block by timelyportfolio 10421261

example in response to rCharts issue #381 ; correlation plot with dimplejs

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<pre><code class='r'>#forked from https://gist.github.com/patilv/7073094

library(rCharts)
library(reshape2)
findata=read.csv(&quot;https://raw.github.com/patilv/rChartsTutorials/master/findata.csv&quot;)
# These are data regarding NCAA athletic department expenses at public universities. Please see the blog post where these charts were originally used 
# regarding more details on the origins of these data.: //analyticsandvisualization.blogspot.com/2013/10/subsidies-revenues-and-expenses-of-ncaa.html
findata=findata[,-c(1:3)] # removing first dummy column - the csv quirk - second column on Rank, and third column on School. Retaining only numeric vars here
corrmatrix&lt;-cor(findata) #store corr matrix
# The following steps are generic and can all be placed in a function with some tweaks to customize output 
corrdata=as.data.frame(corrmatrix)
corrdata$Variable1=names(corrdata)
corrdatamelt=melt(corrdata,id=&quot;Variable1&quot;)
names(corrdatamelt)=c(&quot;Variable1&quot;,&quot;Variable2&quot;,&quot;CorrelationCoefficient&quot;)
corrmatplot = dPlot(
  Variable2 ~ Variable1
  ,z = &quot;CorrelationCoefficient&quot;
  ,data = corrdatamelt
  ,type = 'bubble'
  ,height = 350
  ,width = 500
  ,bounds = list( x = 150, y = 50, width = 330, height = 200)
)
corrmatplot$yAxis ( type= &quot;addCategoryAxis&quot; )
corrmatplot$zAxis (
  type= &quot;addMeasureAxis&quot;
  , outputFormat = &quot;0.5f&quot;
  , overrideMin = -1
  , overrideMax = 1
)
corrmatplot$colorAxis(
  type = &quot;addColorAxis&quot;
  ,colorSeries = 'CorrelationCoefficient'
  ,palette = c('red','white','blue')
  ,outputFormat = &quot;0.5f&quot;
)
corrmatplot


#now do the bar
#corrmatplot$set(type = &quot;bar&quot;)
#corrmatplot
</code></pre>
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    <script type="text/javascript">
  var opts = {
 "dom": "chart1eec43e29dd",
"width":    700,
"height":    350,
"xAxis": {
 "type": "addCategoryAxis",
"showPercent": false 
},
"yAxis": {
 "type": "addCategoryAxis",
"showPercent": false 
},
"zAxis": {
 "type": "addMeasureAxis",
"outputFormat": "0.5f",
"overrideMin":     -1,
"overrideMax":      1 
},
"colorAxis": {
 "type": "addColorAxis",
"colorSeries": "CorrelationCoefficient",
"palette": [ "red", "white", "blue" ],
"outputFormat": "0.5f" 
},
"defaultColors": [],
"layers": [],
"legend": [],
"x": "Variable1",
"y": "Variable2",
"z": "CorrelationCoefficient",
"type": "bubble",
"bounds": {
 "x":    150,
"y":     50,
"width":    330,
"height":    200 
},
"id": "chart1eec43e29dd" 
},
    data = [{"Variable1":"Total.Revenue","Variable2":"Total.Revenue","CorrelationCoefficient":1},{"Variable1":"Total.Expenses","Variable2":"Total.Revenue","CorrelationCoefficient":0.990538233994005},{"Variable1":"Total.Subsidy","Variable2":"Total.Revenue","CorrelationCoefficient":-0.234931071034671},{"Variable1":"Revenue.Less.Expenses","Variable2":"Total.Revenue","CorrelationCoefficient":0.567118793713813},{"Variable1":"Total.Revenue","Variable2":"Total.Expenses","CorrelationCoefficient":0.990538233994005},{"Variable1":"Total.Expenses","Variable2":"Total.Expenses","CorrelationCoefficient":1},{"Variable1":"Total.Subsidy","Variable2":"Total.Expenses","CorrelationCoefficient":-0.219836244366632},{"Variable1":"Revenue.Less.Expenses","Variable2":"Total.Expenses","CorrelationCoefficient":0.448719475057225},{"Variable1":"Total.Revenue","Variable2":"Total.Subsidy","CorrelationCoefficient":-0.234931071034671},{"Variable1":"Total.Expenses","Variable2":"Total.Subsidy","CorrelationCoefficient":-0.219836244366632},{"Variable1":"Total.Subsidy","Variable2":"Total.Subsidy","CorrelationCoefficient":1},{"Variable1":"Revenue.Less.Expenses","Variable2":"Total.Subsidy","CorrelationCoefficient":-0.210485654989721},{"Variable1":"Total.Revenue","Variable2":"Revenue.Less.Expenses","CorrelationCoefficient":0.567118793713813},{"Variable1":"Total.Expenses","Variable2":"Revenue.Less.Expenses","CorrelationCoefficient":0.448719475057225},{"Variable1":"Total.Subsidy","Variable2":"Revenue.Less.Expenses","CorrelationCoefficient":-0.210485654989721},{"Variable1":"Revenue.Less.Expenses","Variable2":"Revenue.Less.Expenses","CorrelationCoefficient":1}];
  var svg = dimple.newSvg("#" + opts.id, opts.width, opts.height);

  //data = dimple.filterData(data, "Owner", ["Aperture", "Black Mesa"])
  var myChart = new dimple.chart(svg, data);
  if (opts.bounds) {
    myChart.setBounds(opts.bounds.x, opts.bounds.y, opts.bounds.width, opts.bounds.height);//myChart.setBounds(80, 30, 480, 330);
  }
  //dimple allows use of custom CSS with noFormats
  if(opts.noFormats) { myChart.noFormats = opts.noFormats; };
  //for markimekko and addAxis also have third parameter measure
  //so need to evaluate if measure provided
  
  //function to build axes
  function buildAxis(position,layer){
    var axis;
    var axisopts = opts[position+"Axis"];
    
    if(axisopts.measure) {
      axis = myChart[axisopts.type](position,layer[position],axisopts.measure);
    } else {
      axis = myChart[axisopts.type](position, layer[position]);
    };
    if(!(axisopts.type === "addPctAxis")) axis.showPercent = axisopts.showPercent;
    if (axisopts.orderRule) axis.addOrderRule(axisopts.orderRule);
    if (axisopts.grouporderRule) axis.addGroupOrderRule(axisopts.grouporderRule);  
    if (axisopts.overrideMin) axis.overrideMin = axisopts.overrideMin;
    if (axisopts.overrideMax) axis.overrideMax = axisopts.overrideMax;
    if (axisopts.overrideMax) axis.overrideMax = axisopts.overrideMax;
    if (axisopts.inputFormat) axis.dateParseFormat = axisopts.inputFormat;
    if (axisopts.outputFormat) axis.tickFormat = axisopts.outputFormat;    
    return axis;
  };
  
  var c = null;
  if(d3.keys(opts.colorAxis).length > 0) {
    c = myChart[opts.colorAxis.type](opts.colorAxis.colorSeries,opts.colorAxis.palette) ;
    if(opts.colorAxis.outputFormat){
      c.tickFormat = opts.colorAxis.outputFormat;
    }
  }
  
  //allow manipulation of default colors to use with dimple
  if(opts.defaultColors.length) {
    //opts.defaultColors = opts.defaultColors[0];
    if (typeof(opts.defaultColors) == "function") {
      //assume this is a d3 scale
      //for now loop through first 20 but need a better way to handle
      defaultColorsArray = [];
      for (var n=0;n<20;n++) {
        defaultColorsArray.push(opts.defaultColors(n));
      };
      opts.defaultColors = defaultColorsArray;
    }
    opts.defaultColors.forEach(function(d,i) {
      opts.defaultColors[i] = new dimple.color(d);
    })
    myChart.defaultColors = opts.defaultColors;
  }  
  
  //do series
  //set up a function since same for each
  //as of now we have x,y,groups,data,type in opts for primary layer
  //and other layers reside in opts.layers
  function buildSeries(layer, hidden){
    //inherit from primary layer if not intentionally changed or xAxis, yAxis, zAxis null
    if (!layer.xAxis) layer.xAxis = opts.xAxis;    
    if (!layer.yAxis) layer.yAxis = opts.yAxis;
    if (!layer.zAxis) layer.zAxis = opts.zAxis;
    
    var x = buildAxis("x", layer);
    x.hidden = hidden;
    
    var y = buildAxis("y", layer);
    y.hidden = hidden;
    
    //z for bubbles
    var z = null;
    if (!(typeof(layer.zAxis) === 'undefined') && layer.zAxis.type){
      z = buildAxis("z", layer);
    };
    
    //here think I need to evaluate group and if missing do null
    //as the group argument
    //if provided need to use groups from layer
    var s = new dimple.series(myChart, null, x, y, z, c, dimple.plot[layer.type], dimple.aggregateMethod.avg, dimple.plot[layer.type].stacked);
    
    //as of v1.1.4 dimple can use different dataset for each series
    if(layer.data){
      //convert to an array of objects
      var tempdata;
      //avoid lodash for now
      datakeys = d3.keys(layer.data)
      tempdata = layer.data[datakeys[1]].map(function(d,i){
        var tempobj = {}
        datakeys.forEach(function(key){
          tempobj[key] = layer.data[key][i]
        })
        return tempobj
      })
      s.data = tempdata;
    }
    
    if(layer.hasOwnProperty("groups")) {
      s.categoryFields = (typeof layer.groups === "object") ? layer.groups : [layer.groups];
      //series offers an aggregate method that we will also need to check if available
      //options available are avg, count, max, min, sum
    }
    if (!(typeof(layer.aggregate) === 'undefined')) {
      s.aggregate = eval(layer.aggregate);
    }
    if (!(typeof(layer.lineWeight) === 'undefined')) {
      s.lineWeight = eval(layer.lineWeight);
    }
    if (!(typeof(layer.barGap) === 'undefined')) {
      s.barGap = eval(layer.barGap);
    }    
  
   /* if (!(typeof(layer.eventHandler) === 'undefined')) {
      layer.eventHandler = (layer.eventHandler.length === "undefined") ? layer.eventHandler : [layer.eventHandler];
      layer.eventHandler.forEach(function(evt){
        s.addEventHandler(evt.event, eval(evt.handler))
      })
    }*/
      
    myChart.series.push(s);
    
    /*placeholder fix domain of primary scale for new series data
    //not working right now but something like this
    //for now just use overrideMin and overrideMax from rCharts
    for( var i = 0; i<2; i++) {
      if (!myChart.axes[i].overrideMin) {
        myChart.series[0]._axisBounds(i==0?"x":"y").min = myChart.series[0]._axisBounds(i==0?"x":"y").min < s._axisBounds(i==0?"x":"y").min ? myChart.series[0]._axisBounds(i==0?"x":"y").min : s._axisBounds(i==0?"x":"y").min;
      }
      if (!myChart.axes[i].overrideMax) {  
        myChart.series[0]._axisBounds(i==0?"x":"y")._max = myChart.series[0]._axisBounds(i==0?"x":"y").max > s._axisBounds(i==0?"x":"y").max ? myChart.series[0]._axisBounds(i==0?"x":"y").max : s._axisBounds(i==0?"x":"y").max;
      }
      myChart.axes[i]._update();
    }
    */
    
    return s;
  };
  
  buildSeries(opts, false);
  if (opts.layers.length > 0) {
    opts.layers.forEach(function(layer){
      buildSeries(layer, true);
    })
  }
  //unsure if this is best but if legend is provided (not empty) then evaluate
  if(d3.keys(opts.legend).length > 0) {
    var l =myChart.addLegend();
    d3.keys(opts.legend).forEach(function(d){
      l[d] = opts.legend[d];
    });
  }
  //quick way to get this going but need to make this cleaner
  if(opts.storyboard) {
    myChart.setStoryboard(opts.storyboard);
  };
  myChart.draw();

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code.R

#forked from https://gist.github.com/patilv/7073094

library(rCharts)
library(reshape2)
findata=read.csv("https://raw.github.com/patilv/rChartsTutorials/master/findata.csv")
# These are data regarding NCAA athletic department expenses at public universities. Please see the blog post where these charts were originally used 
# regarding more details on the origins of these data.: http://analyticsandvisualization.blogspot.com/2013/10/subsidies-revenues-and-expenses-of-ncaa.html
findata=findata[,-c(1:3)] # removing first dummy column - the csv quirk - second column on Rank, and third column on School. Retaining only numeric vars here
corrmatrix<-cor(findata) #store corr matrix
# The following steps are generic and can all be placed in a function with some tweaks to customize output 
corrdata=as.data.frame(corrmatrix)
corrdata$Variable1=names(corrdata)
corrdatamelt=melt(corrdata,id="Variable1")
names(corrdatamelt)=c("Variable1","Variable2","CorrelationCoefficient")
corrmatplot = dPlot(
  Variable2 ~ Variable1
  ,z = "CorrelationCoefficient"
  ,data = corrdatamelt
  ,type = 'bubble'
  ,height = 350
  ,width = 500
  ,bounds = list( x = 150, y = 50, width = 330, height = 200)
)
corrmatplot$yAxis ( type= "addCategoryAxis" )
corrmatplot$zAxis (
  type= "addMeasureAxis"
  , outputFormat = "0.5f"
  , overrideMin = -1
  , overrideMax = 1
)
corrmatplot$colorAxis(
  type = "addColorAxis"
  ,colorSeries = 'CorrelationCoefficient'
  ,palette = c('red','white','blue')
  ,outputFormat = "0.5f"
)
corrmatplot


#now do the bar
#corrmatplot$set(type = "bar")
#corrmatplot