问题
This is very similar to the question @DavidArenburg asked about conditional keyed joins, with an additional bugbear that I can't seem to suss out.
Basically, in addition to a conditional join, I want to define a flag saying at which step of the matching process that the match occurred; my problem is that I can only get the flag to define for all values, not the matched values.
Here's what I hope is a minimal working example:
DT = data.table(
name = c("Joe", "Joe", "Jim", "Carol", "Joe",
"Carol", "Ann", "Ann", "Beth", "Joe", "Joe"),
surname = c("Smith", "Smith", "Jones",
"Clymer", "Smith", "Klein", "Cotter",
"Cotter", "Brown", "Smith", "Smith"),
maiden_name = c("", "", "", "", "", "Clymer",
"", "", "", "", ""),
id = c(1, 1:3, rep(NA, 7)),
year = rep(1:4, c(4, 3, 2, 2)),
flag1 = NA, flag2 = NA, key = "year"
)
DT
# name surname maiden_name id year flag1 flag2
# 1: Joe Smith 1 1 FALSE FALSE
# 2: Joe Smith 1 1 FALSE FALSE
# 3: Jim Jones 2 1 FALSE FALSE
# 4: Carol Clymer 3 1 FALSE FALSE
# 5: Joe Smith NA 2 FALSE FALSE
# 6: Carol Klein Clymer NA 2 FALSE FALSE
# 7: Ann Cotter NA 2 FALSE FALSE
# 8: Ann Cotter NA 3 FALSE FALSE
# 9: Beth Brown NA 3 FALSE FALSE
# 10: Joe Smith NA 4 FALSE FALSE
# 11: Joe Smith NA 4 FALSE FALSE
My approach is, for each year, to first try and match on first name/last name from a prior year; if that fails, then try to match on first name/maiden name. I want to define flag1 to denote an exact match and flag2 to denote a marriage.
for (yr in 2:4) {
#which ids have we hit so far?
existing_ids = DT[.(yr), unique(id)]
#find people in prior years appearing to
# correspond to those people
unmatched =
DT[.(1:(yr - 1))][!id %in% existing_ids, .SD[.N], by = id]
setkey(unmatched, name, surname)
#merge a la Arun, define flag1
setkey(DT, name, surname)
DT[year == yr, c("id", "flag1") := unmatched[.SD, .(id, TRUE)]]
setkey(DT, year)
#repeat, this time keying on name/maiden_name
existing_ids = DT[.(yr), unique(id)]
unmatched =
DT[.(1:(yr - 1))][!id %in% existing_ids, .SD[.N],by=id]
setkey(unmatched, name, surname)
#now define flag2 = TRUE
setkey(DT, name, maiden_name)
DT[year==yr & is.na(id), c("id", "flag2") := unmatched[.SD, .(id, TRUE)]]
setkey(DT, year)
#this is messy, but I'm trying to increment id
# for "new" individuals
setkey(DT, name, surname, maiden_name)
DT[year == yr & is.na(id),
id := unique(
DT[year == yr & is.na(id)],
by = c("name", "surname", "maiden_name")
)[ , count := .I][.SD, count] + DT[ , max(id, na.rm = TRUE)]
]
#re-sort by year at the end
setkey(DT, year)
}
I was hoping that by including the TRUE value in the j argument while I define id, only the matched names (e.g., Joe at the first step) would have their flag updated to TRUE, but this isn't the case--they are all updated:
DT[]
# name surname maiden_name id year flag1 flag2
# 1: Carol Clymer 3 1 FALSE FALSE
# 2: Jim Jones 2 1 FALSE FALSE
# 3: Joe Smith 1 1 FALSE FALSE
# 4: Joe Smith 1 1 FALSE FALSE
# 5: Ann Cotter 4 2 TRUE TRUE
# 6: Carol Klein Clymer 3 2 TRUE TRUE
# 7: Joe Smith 1 2 TRUE FALSE
# 8: Ann Cotter 4 3 TRUE FALSE
# 9: Beth Brown 5 3 TRUE TRUE
# 10: Joe Smith 1 4 TRUE FALSE
# 11: Joe Smith 1 4 TRUE FALSE
Is there any way to update only the matched rows' flag values? Ideal output is as follows:
DT[]
# name surname maiden_name id year flag1 flag2
# 1: Carol Clymer 3 1 FALSE FALSE
# 2: Jim Jones 2 1 FALSE FALSE
# 3: Joe Smith 1 1 FALSE FALSE
# 4: Joe Smith 1 1 FALSE FALSE
# 5: Ann Cotter 4 2 FALSE FALSE
# 6: Carol Klein Clymer 3 2 FALSE TRUE
# 7: Joe Smith 1 2 TRUE FALSE
# 8: Ann Cotter 4 3 TRUE FALSE
# 9: Beth Brown 5 3 FALSE FALSE
# 10: Joe Smith 1 4 TRUE FALSE
# 11: Joe Smith 1 4 TRUE FALSE
回答1:
I think flags are messy here; better to simply identify the source of the id:
dt[,c("flag1","flag2"):=NULL]
# create name -> id table
namemap <- unique(dt[,.(maiden_name,id,year),keyby=.(name,surname)],by=NULL)
# tag original ids
namemap[!is.na(id),src:="original"]
# carried over from earlier years
namemap[, has_oid := any(!is.na(id)), by=key(namemap)]
namemap[(has_oid),`:=`(
id = id[!is.na(id)],
src = ifelse(is.na(id), "history", src)
),by=.(name,surname)]
# carry over for surname changes on marriage
namemap[maiden_name!="",`:=`(
id = namemap[.BY]$id,
src = "maiden"
),by=.(name,maiden_name)]
# create new ids where none exists
namemap[is.na(id),`:=`(
id = .GRP+max(dt$id,na.rm=TRUE),
src = "new"
),by=.(name,surname)]
# copy back to the original table
setkey(dt,name,surname,year)
setkey(namemap,name,surname,year)
dt[namemap,`:=`(
id = i.id,
src = src
)]
which gives
name surname maiden_name id year src
1: Ann Cotter 4 2 new
2: Ann Cotter 4 3 new
3: Beth Brown 5 3 new
4: Carol Clymer 3 1 original
5: Carol Klein Clymer 3 2 maiden
6: Jim Jones 2 1 original
7: Joe Smith 1 1 original
8: Joe Smith 1 1 original
9: Joe Smith 1 2 history
10: Joe Smith 1 4 history
11: Joe Smith 1 4 history
The original ordering of the data is lost, but it is easy to recover if you want it.
回答2:
The key (no pun intended) I think was to realize that the merge was returning NA for the missed IDs, so I should add the flag to unmatched at each step, e.g., at step 1:
unmatched <- dt[.(1:(yr - 1L))
][!id %in% existing_ids,
.SD[.N], by = id][ , flag1 := TRUE]
dt[year == yr, c("id", "flag1") :=
unmatched[.SD, .(id, flag1), on = "name,surname"]]
In the end, this produces:
> dt[ ]
name surname maiden_name id year flag1 flag2
1: Carol Clymer 3 1 FALSE FALSE
2: Jim Jones 2 1 FALSE FALSE
3: Joe Smith 1 1 FALSE FALSE
4: Joe Smith 1 1 FALSE FALSE
5: Ann Cotter 4 2 NA NA
6: Carol Klein Clymer 3 2 NA TRUE
7: Joe Smith 1 2 TRUE FALSE
8: Ann Cotter 4 3 TRUE FALSE
9: Beth Brown 5 3 NA NA
10: Joe Smith 1 4 TRUE FALSE
11: Joe Smith 1 4 TRUE FALSE
One problem remaining is that some flags that should be F have reset to NA; would be nice to be able to set nomatch=F, but I'm not too worried about this side effect--the key for me is knowing when each flag is T.
来源：https://stackoverflow.com/questions/31329939/conditional-keyed-join-update-and-update-a-flag-column-for-matches