I would like to lead/lag variable, but I don't want to lose any points.
For example: x (xts object):
2017-06-30 1
2017-07-31 2
2017-08-31 3
2017-09-30 4
2017-10-31 5
2017-11-30 6
2017-12-31 7
2018-01-31 8
2018-02-28 9
2018-03-31 10
When I do
lag(x,2)
I get:
2017-06-30 NA
2017-07-31 NA
2017-08-31 1
2017-09-30 2
2017-10-31 3
2017-11-30 4
2017-12-31 5
2018-01-31 6
2018-02-28 7
2018-03-31 8
I lost 9 for 2018-04-30 and 10 for 2018-05-31. Is there a way to keep them? I know it is a problem with finding additional dates (2018-04-30 and 2018-05-31), but maybe there is a way to have it done automatically.
Somewhat prolix, but this does the job. This requires dplyr and magrittr.
# Original data frame
df <- data.frame(date = seq(as.Date("2017-07-01"), length=10, by="1 month") - 1, n = 1:10)
# date n
# 1 2017-06-30 1
# 2 2017-07-31 2
# 3 2017-08-31 3
# 4 2017-09-30 4
# 5 2017-10-31 5
# 6 2017-11-30 6
# 7 2017-12-31 7
# 8 2018-01-31 8
# 9 2018-02-28 9
# 10 2018-03-31 10
Next, I define the lag length:
# Length of lag
lag_length <- 2
Here, I create the extra rows to be added:
# Extra rows to add
extra <- data.frame(date = (seq(tail(df$date, 1) + 1, length = lag_length + 1, by = "1 month") - 1)[-1], n = NA)
Finally, I bind them to the original data frame and lag the variable n:
# Bind extra rows and lag 'n' by 'lag_length'
df %<>%
bind_rows(extra) %>%
mutate(n = lag(n, lag_length))
# New data frame
# date n
# 1 2017-06-30 NA
# 2 2017-07-31 NA
# 3 2017-08-31 1
# 4 2017-09-30 2
# 5 2017-10-31 3
# 6 2017-11-30 4
# 7 2017-12-31 5
# 8 2018-01-31 6
# 9 2018-02-28 7
# 10 2018-03-31 8
# 11 2018-04-30 9
# 12 2018-05-31 10