How do I use mutate_at with multiple functions where each function has parameters?

Question

I want to take multiple lagged values of multiple columns in R.

How do I use mutate_at to get the same results as below? Lets say the real example has 30 columns, so it doesn't make sense to write out the lag formula 30x for each time period.

df <- data_frame(time_col = 1:26, col_1 = letters, col_2 = rev(letters))

df %>% mutate(col_1_lag_1 = lag(col_1, n = 1, by = time_col),
              col_2_lag_1 = lag(col_2, n = 1, by = time_col),
              col_1_lag_2 = lag(col_1, n = 2, by = time_col),
              col_2_lag_2 = lag(col_2, n = 2, by = time_col))

I think it should be something like this, but I don't know how to specify both sets of parameters:

df <- data_frame(time_col = 1:26, col_1 = letters, col_2 = rev(letters))

df %>% mutate_at(vars(col_1, col_2), funs(lag, lag), n = 1, n = 2, by = time_col)

Answer 1

A solution with the help from purrr .

library(dplyr)
library(purrr)

df <- data_frame(time_col = 1:26, col_1 = letters, col_2 = rev(letters))

map_dfc(1:2, function(x){
  df2 <- df %>% transmute_at(vars(starts_with("col")), 
                             funs(lag(., n = x, by = time_col)))
  return(df2)
}) %>%
  bind_cols(df, .) %>%
  set_names(c(names(df), paste0("col_", 1:2, "_lag_", rep(1:2, each = 2))))
# # A tibble: 26 x 7
#    time_col col_1 col_2 col_1_lag_1 col_2_lag_1 col_1_lag_2 col_2_lag_2
#       <int> <chr> <chr> <chr>       <chr>       <chr>       <chr>      
#  1        1 a     z     NA          NA          NA          NA         
#  2        2 b     y     a           z           NA          NA         
#  3        3 c     x     b           y           a           z          
#  4        4 d     w     c           x           b           y          
#  5        5 e     v     d           w           c           x          
#  6        6 f     u     e           v           d           w          
#  7        7 g     t     f           u           e           v          
#  8        8 h     s     g           t           f           u          
#  9        9 i     r     h           s           g           t          
# 10       10 j     q     i           r           h           s          
# # ... with 16 more rows

Answer 2

Here is an alternative purrr solution using a nested map_dfc and quasiquotation syntax

bind_cols(
    df,
    map_dfc(c("col_1", "col_2"), function(i) map_dfc(c(1, 2), function(n)
        df %>%
            transmute(!!paste0(i, "_lag_", n, collapse = "") := lag(!!rlang::sym(i), n = n, by = time_col)))))
## A tibble: 26 x 7
#   time_col col_1 col_2 col_1_lag_1 col_1_lag_2 col_2_lag_1 col_2_lag_2
#      <int> <chr> <chr> <chr>       <chr>       <chr>       <chr>
# 1        1 a     z     NA          NA          NA          NA
# 2        2 b     y     a           NA          z           NA
# 3        3 c     x     b           a           y           z
# 4        4 d     w     c           b           x           y
# 5        5 e     v     d           c           w           x
# 6        6 f     u     e           d           v           w
# 7        7 g     t     f           e           u           v
# 8        8 h     s     g           f           t           u
# 9        9 i     r     h           g           s           t
#10       10 j     q     i           h           r           s
## ... with 16 more rows