Find the index of the closest value in data set 2, for each entry in data set one.
A common task in medical work is to find the closest lab value to some index date, for each subject.
neardate(id1, id2, y1, y2, best = c("after", "prior"),
nomatch = NA_integer_)id1 |
vector of subject identifiers for the index group |
id2 |
vector of identifiers for the reference group |
y1 |
normally a vector of dates for the index group, but any orderable data type is allowed |
y2 |
reference set of dates |
best |
if |
nomatch |
the value to return for items without a match |
This routine is closely related to match and to
findInterval, the first of which finds exact matches and
the second closest matches. This finds the closest matching date
within sets of exactly matching identifiers.
Closest date matching is often needed in clinical studies. For
example data set 1 might contain the subject identifier and the date
of some procedure and data set set 2 has the dates and values for
laboratory tests, and the query is to find the first
test value after the intervention but no closer than 7 days.
The id1 and id2 arguments are similar to match in
that we are searching for instances of id1 that will be found
in id2, and the result is the same length as id1.
However, instead of returning the first match with id2 this
routine returns the one that best matches with respect to y1.
The y1 and y2 arguments need not be dates, the function
works for any data type such that the expression
c(y1, y2) gives a sensible, sortable result.
Be careful about matching Date and DateTime values and the impact of
time zones, however, see as.POSIXct.
If y1 and y2 are not of the same class the user is
on their own.
Since there exist pairs of unmatched data types where the result could
be sensible, the routine will in this case proceed under the assumption
that "the user knows what they are doing". Caveat emptor.
the index of the matching observations in the second data set, or
the nomatch value for no successful match
Terry Therneau
data1 <- data.frame(id = 1:10,
entry.dt = as.Date(paste("2011", 1:10, "5", sep='-')))
temp1 <- c(1,4,5,1,3,6,9, 2,7,8,12,4,6,7,10,12,3)
data2 <- data.frame(id = c(1,1,1,2,2,4,4,5,5,5,6,8,8,9,10,10,12),
lab.dt = as.Date(paste("2011", temp1, "1", sep='-')),
chol = round(runif(17, 130, 280)))
#first cholesterol on or after enrollment
indx1 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt)
data2[indx1, "chol"]
# Closest one, either before or after.
#
indx2 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt,
best="prior")
ifelse(is.na(indx1), indx2, # none after, take before
ifelse(is.na(indx2), indx1, #none before
ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) <
abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1)))
# closest date before or after, but no more than 21 days prior to index
indx2 <- ifelse((data1$entry.dt - data2$lab.dt[indx2]) >21, NA, indx2)
ifelse(is.na(indx1), indx2, # none after, take before
ifelse(is.na(indx2), indx1, #none before
ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) <
abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1)))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.