Adding and subtracting values from columns in dataframe - r

Question

I've a dataframe as under:
+-----+---------+-----+-----+-----------------+----------+----------+------------+
| ID | CURRENT | JAN | FEB | CURRENT_IN_2018 | JAN_2018 | FEB_2018 | UNITS_SWAP |
+-----+---------+-----+-----+-----------------+----------+----------+------------+
| 123 | 2 | 3 | 4 | 5 | 6 | 7 | 12 |
| 456 | 1 | 5 | 0 | 4 | 8 | 6 | 6 |
+-----+---------+-----+-----+-----------------+----------+----------+------------+
What I'm trying to do here is subtract the number in UNITS_SWAP from CURRENT_IN_2018, JAN_2018 and FEB_2018 sequentially until the number in UNITS_SWAP reaches zero. Also while doing this, add the number of subtracted UNITS_SWAP from each row to their respective matching rows, for instance if 5 units are deducted from current_in_2018 then add 5 units in CURRENT, so on and so forth for JAN and FEB such that the end result is as under:
+-----+---------+-----+-----+-----------------+----------+----------+------------+
| ID | CURRENT | JAN | FEB | CURRENT_IN_2018 | JAN_2018 | FEB_2018 | UNITS_SWAP |
+-----+---------+-----+-----+-----------------+----------+----------+------------+
| 123 | 7 | 9 | 5 | 0 | 0 | 6 | 0 |
| 456 | 5 | 7 | 0 | 0 | 6 | 6 | 0 |
+-----+---------+-----+-----+-----------------+----------+----------+------------+
Script to load the data:
DF <- data.frame(ID = c(123,456),
CURRENT = c(2,1),
JAN = c(3,5),
FEB=c(4,0),
CURRENT_2018 = c(5,4),
JAN_2018 = c(6,8),
FEB_2018=c(7,6),
UNITS_SWAP =c(12,6))

Answer 1

You could do this - though note that it will overwrite your source DF:
cols <- c('CURRENT', 'JAN', 'FEB')
for (i in 1:NROW(DF)) {
while (DF[i, 'UNITS_SWAP'] > 0) {
for (col in cols) {
excess <- min(DF[i,'UNITS_SWAP'], DF[i, paste0(col, '_2018')])
DF[i, col] <- DF[i, col] + excess
DF[i, paste0(col, '_2018')] <- DF[i, paste0(col, '_2018')] - excess
DF[i, 'UNITS_SWAP'] <- DF[i, 'UNITS_SWAP'] - excess
}
}
}
Because your columns have a definite structure (column matching column_2018), we just need to run through them in the order you specified, and paste _2018 to get the relevant matching column.