Computing NTILE in R for RFM analysis - r

Question

I'm trying to create a dataframe computing 10 percentiles based on the Recency, Frequency and Monetary. I have most of it set up, but I can't figure out why my code is returning three NTILES, when I'm asking for 10. I'm currently at a stand still. The next step will be calculating the percentage of customers in each ntile.
Here is my code:
rm(list = ls())
setwd("/Users/a76475/Documents/Customer_Analytics")
rfm<-read.csv("cdnow_students_transaction.csv")
#Set up test and calibration samples
rfm$DATE <- as.Date(rfm$DATE, format = "%m/%d/%y")
calib <- subset(rfm, rfm$DATE<"1997-09-29")
valid <- subset(rfm, rfm$DATE>"1997-09-30")
#Aggregate for frequency, monetary, and recency -- Calibration
recency<- aggregate(DATE ~ ID, data =calib, FUN = max)
colnames(recency) <- c("ID","Recency")
frequency <- aggregate(DOLLARS ~ ID, data =calib, FUN = length)
colnames(frequency) <- c("ID","Frequency")
monetary <- aggregate(DOLLARS ~ ID, data =calib, FUN = mean)
colnames(frequency) <- c("ID","Monetary")
calib <- merge(frequency, monetary, by = "ID")
calib <- merge(calib, recency, by = "ID")
#Aggregate for frequency, monetary, and recency -- Validation
recency<- aggregate(DATE ~ ID, data =valid, FUN = max)
colnames(recency) <- c("ID","Recency")
frequency <- aggregate(DOLLARS ~ ID, data =valid, FUN = length)
colnames(frequency) <- c("ID","Frequency")
monetary <- aggregate(DOLLARS ~ ID, data =valid, FUN = mean)
colnames(frequency) <- c("ID","Monetary")
valid <- merge(frequency, monetary, by = "ID")
valid <- merge(valid, recency, by = "ID")
colnames(valid) <- c("ID","FREQ","MONETARY","RECENCY")
colnames(calib) <- c("ID","FREQ","MONETARY","RECENCY")
calib$RECENCY <- NULL
#Create recency score
#For validation
for (i in 1:nrow(valid)) {
valid$RECENCY1[i] = as.numeric(max(valid$RECENCY) - valid$RECENCY[i])
}
valid$RECENCY <- valid$RECENCY1
valid$RECENCY1 <- NULL
#For calibration
for (i in 1:nrow(calib)) {
calib$RECENCY1[i] = as.numeric(max(valid$RECENCY) - calib$RECENCY[i])
}
calib$RECENCY <- calib$RECENCY1
calib$RECENCY1 <- NULL
#Merge datasets
rfm <- merge(calib,valid, by="ID", all.x = TRUE)
#Create Column for retention%
require(dplyr)
rfm$monetary.ntile <- ntile(rfm$MONETARY.y,10)
rfm$freq.ntile <- ntile(rfm$FREQ.y,10)
rfm$recency.ntile <- ntile(rfm$RECENCY,10)

Answer 1

For example, if you want 10 buckets for Recency, Frequency and Monetary Ratio:
set.seed(1)
n <- 100
df <- data.frame(
R = runif(n, 1, 365),
F = runif(n, 1, 5),
M = runif(n, 0, 100)
)
apply(df, 2, function(col) {
breaks <- quantile(col, probs=seq(0, 1, length.out = 10))
findInterval(col, breaks)
})