2017-04-27 39 views
1

我一直在寻找一段关于如何处理堆叠条形图中的X轴(因为数据集与单个条形图有点不同)。堆积条形图24小时@ 15分钟数据,只显示2小时刻度

基本上,我有15分钟的间隔24小时的数据。但是,我只想在2小时内显示x轴。

现有小提琴:[https://jsfiddle.net/lucksp/crwb4v5u/][1]

目前,它打印所有的时间间隔。

我已经尝试过time的各种比例选项,但有些东西没有用我设置的方式翻译。

var xScale = d3.scale.ordinal() 
    .domain(dataset[0].map(function(d) { 
    return d.x; 
    })) 
    .rangeRoundBands([0, width - margin.left]); 

var xAxis = d3.svg.axis() 
    .scale(xScale) 
    .orient('bottom') 
    .tickSize(0) 
    .ticks(12) 
    .tickFormat(function(d) { 
    return d; 
    }); 
var rect = groups.selectAll('rect') 
    .data(function(d) { 
    return d; 
    }) 
    .enter() 
    .append('rect') 
    .attr('class', function(d, i) { 
    return 'stacks ' + d.type; 
    }) 
    .classed('stacks', true) 
    .attr('id', function(d, i) { 
    return d.type + '_' + i; 
    }) 
    .attr('x', function(d) { 
    return xScale(d.x); 
    }) 
    .attr('y', function(d) { 
    return yScale(d.y0 + d.y); 
    }) 
    .attr('height', function(d) { 
    return yScale(d.y0) - yScale(d.y0 + d.y); 
    }) 
    .attr('width', xScale.rangeBand()); 

    [1]: https://jsfiddle.net/lucksp/crwb4v5u/ 

我知道这是用户错误,但在过去两天看了这个之后,我现在就诉诸于这个问题。谢谢!

+0

我认为这个问题的一部分是D3不知道“小时”,在你的数据是时间。您可以让d3将该值解析为日期对象,然后让d3处理xAxis滴答声。 [这是一个例子](https://bl.ocks.org/mbostock/1166403)。注意使用parse和d3.time.scale() –

+0

@KarlGalvez我确实在尝试使用time.scale和parse方法,但无法使其工作。我会再试一次。 –

回答

0

您目前正在尝试使用.ticks,只有当您使用的刻度具有内置滴答功能时才能使用。在这种情况下,您的序数量级不会。它将默认使用域中的所有值。

要绕过它,我们可以使用xAxis.tickValues(["custom tick values that match domain vals"])手动设置滴答。检查下面的代码片段。

var data = [{"hour":"0:00","inProgress":3,"inQueue":0},{"hour":"0:15","inProgress":5,"inQueue":3},{"hour":"0:30","inProgress":1,"inQueue":1},{"hour":"0:45","inProgress":1,"inQueue":0},{"hour":"1:00","inProgress":2,"inQueue":0},{"hour":"1:15","inProgress":8,"inQueue":2},{"hour":"1:30","inProgress":5,"inQueue":3},{"hour":"1:45","inProgress":5,"inQueue":1},{"hour":"2:00","inProgress":6,"inQueue":0},{"hour":"2:15","inProgress":6,"inQueue":0},{"hour":"2:30","inProgress":7,"inQueue":0},{"hour":"2:45","inProgress":7,"inQueue":0},{"hour":"3:00","inProgress":8,"inQueue":0},{"hour":"3:15","inProgress":8,"inQueue":0},{"hour":"3:30","inProgress":9,"inQueue":1},{"hour":"3:45","inProgress":9,"inQueue":4},{"hour":"4:00","inProgress":10,"inQueue":6},{"hour":"4:15","inProgress":10,"inQueue":2},{"hour":"4:30","inProgress":10,"inQueue":1},{"hour":"4:45","inProgress":11,"inQueue":0},{"hour":"5:00","inProgress":11,"inQueue":0},{"hour":"5:15","inProgress":12,"inQueue":0},{"hour":"5:30","inProgress":12,"inQueue":0},{"hour":"5:45","inProgress":13,"inQueue":0},{"hour":"6:00","inProgress":13,"inQueue":0},{"hour":"6:15","inProgress":14,"inQueue":0},{"hour":"6:30","inProgress":14,"inQueue":0},{"hour":"6:45","inProgress":15,"inQueue":0},{"hour":"7:00","inProgress":15,"inQueue":3},{"hour":"7:15","inProgress":15,"inQueue":1},{"hour":"7:30","inProgress":16,"inQueue":0},{"hour":"7:45","inProgress":16,"inQueue":0},{"hour":"8:00","inProgress":17,"inQueue":2},{"hour":"8:15","inProgress":17,"inQueue":3},{"hour":"8:30","inProgress":18,"inQueue":1},{"hour":"8:45","inProgress":18,"inQueue":0},{"hour":"9:00","inProgress":19,"inQueue":0},{"hour":"9:15","inProgress":19,"inQueue":0},{"hour":"9:30","inProgress":20,"inQueue":0},{"hour":"9:45","inProgress":20,"inQueue":0},{"hour":"10:00","inProgress":20,"inQueue":0},{"hour":"10:15","inProgress":21,"inQueue":1},{"hour":"10:30","inProgress":21,"inQueue":4},{"hour":"10:45","inProgress":22,"inQueue":6},{"hour":"11:00","inProgress":22,"inQueue":2},{"hour":"11:15","inProgress":23,"inQueue":1},{"hour":"11:30","inProgress":23,"inQueue":0},{"hour":"11:45","inProgress":3,"inQueue":0},{"hour":"12:00","inProgress":5,"inQueue":0},{"hour":"12:15","inProgress":1,"inQueue":0},{"hour":"12:30","inProgress":1,"inQueue":0},{"hour":"12:45","inProgress":2,"inQueue":0},{"hour":"13:00","inProgress":8,"inQueue":0},{"hour":"13:15","inProgress":5,"inQueue":0},{"hour":"13:30","inProgress":5,"inQueue":0},{"hour":"13:45","inProgress":6,"inQueue":3},{"hour":"14:00","inProgress":6,"inQueue":1},{"hour":"14:15","inProgress":7,"inQueue":0},{"hour":"14:30","inProgress":7,"inQueue":0},{"hour":"14:45","inProgress":8,"inQueue":2},{"hour":"15:00","inProgress":8,"inQueue":3},{"hour":"15:15","inProgress":9,"inQueue":1},{"hour":"15:30","inProgress":9,"inQueue":0},{"hour":"15:45","inProgress":10,"inQueue":0},{"hour":"16:00","inProgress":10,"inQueue":0},{"hour":"16:15","inProgress":10,"inQueue":0},{"hour":"16:30","inProgress":11,"inQueue":0},{"hour":"16:45","inProgress":11,"inQueue":0},{"hour":"17:00","inProgress":12,"inQueue":1},{"hour":"17:15","inProgress":12,"inQueue":4},{"hour":"17:30","inProgress":13,"inQueue":6},{"hour":"17:45","inProgress":13,"inQueue":2},{"hour":"18:00","inProgress":14,"inQueue":1},{"hour":"18:15","inProgress":14,"inQueue":0},{"hour":"18:30","inProgress":15,"inQueue":0},{"hour":"18:45","inProgress":15,"inQueue":0},{"hour":"19:00","inProgress":15,"inQueue":0},{"hour":"19:15","inProgress":16,"inQueue":0},{"hour":"19:30","inProgress":16,"inQueue":0},{"hour":"19:45","inProgress":17,"inQueue":0},{"hour":"20:00","inProgress":17,"inQueue":0},{"hour":"20:15","inProgress":18,"inQueue":0},{"hour":"20:30","inProgress":18,"inQueue":3},{"hour":"20:45","inProgress":19,"inQueue":1},{"hour":"21:00","inProgress":19,"inQueue":0},{"hour":"21:15","inProgress":20,"inQueue":0},{"hour":"21:30","inProgress":20,"inQueue":2},{"hour":"21:45","inProgress":20,"inQueue":3},{"hour":"22:00","inProgress":21,"inQueue":1},{"hour":"22:15","inProgress":21,"inQueue":0},{"hour":"22:30","inProgress":22,"inQueue":0},{"hour":"22:45","inProgress":22,"inQueue":0},{"hour":"23:00","inProgress":23,"inQueue":0},{"hour":"23:15","inProgress":23,"inQueue":0},{"hour":"23:30","inProgress":1,"inQueue":0},{"hour":"23:45","inProgress":2,"inQueue":1}]; 
 

 
var margin = {top: 20, right: 50, bottom: 30, left: 20}, 
 
    width = 500, 
 
    height = 300; 
 

 
// Transpose the data into layers 
 
var dataset = d3.layout.stack()(['inProgress', 'inQueue'].map(function(types) { 
 
    return data.map(function(d) { 
 
    return { 
 
     x: d.hour, 
 
     y: +d[types], 
 
     type: types 
 
    }; 
 
    }); 
 
})); 
 

 
var svg = d3.select('svg'), 
 
    margin = {top: 40, right: 10, bottom: 20, left: 10}, 
 
    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 + ")"); 
 

 
// Set x, y and colors 
 
var xScale = d3.scale.ordinal() 
 
    .domain(dataset[0].map(function(d) { 
 
    return d.x; 
 
    })) 
 
    .rangeRoundBands([0, width - margin.left]); 
 

 
var yScale = d3.scale.linear() 
 
    .domain([0, d3.max(dataset, function(d) { 
 
    return d3.max(d, function(d) { 
 
     return d.y0 + d.y; 
 
    }); 
 
    })]) 
 
    .range([height, 0]); 
 

 
var colors = ['#56a8f8', '#c34434']; 
 

 
// Define and draw axes 
 
var yAxis = d3.svg.axis() 
 
    .scale(yScale) 
 
    .orient('left') 
 
    .ticks(5) 
 
    .tickSize(0) 
 
    .tickFormat(function(d) { 
 
    return d; 
 
    }); 
 

 
var xAxis = d3.svg.axis() 
 
    .scale(xScale) 
 
    .orient('bottom') 
 
    .tickSize(0) 
 
    .ticks(12) // this 
 
    .tickFormat(function(d) { 
 
    return d; // and this will not work with an ordinal scale 
 
    }); 
 
    
 
    
 
xAxis.tickValues(["0:00", "2:00", "4:00", "6:00", "8:00", "10:00", "12:00", "14:00", "16:00", "18:00", "20:00", "22:00"]); 
 

 
svg.append('g') 
 
    .attr('class', 'y axis') 
 
    .call(yAxis); 
 

 
svg.append('g') 
 
    .attr('class', 'x axis') 
 
    .attr('transform', 'translate(0,' + height + ')') 
 
    .call(xAxis); 
 

 
// Create groups for each series, rects for each segment 
 
var groups = svg.selectAll('g.bar-stacks') 
 
    .data(dataset) 
 
    .enter().append('g') 
 
    .attr('class', function(d, i) { 
 
    return 'bar-stacks ' + d[i].type; 
 
    }) 
 
    .classed('bar-stacks', true) 
 
    .style('fill', function(d, i) { 
 
    return colors[i]; 
 
    }); 
 

 
var rect = groups.selectAll('rect') 
 
    .data(function(d) { 
 
    return d; 
 
    }) 
 
    .enter() 
 
    .append('rect') 
 
    .attr('class', function(d, i) { 
 
    return 'stacks ' + d.type; 
 
    }) 
 
    .classed('stacks', true) 
 
    .attr('id', function(d, i) { 
 
    return d.type + '_' + i; 
 
    }) 
 
    .attr('x', function(d) { 
 
    return xScale(d.x); 
 
    }) 
 
    .attr('y', function(d) { 
 
    return yScale(d.y0 + d.y); 
 
    }) 
 
    .attr('height', function(d) { 
 
    return yScale(d.y0) - yScale(d.y0 + d.y); 
 
    }) 
 
    .attr('width', xScale.rangeBand());
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.4.11/d3.min.js"></script> 
 
<div> 
 
<svg width="600" height="300"></svg> 
 
</div>

+0

非常感谢!出于好奇,是否有更好的方式来重构X和Y值的生成方式,或者这是否正确? –

+0

@PhilipL是的,我认为根据您的数据,时间尺度会更适合您的X轴,但这是一个不同的问题。 – sparta93