d3.csv("dji.csv", function(error, csv) { if (error) throw error; var data = d3.nest() .key(function(d) { return d.Date; }) .rollup(function(d) { return (d[0].Close - d[0].Open) / d[0].Open; }) .map(csv); rect.filter(function(d) { return d in data; }) .attr("class", function(d) { return "day " + color(data[d]); }) .select("title") .text(function(d) { return d + ": " + percent(data[d]); }); });
// fetch initial data useEffect(() => { const fetchData = async () => { setData( await d3.csv( "https://gist.githubusercontent.com/mbostock/77a98cd519be20ea1f8e33bbd3617ac2/raw/574433e95e983b288a54f4d2217cb39d1557cd8d/mtcars.csv", ({ name, mpg: x, hp: y }) => ({ name, x: +x, y: +y }) ) ); }; fetchData(); }, []);
_loadCSV() { d3.csv('/worldcup.csv').then(csv => this.setState({ csv })); }
d3.csv("Home_Office_Air_Travel_Data_2011.csv", type, function(error, data) { if (error) throw error; var d3root = d3.hierarchy({values: nest.entries(data)}, function(d) { return d.values; }) .sum(function(d) { return d.values; }) .sort(function(a, b) { return b.value - a.value; }); treemap(d3root); var node = d3.select(root) .selectAll(".node") .data(d3root.leaves()) .enter().append("div") .attr("class", "nodeNTM ") .style("left", function(d) { return d.x0 + "px"; }) .style("top", function(d) { return d.y0 + "px"; }) .style("width", function(d) { return d.x1 - d.x0 + "px"; }) .style("height", function(d) { return d.y1 - d.y0 + "px"; }); node.append("div") .attr("class", "node-labelNTM ") .text(function(d) { return d.parent.parent.data.key + " to " + d.parent.data.key + "\n" + d.data.key; }); node.append("div") .attr("class", "node-valueNTM ") .text(function(d) { return format(d.value); }); });
const loadAllData = (callback = _.noop) => { d3.queue() .defer(d3.json, 'data/us.json') .defer(d3.csv, 'data/us-county-names-normalized.csv') .defer(d3.csv, 'data/county-median-incomes.csv', cleanIncomes) .defer(d3.csv, 'data/h1bs-2012-2016.csv', cleanSalary) .defer(d3.tsv, 'data/us-state-names.tsv', cleanUSStateName) .await((error, us, countyNames, medianIncomes, techSalaries, USstateNames) => {
.padding(1.5); d3.csv("flare.csv", function(d) { d.value = +d.value; if (d.value) return d;
d3.csv("data.csv", function(d, i, columns) { return { state: d.State,
componentWillMount() { d3.csv('data/prepped_dataPop.csv', function(data){ this.setState({data:data}) }.bind(this))
const loadAllData = (callback = _.noop) => { d3.queue() .defer(d3.json, 'data/us.json') .defer(d3.csv, 'data/us-county-names-normalized.csv') .defer(d3.csv, 'data/county-median-incomes.csv', cleanIncomes) .defer(d3.csv, 'data/h1bs-2012-2016.csv', cleanSalary) .defer(d3.tsv, 'data/us-state-names.tsv', cleanUSStateName) .await((error, us, countyNames, medianIncomes, techSalaries, USstateNames) => {