Example of creating multiple lags with dplyr

multiple_lags.R

library(dplyr)

d <- data_frame(x = seq_len(100))
d
#> # A tibble: 100 x 1
#>        x
#>    <int>
#>  1     1
#>  2     2
#>  3     3
#>  4     4
#>  5     5
#>  6     6
#>  7     7
#>  8     8
#>  9     9
#> 10    10
#> # ... with 90 more rows

lags <- seq(10)
lag_names <- paste("lag", formatC(lags, width = nchar(max(lags)), flag = "0"), 
  sep = "_")
lag_functions <- setNames(paste("dplyr::lag(., ", lags, ")"), lag_names)

d %>% mutate_at(vars(x), funs_(lag_functions))
#> # A tibble: 100 x 11
#>        x lag_01 lag_02 lag_03 lag_04 lag_05 lag_06 lag_07 lag_08 lag_09
#>    <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>
#>  1     1     NA     NA     NA     NA     NA     NA     NA     NA     NA
#>  2     2      1     NA     NA     NA     NA     NA     NA     NA     NA
#>  3     3      2      1     NA     NA     NA     NA     NA     NA     NA
#>  4     4      3      2      1     NA     NA     NA     NA     NA     NA
#>  5     5      4      3      2      1     NA     NA     NA     NA     NA
#>  6     6      5      4      3      2      1     NA     NA     NA     NA
#>  7     7      6      5      4      3      2      1     NA     NA     NA
#>  8     8      7      6      5      4      3      2      1     NA     NA
#>  9     9      8      7      6      5      4      3      2      1     NA
#> 10    10      9      8      7      6      5      4      3      2      1
#> # ... with 90 more rows, and 1 more variables: lag_10 <int>

This was great! As funs_() is now deprecated, I had to do some reading up, and I came up with the following as a possible alternative without using funs_(): https://gist.github.com/mpettis/c4a4e930e6e0d69b25249484378e9f5f

The hard part for me is the string -> function part, which works for functions that already have names, but parsing a string specifying a function was hard -- lambda function parsing worked for me.

Thank you so much. Your code helps me a lot.

See my example doing similar here: https://gist.github.com/brshallo/cadaa40cef6387e28924b6c3756627c9

Here is another alternative solution using across, which gives a little more flexibility in selecting which columns to lag.

https://gist.github.com/RJHKnight/22dbe5a3ef1d2701afd48370a1f1742c