[R] Data Table Merge Help
bgunter.4567 at gmail.com
Thu Feb 1 22:01:16 CET 2018
Did you search first? (This is suggested by the posting guide -- below
-- prior to posting).
"merge 2 data.tables in R" brought up what looked like useful stuff,
in particular the merge() function for data tables. If this does not
do what you want, it may help to explain why not.
Alternatively, there is a merge.data.frame function that may do the
job if you first convert your data.table to a data.frame.
As I do not use the data.table package, you or others may have to fill
in details to make these work -- if they *can* work.
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Thu, Feb 1, 2018 at 9:45 AM, Graeve, Nick <Graeve.Nick at principal.com> wrote:
> I'm not sure if this is an appropriate use of this mailing list or not, please let me know if it isn't. I'm struggling to figure out how to merge two data tables based on max effective date logic compared to when a payment occurred. My dtDistributions DT is a transactional dataset while dtDepartments is a domain data set containing all department names and the effective date of when department name changes have occurred. For the Bob example below, there was a payment on 2016-01-01 which occurred in H229000. In 2012, this department was named "Modified Name", in 2019 the department will be named "Final Name". When I merge these two tables, I'd like it to pull the transactional data and match it up to department name "Modified Name" since that was the active department name at the time of that transaction. I've read documentation on foverlaps, but I'm not sure if this problem is considered a range of dates or not. At the bottom of this post is a temporarily solution that is working but it runs for a long time due to the amount of data in my actual source.
> Here is some sample data to get started:
> dtDistributions <- data.table(PayeeName = c("Bob", "Tracy", "Tom"),
> Department = factor(c("H229000", "H135000", "H047800")),
> Amount = c(5, 34, 87),
> PaymentDT = as.Date(c("2016-01-01", "2015-01-01", "2015-01-01")))
> dtDepartments <- data.table(Department = factor(c("H229000", "H229000", "H229000", "H135000", "H047800")),
> EffDT = as.Date(c("2019-01-01", "2012-01-01", "1901-01-01", "1901-01-01", "1901-01-01")),
> Descr = c("Final Name","Modified Name","Original Name","Payables","Postal"))
> Here is the output I would like to see:
> PayeeName Department PaymentDT Amount
> Bob Modified Name 2016-01-01 5
> Tracy Payables 2015-01-01 34
> Tom Postal 2015-01-01 87
> I was able to get this working by using the sqldf library, but it runs for a very long time in my actual dataset and I'd like to use data.table if at all possible.
> joinString <- "SELECT A.PayeeName, B.Descr, A.PaymentDT, A.Amount
> FROM dtDistributions A, dtDepartments B
> WHERE A.DEPARTMENT = B.Department
> AND B.EffDT = (SELECT MAX(ED.EffDT)
> FROM dtDepartments ED
> WHERE B.Department = ED.Department
> AND ED.EffDT <= A.PaymentDT)"
> finalDT <- data.table(sqldf(joinString))
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