I'm trying to create new rows based on overlapping time periods of existing rows. For example, I'd like to turn this:
Customer_Product <- data.table(Customer=c("A01","A01","A01", "A02", "A02", "A02", "A03", "A03", "A03"),
Product=c("Prod1","Prod2","Prod3","Prod1","Prod2","Prod3","Prod1","Prod2","Prod3"),
Start_Date=c("1/1/2015", "3/1/2015", "4/1/2015", "1/1/2015", "3/1/2015", "4/1/2015", "1/1/2015", "3/1/2015", "4/1/2015"),
End_Date=c("2/1/2015","5/1/2015","5/1/2015","2/1/2015","5/1/2015","6/1/2015","2/1/2015","6/1/2015","5/1/2015"))
Customer Product Start_Date End_Date 1: A01 Prod1 1/1/2015 2/1/2015 2: A01 Prod2 3/1/2015 5/1/2015 3: A01 Prod3 4/1/2015 5/1/2015 4: A02 Prod1 1/1/2015 2/1/2015 5: A02 Prod2 3/1/2015 5/1/2015 6: A02 Prod3 4/1/2015 6/1/2015 7: A03 Prod1 1/1/2015 2/1/2015 8: A03 Prod2 3/1/2015 6/1/2015 9: A03 Prod3 4/1/2015 5/1/2015
Into something like this:
Customer_Product_Combo <- data.table(Customer=c("A01","A01","A01", "A02", "A02", "A02", "A02","A03", "A03","A03","A03"),
Product_or_Combination=c("Prod1","Prod2","Prod2/Prod3","Prod1","Prod2","Prod2/Prod3","Prod3","Prod1","Prod2","Prod2/Prod3","Prod2"),
Start_Date=c("1/1/2015","3/1/2015","4/1/2015","1/1/2015","3/1/2015","4/1/2015","5/1/2015","1/1/2015","3/1/2015","4/1/2015","5/1/2015"),
End_Date=c("2/1/2015","4/1/2015","5/1/2015","2/1/2015","4/1/2015","5/1/2015","6/1/2015","2/1/2015","4/1/2015","5/1/2015","6/1/2015"))
Customer Product_or_Combination Start_Date End_Date 1: A01 Prod1 1/1/2015 2/1/2015 2: A01 Prod2 3/1/2015 4/1/2015 3: A01 Prod2/Prod3 4/1/2015 5/1/2015 4: A02 Prod1 1/1/2015 2/1/2015 5: A02 Prod2 3/1/2015 4/1/2015 6: A02 Prod2/Prod3 4/1/2015 5/1/2015 7: A02 Prod3 5/1/2015 6/1/2015 8: A03 Prod1 1/1/2015 2/1/2015 9: A03 Prod2 3/1/2015 4/1/2015 10: A03 Prod2/Prod3 4/1/2015 5/1/2015 11: A03 Prod2 5/1/2015 6/1/2015
I've been looking into IRanges, because it seems like disjoin() may be a possible solution, but I can't see any way to inherit/merge the "Prod" data.
I've also been trying to sketch out something using lead/lag in dplyr followed by a gather/merge cycle, but it's also worth noting that I could have instances where more than 2 "Prod"s overlap, and then the logic just gets messy.
Is there a reasonable way to do this? Any help is greatly appreciated!
I'm using the data you posted (as a data.frame)
Customer_Product <- data.frame(Customer=c("A01","A01","A01", "A02", "A02", "A02", "A03", "A03", "A03"),
Product=c("Prod1","Prod2","Prod3","Prod1","Prod2","Prod3","Prod1","Prod2","Prod3"),
Start_Date=c("1/1/2015", "3/1/2015", "4/1/2015", "1/1/2015", "3/1/2015", "4/1/2015", "1/1/2015", "3/1/2015", "4/1/2015"),
End_Date=c("2/1/2015","5/1/2015","5/1/2015","2/1/2015","5/1/2015","6/1/2015","2/1/2015","6/1/2015","5/1/2015"))
Here's a possible solution:
library(tidyverse)
library(data.table)
library(lubridate)
Customer_Product %>%
mutate_at(vars(matches("Date")), dmy) %>% # update to date columns (if needed)
mutate(day = map2(Start_Date, End_Date, ~seq(.x, .y, "day"))) %>% # create sequence of days between start and end
unnest() %>% # unnest data
group_by(Customer, day) %>% # for each customer and day
summarise(Product = paste0(Product, collapse = "/")) %>% # find corresponding products
group_by(Customer, Product, id = rleid(Product)) %>% # for each customer, product combination and position of product combination
summarise(Start_Date = min(day), # get start date
End_Date = max(day)) %>% # get end date
ungroup() %>% # ungroup
select(-id) %>% # remove id column
arrange(Customer, Start_Date) # order rows (if needed)
# # A tibble: 11 x 4
# Customer Product Start_Date End_Date
# <fct> <chr> <date> <date>
# 1 A01 Prod1 2015-01-01 2015-01-02
# 2 A01 Prod2 2015-01-03 2015-01-03
# 3 A01 Prod2/Prod3 2015-01-04 2015-01-05
# 4 A02 Prod1 2015-01-01 2015-01-02
# 5 A02 Prod2 2015-01-03 2015-01-03
# 6 A02 Prod2/Prod3 2015-01-04 2015-01-05
# 7 A02 Prod3 2015-01-06 2015-01-06
# 8 A03 Prod1 2015-01-01 2015-01-02
# 9 A03 Prod2 2015-01-03 2015-01-03
#10 A03 Prod2/Prod3 2015-01-04 2015-01-05
#11 A03 Prod2 2015-01-06 2015-01-06
Note that this solution doesn't allow for date range overlap in your output table.
For example, if you have Prod2/Prod3 during 4/1/2015 - 5/1/2015 you won't get Prod2 during 5/1/2015 - 6/1/2015, but 6/1/2015 - 6/1/2015, as 5/1/2015 is covered in Prod2/Prod3.