Claude Seidman cseidman

## plotInitialData.R
# Ages are more relevant when we bucket them
ds$AgeRanges <- cut(ds$Age,breaks=c(0,30,50,70,90),labels=c("11-30","31-50","51-70","71+"))
# Income ranges are also more interesting when we bin them
ds$IncomeCategories <- cut(x=ds$YearlyIncome,breaks=c(0,20000,50000,70000,100000,250000),labels=c("Low","Lower","Middle","Upper","Wealthy"))

# Helps with building the plots
ds$Count <- 1

features <- c("AgeRanges","MaritalStatus","Gender",
              "IncomeCategories","TotalChildren","NumberChildrenAtHome",
	# Ages are more relevant when we bucket them
	ds$AgeRanges <- cut(ds$Age,breaks=c(0,30,50,70,90),labels=c("11-30","31-50","51-70","71+"))
	# Income ranges are also more interesting when we bin them
	ds$IncomeCategories <- cut(x=ds$YearlyIncome,breaks=c(0,20000,50000,70000,100000,250000),labels=c("Low","Lower","Middle","Upper","Wealthy"))

	# Helps with building the plots
	ds$Count <- 1

	features <- c("AgeRanges","MaritalStatus","Gender",
	"IncomeCategories","TotalChildren","NumberChildrenAtHome",