My questions concerns lagging data in r where r should be aware of the time index. I hope the question has not been asked in any further thread. Lets consider a simple setup:
df <- data.frame(date=as.Date(c("1990-01-01","1990-02-01","1990-01-15","1990-03-01","1990-05-01","1990-07-01","1993-01-02")), value=1:7)
This code should generate a table like
| date | value |
|---|---|
| 1990-01-01 | 1 |
| 1990-02-01 | 2 |
| 1990-01-15 | 3 |
| 1990-03-01 | 4 |
| 1990-05-01 | 5 |
| 1990-07-01 | 6 |
And my aim is now to try to lag the "value" by e.g. one month such that e.g when I try to compute the lagged value of "1990-05-01" (which would be 1990-04-01 but is not present in the data) should then generate an NA in the specific row. When I use the standard lag function r is not aware of the time index and simply uses the value "4" of 1990-03-01 which is not what I want. Has anyone an idea what I could do here?
Thanks in advance! :)
All the best,
Leon
You can try %m-% for lagged month like below
library(lubridate)
transform(
df,
value_lag = value[match(date %m-% months(1), date)]
)
which gives
date value value_lag
1 1990-01-01 1 NA
2 1990-02-01 2 1
3 1990-01-15 3 NA
4 1990-03-01 4 2
5 1990-05-01 5 NA
6 1990-07-01 6 NA
7 1993-01-02 7 NA