README.md

Objective

Visualize this data however you see fit and tell a story of any specific trends you see. This should be a combination of a visual graph, coupled with a couple of paragraphs on your methodology, insights, and analysis.

Summary/Findings

After investigating the data, I produced a few SQL query results (see below) and charts (see attached images). But I didn't find any trends worth highlighting or stories worth telling.

I did, however, take interest in noting the existence of unresolved violations. Under the assumption that city officials have an interest in closing all violations, I built a web-based visualization tool to help identify and track them.

View the tool live at http://bl.ocks.org/s2t2/raw/2ba2fe83a852b53fe804/.

Maybe front-end or back-end development would have been a more applicable primary skill to choose for evaluation.

Methodology/Notes/Process (stream of conscious)

Download the csv file to local machine.

Import into a new database table, cfa.violations, on localhost.

Explore and diagnose the data via SQL queries. Focus on "finding a trend".

-- 545 rows (1 per violation)
-- are violation_ids unique identifiers? (yes)
SELECT violation_id, count(*)
FROM cfa.violations
GROUP BY 1
ORDER BY 2 desc

-- how many violations per inspection?
-- between 1 and 11, with a long tail starting to form around 3)
-- ten inspections resulted in greater than 3 violations, these might be worth looking into
SELECT inspection_id, count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1
ORDER BY 2 desc

-- how many violations per category?
SELECT violation_category, count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1
ORDER BY 2 DESC

=>

violation_category	violation_count
Animals and Pests	180
Garbage and Refuse	126
Unsanitary Conditions	83
Vegetation	67
Building Conditions	62
Chemical Hazards	17
Biohazards	7
Air Pollutants and Odors	2
Retail Food	1

-- how many violations per type?
SELECT violation_type, count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1
ORDER BY 2 DESC

=>

violation_type	violation_count
Refuse Accumulation	106
Unsanitary conditions, not specified	74
Overgrown Vegetation	66
Animals and Pests, not specified	41
Animal Feces	37
Insects, not specified	33
Rodents, not specified	28
Mold or Mildew	28
Lead Hazard	17
Abandoned Vehicle	13
Mice	11
Inadequate Pest Exclusion	10
Bed Bugs	7
Cockroaches	7
Damaged or defective walls, floors or ceilings	7
Refuse Not Properly Stored	6
Unsanitary Floors or Walls	6
Rats	5
Non-functioning Smoke Detector	5
Human Feces	4
Standing Water	3
Inadequate Ventilation	2
Animals, Prohibited number	2
Inoperable Windows	2
Barrier to Emergency Ingress or Egress	2
Sewage Hazard	2
Building Dampness or Water Intrusion	2
Unsanitary Public Areas	2
Flies	2
Nuisance Odors	2
Inadequate Lighting	1
Poison Ivy or Poison Oak	1
Inadequate or Improper Kitchen Facilities	1
Animal Urine	1
Moderate risk food holding temperature	1
Dogs	1
Mosquitos	1
Unsanitary Toilets or Bathrooms	1
No or inadequate hot or cold water	1
Human Urine	1
Improper Refuse Storage	1
Inadequate Heating	1
Pidgeons	1

-- whats the relationship between violation categories and types? do they nest nicely?
-- they do
-- it might be interesting to show the top violation types by category...
SELECT violation_category, "|", violation_type, "|", count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1,2,3,4
ORDER BY 1,5 DESC

=>

violation_category	violation_type	violation_count
Air Pollutants and Odors	Nuisance Odors	2
Animals and Pests	Animals and Pests, not specified	41
Animals and Pests	Animal Feces	37
Animals and Pests	Insects, not specified	33
Animals and Pests	Rodents, not specified	28
Animals and Pests	Mice	11
Animals and Pests	Bed Bugs	7
Animals and Pests	Cockroaches	7
Animals and Pests	Rats	5
Animals and Pests	Standing Water	3
Animals and Pests	Animals, Prohibited number	2
Animals and Pests	Flies	2
Animals and Pests	Animal Urine	1
Animals and Pests	Dogs	1
Animals and Pests	Mosquitos	1
Animals and Pests	Pidgeons	1
Biohazards	Human Feces	4
Biohazards	Sewage Hazard	2
Biohazards	Human Urine	1
Building Conditions	Mold or Mildew	28
Building Conditions	Inadequate Pest Exclusion	10
Building Conditions	Damaged or defective walls, floors or ceilings	7
Building Conditions	Non-functioning Smoke Detector	5
Building Conditions	Barrier to Emergency Ingress or Egress	2
Building Conditions	Building Dampness or Water Intrusion	2
Building Conditions	Inadequate Ventilation	2
Building Conditions	Inoperable Windows	2
Building Conditions	Inadequate Heating	1
Building Conditions	Inadequate Lighting	1
Building Conditions	Inadequate or Improper Kitchen Facilities	1
Building Conditions	No or inadequate hot or cold water	1
Chemical Hazards	Lead Hazard	17
Garbage and Refuse	Refuse Accumulation	106
Garbage and Refuse	Abandoned Vehicle	13
Garbage and Refuse	Refuse Not Properly Stored	6
Garbage and Refuse	Improper Refuse Storage	1
Retail Food	Moderate risk food holding temperature	1
Unsanitary Conditions	Unsanitary conditions, not specified	74
Unsanitary Conditions	Unsanitary Floors or Walls	6
Unsanitary Conditions	Unsanitary Public Areas	2
Unsanitary Conditions	Unsanitary Toilets or Bathrooms	1
Vegetation	Overgrown Vegetation	66
Vegetation	Poison Ivy or Poison Oak	1

-- how many violations over time? (by day)
-- there are some spikes but it doesn't look like there are any trends in overall counts.
SELECT
  DATE(violation_date) AS violation_on
  ,count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1
ORDER BY 1 desc

-- how many violations over time? (by week)
-- not seeing anything...
SELECT
  yearweek(violation_date) AS violation_on
  ,count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1
ORDER BY 1 DESC

-- how many violations over time? (by month)
-- are there more violations during summer months?
-- i don't think so
SELECT
  MONTH(violation_date) AS violation_month
  ,monthname(violation_date) AS violation_month_name
  ,count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1,2
ORDER BY 1

=>

violation_month	violation_month_name	violation_count
1	January	20
2	February	38
3	March	32
4	April	41
5	May	24
6	June	59
7	July	71
8	August	41
9	September	73
10	October	42
11	November	51
12	December	53

-- are there any kinds of violation that are trending differently over time?
SELECT 
yearweek(violation_date) AS violation_week
,count(DISTINCT CASE WHEN violation_category = 'Garbage and Refuse' THEN violation_id END) AS garbage_count
,count(DISTINCT CASE WHEN violation_category = 'Unsanitary Conditions' THEN violation_id END) AS sanitation_count
,count(DISTINCT CASE WHEN violation_category = 'Animals and Pests' THEN violation_id END) AS pest_count
,count(DISTINCT CASE WHEN violation_category = 'Building Conditions' THEN violation_id END) AS building_count
,count(DISTINCT CASE WHEN violation_category = 'Vegetation' THEN violation_id END) AS plant_count
,count(DISTINCT CASE WHEN violation_category = 'Chemical Hazards' THEN violation_id END) AS chem_count
,count(DISTINCT CASE WHEN violation_category = 'Biohazards' THEN violation_id END) AS bio_count
,count(DISTINCT CASE WHEN violation_category = 'Air Pollutants and Odors' THEN violation_id END) AS pollution_count
,count(DISTINCT CASE WHEN violation_category = 'Retail Food' THEN violation_id END) AS food_count
FROM cfa.violations
GROUP BY 1
ORDER BY 1 desc

=>

violation_week	garbage_count	sanitation_count	pest_count	building_count	plant_count	chem_count	bio_count	pollution_count	food_count
201252	0	0	4	2	0	0	0	0	0
201251	2	2	9	5	0	0	2	1	1
201250	3	3	4	0	0	0	3	0	0
201249	2	1	3	2	2	1	0	1	0
201248	5	0	1	3	1	0	0	0	0
201247	2	0	3	1	0	0	0	0	0
201246	1	4	2	4	0	1	0	0	0
201245	1	4	5	4	1	0	0	0	0
201244	2	3	2	2	0	0	0	0	0
201243	2	2	6	4	2	0	0	0	0
201242	0	1	5	4	1	0	0	0	0
201241	0	0	2	1	0	0	0	0	0
201240	3	2	5	1	0	0	0	0	0
201239	2	1	5	0	3	0	1	0	0
201238	4	14	3	4	2	0	0	0	0
201237	3	11	11	2	0	0	0	0	0
201236	2	0	3	0	2	0	0	0	0
201235	6	1	5	2	1	0	0	0	0
201234	2	1	10	0	0	0	0	0	0
201233	3	0	4	0	1	0	0	0	0
201232	1	0	1	0	0	0	0	0	0
201231	1	0	4	0	1	0	0	0	0
201230	5	1	8	7	8	0	0	0	0
201229	5	0	1	0	1	0	0	0	0
201228	6	1	2	0	5	0	0	0	0
201227	8	0	7	1	1	1	0	0	0
201226	6	0	2	0	0	3	0	0	0
201225	3	5	7	1	5	0	0	0	0
201224	1	4	3	1	2	2	0	0	0
201223	2	2	3	0	5	0	0	0	0
201222	2	1	0	0	0	0	0	0	0
201221	0	0	1	1	3	0	0	0	0
201220	2	1	4	0	2	0	0	0	0
201219	1	1	1	0	0	0	0	0	0
201218	0	1	4	1	1	0	0	0	0
201217	1	0	3	0	0	0	0	0	0
201216	2	2	4	3	1	1	0	0	0
201215	1	0	1	0	2	0	1	0	0
201214	7	1	5	2	3	0	0	0	0
201213	2	1	2	1	3	2	0	0	0
201212	3	2	0	0	3	1	0	0	0
201211	0	0	2	0	1	1	0	0	0
201210	0	0	5	1	0	0	0	0	0
201209	2	0	0	0	1	1	0	0	0
201208	1	3	11	0	0	0	0	0	0
201207	8	1	1	1	1	0	0	0	0
201206	0	1	1	0	1	3	0	0	0
201205	2	0	1	0	1	0	0	0	0
201204	0	1	0	0	0	0	0	0	0
201202	5	3	3	1	0	0	0	0	0
201201	4	1	1	0	0	0	0	0	0

-- ... by month?
SELECT 
MONTH(violation_date) AS violation_month
,"|"
,MONTHname(violation_date) AS violation_month
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Garbage and Refuse' THEN violation_id END) AS garbage_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Unsanitary Conditions' THEN violation_id END) AS sanitation_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Animals and Pests' THEN violation_id END) AS pest_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Building Conditions' THEN violation_id END) AS building_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Vegetation' THEN violation_id END) AS plant_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Chemical Hazards' THEN violation_id END) AS chem_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Biohazards' THEN violation_id END) AS bio_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Air Pollutants and Odors' THEN violation_id END) AS pollution_count
,"|"
,count(DISTINCT CASE WHEN violation_category = 'Retail Food' THEN violation_id END) AS food_count
FROM cfa.violations
GROUP BY 1,2,3
ORDER BY 1 DESC

=>

violation_month	violation_month_name	garbage_count	sanitation_count	pest_count	building_count	plant_count	chem_count	bio_count	pollution_count	food_count
12	December	7	6	20	9	2	1	5	2	1
11	November	11	10	13	14	2	1	0	0	0
10	October	5	6	18	10	3	0	0	0	0
9	September	11	26	22	6	7	0	1	0	0
8	August	13	2	22	2	2	0	0	0	0
7	July	24	2	20	8	16	1	0	0	0
6	June	13	12	15	2	12	5	0	0	0
5	May	4	3	9	2	6	0	0	0	0
4	April	11	3	14	5	6	1	1	0	0
3	March	6	3	9	2	8	4	0	0	0
2	February	11	5	14	1	3	4	0	0	0
1	January	10	5	4	1	0	0	0	0	0

-- what proportion of the cases are closed?
-- 429 (79%) are closed; 116 (21%) are still open; 
SELECT 
 IF(violation_date_closed = '0000-00-00 00:00:00', 'open', 'closed') AS resolution_status
 ,count(DISTINCT violation_id) AS violation_count
FROM cfa.violations
GROUP BY 1

-- what is the open-to-close ratio by category?
-- 
SELECT 
 violation_category
 ,count(DISTINCT CASE WHEN violation_date_closed = '0000-00-00 00:00:00' THEN violation_id END) AS open_count
 ,count(DISTINCT CASE WHEN violation_date_closed <> '0000-00-00 00:00:00' THEN violation_id END) AS closed_count
 ,count(DISTINCT CASE WHEN violation_date_closed = '0000-00-00 00:00:00' THEN violation_id END) / count(DISTINCT violation_id) AS open_ratio
FROM cfa.violations
GROUP BY 1 
ORDER BY 4 desc 

=>

violation_category	open_count	closed_count	open_ratio
Air Pollutants and Odors	1	1	0.5000
Building Conditions	19	43	0.3065
Animals and Pests	42	138	0.2333
Unsanitary Conditions	18	65	0.2169
Garbage and Refuse	24	102	0.1905
Vegetation	10	57	0.1493
Biohazards	1	6	0.1429
Chemical Hazards	1	16	0.0588
Retail Food	0	1	0.0000

SELECT 
 violation_category
 ,"|"
 ,violation_type
 ,"|"
 ,count(DISTINCT CASE WHEN violation_date_closed = '0000-00-00 00:00:00' THEN violation_id END) AS open_count
  ,"|"
  ,count(DISTINCT CASE WHEN violation_date_closed <> '0000-00-00 00:00:00' THEN violation_id END) AS closed_count
  ,"|"
  ,count(DISTINCT CASE WHEN violation_date_closed = '0000-00-00 00:00:00' THEN violation_id END) / count(DISTINCT violation_id) AS open_ratio
FROM cfa.violations
GROUP BY 1 ,2,3
ORDER BY 9 desc

=>

violation_category	violation_type	open_count	closed_count	open_ratio
Garbage and Refuse	Improper Refuse Storage	1	0	1.0000
Building Conditions	Inoperable Windows	2	0	1.0000
Building Conditions	Inadequate Heating	1	0	1.0000
Building Conditions	Inadequate Lighting	1	0	1.0000
Animals and Pests	Pidgeons	1	0	1.0000
Animals and Pests	Standing Water	2	1	0.6667
Unsanitary Conditions	Unsanitary Floors or Walls	4	2	0.6667
Animals and Pests	Mice	7	4	0.6364
Animals and Pests	Rats	3	2	0.6000
Air Pollutants and Odors	Nuisance Odors	1	1	0.5000
Unsanitary Conditions	Unsanitary Public Areas	1	1	0.5000
Building Conditions	Building Dampness or Water Intrusion	1	1	0.5000
Biohazards	Sewage Hazard	1	1	0.5000
Building Conditions	Barrier to Emergency Ingress or Egress	1	1	0.5000
Building Conditions	Damaged or defective walls, floors or ceilings	3	4	0.4286
Animals and Pests	Bed Bugs	2	5	0.2857
Animals and Pests	Cockroaches	2	5	0.2857
Building Conditions	Mold or Mildew	7	21	0.2500
Animals and Pests	Rodents, not specified	6	22	0.2143
Building Conditions	Non-functioning Smoke Detector	1	4	0.2000
Building Conditions	Inadequate Pest Exclusion	2	8	0.2000
Garbage and Refuse	Refuse Accumulation	21	85	0.1981
Animals and Pests	Animals and Pests, not specified	8	33	0.1951
Animals and Pests	Animal Feces	7	30	0.1892
Unsanitary Conditions	Unsanitary conditions, not specified	13	61	0.1757
Garbage and Refuse	Abandoned Vehicle	2	11	0.1538
Vegetation	Overgrown Vegetation	10	56	0.1515
Animals and Pests	Insects, not specified	4	29	0.1212
Chemical Hazards	Lead Hazard	1	16	0.0588
Building Conditions	Inadequate Ventilation	0	2	0.0000
Animals and Pests	Dogs	0	1	0.0000
Animals and Pests	Flies	0	2	0.0000
Unsanitary Conditions	Unsanitary Toilets or Bathrooms	0	1	0.0000
Animals and Pests	Animal Urine	0	1	0.0000
Garbage and Refuse	Refuse Not Properly Stored	0	6	0.0000
Biohazards	Human Feces	0	4	0.0000
Building Conditions	No or inadequate hot or cold water	0	1	0.0000
Vegetation	Poison Ivy or Poison Oak	0	1	0.0000
Retail Food	Moderate risk food holding temperature	0	1	0.0000
Animals and Pests	Animals, Prohibited number	0	2	0.0000
Biohazards	Human Urine	0	1	0.0000
Animals and Pests	Mosquitos	0	1	0.0000
Building Conditions	Inadequate or Improper Kitchen Facilities	0	1	0.0000

-- which kinds of violations take longest to resolve?
SELECT
  violation_id
  ,violation_category
  ,violation_type
  ,violation_date
  ,IF(violation_date_closed = '0000-00-00 00:00:00', '2012-12-31 23:59:59', violation_date_closed) AS minimum_resolved_at
  ,TIMESTAMPDIFF(DAY, violation_date, IF(violation_date_closed = '0000-00-00 00:00:00', '2012-12-31 23:59:59', violation_date_closed)) AS min_time_to_close_in_days
FROM cfa.violations
ORDER BY 6 desc

SELECT
  violation_category
  ,violation_type
  ,count(DISTINCT violation_id) AS violation_count
  ,AVG(
    TIMESTAMPDIFF(DAY, violation_date, 
      IF(violation_date_closed = '0000-00-00 00:00:00', '2012-12-31 23:59:59', violation_date_closed)
    ) 
  ) AS average_min_time_to_close_in_days
FROM cfa.violations
GROUP BY 1,2
ORDER BY 4 DESC

=>

violation_category	violation_type	violation_count	average_min_time_to_close_in_days
Building Conditions	Inadequate Heating	1	159.0000
Garbage and Refuse	Improper Refuse Storage	1	159.0000
Building Conditions	Inadequate Lighting	1	159.0000
Building Conditions	Inoperable Windows	2	103.0000
Building Conditions	Building Dampness or Water Intrusion	2	87.0000
Chemical Hazards	Lead Hazard	17	82.0000
Building Conditions	Inadequate Ventilation	2	76.0000
Building Conditions	Damaged or defective walls, floors or ceilings	7	76.0000
Unsanitary Conditions	Unsanitary conditions, not specified	74	74.8784
Animals and Pests	Animals and Pests, not specified	41	74.7073
Animals and Pests	Insects, not specified	33	72.6061
Garbage and Refuse	Refuse Accumulation	106	62.2358
Vegetation	Overgrown Vegetation	66	60.0455
Animals and Pests	Pidgeons	1	60.0000
Animals and Pests	Rodents, not specified	28	57.7857
Animals and Pests	Animal Feces	37	55.4865
Garbage and Refuse	Abandoned Vehicle	13	49.3846
Building Conditions	Non-functioning Smoke Detector	5	44.4000
Animals and Pests	Cockroaches	7	43.7143
Animals and Pests	Bed Bugs	7	42.8571
Animals and Pests	Mice	11	41.7273
Building Conditions	Mold or Mildew	28	39.9286
Unsanitary Conditions	Unsanitary Public Areas	2	39.5000
Unsanitary Conditions	Unsanitary Floors or Walls	6	36.3333
Building Conditions	No or inadequate hot or cold water	1	36.0000
Animals and Pests	Flies	2	35.0000
Retail Food	Moderate risk food holding temperature	1	34.0000
Building Conditions	Inadequate Pest Exclusion	10	33.2000
Animals and Pests	Standing Water	3	33.0000
Building Conditions	Barrier to Emergency Ingress or Egress	2	32.5000
Unsanitary Conditions	Unsanitary Toilets or Bathrooms	1	28.0000
Air Pollutants and Odors	Nuisance Odors	2	27.0000
Biohazards	Human Feces	4	23.2500
Building Conditions	Inadequate or Improper Kitchen Facilities	1	22.0000
Vegetation	Poison Ivy or Poison Oak	1	22.0000
Animals and Pests	Dogs	1	17.0000
Animals and Pests	Rats	5	16.8000
Garbage and Refuse	Refuse Not Properly Stored	6	15.6667
Animals and Pests	Animal Urine	1	9.0000
Animals and Pests	Animals, Prohibited number	2	7.5000
Biohazards	Sewage Hazard	2	7.0000
Animals and Pests	Mosquitos	1	0.0000
Biohazards	Human Urine	1	0.0000

index.html

<html>
<head>
  <title>CfA Violations 2016</title>
</head>
<body>
  <div id="page">
    <h1>Violations</h1>
    <div id="menu">
      <select id="category-selector">
        <option value="All Categories" selected="true">All Categories</option>
        <option value="Garbage and Refuse">Garbage and Refuse</option>
        <option value="Unsanitary Conditions">Unsanitary Conditions</option>
        <option value="Animals and Pests">Animals and Pests</option>
        <option value="Building Conditions">Building Conditions</option>
        <option value="Vegetation">Vegetation</option>
        <option value="Chemical Hazards">Chemical Hazards</option>
        <option value="Biohazards">Biohazards</option>
        <option value="Air Pollutants and Odors">Air Pollutants and Odors</option>
        <option value="Retail Food">Retail Food</option>
      </select>
    </div>
    <div id="chart-container"></div>
    <div id="list-container">
      <h2>Unresolved Violations</h2>
    </div>
    <footer>
      <a href="http://bl.ocks.org/s2t2/raw/2ba2fe83a852b53fe804">view</a>
      &nbsp;|&nbsp;
      <a href="https://gist.github.com/s2t2/2ba2fe83a852b53fe804">source</a>
    </footer>
  </div>
  <script src="http://d3js.org/d3.v3.min.js"></script>
  <script src="http://ajax.googleapis.com/ajax/libs/jquery/1.8.2/jquery.min.js"></script>
  <script src="http://code.highcharts.com/stock/highstock.js"></script>
  <script type="text/javascript">

        var category_selector = document.getElementById("category-selector");
        var selected_category = category_selector.value;
        category_selector.addEventListener("change", selectCategory, false)

        var selected_data;

        var list = d3.select("div#list-container").append("ul");

        var dateFormatter = d3.time.format("%Y-%m-%d");

        function selectCategory(){
            selected_category = this.value
            updateChart()
        };

        function updateChart(){
            d3.csv("violations.csv", function(csv_data){
                if(selected_category == "All Categories"){
                    selected_data = csv_data;
                } else {
                    selected_data = csv_data.filter(function(d){
                        return d["violation_category"] == selected_category
                    })
                };

                //console.log(selected_category)
                //console.log(selected_data.length)
                //console.log(selected_data[0])

                var violations_grouped_by_day = d3.nest()
                    .key(function(d) { return new Date(d["violation_date"]).getTime(); })
                    .rollup(function(leaves) {
                      return {
                        violation_count: leaves.length,
                      }
                    })
                    .sortKeys(d3.ascending)
                    .entries(selected_data)

                chart_series = violations_grouped_by_day.map(function(g){
                  return [parseInt(g.key), g.values["violation_count"]]
                });

                //console.log(chart_series)
                //console.log(chart_series[0])

                $('#chart-container').highcharts('StockChart', {
                    chart: {
                        alignTicks: false
                    },
                    rangeSelector: {
                        enabled:false
                    },
                    legend: {
                      enabled:true,
                    },
                    series: [{
                        type: 'column',
                        name: selected_category,
                        data: chart_series,
                        dataGrouping: {
                            units: [
                                [
                                    'week', // unit name
                                    [1] // allowed multiples
                                ],
                                ['month',[1, 2, 3, 4, 6]]
                            ]
                        }
                    }],
                    xAxis: {
                        ordinal: false, // spaces axis properly. h/t http://stackoverflow.com/a/13439281/670433
                        type: 'datetime',
                        dateTimeLabelFormats: {
                            day: '%m/%d',
                            week: '%m/%d'
                        }
                    }
                });

                //console.log(selected_data.length)

                var unresolved_violations = selected_data.filter(function(d){
                    return d["violation_date_closed"] == ""
                }).sort(function(a,b){
                    return d3.ascending(a["violation_date"], b["violation_date"]);
                });

                //console.log(unresolved_violations.length)

                var list_item = list.selectAll(".violation-list-item")
                    .data(unresolved_violations)

                list_item.enter().append("li")
                    .attr("class","violation-list-item")

                list_item
                    .text(function(d){
                        return dateFormatter(new Date(d["violation_date"])) + " -- #" + d["violation_id"] + " -- " + d["violation_category"] + " (" + d["violation_type"] + ")"
                    })

                list_item.exit().remove();

            });

        };

        updateChart()

  </script>
</body>
</html>

Open Violations by Month.jpg

Violations by Month.jpg

Violations by Week.jpg

violations.csv

violations.twbx