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maelafifi/.block

Last active May 7, 2017 04:20
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scatter
Raw
.block
license: mit
Raw
DataFinal.csv
Occupation workersMen earningsMen earningsWomen workersWomen
Chief executives 832 2419 1876 318
General and operations managers 626 1358 1037 250
Marketing and sales managers 518 1745 1142 403
Administrative services managers 101 1398 952 71
Computer and information systems managers 443 1756 1680 150
Financial managers 527 1670 1157 578
Human resources managers 73 1737 1283 190
Industrial production managers 201 1409 1219 66
Purchasing managers 95 1400 1169 92
Transportation storage and distribution managers 237 990 988 51
Education administrators 297 1528 1280 541
Food service managers 410 853 632 350
Lodging managers 59 1068 764 64
Medical and health services managers 133 1610 1254 399
Real estate managers 181 1157 815 221
Social/community service managers 115 1206 992 248
Managers - all other 1845 1542 1188 1115
Wholesale and retail buyers 68 942 765 83
Purchasing agents 128 1037 974 125
Adjusters appraisers examiners 114 1139 912 206
Compliance officers 106 1274 1163 146
Human resources workers 166 1364 1089 465
Management analysts 332 1586 1342 253
Market research analysts 116 1345 1097 130
Business operations - all other 110 1263 1001 146
Accountants and auditors 559 1441 1018 892
Financial analysts 173 1747 1252 104
Personal financial advisors 258 1714 953 142
Credit counselors and loan officers 151 1331 926 200
Computer systems analysts 314 1475 1328 182
Computer programmers 300 1466 1312 104
Software developers/systems software 1084 1863 1553 266
Computer support specialists 367 1078 1014 124
Computer occupations all other 418 1244 1055 106
Operations research analysts 59 1382 1300 66
Engineers all other 430 1659 1401 63
Engineering technicians 275 1075 793 75
Medical scientists 69 1250 1169 57
Physical scientists - all other 131 1727 1323 111
Life physical and social science 62 949 826 57
Counselors 184 892 907 473
Social workers 127 1039 884 557
Clergy 315 1070 893 53
Lawyers 446 2086 1619 299
Postsecondary teachers 535 1356 1152 445
Elementary/middle school teachers 605 1126 981 2231
Secondary school teachers 403 1146 1074 562
Other teachers and instructors 156 1135 786 214
Teacher assistants 56 501 525 532
Designers 290 1267 922 297
Producers and directors 78 1228 1030 50
Editors 64 1112 1035 56
Pharmacists 89 2096 1839 133
Physicians and surgeons 497 2343 1476 308
Physical therapists 69 1348 1306 128
Registered nurses 285 1261 1143 2213
Clinical lab tech 97 1071 834 198
Diagnostic related tech 79 1156 936 175
EMT and paramedics 114 821 650 60
Health practitioner support tech 113 712 643 361
Nursing psychiatric and home health aides 194 534 498 1192
Bailiffs correctional officers and jailers 275 793 672 91
Police and sheriff's patrol officers 609 1008 938 100
Security guards/surveillance officers 576 597 516 152
Chefs and head cooks 288 632 519 68
Food Prep Supervisors 174 666 485 251
Cooks 892 459 421 515
Food preparation workers 215 432 413 262
Bartenders 131 701 498 122
Food preparation and Servers 72 381 402 125
Waiters and waitresses 342 504 441 607
Dining attendants 82 465 407 51
Housekeeping supervisors 119 702 524 71
Janitors 1139 564 476 441
Maids 120 497 427 661
Gaming supervisors 68 840 717 59
Personal care aides 125 514 469 636
Recreation and fitness workers 92 668 517 100
Retail sales supervisors 1321 857 630 1047
Non-retail sales supervisors 552 1161 1004 230
Cashiers 402 475 403 965
Retail salespersons 1103 730 514 728
Advertising sales agents 122 1034 999 81
Insurance sales agents 218 1166 676 205
Financial service agents 155 1458 951 63
Sales reps - all other 282 1202 826 145
Sales - wholesale and manufacturing 819 1140 872 302
Real estate brokers and sales agents 209 1222 780 284
Sales and related workers all other 103 946 709 68
Office supervisors 383 942 809 819
Accountants 115 790 716 641
Customer service 664 676 623 1185
Receptionists 103 600 581 848
Dispatchers 126 734 671 153
Postal service clerks 58 999 805 53
Postal service carriers 185 1020 931 119
Production planning and expediting clerks 113 935 765 111
Shipping receiving and traffic clerks 322 602 583 151
Stock clerks and order fillers 643 533 526 371
Secretaries and administrative assistants 133 831 708 2078
Data entry 51 655 630 170
Office clerks general 140 707 648 760
Office and administrative support 107 855 751 351
Miscellaneous agricultural workers 547 524 423 111
Production and operations supervisors 599 972 723 131
Electrical electronics and electromechanical assemblers 55 618 554 50
Miscellaneous assemblers and fabricators 645 625 559 345
Bakers 55 562 480 83
Butchers and other meat poultry and fish processing workers 183 571 449 61
Laundry and dry-cleaning workers 55 486 436 80
Sewing machine operators 52 407 452 114
Inspectors testers sorters samplers and weighers 445 834 584 241
Packaging and filling machine operators and tenders 112 574 425 113
Production workers all other 641 690 515 246
Bus drivers 197 691 589 146
Driver/sales workers and truck drivers 2689 787 630 120
Industrial truck and tractor operators 510 604 563 54
Laborers 1196 580 509 221
Packers and packagers 166 468 437 216
Raw
index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
svg
{
font: 10px sans-serif;
padding: 10px;
}
.axis,
.frame
{
shape-rendering: crispEdges;
}
.axis line
{
stroke: #ddd;
}
.axis path
{
display: none;
}
.cell text
{
font-weight: bold;
text-transform: capitalize;
}
.frame
{
fill: none;
stroke: #aaa;
}
.tooltip {
position: absolute;
width: 200px;
height: 75px;
background: white;
}
circle
{
fill-opacity: 0;
}
circle.hidden
{
fill: #ccc !important;
}
.extent
{
fill: #000;
fill-opacity: .125;
stroke: #fff;
}
</style>
<body>
<script src="https://d3js.org/d3.v3.min.js"></script>
<script>
var width = 960,
size = 110,
padding = 20;
var x = d3.scale.linear()
.range([padding / 2, size - padding / 2]);
var y = d3.scale.linear()
.range([size - padding / 2, padding / 2]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("bottom")
.ticks(5)
.tickFormat(function(d, i)
{
return d/1000 + "k";
});
var yAxis = d3.svg.axis()
.scale(y)
.orient("left")
.ticks(5)
.tickFormat(function(d, i)
{
return d/1000 + "k";
});
d3.csv("DataFinal.csv", function(error, data)
{
if (error) throw error;
var domainPerField = {},
field = d3.keys(data[0]).filter(function(d) { return d !== "Occupation"; }),
n = field.length;
field.forEach(function(field1)
{
domainPerField[field1] = d3.extent(data, function(d) { return +d[field1]; });
});
xAxis.tickSize(size * n);
yAxis.tickSize(-size * n);
var svg = d3.select("body").append("svg")
.attr("width", size * n + padding)
.attr("height", size * n + padding)
.append("g")
.attr("transform", "translate(" + padding + "," + padding / 2 + ")");
var tooltip = d3.select("body").append("div")
.attr("class", "tooltip")
.style("opacity", 0);
svg.selectAll(".x.axis")
.data(field)
.enter().append("g")
.attr("class", "x axis")
.attr("transform", function(d, i) { return "translate(" + (n - i - 1) * size + ",0)"; })
.each(function(d) { x.domain(domainPerField[d]); d3.select(this).call(xAxis); });
svg.selectAll(".y.axis")
.data(field)
.enter().append("g")
.attr("class", "y axis")
.attr("transform", function(d, i) { return "translate(0," + i * size + ")"; })
.each(function(d) { y.domain(domainPerField[d]); d3.select(this).call(yAxis); });
var cell = svg.selectAll(".cell")
.data(cross(field, field))
.enter()
.append("g")
.attr("class", "cell")
.attr("transform", function(d)
{
return "translate(" + (n - d.i - 1) * size + "," + d.j * size + ")";
})
.each(plot);
// Titles for the diagonal.
cell.filter(function(d){ return d.i === d.j;})
.append("text")
.attr("x", padding)
.attr("dx", "-1.01em")
.attr("y", padding)
.attr("dy", "-1.2em")
.text(function(d)
{
if(d.x === "earningsMen")
{
return "Median Mens Earning (Weekly)";
}
else if(d.x === "earningsWomen")
{
return "Median Womens Earning (Weekly)";
}
else if(d.x === "earningsTotal")
{
return "Median Total Earnings (Weekly)";
}
else if(d.x === "workersWomen")
{
return "Women Workers (In Thousands)";
}
else if(d.x === "workersMen")
{
return "Men Workers (In Thousands)";
}
});
function plot(p)
{
var cell = d3.select(this);
x.domain([0,d3.max(domainPerField[p.x])+100]);
y.domain([0,d3.max(domainPerField[p.y])+100]);
cell.append("rect")
.attr("class", "frame")
.attr("x", padding / 2)
.attr("y", padding / 2)
.attr("width", size - padding)
.attr("height", size - padding);
cell.selectAll("circle")
.data(data)
.enter()
.append("circle")
.attr("cx", function(d) { return x(d[p.x]); })
.attr("cy", function(d) { return y(d[p.y]); })
.attr("r", 2)
.style("stroke", function(d)
{
if(p.y === "workersMen")
{
var colorScale = d3.scale.linear()
.domain([51, 1869])
.range(["#b3ffb3","#003300"]);
return colorScale(+d[p.y]);
}
else if(p.y === "workersTotal")
{
var colorScale = d3.scale.linear()
.domain([105, 2960])
.range(["#b3ffb3","#003300"]);
return colorScale(+d[p.y]);
}
else if(p.y === "earningsMen")
{
var colorScale = d3.scale.linear()
.domain([407, 2419])
.range(["#b3ffb3","#003300"]);
return colorScale(+d[p.y]);
}
else if(p.y === "earningsTotal")
{
var colorScale = d3.scale.linear()
.domain([414, 2303])
.range(["#b3ffb3","#003300"]);
return colorScale(+d[p.y]);
}
else if(p.y === "workersWomen")
{
var colorScale = d3.scale.linear()
.domain([51, 2231])
.range(["#b3ffb3","#003300"]);
return colorScale(+d[p.y]);
}
else if(p.y === "earningsWomen")
{
var colorScale = d3.scale.linear()
.domain([403, 1876])
.range(["#b3ffb3","#003300"]);
return colorScale(+d[p.y]);
}
else
{
var colorScale = d3.scale.linear()
.domain([.405, .79])
.range(["#b3ffb3","#003300"]);
return colorScale(+d[p.y]);
}
})
.on("mouseover", function(d) {
var y;
var x;
if(p.y === "workersMen")
{
y = "Men Workers (thousands): "
}
else if(p.y === "workersWomen")
{
y = "Women Workers (thousands): "
}
else if(p.y === "earningsWomen")
{
y = "Average Women Earning: "
}
else if(p.y === "earningsMen")
{
y = "Average Men Earning: "
}
else if(p.y === "earningsTotal")
{
y = "Average Overall Earning: "
}
if(p.x === "workersMen")
{
x = "Men Workers (thousands): "
}
else if(p.x === "workersWomen")
{
x = "Women Workers (thousands): "
}
else if(p.x === "earningsWomen")
{
x = "Average Women Earning: "
}
else if(p.x === "earningsMen")
{
x = "Average Men Earning: "
}
else if(p.x === "earningsTotal")
{
x = "Average Overall Earning: "
}
tooltip.transition()
.duration(200)
.style("opacity", .9);
tooltip.html(d.Occupation + "<br/>" +
y +d[p.y]
+ "<br/> "+ x +d[p.x])
.style("left", (d3.event.pageX + 5) + "px")
.style("top", (d3.event.pageY - 28) + "px");
})
.on("mouseout", function(d) {
tooltip.transition()
.duration(500)
.style("opacity", 0);
});
;
}
});
function cross(a, b)
{
var c = [], n = a.length, m = b.length, i, j;
for (i = -1; ++i < n;) for (j = -1; ++j < m;)
{
c.push({x: a[i], i: i, y: b[j], j: j});
}
return c;
}
</script>
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