curran/README.md

## README.md

      
    Raw
  

              README.md
            
          
    This is a dataset on crime in 2014, subdivided by race and offense. Originally found in table 43 in the FBI Web Site "2014 Crime in the United States". This is the same data as in a previous block, but normalized such that "Race" is now a single column rather than a collection of different columns. This allows the data to be easily used with d3.nest and other utilities/
This example page provides the basic code required to load the data and display it on the page (as JSON) using D3.js.
Built with blockbuilder.org
forked from curran's block: Crime by Race Dataset


## data.csv

          
            Offense charged
            Total
            White
            Black or African American
            American Indian or Alaska Native
            Asian
            Native Hawaiian or Other Pacific Islander

            
              TOTAL
              8730665
              6056687
              2427683
              135599
              100067
              10629

            
              Murder and nonnegligent manslaughter
              8230
              3807
              4224
              83
              107
              9

            
              Rape
              16326
              10977
              4888
              212
              222
              27

            
              Robbery
              74077
              31354
              41379
              616
              617
              111

            
              Aggravated assault
              291600
              185612
              96511
              4372
              4507
              598

            
              Burglary
              186794
              126242
              56504
              1703
              1999
              346

            
              Larceny-theft
              971199
              671260
              271788
              15869
              11355
              927

            
              Motor vehicle theft
              53456
              35551
              16391
              668
              677
              169

            
              Arson
              7298
              5338
              1709
              142
              98
              11

            
              Violent crime
              390233
              231750
              147002
              5283
              5453
              745

            
              Property crime
              1218747
              838391
              346392
              18382
              14129
              1453

            
              Other assaults
              853887
              558181
              272068
              13618
              9173
              847

            
              Forgery and counterfeiting
              44336
              28272
              15095
              279
              646
              44

            
              Fraud
              109576
              72424
              34853
              1116
              1124
              59

            
              Embezzlement
              12678
              7851
              4518
              87
              210
              12

            
              Stolen property; buying, receiving, possessing
              69912
              45816
              22538
              662
              812
              84

            
              Vandalism
              154755
              108531
              41723
              2761
              1556
              184

            
              Weapons; carrying, possessing, etc.
              109891
              62920
              44705
              803
              1307
              156

            
              Prostitution and commercialized vice
              37030
              19867
              15483
              199
              1434
              47

            
              Sex offenses (except rape and prostitution)
              43125
              31279
              10462
              640
              688
              56

            
              Drug abuse violations
              1216225
              837851
              353862
              10071
              12893
              1548

            
              Gambling
              4363
              1560
              2568
              16
              192
              27

            
              Offenses against the family and children
              79075
              50912
              26048
              1521
              574
              20

            
              Driving under the influence
              863598
              722451
              112107
              12048
              15938
              1054

            
              Liquor laws
              246304
              197559
              35727
              9539
              3302
              177

            
              Drunkenness
              327325
              264906
              51485
              6952
              3552
              430

            
              Disorderly conduct
              338636
              213342
              114802
              7734
              2493
              265

            
              Vagrancy
              21577
              14819
              6116
              443
              182
              17

            
              All other offenses (except traffic)
              2546822
              1726091
              750488
              42980
              23946
              3317

            
              Suspicion
              1057
              557
              472
              15
              13
              0

            
              Curfew and loitering law violations
              41513
              21357
              19169
              450
              450
              87

## data.json
{
  "columns":[
    { "name": "Offense charged", "type": "string" },
    { "name": "Total", "type": "number" },
    { "name": "White", "type": "number" },
    { "name": "Black or African American", "type": "number" },
    { "name": "American Indian or Alaska Native", "type": "number" },
    { "name": "Asian", "type": "number" },
    { "name": "Native Hawaiian or Other Pacific Islander", "type": "number" }
  ]
}

## index.html
<!DOCTYPE html>
<head>
  <meta charset="utf-8">
  <script src="//cdnjs.cloudflare.com/ajax/libs/d3/3.5.9/d3.min.js"></script>
  <script src="//curran.github.io/dsv-dataset/dsv-dataset-v0.2.1.js"></script>
</head>

<body>
  <script>

    function render(data){

      // This computes totals for each offense.
      // Could be a bar chart.
      var offenses = d3.nest()
        .key(function (d){ return d["Offense charged"];})
        .rollup(function(leaves) {
          return d3.sum(leaves, function(d) { return d.count; });
        })
        .entries(data);
      d3.select("body")
        .append("pre")
        .text(JSON.stringify(offenses));

      // This computes totals for each race.
      // Could be a bar chart.
      var offenses = d3.nest()
        .key(function (d){ return d["Race"];})
        .rollup(function(leaves) {
          return d3.sum(leaves, function(d) { return d.count; });
        })
        .entries(data);
      d3.select("body")
        .append("pre")
        .text(JSON.stringify(offenses));

      // This shows the full data.
      // Could be a stacked or grouped bar chart.
      d3.select("body")
        .append("pre")
        .text(JSON.stringify(data, null, 2));
    }

    d3.csv("data.csv", function(data){
      d3.json("data.json", function(metadata){

        var dataset = dsvDataset.parse({
          data: data,
          metadata: metadata
        });

        // Transform the table so "Race" is a single column.
        data = normalize(dataset.data, "Race", "count", [
          "White",
          "Black or African American",
          "American Indian or Alaska Native",
          "Asian",
          "Native Hawaiian or Other Pacific Islander"
        ]);

        // Remove the "TOTAL" values.
        data = data.filter(function (d){
          return d["Offense charged"] !== "TOTAL";
        });

        data.forEach(function (d){
          delete d["Total"];
        });

        render(data);
      });
    });

    function normalize(dataIn, dimension, measure, values){

      var data = [];

      // All the columns not in "values".
      var columns = Object.keys(dataIn[0]).filter(function (column){
        return !values.some(function (value){
          return column === value;
        });
      });

      dataIn.forEach(function (d){
        values.forEach(function (value){
          var row = {};

          // Pivot the given value columns into rows.
          row[dimension] = value;
          row[measure] = d[value];

          // Copy over values from other columns.
          columns.forEach(function (column){
            row[column] = d[column];
          });

          data.push(row);
        });
      });

      return data;
    }

  </script>
</body>
Offense charged	Total	White	Black or African American	American Indian or Alaska Native	Asian	Native Hawaiian or Other Pacific Islander
TOTAL	8730665	6056687	2427683	135599	100067	10629
Murder and nonnegligent manslaughter	8230	3807	4224	83	107	9
Rape	16326	10977	4888	212	222	27
Robbery	74077	31354	41379	616	617	111
Aggravated assault	291600	185612	96511	4372	4507	598
Burglary	186794	126242	56504	1703	1999	346
Larceny-theft	971199	671260	271788	15869	11355	927
Motor vehicle theft	53456	35551	16391	668	677	169
Arson	7298	5338	1709	142	98	11
Violent crime	390233	231750	147002	5283	5453	745
Property crime	1218747	838391	346392	18382	14129	1453
Other assaults	853887	558181	272068	13618	9173	847
Forgery and counterfeiting	44336	28272	15095	279	646	44
Fraud	109576	72424	34853	1116	1124	59
Embezzlement	12678	7851	4518	87	210	12
Stolen property; buying, receiving, possessing	69912	45816	22538	662	812	84
Vandalism	154755	108531	41723	2761	1556	184
Weapons; carrying, possessing, etc.	109891	62920	44705	803	1307	156
Prostitution and commercialized vice	37030	19867	15483	199	1434	47
Sex offenses (except rape and prostitution)	43125	31279	10462	640	688	56
Drug abuse violations	1216225	837851	353862	10071	12893	1548
Gambling	4363	1560	2568	16	192	27
Offenses against the family and children	79075	50912	26048	1521	574	20
Driving under the influence	863598	722451	112107	12048	15938	1054
Liquor laws	246304	197559	35727	9539	3302	177
Drunkenness	327325	264906	51485	6952	3552	430
Disorderly conduct	338636	213342	114802	7734	2493	265
Vagrancy	21577	14819	6116	443	182	17
All other offenses (except traffic)	2546822	1726091	750488	42980	23946	3317
Suspicion	1057	557	472	15	13	0
Curfew and loitering law violations	41513	21357	19169	450	450	87
	{
	"columns":[
	{ "name": "Offense charged", "type": "string" },
	{ "name": "Total", "type": "number" },
	{ "name": "White", "type": "number" },
	{ "name": "Black or African American", "type": "number" },
	{ "name": "American Indian or Alaska Native", "type": "number" },
	{ "name": "Asian", "type": "number" },
	{ "name": "Native Hawaiian or Other Pacific Islander", "type": "number" }
	]
	}
	<!DOCTYPE html>
	<head>
	<meta charset="utf-8">
	<script src="//cdnjs.cloudflare.com/ajax/libs/d3/3.5.9/d3.min.js"></script>
	<script src="//curran.github.io/dsv-dataset/dsv-dataset-v0.2.1.js"></script>
	</head>

	<body>
	<script>

	function render(data){

	// This computes totals for each offense.
	// Could be a bar chart.
	var offenses = d3.nest()
	.key(function (d){ return d["Offense charged"];})
	.rollup(function(leaves) {
	return d3.sum(leaves, function(d) { return d.count; });
	})
	.entries(data);
	d3.select("body")
	.append("pre")
	.text(JSON.stringify(offenses));

	// This computes totals for each race.
	// Could be a bar chart.
	var offenses = d3.nest()
	.key(function (d){ return d["Race"];})
	.rollup(function(leaves) {
	return d3.sum(leaves, function(d) { return d.count; });
	})
	.entries(data);
	d3.select("body")
	.append("pre")
	.text(JSON.stringify(offenses));

	// This shows the full data.
	// Could be a stacked or grouped bar chart.
	d3.select("body")
	.append("pre")
	.text(JSON.stringify(data, null, 2));
	}

	d3.csv("data.csv", function(data){
	d3.json("data.json", function(metadata){

	var dataset = dsvDataset.parse({
	data: data,
	metadata: metadata
	});

	// Transform the table so "Race" is a single column.
	data = normalize(dataset.data, "Race", "count", [
	"White",
	"Black or African American",
	"American Indian or Alaska Native",
	"Asian",
	"Native Hawaiian or Other Pacific Islander"
	]);

	// Remove the "TOTAL" values.
	data = data.filter(function (d){
	return d["Offense charged"] !== "TOTAL";
	});

	data.forEach(function (d){
	delete d["Total"];
	});

	render(data);
	});
	});

	function normalize(dataIn, dimension, measure, values){

	var data = [];

	// All the columns not in "values".
	var columns = Object.keys(dataIn[0]).filter(function (column){
	return !values.some(function (value){
	return column === value;
	});
	});

	dataIn.forEach(function (d){
	values.forEach(function (value){
	var row = {};

	// Pivot the given value columns into rows.
	row[dimension] = value;
	row[measure] = d[value];

	// Copy over values from other columns.
	columns.forEach(function (column){
	row[column] = d[column];
	});

	data.push(row);
	});
	});

	return data;
	}

	</script>
	</body>