R Map returning 2 different data.frame, using by bind_rows

I have a list of .csv files that I am trying to filter one by one (I can't filter when regrouped because I have too much data to load it at the same time).

I want :

To filter my data, then aggregate it in one single data.frame
To have a count of the data I filtered

Here is a (fake) example of my data :

library(tidyverse)
df_list=data.frame(a=seq(1,20,1), b=seq(41,60,1), c=seq(81,100,1)) %>% map(~{ 
  data.frame( a=.x, b=.x*2, c=.x*3)})

I then managed to do :

regrouped_data=df_list %>% map(~{
# Filter
  d2=.x %>% filter(a>5) 
# Count
  print(
    tribble(~date,~initial,~final,
            "name",nrow(.x),nrow(d2)
            )
  )
  return(d2)
}) %>% bind_rows()

The problem is : I need all the data.table to be assembled in one (because I have a lot of files to filter). How can I do that ?

Solution

Can be nice to lay everything out so the logic is clear in a straightforward loop:

 filterCount <- function(){
  
  for(i in 1:length(df_list)){
    
    data_flt <- df_list[[i]] %>% 
      filter(a>5)
    
    count_flt <- tibble(date = i,
                        nrow.total = nrow(df_list[[i]]),
                        nrow.flt = nrow(data_flt))
    
    if(i == 1){
      
      data_out <- data_flt
      count_out <- count_flt
      
    } else {
      
      data_out <- bind_rows(data_out, data_flt)
      count_out <- bind_rows(count_out, count_flt)
      
    }
    
  }

  return(list(data_out, count_out))
  
}