data.table merge based on date ranges
data.table, r
Solution
Version 1 (updated for data.table v1.9.4+)
Try this:
# Policies table; I've added policyNumber 126:
policies<-data.table(policyNumber=c(123,123,124,125,126),
EFDT=as.Date(c("2012-01-01","2013-01-01","2013-01-01","2013-02-01","2013-02-01")),
EXDT=as.Date(c("2013-01-01","2014-01-01","2014-01-01","2014-02-01","2014-02-01")))
# Claims table; I've added two claims for 126 that are before and after the policy dates:
claims<-data.table(claimNumber=c(1,2,3,4,5,6),
policyNumber=c(123,123,123,124,126,126),
lossDate=as.Date(c("2012-2-1","2012-8-15","2013-1-1","2013-10-31","2012-06-01","2014-03-01")),
claimAmount=c(10,20,20,15,5,25))
# Set the keys for policies and claims so we can join them:
setkey(policies,policyNumber,EFDT)
setkey(claims,policyNumber,lossDate)
# Join the tables using roll
# ans<-policies[claims,list(EFDT,EXDT,claimNumber,lossDate,claimAmount,inPolicy=F),roll=T][,EFDT:=NULL] ## This worked with earlier versions of data.table, but broke when they updated the by-without-by behavior...
ans<-policies[claims,list(.EFDT=EFDT,EXDT,claimNumber,lossDate,claimAmount,inPolicy=F),by=.EACHI,roll=T][,`:=`(EFDT=.EFDT, .EFDT=NULL)]
# The claim should have inPolicy==T where lossDate is between EFDT and EXDT:
ans[lossDate>=EFDT & lossDate<=EXDT, inPolicy:=T]
# Set the keys again, but this time we'll join on both dates:
setkey(ans,policyNumber,EFDT,EXDT)
setkey(policies,policyNumber,EFDT,EXDT)
# Union the ans table with policies that don't have any claims:
ans<-rbindlist(list(ans, ans[policies][is.na(claimNumber)]))
ans
# policyNumber EFDT EXDT claimNumber lossDate claimAmount inPolicy
#1: 123 2012-01-01 2013-01-01 1 2012-02-01 10 TRUE
#2: 123 2012-01-01 2013-01-01 2 2012-08-15 20 TRUE
#3: 123 2013-01-01 2014-01-01 3 2013-01-01 20 TRUE
#4: 124 2013-01-01 2014-01-01 4 2013-10-31 15 TRUE
#5: 126 <NA> <NA> 5 2012-06-01 5 FALSE
#6: 126 2013-02-01 2014-02-01 6 2014-03-01 25 FALSE
#7: 125 2013-02-01 2014-02-01 NA <NA> NA NA
Version 2
@Arun suggested using the new `foverlaps` function from `data.table`. My attempt below seems harder, not easier, so please let me know how to improve it.
## The foverlaps function requires both tables to have a start and end range, and the "y" table to be keyed
claims[, lossDate2:=lossDate] ## Add a redundant lossDate column to use as the end range for claims
setkey(policies, policyNumber, EFDT, EXDT) ## Set the key for policies ("y" table)
## Find the overlaps, remove the redundant lossDate2 column, and add the inPolicy column:
ans2 <- foverlaps(claims, policies, by.x=c("policyNumber", "lossDate", "lossDate2"))[, `:=`(inPolicy=T, lossDate2=NULL)]
## Update rows where the claim was out of policy:
ans2[is.na(EFDT), inPolicy:=F]
## Remove duplicates (such as policyNumber==123 & claimNumber==3),
## and add policies with no claims (policyNumber==125):
setkey(ans2, policyNumber, claimNumber, lossDate, EFDT) ## order the results
setkey(ans2, policyNumber, claimNumber) ## set the key to identify unique values
ans2 <- rbindlist(list(
unique(ans2), ## select only the unique values
policies[!.(ans2[, unique(policyNumber)])] ## policies with no claims
), fill=T)
ans2
## policyNumber EFDT EXDT claimNumber lossDate claimAmount inPolicy
## 1: 123 2012-01-01 2013-01-01 1 2012-02-01 10 TRUE
## 2: 123 2012-01-01 2013-01-01 2 2012-08-15 20 TRUE
## 3: 123 2012-01-01 2013-01-01 3 2013-01-01 20 TRUE
## 4: 124 2013-01-01 2014-01-01 4 2013-10-31 15 TRUE
## 5: 126 <NA> <NA> 5 2012-06-01 5 FALSE
## 6: 126 <NA> <NA> 6 2014-03-01 25 FALSE
## 7: 125 2013-02-01 2014-02-01 NA <NA> NA NA
Version 3
Using `foverlaps()`, another version:
require(data.table) ## 1.9.4+
setDT(claims)[, lossDate2 := lossDate]
setDT(policies)[, EXDTclosed := EXDT-1L]
setkey(claims, policyNumber, lossDate, lossDate2)
foverlaps(policies, claims, by.x=c("policyNumber", "EFDT", "EXDTclosed"))
`foverlaps()` requires both start and end ranges/intervals. Therefore, we duplicate `lossDate` column on to `lossDate2`.
Since `EXDT` needs to be open interval, we subtract one from it, and place it in a new column `EXDTclosed`.
Now, we set the key. `foverlaps()` requires the last two key columns to be intervals. So they're specified last. And we also want overlapping join to first match by `policyNumber`. Hence, it's also specified in the key.
We need to set key on `claims` (check `?foverlaps`). We don't have to set key on `policies`. But you can if you wish (then you can skip `by.x` argument as it by default takes the key value). Since we don't set the key for `policies` here, we'll specify explicitly the corresponding columns in `by.x` argument. The overlap type by default is `any`, which we don't have to change (and therefore not specified). This results in:
# policyNumber claimNumber lossDate claimAmount lossDate2 EFDT EXDT EXDTclosed
# 1: 123 1 2012-02-01 10 2012-02-01 2012-01-01 2013-01-01 2012-12-31
# 2: 123 2 2012-08-15 20 2012-08-15 2012-01-01 2013-01-01 2012-12-31
# 3: 123 3 2013-01-01 20 2013-01-01 2013-01-01 2014-01-01 2013-12-31
# 4: 124 4 2013-10-31 15 2013-10-31 2013-01-01 2014-01-01 2013-12-31
# 5: 125 NA <NA> NA <NA> 2013-02-01 2014-02-01 2014-01-31
Problem
I have two tables, `policies` and `claims` ``` policies<-data.table(policyNumber=c(123,123,124,125), EFDT=as.Date(c("2012-1-1","2013-1-1","2013-1-1","2013-2-1")), EXDT=as.Date(c("2013-1-1","2014-1-1","2014-1-1","2014-2-1"))) > policies policyNumber EFDT EXDT 1: 123 2012-01-01 2013-01-01 2: 123 2013-01-01 2014-01-01 3: 124 2013-01-01 2014-01-01 4: 125 2013-02-01 2014-02-01 claims<-data.table(claimNumber=c(1,2,3,4), policyNumber=c(123,123,123,124), lossDate=as.Date(c("2012-2-1","2012-8-15","2013-1-1","2013-10-31")), claimAmount=c(10,20,20,15)) > claims claimNumber policyNumber lossDate claimAmount 1: 1 123 2012-02-01 10 2: 2 123 2012-08-15 20 3: 3 123 2013-01-01 20 4: 4 124 2013-10-31 15 ``` The policy table really contains policy-terms, since each row is uniquely identified by a policy number along with an effective date. I want to merge the two tables in a way that associates claims with policy-terms. A claim is associated with a policy term if it has the same policy number and the lossDate of the claim falls within the effective date and expiration date of the policy-term (effective dates are inclusive bounds and expiration dates are exclusive bounds.) How do I merge the tables in this way? This should be similar to a left outer join. The result should look like ``` policyNumber EFDT EXDT claimNumber lossDate claimAmount 1: 123 2012-01-01 2013-01-01 1 2012-02-01 10 2: 123 2012-01-01 2013-01-01 2 2012-08-15 20 3: 123 2013-01-01 2014-01-01 3 2013-01-01 20 4: 124 2013-01-01 2014-01-01 4 2013-10-31 15 5: 125 2013-02-01 2014-02-01 NA <NA> NA ```