我正在应用程序中实现d3.js图表，我不想将文件用作数据集，我只想将inline json用作数据集(JSON(JSON(将在In中动态生成应用程序(。
我已经使用以下代码完成了可重复使用的响应迅速多行图的实施。
var data1 = [{"我的json数据在此处"}]; d3.json(''，函数(错误，数据({ data1 .foreach(function(d({ d.year = d.year; d.variaiblea = d.variablea; d.variableb = d.variableb; d.variablec = d.variablec; d.temp = d.temp; }(; var Chart = Makelinechart( data1 ，'Year'，{ '设备1'：{列：'variablea'}， '设备2'：{列：'variableb'}， '设备3'：{列：'variablec'}， '设备4'：{列：'variabled'} }，{xaxis：'YARS'，YAXIS：'温度'}(; Chart.Bind("＃Chart-line1"(; Chart.Render((;}(;
在这里，我正在调用d3.json((，但是文件名是空白，并且在代码中也没有在代码中使用data 。而不是"数据"我正在使用" data1"。它的工作原理完美...
现在，我想为分组的条形图实现相同的目标，但是此图表数据绑定方法与"可重复使用的响应式多行图"不同。以下是解析"分组条形图"数据的代码。
d3.csv("\data.csv", function(d, i, columns) {
for (var i = 1, n = columns.length; i < n; ++i) d[columns[i]] = +d[columns[i]];
return d;
}, function(error, data) {
if (error) throw error;
var keys = data.columns.slice(1);
// Rest of code to bind parsed data to chart
});
在分组的条形图上完成代码
所以我如何用inline json替换 data.csv 。
方法d3.json和d3.csv是AJAX调用，旨在从服务器中获取数据。如果您有在线JSON，则不需要这些电话。您的第一个示例这样的事实只是副作用。您的d3.json调用失败，但仍执行呼叫函数。这只是不必要的，应该写为：
var data1 = [ { "My JSON data here" } ];
data1.forEach(function (d) {
d.year = +d.year;
d.variableA = +d.variableA;
d.variableB = +d.variableB;
d.variableC = +d.variableC;
d.Temp = +d.Temp;
});
var chart = makeLineChart(data1, 'year', {
'Device 1': { column: 'variableA' },
'Device 2': { column: 'variableB' },
'Device 3': { column: 'variableC' },
'Device 4': { column: 'variableD' }
}, { xAxis: 'Years', yAxis: 'Temperature' });
chart.bind("#chart-line1");
chart.render();
在您的第二个图表上再次不需要呼叫d3.csv。但是，从CSV格式到JSON发生了一些处理。您需要在创建JSON和图表的其余部分中复制它，您需要以下内容：
...
var z = d3.scaleOrdinal()
.range(["#98abc5", "#8a89a6", "#7b6888", "#6b486b", "#a05d56", "#d0743c", "#ff8c00"]);
var data = [{"State":"CA","Under 5 Years":2704659,"5 to 13 Years":4499890,"14 to 17 Years":2159981,"18 to 24 Years":3853788,"25 to 44 Years":10604510,"45 to 64 Years":8819342,"65 Years and Over":4114496},{"State":"TX","Under 5 Years":2027307,"5 to 13 Years":3277946,"14 to 17 Years":1420518,"18 to 24 Years":2454721,"25 to 44 Years":7017731,"45 to 64 Years":5656528,"65 Years and Over":2472223}];
var keys = ["Under 5 Years", "5 to 13 Years", "14 to 17 Years", "18 to 24 Years", "25 to 44 Years", "45 to 64 Years", "65 Years and Over"]
x0.domain(data.map(function(d) { return d.State; }));
x1.domain(keys).rangeRound([0, x0.bandwidth()]);
...
这是运行的代码没有呼叫d3.csv：
<!DOCTYPE html>
<style>
.axis .domain {
display: none;
}
</style>
<svg width="960" height="500"></svg>
<script src="https://d3js.org/d3.v4.min.js"></script>
<script>
var svg = d3.select("svg"),
margin = {top: 20, right: 20, bottom: 30, left: 40},
width = +svg.attr("width") - margin.left - margin.right,
height = +svg.attr("height") - margin.top - margin.bottom,
g = svg.append("g").attr("transform", "translate(" + margin.left + "," + margin.top + ")");
var x0 = d3.scaleBand()
.rangeRound([0, width])
.paddingInner(0.1);
var x1 = d3.scaleBand()
.padding(0.05);
var y = d3.scaleLinear()
.rangeRound([height, 0]);
var z = d3.scaleOrdinal()
.range(["#98abc5", "#8a89a6", "#7b6888", "#6b486b", "#a05d56", "#d0743c", "#ff8c00"]);
var data = [{"State":"CA","Under 5 Years":2704659,"5 to 13 Years":4499890,"14 to 17 Years":2159981,"18 to 24 Years":3853788,"25 to 44 Years":10604510,"45 to 64 Years":8819342,"65 Years and Over":4114496},{"State":"TX","Under 5 Years":2027307,"5 to 13 Years":3277946,"14 to 17 Years":1420518,"18 to 24 Years":2454721,"25 to 44 Years":7017731,"45 to 64 Years":5656528,"65 Years and Over":2472223},{"State":"NY","Under 5 Years":1208495,"5 to 13 Years":2141490,"14 to 17 Years":1058031,"18 to 24 Years":1999120,"25 to 44 Years":5355235,"45 to 64 Years":5120254,"65 Years and Over":2607672},{"State":"FL","Under 5 Years":1140516,"5 to 13 Years":1938695,"14 to 17 Years":925060,"18 to 24 Years":1607297,"25 to 44 Years":4782119,"45 to 64 Years":4746856,"65 Years and Over":3187797},{"State":"IL","Under 5 Years":894368,"5 to 13 Years":1558919,"14 to 17 Years":725973,"18 to 24 Years":1311479,"25 to 44 Years":3596343,"45 to 64 Years":3239173,"65 Years and Over":1575308},{"State":"PA","Under 5 Years":737462,"5 to 13 Years":1345341,"14 to 17 Years":679201,"18 to 24 Years":1203944,"25 to 44 Years":3157759,"45 to 64 Years":3414001,"65 Years and Over":1910571}];
var keys = ["Under 5 Years", "5 to 13 Years", "14 to 17 Years", "18 to 24 Years", "25 to 44 Years", "45 to 64 Years", "65 Years and Over"];
x0.domain(data.map(function(d) { return d.State; }));
x1.domain(keys).rangeRound([0, x0.bandwidth()]);
y.domain([0, d3.max(data, function(d) { return d3.max(keys, function(key) { return d[key]; }); })]).nice();
g.append("g")
.selectAll("g")
.data(data)
.enter().append("g")
.attr("transform", function(d) { return "translate(" + x0(d.State) + ",0)"; })
.selectAll("rect")
.data(function(d) { return keys.map(function(key) { return {key: key, value: d[key]}; }); })
.enter().append("rect")
.attr("x", function(d) { return x1(d.key); })
.attr("y", function(d) { return y(d.value); })
.attr("width", x1.bandwidth())
.attr("height", function(d) { return height - y(d.value); })
.attr("fill", function(d) { return z(d.key); });
g.append("g")
.attr("class", "axis")
.attr("transform", "translate(0," + height + ")")
.call(d3.axisBottom(x0));
g.append("g")
.attr("class", "axis")
.call(d3.axisLeft(y).ticks(null, "s"))
.append("text")
.attr("x", 2)
.attr("y", y(y.ticks().pop()) + 0.5)
.attr("dy", "0.32em")
.attr("fill", "#000")
.attr("font-weight", "bold")
.attr("text-anchor", "start")
.text("Population");
var legend = g.append("g")
.attr("font-family", "sans-serif")
.attr("font-size", 10)
.attr("text-anchor", "end")
.selectAll("g")
.data(keys.slice().reverse())
.enter().append("g")
.attr("transform", function(d, i) { return "translate(0," + i * 20 + ")"; });
legend.append("rect")
.attr("x", width - 19)
.attr("width", 19)
.attr("height", 19)
.attr("fill", z);
legend.append("text")
.attr("x", width - 24)
.attr("y", 9.5)
.attr("dy", "0.32em")
.text(function(d) { return d; });
</script>