Initial commit for correspondence analysis

ca.R

@@ -0,0 +1,43 @@
+library(dplyr)
+library(ggplot2)
+
+dfData <- readr::read_csv("CleanData.csv")
+
+injuryGender <- table(dfData$BikeInjury, dfData$BikeGender)
+injuryGender
+
+cMat <- injuryGender / sum(injuryGender)
+cMat
+
+library(ca)
+data("smoke")
+ca(smoke)
+
+# dfSmall <- dfData %>% 
+#   select(BikeInjury, BikeGender, Locality, Light_Cond, Rd_Class)
+# 
+# dfInjuryGender <- dfSmall %>% 
+#   group_by(BikeGender, BikeInjury) %>% 
+#   summarise(Count = n())
+# 
+# mojo <- ggplot(dfInjuryGender, aes(BikeGender, BikeInjury, fill = Count)) + geom_tile()
+# mojo
+# 
+# dfInjuryGender <- dfSmall %>% 
+#   mutate(GenderPos = as.factor(BikeGender)
+#          , InjuryPos = as.factor(BikeInjury)
+#          , GenderPos = as.integer(GenderPos)
+#          , InjuryPos = as.integer(InjuryPos))
+# dfInjuryGender$IncidentNum <- seq(1, nrow(dfInjuryGender))
+# 
+# dfDistance <- dist(dfInjuryGender[, c("GenderPos", "InjuryPos")], method = "manhattan")
+# 
+# dfInjuryGenderLocality <- dfSmall %>% 
+#   mutate(GenderPos = as.factor(BikeGender)
+#          , InjuryPos = as.factor(BikeInjury)
+#          , LocalityPos = as.factor(Locality)
+#          , GenderPos = as.integer(GenderPos)
+#          , InjuryPos = as.integer(InjuryPos)
+#          , Local = as.integer(LocalityPos))
+# 
+# moreDist <- dist(dfInjuryGenderLocality[, c("GenderPos", "InjuryPos", "LocalityPos")], method = "manhattan")