Replicate rows by different N - r

Question

I’ve the following data
mydata <- data.frame(id=c(1,2,3,4,5), n=c(2.63, 1.5, 0.5, 3.5, 4))
1) I need to repeat number of rows for each id by n. For example, n=2.63 for id=1, then I need to replicated id=1 row three times. If n=0.5, then I need to replicate it only one time... so n needs to be round up.
2) Create a new variable called t, where the sum of t for each id must equal to n.
3) Create another new variable called accumulated.t
Here how the output looks like:
id n t accumulated.t
1 2.63 1 1
1 2.63 1 2
1 2.63 0.63 2.63
2 1.5 1 1
2 1.5 0.5 1.5
3 0.5 0.5 0.5
4 3.5 1 1
4 3.5 1 2
4 3.5 1 3
4 3.5 0.5 3.5
5 4 1 1
5 4 1 2
5 4 1 3
5 4 1 4

Answer 1

Get the ceiling of 'n' column and use that to expand the rows of 'mydata' (rep(1:nrow(mydata), ceiling(mydata$n)))
Using data.table, we convert the 'data.frame' to 'data.table' (setDT(mydata1)), grouped by 'id' column, we replicate (rep) 1 with times specified as the trunc of the first value of 'n' (rep(1, trunc(n[1]))). Take the difference between the unique value of 'n' per group and the sum of 'tmp' (n[1]-sum(tmp)). If the difference is greater than 0, we concatenate 'tmp' and 'tmp2' (c(tmp, tmp2)) or if it is '0', we take only 'tmp'. This can be placed in a list to create the two columns 't' and the cumulative sum of 'tmp3 (cumsum(tmp3)).
library(data.table)
mydata1 <- mydata[rep(1:nrow(mydata),ceiling(mydata$n)),]
setDT(mydata1)[, c('t', 'taccum') := {
tmp <- rep(1, trunc(n[1]))
tmp2 <- n[1]-sum(tmp)
tmp3= if(tmp2==0) tmp else c(tmp, tmp2)
list(tmp3, cumsum(tmp3)) },
by = id]
mydata1
# id n t taccum
# 1: 1 2.63 1.00 1.00
# 2: 1 2.63 1.00 2.00
# 3: 1 2.63 0.63 2.63
# 4: 2 1.50 1.00 1.00
# 5: 2 1.50 0.50 1.50
# 6: 3 0.50 0.50 0.50
# 7: 4 3.50 1.00 1.00
# 8: 4 3.50 1.00 2.00
# 9: 4 3.50 1.00 3.00
#10: 4 3.50 0.50 3.50
#11: 5 4.00 1.00 1.00
#12: 5 4.00 1.00 2.00
#13: 5 4.00 1.00 3.00
#14: 5 4.00 1.00 4.00

Answer 2

An alternative that utilizes base R.
mydata <- data.frame(id=c(1,2,3,4,5), n=c(2.63, 1.5, 0.5, 3.5, 4))
mynewdata <- data.frame(id = rep(x = mydata$id,times = ceiling(x = mydata$n)),
n = mydata$n[match(x = rep(x = mydata$id,ceiling(mydata$n)),table = mydata$id)],
t = rep(x = mydata$n / ceiling(mydata$n),times = ceiling(mydata$n)))
mynewdata$t.accum <- unlist(x = by(data = mynewdata$t,INDICES = mynewdata$id,FUN = cumsum))
We start by creating a data.frame with three columns, id, n, and t. id is calculated using rep and ceiling to repeat the ID variable the number of appropriate times. n is obtained by using match to look up the right value in mydata$n. t is obtained by obtaining the ratio of n and ceiling of n, and then repeating it the appropriate amount of times (in this case, ceiling of n again.
Then, we use cumsum to get the cumulative sum, called using by to allow by-group processing for each group of IDs. You could probably use tapply() here as well.