country + "-" + c[0].year
});
n_svg.selectAll('.axis.x.left')
.transition()
.duration(1000)
.call(xAxisLeft);
n_svg.selectAll('.axis.x.right')
.transition()
.duration(1000)
.call(xAxisRight);
var c_data = d3.nest().key(function(d){return d.sex}).entries(c.values);
var male_data = c_data[0].values;
var female_data = c_data[1].values;
n_svg.select(".lbg").selectAll('rect.bar.left')
.data(male_data)
.transition()
.duration(1000)
.attr('width', function(d) { return xScale(percentage(d.population)); })
.attr('height', yScale.rangeBand());
n_svg.select(".rbg").selectAll('rect.bar.right')
.data(female_data)
.transition()
.duration(1000)
.attr('width', function(d) { return xScale(percentage(d.population)); })
.attr('height', yScale.rangeBand());
});
}
var years = [];
var year_idx = 1;
year_data.forEach(function(y) {
years.push(y.key)
})
var year_interval = setInterval(function() {
update([year_idx]);
year_idx++;
// if the year index exceeds the data length,
// stop the interval
if(year_idx >= years.length ) {
clearInterval(year_interval);
var buttons = d3.select("div.chart")
.append("div")
.attr("class", "years_buttons")
.selectAll("div")
.data(years)
.enter()
.append("div")
.text(function(d) {
return d;
});
buttons.on("click", function(d) {
d3.select(".years_buttons")
.selectAll("div")
.transition()
.duration(500)
.style("color", "black")
.style("background", "white");
d3.select(this)
.transition()
.duration(500)
.style("background", "black")
.style("color", "white");
update(years.indexOf(d))
})
} else {
// auto-update debugging
// console.log("it's okay")
}}, 1000);
}
</script>
<title></title>
</head>
<body>
<div class="chart"></div>
<script type="text/javascript">
d3.csv("population.csv", function(d){
d.population = +d.population;
return d;
}, draw)
</script>
<div>
<h2 class="expl_header">Main Findings</h2>
<li class="expl">Before mid 20th century, South Korea and Japan had expansive pyramid population structure which is often found in developing countries.</li>
<li class="expl">There was a dramatic shift in young male adult population (20-39) in Japan between 1940 and 1945, which is likely to be caused by the Second World War.</li>
<li class="expl">The Korean population statistics in 1950 is considered to be more or less inaccuarate due to the Korean War.</li>
<li class="expl">In 1955, Korea saw less number of 0-4 babies than 5-9 kids for the first time in its modern history, and that age cohort keeps shrinking since then.</li>
<li class="expl">After 1990s Korea's population structure has become similar to that of Japan. This transformation is fueled by the atomization of extended families, the influx of western culture(individualism) and sudden economic recessions(IMF, dot-com bubble bust).</li>
</div>
</body>
</html>
Raw
population.csv
country year age_bin sex population
korea 1925 0-4 male 1560053
korea 1925 0-4 female 1509533
korea 1925 5-9 male 1200503
korea 1925 5-9 female 1123590
korea 1925 10-14 male 1117122
korea 1925 10-14 female 1040101
korea 1925 15-19 male 964186
korea 1925 15-19 female 912881
korea 1925 20-24 male 749424
korea 1925 20-24 female 720859
korea 1925 25-29 male 754495
korea 1925 25-29 female 718047
korea 1925 30-34 male 654292
korea 1925 30-34 female 605105
korea 1925 35-39 male 594448
korea 1925 35-39 female 545399
korea 1925 40-44 male 497970
korea 1925 40-44 female 451578
korea 1925 45-49 male 425428
korea 1925 45-49 female 392262
korea 1925 50-54 male 347447
korea 1925 50-54 female 326250
korea 1925 55-59 male 291999
korea 1925 55-59 female 296371
korea 1925 60-64 male 231498
korea 1925 60-64 female 248627
korea 1925 65-69 male 181586
korea 1925 65-69 female 207868
korea 1925 70-74 male 94614
korea 1925 70-74 female 112990
korea 1925 75-79 male 45498
korea 1925 75-79 female 58776
korea 1925 80-84 male 12674
korea 1925 80-84 female 18238
korea 1925 85+ male 2913
korea 1925 85+ female 5405
korea 1930 0-4 male 1661240
korea 1930 0-4 female 1620443
korea 1930 5-9 male 1361625
korea 1930 5-9 female 1296035
korea 1930 10-14 male 1153608
korea 1930 10-14 female 1066871
korea 1930 15-19 male 1058199
korea 1930 15-19 female 993740
korea 1930 20-24 male 860573
korea 1930 20-24 female 850970
korea 1930 25-29 male 692154
korea 1930 25-29 female 679822
korea 1930 30-34 male 706726
korea 1930 30-34 female 677336
korea 1930 35-39 male 618681
korea 1930 35-39 female 578721
korea 1930 40-44 male 548241
korea 1930 40-44 female 507159
korea 1930 45-49 male 460921
korea 1930 45-49 female 428224
korea 1930 50-54 male 379603
korea 1930 50-54 female 357839
korea 1930 55-59 male 305594
korea 1930 55-59 female 302361
korea 1930 60-64 male 235138
korea 1930 60-64 female 248612
korea 1930 65-69 male 171553
korea 1930 65-69 female 196183
korea 1930 70-74 male 114519
korea 1930 70-74 female 137739
korea 1930 75-79 male 50191
korea 1930 75-79 female 66432
korea 1930 80-84 male 0
korea 1930 80-84 female 0
korea 1930 85+ male 0
korea 1930 85+ female 0
korea 1935 0-4 male 1864127
korea 1935 0-4 female 1807454
korea 1935 5-9 male 1478064
korea 1935 5-9 female 1408407
korea 1935 10-14 male 1301810
korea 1935 10-14 female 1229821
korea 1935 15-19 male 1080314
korea 1935 15-19 female 1021591
korea 1935 20-24 male 959748
korea 1935 20-24 female 937281
korea 1935 25-29 male 811545
korea 1935 25-29 female 802225
korea 1935 30-34 male 652568
korea 1935 30-34 female 633374
korea 1935 35-39 male 669307
korea 1935 35-39 female 639446
korea 1935 40-44 male 572611
korea 1935 40-44 female 540153
korea 1935 45-49 male 508714
korea 1935 45-49 female 482679
korea 1935 50-54 male 409120
korea 1935 50-54 female 388381
korea 1935 55-59 male 334045
korea 1935 55-59 female 331983
korea 1935 60-64 male 248205
korea 1935 60-64 female 255063
korea 1935 65-69 male 179466
korea 1935 65-69 female 202173
korea 1935 70-74 male 113398
korea 1935 70-74 female 134568
korea 1935 75-79 male 62583
korea 1935 75-79 female 83215
korea 1935 80-84 male 0
korea 1935 80-84 female 0
korea 1935 85+ male 0
korea 1935 85+ female 0
korea 1940 0-4 male 1974157
korea 1940 0-4 female 1923501
korea 1940 5-9 male 1655425
korea 1940 5-9 female 1581813
korea 1940 10-14 male 1398317
korea 1940 10-14 female 1322947
korea 1940 15-19 male 1142250
korea 1940 15-19 female 1121413
korea 1940 20-24 male 908119
korea 1940 20-24 female 926974
korea 1940 25-29 male 845382
korea 1940 25-29 female 859217
korea 1940 30-34 male 725152
korea 1940 30-34 female 731569
korea 1940 35-39 male 606498
korea 1940 35-39 female 598909
korea 1940 40-44 male 608860
korea 1940 40-44 female 592442
korea 1940 45-49 male 515050
korea 1940 45-49 female 501824
korea 1940 50-54 male 445617
korea 1940 50-54 female 439414
korea 1940 55-59 male 355954
korea 1940 55-59 female 357287
korea 1940 60-64 male 270603
korea 1940 60-64 female 281946
korea 1940 65-69 male 186391
korea 1940 65-69 female 186391
korea 1940 70-74 male 115515
korea 1940 70-74 female 140678
korea 1940 75-79 male 56882
korea 1940 75-79 female 76336
korea 1940 80-84 male 0
korea 1940 80-84 female 0
korea 1940 85+ male 0
korea 1940 85+ female 0
korea 1945 0-4 male 2159146
korea 1945 0-4 female 2119472
korea 1945 5-9 male 1815269
korea 1945 5-9 female 1747262
korea 1945 10-14 male 1545793
korea 1945 10-14 female 1462954
korea 1945 15-19 male 1174771
korea 1945 15-19 female 1175654
korea 1945 20-24 male 889337
korea 1945 20-24 female 979989
korea 1945 25-29 male 812916
korea 1945 25-29 female 858660
korea 1945 30-34 male 768255
korea 1945 30-34 female 799205
korea 1945 35-39 male 675127
korea 1945 35-39 female 681636
korea 1945 40-44 male 575438
korea 1945 40-44 female 563422
korea 1945 45-49 male 554141
korea 1945 45-49 female 531905
korea 1945 50-54 male 451506
korea 1945 50-54 female 451128
korea 1945 55-59 male 366961
korea 1945 55-59 female 377014
korea 1945 60-64 male 293122
korea 1945 60-64 female 314342
korea 1945 65-69 male 200965
korea 1945 65-69 female 225093
korea 1945 70-74 male 136940
korea 1945 70-74 female 165109
korea 1945 75-79 male 65901
korea 1945 75-79 female 88320
korea 1945 80-84 male 24464
korea 1945 80-84 female 36987
korea 1945 85+ male 11121
korea 1945 85+ female 20849
korea 1950 0-4 male 0
korea 1950 0-4 female 0
korea 1950 5-9 male 2991580
korea 1950 5-9 female 2886197
korea 1950 10-14 male 1282027
korea 1950 10-14 female 1232613
korea 1950 15-19 male 1029625
korea 1950 15-19 female 993026
korea 1950 20-24 male 863715
korea 1950 20-24 female 854011
korea 1950 25-29 male 759752
korea 1950 25-29 female 735565
korea 1950 30-34 male 652043
korea 1950 30-34 female 613678
korea 1950 35-39 male 589925
korea 1950 35-39 female 552259
korea 1950 40-44 male 488270
korea 1950 40-44 female 459063
korea 1950 45-49 male 393673
korea 1950 45-49 female 380476
korea 1950 50-54 male 340893
korea 1950 50-54 female 340741
korea 1950 55-59 male 294192
korea 1950 55-59 female 322327
korea 1950 60-64 male 485605
korea 1950 60-64 female 590121
korea 1950 65-69 male 0
korea 1950 65-69 female 0
korea 1950 70-74 male 0
korea 1950 70-74 female 0
korea 1950 75-79 male 0
korea 1950 75-79 female 0
korea 1950 80-84 male 0
korea 1950 80-84 female 0
korea 1950 85+ male 0
korea 1950 85+ female 0
korea 1955 0-4 male 1742778
korea 1955 0-4 female 1633870
korea 1955 5-9 male 1495871
korea 1955 5-9 female 1371517
korea 1955 10-14 male 1371568
korea 1955 10-14 female 1249453
korea 1955 15-19 male 1256904
korea 1955 15-19 female 1138007
korea 1955 20-24 male 808143
korea 1955 20-24 female 946257
korea 1955 25-29 male 635243
korea 1955 25-29 female 803884
korea 1955 30-34 male 679017
korea 1955 30-34 female 710431
korea 1955 35-39 male 585542
korea 1955 35-39 female 583037
korea 1955 40-44 male 530158
korea 1955 40-44 female 523904
korea 1955 45-49 male 496405
korea 1955 45-49 female 451476
korea 1955 50-54 male 337483
korea 1955 50-54 female 342418
korea 1955 55-59 male 295560
korea 1955 55-59 female 319434
korea 1955 60-64 male 217405
korea 1955 60-64 female 263101
korea 1955 65-69 male 156405
korea 1955 65-69 female 203113
korea 1955 70-74 male 80971
korea 1955 70-74 female 110771
korea 1955 75-79 male 0
korea 1955 75-79 female 0
korea 1955 80-84 male 0
korea 1955 80-84 female 0
korea 1955 85+ male 0
korea 1955 85+ female 0
korea 1960 0-4 male 1820312
korea 1960 0-4 female 1729252
korea 1960 5-9 male 1958379
korea 1960 5-9 female 1823172
korea 1960 10-14 male 1480279
korea 1960 10-14 female 1341976
korea 1960 15-19 male 1248791
korea 1960 15-19 female 1134363
korea 1960 20-24 male 1175602
korea 1960 20-24 female 1103847
korea 1960 25-29 male 916751
korea 1960 25-29 female 996435
korea 1960 30-34 male 727096
korea 1960 30-34 female 829238
korea 1960 35-39 male 687559
korea 1960 35-39 female 729178
korea 1960 40-44 male 598867
korea 1960 40-44 female 588603
korea 1960 45-49 male 518017
korea 1960 45-49 female 515744
korea 1960 50-54 male 444283
korea 1960 50-54 female 440293
korea 1960 55-59 male 318745
korea 1960 55-59 female 345793
korea 1960 60-64 male 257447
korea 1960 60-64 female 309124
korea 1960 65-69 male 174206
korea 1960 65-69 female 230526
korea 1960 70-74 male 120719
korea 1960 70-74 female 173283
korea 1960 75-79 male 55635
korea 1960 75-79 female 85028
korea 1960 80-84 male 25744
korea 1960 80-84 female 42696
korea 1960 85+ male 8186
korea 1960 85+ female 15983
korea 1965 0-4 male 2318664
korea 1965 0-4 female 2162257
korea 1965 5-9 male 2391295
korea 1965 5-9 female 2221577
korea 1965 10-14 male 1857472
korea 1965 10-14 female 1732555
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japan 1970 75-79 male 536369
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japan 1970 80-84 male 243532
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japan 1970 85+ male 90344
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japan 1975 0-4 male 5127359
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japan 1975 80-84 male 307763
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japan 1975 85+ male 122647
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japan 1980 15-19 male 4223685
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japan 1980 20-24 male 3960116
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japan 1980 75-79 male 848714
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japan 1980 85+ male 171959
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japan 1985 80-84 male 542243
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japan 1985 85+ male 255975
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japan 1990 0-4 male 3326039
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japan 1990 5-9 male 3821833
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japan 1990 30-34 male 3925353
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japan 1990 55-59 male 3783367
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japan 1990 60-64 male 3236549
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japan 1990 65-69 male 2194783
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japan 1990 70-74 male 1559972
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japan 1990 75-79 male 1197457
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japan 1990 80-84 male 678385
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japan 1990 85+ male 357040
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japan 1995 0-4 male 3070015
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japan 1995 55-59 male 3906621
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japan 1995 75-79 male 1260411
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japan 1995 80-84 male 824492
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japan 1995 85+ male 479086
japan 1995 85+ female 1100659
japan 2000 0-4 male 3022521
japan 2000 0-4 female 2881577
japan 2000 5-9 male 3083431
japan 2000 5-9 female 2938358
japan 2000 10-14 male 3353150
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japan 2000 15-19 male 3833984
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japan 2000 25-29 male 4965277
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japan 2000 30-34 male 4436818
japan 2000 30-34 female 4339792
japan 2000 35-39 male 4096286
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japan 2000 40-44 male 3924171
japan 2000 40-44 female 3876048
japan 2000 45-49 male 4467772
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japan 2000 50-54 male 5210038
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japan 2000 55-59 male 4290239
japan 2000 55-59 female 4443933
japan 2000 60-64 male 3749528
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japan 2000 75-79 male 1625822
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japan 2000 80-84 male 915268
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japan 2000 85+ male 653475
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japan 2005 0-4 male 2854502
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japan 2005 75-79 male 2256317
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japan 2005 80-84 male 1222635
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japan 2005 85+ male 810898
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japan 2010 0-4 male 2710581
japan 2010 0-4 female 2586167
japan 2010 5-9 male 2859805
japan 2010 5-9 female 2725856
japan 2010 10-14 male 3031943
japan 2010 10-14 female 2889092
japan 2010 15-19 male 3109229
japan 2010 15-19 female 2954128
japan 2010 20-24 male 3266240
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japan 2010 25-29 male 3691723
japan 2010 25-29 female 3601978
japan 2010 30-34 male 4221011
japan 2010 30-34 female 4120486
japan 2010 35-39 male 4950122
japan 2010 35-39 female 4836227
japan 2010 40-44 male 4400375
japan 2010 40-44 female 4341490
japan 2010 45-49 male 4027969
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japan 2010 50-54 male 3809576
japan 2010 50-54 female 3834923
japan 2010 55-59 male 4287489
japan 2010 55-59 female 4376245
japan 2010 60-64 male 4920468
japan 2010 60-64 female 5116781
japan 2010 65-69 male 3921774
japan 2010 65-69 female 4288399
japan 2010 70-74 male 3225503
japan 2010 70-74 female 3737799
japan 2010 75-79 male 2582940
japan 2010 75-79 female 3358073
japan 2010 80-84 male 1692584
japan 2010 80-84 female 2643680
japan 2010 85+ male 1047611
japan 2010 85+ female 2747322
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