我正在尝试使用Google可视化工具创建一个仪表板，我遇到了一些问题。
我通过在ChartWrapper中设置calc属性来引入所有文本和动态计算图表数据的数据，当数据表准备就绪时，我调用一个函数来设置图表的视图。完整的代码如下。
我遇到的问题是：
非常感谢任何帮助。如果您对处理此类数据的更好方法有任何建议，我会全力以赴。
我创造了一个小提琴：https://jsfiddle.net/kq8fcdee/1/
完整代码：
<!DOCTYPE html>
<html>
<head>
<title></title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<script type="text/javascript" src="https://www.gstatic.com/charts/loader.js"></script>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js"></script>
<style>
#table_div{display:none;}
.chart_div{display:inline-block;border:1px solid #888;margin-right:10px;}
</style>
</head>
<body>
<!--Div that will hold the dashboard-->
<div id="dashboard_div">
<!--Divs that will hold each control and chart-->
<div id="filter_div"></div>
<div id="table_div"></div>
</div>
<script>
var data=
[
["Gender","Last time","Origin\/transfer","How arrived","Reason for flight","Section","Shop concessions","Purchase merchandise","Purchase services","Connect to WIFI","Overall experience","Country","Age group","Zip code","Education","Employment Status","Employment Status Group","Household income","Hispanic","Race","2. Children"],
["1. Male","3. 1-2 years","1. Starting","8. Other","3. Other","1. First\/Business","1. Yes","2. No","1. Yes","2. No","4. Very good","2. Other","5. 45-54",75201,"5. College degree","4. Student","2. Not Employed","4. 100k - 150k","1. Yes","4. Native American \/ American Indian","1. No children"],
["2. Female","2. 6-12 months","2. Connection","1. Drove self","1. Business","1. First\/Business","2. No","1. Yes","1. Yes","1. Yes","4. Very good","1. USA","3. 26-34",32003,"4. Some college","2. Part-time","1. Employed","2. 40k - 75k","1. Yes","2. Black \/ African American","1. No children"],
["1. Male","2. 6-12 months","2. Connection","5.R ental","2. Leisure","2. Economy\/Coach","1. Yes","2. No","1. Yes","2. No","4. Very good","1. USA","5. 45-54",64101,"4. Some college","5. Homemaker","2. Not Employed","5. 150k+","1. Yes","5. Other","1. No children"],
["2. Female","2. 6-12 months","2. Connection","1. Drove self","2. Leisure","1. First\/Business","2. No","2. No","1. Yes","2. No","4. Very good","1. USA","5. 45-54",75201,"2. Some HS","4. Student","2. Not Employed","4. 100k - 150k","2. No","4. Native American \/ American Indian","Children"],
["2. Female","2. 6-12 months","2. Connection","5.R ental","2. Leisure","2. Economy\/Coach","1. Yes","2. No","1. Yes","2. No","4. Very good","1. USA","2. 22-25",84101,"4. Some college","4. Student","2. Not Employed","4. 100k - 150k","1. Yes","4. Native American \/ American Indian","1. No children"],
["2. Female","5. 3+ years","1. Starting","4. Bus\/shuttle","2. Leisure","1. First\/Business","2. No","2. No","2. No","1. Yes","4. Very good","2. Other","3. 26-34",32003,"5. College degree","6. Unemployed not looking","2. Not Employed","6. Refused","2. No",null,"1. No children"],
["2. Female","4. 2-3 years","1. Starting","8. Other","3. Other","2. Economy\/Coach","2. No","2. No","2. No","2. No","1. Poor","2. Other","6. 55-64",99501,"4. Some college","3. Retired","2. Not Employed","3. 75k - 100k","2. No","3. Asian","1. No children"],
["2. Female","5. 3+ years","1. Starting","5.R ental","3. Other","1. First\/Business","2. No","1. Yes","2. No","1. Yes","1. Poor","1. USA","1. 16-21",15668,"4. Some college","1. Full-time","1. Employed","1. < 40k","2. No","1. White \/ Caucasian","1. No children"]
];
var proc = function(){
//get the data from the server (php that gets data from mysql and reruns json array
//$.getJSON('getdata.php',function(data) {
//load the library
google.charts.load('current', {'packages':['corechart', 'controls']});
//function that is called when libary loaded
google.charts.setOnLoadCallback(function() {
nCols=data[0].length;
charts=[];
data = google.visualization.arrayToDataTable(data);
dashboard = new google.visualization.Dashboard(document.getElementById('dashboard_div'));
//create Gender filter
var genderSelection = new google.visualization.ControlWrapper({
'controlType': 'CategoryFilter',
'containerId': 'filter_div',
'options': {
'filterColumnLabel': 'Gender',
}
});
//create the table
var table = new google.visualization.ChartWrapper({
'chartType': 'Table',
'dataTable':data,
'containerId': 'table_div',
'options': {'width': '800px'}
});
//create the charts (one chart for each column in the table)
for(qidx=0;qidx < nCols;qidx++) {
var createChart = function(qidx) {
//add place for chart
$('#dashboard_div').append('<div class="chart_div" id="chart'+qidx+'_div"></div>');
//create chart
charts[qidx] = new google.visualization.ChartWrapper({
'chartType': 'ColumnChart',
'containerId': 'chart'+qidx+'_div',
'options': {
'title': data.getColumnLabel(qidx),
'xwidth': 400,
'xheight': 300,
'vAxis': {
'minValue': 0,
'maxValue': 100
},
'chartArea': {
'width': '80%',
'height': '60%',
'top': 30,
},
'legend':'bottom'
},
'view': {
'columns': [qidx, {calc:function(dataTable, rowNum) {
var curr_stat = dataTable.getValue(rowNum,qidx);
var distinct_values = dataTable.getDistinctValues(qidx);
var count = 0;
var numRows=dataTable.getNumberOfRows();
for (var ii=0; ii<numRows; ii++) {
stat = dataTable.getValue(ii,qidx);
if (stat == curr_stat) {
count++;
}
}
return count/numRows*100; //return percentage
}, type:'number', label: '1Q'}]
}
});
}(qidx);
}
//set the rows of the chart
function setChartRows () {
//loop though the charts
for(qidx=0;qidx < nCols;qidx++) {
var dt = charts[qidx].getDataTable();
var rows = dt.getNumberOfRows();
var distinct_values = dt.getDistinctValues(qidx);
var arRows = [];
for (var ii=0; ii<distinct_values.length; ii++) {
for (var jj=0; jj<rows; jj++) {
var curr_stat = dt.getValue(jj,qidx);
if (curr_stat == distinct_values[ii]) {
arRows.push(jj);
break;
}
}
}
var view = charts[qidx].getView() || {};
view.rows = arRows;
charts[qidx].setView(view);
charts[qidx].draw();
}
}
//when the table is created/changed update the chart
google.visualization.events.addListener(table, 'ready', setChartRows);
charts.push(table);
dashboard.bind(genderSelection, charts);
dashboard.draw(data);
});
//});
};
//run
proc();
//refresh every 5 minutes
var run = setInterval(proc,300000)
</script>
</body>
</html>
答案 0 :(得分：0)
1＆amp; 2）通过从charts
dashboard，可以纠正这些问题
当charts事件在'ready'上触发时，table可以独立绘制
使用table图表
这将防止错误和首次加载时绘制初始原始数据
3）自定义标签排序，在数据数组{}
数据表中的每个单元格必须具有值（v:），
并且可选地，可以具有格式化的值（f:）
在数据数组中，而不是使用 - ＆gt; "1. Male"
使用对象表示法 - ＆gt; {v: "1", f: "Male"}
note ：图表默认显示格式化值
但是，必须在CategoryFilter
设置选项 - ＆gt; useFormattedValue: true - 在控件中显示格式化值
设置选项 - ＆gt; ui.sortValues: false - 显示数据中的类别
请参阅以下工作代码段
数据中的前两列已更新为使用上述对象表示法...
//load the library
google.charts.load('current', {
callback: function () {
var data = [
["Gender","Last time","Origin\/transfer","How arrived","Reason for flight","Section","Shop concessions","Purchase merchandise","Purchase services","Connect to WIFI","Overall experience","Country","Age group","Zip code","Education","Employment Status","Employment Status Group","Household income","Hispanic","Race","2. Children"],
[{v: "1", f: "Male"},{v: "3", f: "1-2 years"},"1. Starting","8. Other","3. Other","1. First\/Business","1. Yes","2. No","1. Yes","2. No","4. Very good","2. Other","5. 45-54",75201,"5. College degree","4. Student","2. Not Employed","4. 100k - 150k","1. Yes","4. Native American \/ American Indian","1. No children"],
[{v: "2", f: "Female"},{v: "2", f: "6-12 months"},"2. Connection","1. Drove self","1. Business","1. First\/Business","2. No","1. Yes","1. Yes","1. Yes","4. Very good","1. USA","3. 26-34",32003,"4. Some college","2. Part-time","1. Employed","2. 40k - 75k","1. Yes","2. Black \/ African American","1. No children"],
[{v: "1", f: "Male"},{v: "2", f: "6-12 months"},"2. Connection","5.R ental","2. Leisure","2. Economy\/Coach","1. Yes","2. No","1. Yes","2. No","4. Very good","1. USA","5. 45-54",64101,"4. Some college","5. Homemaker","2. Not Employed","5. 150k+","1. Yes","5. Other","1. No children"],
[{v: "2", f: "Female"},{v: "2", f: "6-12 months"},"2. Connection","1. Drove self","2. Leisure","1. First\/Business","2. No","2. No","1. Yes","2. No","4. Very good","1. USA","5. 45-54",75201,"2. Some HS","4. Student","2. Not Employed","4. 100k - 150k","2. No","4. Native American \/ American Indian","Children"],
[{v: "2", f: "Female"},{v: "2", f: "6-12 months"},"2. Connection","5.R ental","2. Leisure","2. Economy\/Coach","1. Yes","2. No","1. Yes","2. No","4. Very good","1. USA","2. 22-25",84101,"4. Some college","4. Student","2. Not Employed","4. 100k - 150k","1. Yes","4. Native American \/ American Indian","1. No children"],
[{v: "2", f: "Female"},{v: "5", f: "3+ years"},"1. Starting","4. Bus\/shuttle","2. Leisure","1. First\/Business","2. No","2. No","2. No","1. Yes","4. Very good","2. Other","3. 26-34",32003,"5. College degree","6. Unemployed not looking","2. Not Employed","6. Refused","2. No",null,"1. No children"],
[{v: "2", f: "Female"},{v: "4", f: "2-3 years"},"1. Starting","8. Other","3. Other","2. Economy\/Coach","2. No","2. No","2. No","2. No","1. Poor","2. Other","6. 55-64",99501,"4. Some college","3. Retired","2. Not Employed","3. 75k - 100k","2. No","3. Asian","1. No children"],
[{v: "2", f: "Female"},{v: "5", f: "3+ years"},"1. Starting","5.R ental","3. Other","1. First\/Business","2. No","1. Yes","2. No","1. Yes","1. Poor","1. USA","1. 16-21",15668,"4. Some college","1. Full-time","1. Employed","1. < 40k","2. No","1. White \/ Caucasian","1. No children"]
];
nCols=data[0].length;
charts=[];
data = google.visualization.arrayToDataTable(data);
dashboard = new google.visualization.Dashboard(document.getElementById('dashboard_div'));
//create Gender filter
var genderSelection = new google.visualization.ControlWrapper({
'controlType': 'CategoryFilter',
'containerId': 'filter_div',
'options': {
'filterColumnLabel': 'Gender',
'useFormattedValue': true,
'ui': {
'sortValues': false
}
}
});
//create the table
var table = new google.visualization.ChartWrapper({
'chartType': 'Table',
'dataTable': data,
'containerId': 'table_div',
'options': {'width': '800px'}
});
//create the charts
for(qidx=0;qidx < nCols;qidx++) {
var createChart = function(qidx) {
//add place for chart
$('#dashboard_div').append('<div class="chart_div" id="chart'+qidx+'_div"></div>');
//create chart
charts[qidx] = new google.visualization.ChartWrapper({
'chartType': 'ColumnChart',
'containerId': 'chart'+qidx+'_div',
'options': {
'title': data.getColumnLabel(qidx),
'xwidth': 400,
'xheight': 300,
'vAxis': {
'minValue': 0,
'maxValue': 100
},
'chartArea': {
'width': '80%',
'height': '60%',
'top': 30,
},
'legend':'bottom'
},
'view': {
'columns': [qidx, {calc:function(dataTable, rowNum) {
var curr_stat = dataTable.getValue(rowNum,qidx);
var distinct_values = dataTable.getDistinctValues(qidx);
var count = 0;
var numRows=dataTable.getNumberOfRows();
for (var ii=0; ii<numRows; ii++) {
stat = dataTable.getValue(ii,qidx);
if (stat == curr_stat) {
count++;
}
}
return count/numRows*100; //return percentage
}, type:'number', label: '1Q'}]
}
});
}(qidx);
}
//set the rows of the chart
function setChartRows (dt) {
//loop though the charts
for(qidx=0;qidx < nCols;qidx++) {
var rows = dt.getNumberOfRows();
var distinct_values = dt.getDistinctValues(qidx);
var arRows = [];
for (var ii=0; ii<distinct_values.length; ii++) {
for (var jj=0; jj<rows; jj++) {
var curr_stat = dt.getValue(jj,qidx);
if (curr_stat == distinct_values[ii]) {
arRows.push(jj);
break;
}
}
}
var view = charts[qidx].getView() || {};
view.rows = arRows;
charts[qidx].setDataTable(dt);
charts[qidx].setView(view);
charts[qidx].draw();
}
}
//when the table is created/changed update the chart
google.visualization.events.addListener(table, 'ready', function () {
setChartRows(table.getDataTable());
});
dashboard.bind(genderSelection, table);
dashboard.draw(data);
},
packages: ['corechart', 'controls']
});#table_div{display:none;}
.chart_div{display:inline-block;border:1px solid #888;margin-right:5px;}<script src="https://www.gstatic.com/charts/loader.js"></script>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js"></script>
<div id="dashboard_div">
<div id="filter_div"></div>
<div id="table_div"></div>
</div>