【发布时间】：2019-08-23 15:17:09
【问题描述】：
我有一个格式如下的数据表：
Month,KPI,Type,Unit,S40401,S40402,S40403
JAN,A,Units FTP,PC,2000,4000,6000
JAN,B,Invoice Sales FTP,EUR,2000,4000,6000
JAN,C,Gross Sales Actual FTP,EUR,2000,4000,6000
JAN,D,Net Sales FTP,EUR,2000,4000,6000
JAN,E,CMC FTP,EUR,2000,4000,6000
FEB,A,Units FTP,PC,2000,4000,6000
FEB,B,Invoice Sales FTP,EUR,2000,4000,6000
FEB,C,Gross Sales Actual FTP,EUR,2000,4000,6000
FEB,D,Net Sales FTP,EUR,2000,4000,6000
FEB,E,CMC FTP,EUR,2000,4000,6000
...
...
...
如果该数据仅包含一个变量/KPI（例如 A-E 中的一个），则将数据解析为 D3 图表完全没有问题。但由于我想选择特定的行，它变得复杂。我无法调整 CSV 文件，数据必须采用这种结构。所以我唯一的解决方案是按行过滤。
如何使用 .filter() 过滤数据以解析特定行？ 例如：以仅来自“A-rows”或“B-rows”的方式过滤数据 选择了“KPI”列？
我想导入 CSV 的这部分代码需要过滤功能。我已经尝试过但没有成功：
<script>
var freqData;
d3.csv("export.csv", function(data) {
data = csv.filter(function(row) {
return row['KPI'] == 'C';
freqData = data.map(function(d) { return {
Month: d.Month,
freq: {
S40401: +d.S40401,
S40402: +d.S40402,
S40403: +d.S40403
}}
});
dashboard('#dashboard',freqData);
});
</script>
在整个代码下面：
<script>
function dashboard(id, fData){
var barColor = 'steelblue';
function segColor(c){ return {S40401:"#04B404",S40402:"#045FB4",S40403:"#B40404"}[c]; }
// compute total for each state.
fData.forEach(function(d){d.total=d.freq.S40401+d.freq.S40402+d.freq.S40403;});
// function to handle histogram.
function histoGram(fD){
var hG={}, hGDim = {t: 60, r: 0, b: 30, l: 0};
hGDim.w = 500 - hGDim.l - hGDim.r,
hGDim.h = 300 - hGDim.t - hGDim.b;
//create svg for histogram.
var hGsvg = d3.select(id).append("svg")
.attr("width", hGDim.w + hGDim.l + hGDim.r)
.attr("height", hGDim.h + hGDim.t + hGDim.b).append("g")
.attr("transform", "translate(" + hGDim.l + "," + hGDim.t + ")");
// create function for x-axis mapping.
var x = d3.scale.ordinal().rangeRoundBands([0, hGDim.w], 0.1)
.domain(fD.map(function(d) { return d[0]; }));
// Add x-axis to the histogram svg.
hGsvg.append("g").attr("class", "x axis")
.attr("transform", "translate(0," + hGDim.h + ")")
.call(d3.svg.axis().scale(x).orient("bottom"));
// Create function for y-axis map.
var y = d3.scale.linear().range([hGDim.h, 0])
.domain([0, d3.max(fD, function(d) { return d[1]; })]);
// Create bars for histogram to contain rectangles and freq labels.
var bars = hGsvg.selectAll(".bar").data(fD).enter()
.append("g").attr("class", "bar");
//create the rectangles.
bars.append("rect")
.attr("x", function(d) { return x(d[0]); })
.attr("y", function(d) { return y(d[1]); })
.attr("width", x.rangeBand())
.attr("height", function(d) { return hGDim.h - y(d[1]); })
.attr('fill',barColor)
.on("mouseover",mouseover)// mouseover is defined below.
.on("mouseout",mouseout);// mouseout is defined below.
//Create the frequency labels above the rectangles.
bars.append("text").text(function(d){ return d3.format(".3f")(d[1])})
.attr("x", function(d) { return x(d[0])+x.rangeBand()/2; })
.attr("y", function(d) { return y(d[1])-5; })
.attr("text-anchor", "middle");
function mouseover(d){ // utility function to be called on mouseover.
// filter for selected state.
var st = fData.filter(function(s){ return s.Month == d[0];})[0],
nD = d3.keys(st.freq).map(function(s){ return {type:s, freq:st.freq[s]};});
// call update functions of pie-chart and legend.
pC.update(nD);
leg.update(nD);
}
function mouseout(d){ // utility function to be called on mouseout.
// reset the pie-chart and legend.
pC.update(tF);
leg.update(tF);
}
// create function to update the bars. This will be used by pie-chart.
hG.update = function(nD, color){
// update the domain of the y-axis map to reflect change in frequencies.
y.domain([0, d3.max(nD, function(d) { return d[1]; })]);
// Attach the new data to the bars.
var bars = hGsvg.selectAll(".bar").data(nD);
// transition the height and color of rectangles.
bars.select("rect").transition().duration(500)
.attr("y", function(d) {return y(d[1]); })
.attr("height", function(d) { return hGDim.h - y(d[1]); })
.attr("fill", color);
// transition the frequency labels location and change value.
bars.select("text").transition().duration(500)
.text(function(d){ return d3.format(".3f")(d[1])})
.attr("y", function(d) {return y(d[1])-5; });
}
return hG;
}
// function to handle pieChart.
function pieChart(pD){
var pC ={}, pieDim ={w:250, h: 250};
pieDim.r = Math.min(pieDim.w, pieDim.h) / 2;
// create svg for pie chart.
var piesvg = d3.select(id).append("svg")
.attr("width", pieDim.w).attr("height", pieDim.h).append("g")
.attr("transform", "translate("+pieDim.w/2+","+pieDim.h/2+")");
// create function to draw the arcs of the pie slices.
var arc = d3.svg.arc().outerRadius(pieDim.r - 10).innerRadius(0);
// create a function to compute the pie slice angles.
var pie = d3.layout.pie().sort(null).value(function(d) { return d.freq; });
// Draw the pie slices.
piesvg.selectAll("path").data(pie(pD)).enter().append("path").attr("d", arc)
.each(function(d) { this._current = d; })
.style("fill", function(d) { return segColor(d.data.type); })
.on("mouseover",mouseover).on("mouseout",mouseout);
// create function to update pie-chart. This will be used by histogram.
pC.update = function(nD){
piesvg.selectAll("path").data(pie(nD)).transition().duration(500)
.attrTween("d", arcTween);
}
// Utility function to be called on mouseover a pie slice.
function mouseover(d){
// call the update function of histogram with new data.
hG.update(fData.map(function(v){
return [v.Month,v.freq[d.data.type]];}),segColor(d.data.type));
}
//Utility function to be called on mouseout a pie slice.
function mouseout(d){
// call the update function of histogram with all data.
hG.update(fData.map(function(v){
return [v.Month,v.total];}), barColor);
}
// Animating the pie-slice requiring a custom function which specifies
// how the intermediate paths should be drawn.
function arcTween(a) {
var i = d3.interpolate(this._current, a);
this._current = i(0);
return function(t) { return arc(i(t)); };
}
return pC;
}
// function to handle legend.
function legend(lD){
var leg = {};
// create table for legend.
var legend = d3.select(id).append("table").attr('class','legend');
// create one row per segment.
var tr = legend.append("tbody").selectAll("tr").data(lD).enter().append("tr");
// create the first column for each segment.
tr.append("td").append("svg").attr("width", '16').attr("height", '16').append("rect")
.attr("width", '16').attr("height", '16')
.attr("fill",function(d){ return segColor(d.type); });
// create the second column for each segment.
tr.append("td").text(function(d){ return d.type;});
// create the third column for each segment.
tr.append("td").attr("class",'legendFreq')
.text(function(d){ return d3.format(".3f")(d.freq);});
// create the fourth column for each segment.
tr.append("td").attr("class",'legendPerc')
.text(function(d){ return getLegend(d,lD);});
// Utility function to be used to update the legend.
leg.update = function(nD){
// update the data attached to the row elements.
var l = legend.select("tbody").selectAll("tr").data(nD);
// update the frequencies.
l.select(".legendFreq").text(function(d){ return d3.format(".3f")(d.freq);});
// update the percentage column.
l.select(".legendPerc").text(function(d){ return getLegend(d,nD);});
}
function getLegend(d,aD){ // Utility function to compute percentage.
return d3.format("%")(d.freq/d3.sum(aD.map(function(v){ return v.freq; })));
}
return leg;
}
// calculate total frequency by segment for all state.
var tF = ['S40401','S40402','S40403'].map(function(d){
return {type:d, freq: d3.sum(fData.map(function(t){ return t.freq[d];}))};
});
// calculate total frequency by state for all segment.
var sF = fData.map(function(d){return [d.Month,d.total];});
var hG = histoGram(sF), // create the histogram.
pC = pieChart(tF), // create the pie-chart.
leg= legend(tF); // create the legend.
}
</script>
<script>
var freqData;
d3.csv("dataset_sales.csv", function(data) {
freqData = data.map(function(d) { return {
Month: d.Month,
freq: {
S40401: +d.S40401,
S40402: +d.S40402,
S40403: +d.S40403
}}
});
dashboard('#dashboard',freqData);
});
</script>
【问题讨论】：
标签： javascript csv parsing d3.js filter