1. #forked from https://gist.github.com/patilv/7073094
  3. library(rCharts)
  4. library(reshape2)
  5. findata=read.csv("https://raw.github.com/patilv/rChartsTutorials/master/findata.csv")
  6. # These are data regarding NCAA athletic department expenses at public universities. Please see the blog post where these charts were originally used 
  7. # regarding more details on the origins of these data.: //analyticsandvisualization.blogspot.com/2013/10/subsidies-revenues-and-expenses-of-ncaa.html
  8. 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
  9. corrmatrix<-cor(findata) #store corr matrix
  10. # The following steps are generic and can all be placed in a function with some tweaks to customize output 
  11. corrdata=as.data.frame(corrmatrix)
  12. corrdata$Variable1=names(corrdata)
  13. corrdatamelt=melt(corrdata,id="Variable1")
  14. names(corrdatamelt)=c("Variable1","Variable2","CorrelationCoefficient")
  15. corrmatplot = dPlot(
  16.   Variable2 ~ Variable1
  17.   ,z = "CorrelationCoefficient"
  18.   ,data = corrdatamelt
  19.   ,type = 'bubble'
  20.   ,height = 350
  21.   ,width = 500
  22.   ,bounds = list( x = 150, y = 50, width = 330, height = 200)
  23. )
  24. corrmatplot$yAxis ( type= "addCategoryAxis" )
  25. corrmatplot$zAxis (
  26.   type= "addMeasureAxis"
  27.   , outputFormat = "0.5f"
  28.   , overrideMin = -1
  29.   , overrideMax = 1
  30. )
  31. corrmatplot$colorAxis(
  32.   type = "addColorAxis"
  33.   ,colorSeries = 'CorrelationCoefficient'
  34.   ,palette = c('red','white','blue')
  35.   ,outputFormat = "0.5f"
  36. )
  37. corrmatplot
  40. #now do the bar
  41. #corrmatplot$set(type = "bar")
  42. #corrmatplot