How to get a new column in a data frame which has only elements which appear in the set more than once in R - r

Question

Data:
DB1 <- data.frame(orderItemID = c(1,2,3,4,5,6,7,8,9,10),
orderDate = c("1.1.12","1.1.12","1.1.12","1.1.12","1.1.12", "1.1.12","1.1.12","1.1.12","2.1.12","2.1.12"),
itemID = c(2,3,2,5,12,4,2,3,1,5),
size = factor(c("l", "s", "xl", "xs","m", "s", "l", "m", "xxs", "xxl")),
color = factor(c("blue", "black", "blue", "orange", "red", "navy", "red", "purple", "white", "black")),
customerID = c(33, 15, 1, 33, 14, 55, 33, 78, 94, 23))
Expected output:
selection_order = c("yes","no","no","no","no","no","yes","no","no","no")
In the data set I have items with the same size or the same color, the same ItemID. Every registered user has his unique customerID.
I want to identify when a user orders products (more then one) with the same itemID (in different sizes or colors = for example the user with the customerID = 33 orders the same item (ItemID = 2) in two different colors) and mark it in a new column named like "selection order"(for example) with "Yes" or "No". It should NOT show me a "Yes", when he or she orders an item with an other ID. I just want to get a "yes", when there is an order (at the same day or in the past) with the same ID more then once - regardless from other ID´s (other products).
I've tried a lot already,but nothing works. There are a few thousand different userID's and ItemId's-so I can´t subset for every Id. I tried it with the duplicated function - but it's not leading to a satisfactory solution:
The problem is, that if the same person orders more then one object (customerID is duplicated then) and another person(customerId) orders an item with the same Id (itemId is duplicated then) it gives me a "yes": and it must be a "No" in this case. (in the example the duplicate function will give me an "yes" at orderItemID 4 instead of an "no")

Answer 1

I think I understand what is your desired output now, try
library(data.table)
setDT(DB1)[, selection_order := .N > 1, by = list(customerID, itemID)]
DB1
# orderItemID orderDate itemID size color customerID selection_order
# 1: 1 1.1.12 2 l blue 33 TRUE
# 2: 2 1.1.12 3 s black 15 FALSE
# 3: 3 1.1.12 2 xl blue 1 FALSE
# 4: 4 1.1.12 5 xs orange 33 FALSE
# 5: 5 1.1.12 12 m red 14 FALSE
# 6: 6 1.1.12 4 s navy 55 FALSE
# 7: 7 1.1.12 2 l red 33 TRUE
# 8: 8 1.1.12 3 m purple 78 FALSE
# 9: 9 2.1.12 1 xxs white 94 FALSE
# 10: 10 2.1.12 5 xxl black 23 FALSE
In order to convert back to a data.frame, use DB1 <- as.data.frame(DB1) (for older versions) or setDF(DB1) for the lates data.table version.
You can do it (less efficiently) with base R too
transform(DB1, selection_order = ave(itemID, list(customerID, itemID), FUN = function(x) length(x) > 1))
Or using the dplyr package
library(dplyr)
DB1 %>%
group_by(customerID, itemID) %>%
mutate(selection_order = n() > 1)

Answer 2

The following code will append a new column selection.order to your data frame if the row represents a duplicate (customerID, itemID) tuple.
# First merge together the table to itself
m<- merge(x=DB1,y=DB1,by=c("customerID","itemID"))
# Now find duplicate instances of orderItemID, note this is assumed to be UNIQUE
m$selection.order<-sapply(m$orderItemID.x,function(X) sum(m$orderItemID.x==X)) > 1
m <- m[,c("orderItemID.x","selection.order")]
# Merge the two together
DB1<- merge(DB1, unique(m), by.x="orderItemID",by.y="orderItemID.x",all.x=TRUE,all.y=FALSE)

Answer 3

If you just want the subset, as you say in the title, then do this:
DB1[duplicated(DB1[c("itemID", "customerID")]),]
If you want the column, then:
f <- interaction(DB1$itemID, DB1$customerID)
DB1$multiple <- table(f)[f] > 1L
Note that is also easy to get the actual count by simplifying the last line above.