mutate list of dataframes with another list

Question

I've got a list of 17 dataframes and a list of 17 dates. They are ordered and correspond to each other. In other words, list_of_dfs[[1]] corresponds to dates[[1]] and so forth. The list of dates, below, are date objects using lubridate::ymd .

> dates
[1] "2004-10-10" "2005-10-10" "2006-10-10" "2007-10-10" "2008-10-10" "2009-10-10" "2010-10-10" "2011-10-10" "2012-10-10" "2013-10-10" "2014-10-10" "2015-10-10" "2016-10-10" "2017-10-10"
[15] "2018-10-10" "2019-10-10" "2020-10-10"

I would like to mutate a subset of variables in each dataframe such that I am subtracting the subset from the corresponding object in dates . For example, I could do the following for the first item.

list_of_dfs[[1]] <- list_of_dfs[[1]] %>% `
  mutate_at(.vars = vars(contains('string')),
            .funs = funs(dates[[1]] - .)

Is there a way that I incorporate the above into a map or lapply like command that will allow me to iterate through dates ?

My closet approximation would be something like

list_of_dfs <- list_of_dfs %>%
      map(., function(x) mutate_at(x,
                               .vars = vars(contains('string')),
                               .funs =  funs(dates - .)))

which can't take a list object in .funs as shown above.

Answer 1

We can use map2 as we are doing the subtraction from corresponding elements of 'dates' list

library(dplyr)
library(purrr)
list_of_dfs2 <- map2(list_of_dfs, dates, ~ {date <- .y
                    .x   %>%
                          mutate_at(vars(contains('string')), ~ date - as.Date(.))})

In the devel version of dplyr , across can be used along with mutate

list_of_dfs2 <- map2(list_of_dfs, dates, ~ { date <- .y
                 .x %>%
                    mutate(across(contains('string'), ~ date - as.Date(.x)))
   })

data

list_of_dfs <- list(data.frame(string1 = Sys.Date() -   1:6, string2 = Sys.Date()), 
          data.frame(string1 = Sys.Date() -   1:6, string2 = Sys.Date()))
dates <- Sys.Date() + 1:2