I have two sets of data:
First set:
patient<-c("A","A","B","B","C","C","C","C")
arrival<-c("11:00","11:00","13:00","13:00","14:00","14:00","14:00","14:00")
lastRow<-c("","Yes","","Yes","","","","Yes")
data1<-data.frame(patient,arrival,lastRow)
Another set of data:
patient<-c("A","A","A","A","B","B","B","C","C","C")
availableSlot<-c("11:15","11:35","11:45","11:55","12:55","13:55","14:00","14:00","14:10","17:00")
data2<-data.frame(patient, availableSlot)
I want to create add a column to the first dataset such that for each last row of each patient, it shows the available slot that is closest to the arrival time:
The results would be:
patient arrival lastRow availableSlot
A 11:00
A 11:00 Yes 11:15
B 13:00
B 13:00 Yes 12:55
C 14:00
C 14:00
C 14:00
C 14:00 Yes 14:00
Would appreciate if anyone can tell me how I can implement this in R.
I'd use data.table, first cleaning up by converting to ITime and ignoring redundant rows:
library(data.table)
setDT(data1)[, arrival := as.ITime(as.character(arrival))]
setDT(data2)[, availableSlot := as.ITime(as.character(availableSlot))]
DT1 = unique(data1, by="patient", fromLast=TRUE)
Then you can do a "rolling join":
res = data2[DT1, on=.(patient, availableSlot = arrival), roll="nearest",
.(patient, availableSlot = x.availableSlot)]
# patient availableSlot
# 1: A 11:15:00
# 2: B 12:55:00
# 3: C 14:00:00
How it works
The syntax is x[i, on=, roll=, j]
.
on=
are the merge-by columns. i
, we are looking for matches in x
.roll="nearest"
, the final column in the on=
is "rolled" to its nearest match.on=
columns in the original tables can be referenced with x.*
and i.*
prefixes.j
argument should give a list of columns, and .()
is an alias for list()
here.Check out the package's introductory materials at http://r-datatable.com/Getting-started and type ?data.table
for the docs relevant to rolling joins.
I would stop at res
, but if you really want it back in your original table...
# a very nonstandard step:
data1[lastRow == "Yes", availableSlot := res$availableSlot ]
# patient arrival lastRow availableSlot
# 1: A 11:00:00 <NA>
# 2: A 11:00:00 Yes 11:15:00
# 3: B 13:00:00 <NA>
# 4: B 13:00:00 Yes 12:55:00
# 5: C 14:00:00 <NA>
# 6: C 14:00:00 <NA>
# 7: C 14:00:00 <NA>
# 8: C 14:00:00 Yes 14:00:00
Now, data1
has availableSlot
in a new column, similar to when you do data1$col <- val
.
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