R-Adding rows between rows of a data frame [closed] - r

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I have a dataframe that looks like this:
The second two rows show the Albumin lab values 3 and 63 hours after admission of patient. Between these two rows, I want to add 59 new rows incrementing HoursFromAdmitLab values by one each time so I have one row for each hour after admission. for the 59 newly added rows between first and second row, I want to repeat the first row values of every column with the exception that I want AbnormalityCode and Value be NA and as mentioned before, HoursFromAdmitLab be incremented by 1 in each row.
So I want to have one row for each hour after admission (HoursFromAdmitLab) and for the hours that lab was not taken I want the Value and AbnormalityCode as NA that means there is no value available. The second row of my result data frame should look like this:
I want to repeat this process between second and third row and so on. I tried to this with a loop but it takes for ever and I know there should be a better way.

Answer 1

One possible way to achieve this is to use two different joins:
join with columns which should not be filled
join with columns which should be filled using a rolling join to fill
The data.table package is used for this as the OP has indicated that performance could be crucial for his settings.
library(data.table) # CRAN version 1.10.4
# make sure data is in correct order
setorder(setDT(DT), GUID, Hours)
# create sequence of hours for each case
Hours <- DT[, .(Hours = seq(min(Hours), max(Hours))), by = GUID]
# 1st join with columns which should not be filled
tmp <- DT[, c("GUID", "Hours", "Value", "AbnormCode")][Hours, on = c("GUID", "Hours")]
# 2nd, rolling join with columns which should be filled
result <- DT[, -c("Value", "AbnormCode")][tmp, on = .(GUID, Hours), roll = TRUE]
result
# GUID BirthYearNum GenderCode Hours Value AbnormCode
# 1: 27632200200 1949 Female 3 4.3 N
# 2: 27632200200 1949 Female 4 NA NA
# 3: 27632200200 1949 Female 5 NA NA
# 4: 27632200200 1949 Female 6 NA NA
# 5: 27632200200 1949 Female 7 NA NA
# ---
#273: 27632200200 1949 Female 275 NA NA
#274: 27632200200 1949 Female 276 NA NA
#275: 27632200200 1949 Female 277 NA NA
#276: 27632200200 1949 Female 278 NA NA
#277: 27632200200 1949 Female 279 3.0 L
Note that the approach relies on GUID being the unique key, i.e., it is assumed that a separate sequence has to be created for each GUID.
Data
As the OP has failed to provide reproducible data the following data are used:
library(data.table)
DT <- data.table(
GUID = "27632200200",
BirthYearNum = 1949L,
GenderCode = "Female",
Hours = c(3, 63, 111, 159, 231, 279),
Value = c(4.3, 3.8, 3.6, 3.3, 3, 3),
AbnormCode = c(rep("N", 3), rep("L", 3))
)
DT
# GUID BirthYearNum GenderCode Hours Value AbnormCode
#1: 27632200200 1949 Female 3 4.3 N
#2: 27632200200 1949 Female 63 3.8 N
#3: 27632200200 1949 Female 111 3.6 N
#4: 27632200200 1949 Female 159 3.3 L
#5: 27632200200 1949 Female 231 3.0 L
#6: 27632200200 1949 Female 279 3.0 L
Note that HoursFromAdmitLab has been abbreviated to Hours and AbnormalityCode to AbnormCode.