Hi everyone,
Please forgive me if my question is simple and my code terrible, I'm new to
R. I am not looking for a ready-made answer, but I would really appreciate
it if someone could share conceptual hints for programming, or point me
toward an R function/package that could speed up my processing time.
Thanks a lot for your help!
##
My dataframe includes the variables 'year', 'id', and 'eif' and has +/- 1.9
million id-year observations
I would like to do 2 things:
-1- I want to create a 'conditional_time' variable, which increases in
increments of 1 every year, but which resets during year(t) if event 'eif'
occured for this 'id' at year(t-1). It should also reset when we switch to a
new 'id'. For example:
dataframe = test
year id eif conditional_time
1990 1010 0 1
1991 1010 0 2
1992 1010 1 3
1993 1010 0 1
1994 1010 0 2
1995 1010 0 3
1996 1010 0 4
1997 1010 1 5
1998 1010 0 1
1999 1010 0 2
2000 1010 0 3
2001 1010 0 4
2002 1010 0 5
2003 1010 0 6
1990 2010 0 1
1991 2010 0 2
1992 2010 0 3
1993 2010 0 4
1994 2010 0 5
1995 2010 0 6
1996 2010 0 7
1997 2010 0 8
1998 2010 0 9
1999 2010 0 10
2000 2010 0 11
2001 2010 1 12
2002 2010 0 1
2003 2010 0 2
-2- In a copy of the original dataframe, drop all id-year rows that
correspond to years after a given id has experienced his first 'eif' event.
I have written the code below to take care of -1-, but it is incredibly
inefficient. Given the size of my database, and considering how slow my
computer is, I don't think it's practical to use it. Also, it depends on
correct sorting of the dataframe, which might generate errors.
##
for (i in 1:nrow(test)) {
if (i == 1) { # If first id-year
cond_time <- 1
test[i, 4] <- cond_time
} else if ((test[i-1, 1]) != (test[i, 4])) { # If new id
cond_time <- 1
test[i, 4] <- cond_time
} else { # Same id as previous row
if (test[i, 3] == 0) {
test[i, 4] <- sum(cond_time, 1)
cond_time <- test[i, 6]
} else {
test[i, 4] <- sum(cond_time, 1)
cond_time <- 0
}
}
}
--
Vincent Arel
M.A. Student, McGill University
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