Here are some tidyverse and custom function options that might work depending on your needs:
library(tidyverse)
# custom function to generate, filter, and mutate the data:
combine_dfs <- function(i){
 data_frame(x = rnorm(5), y = runif(5)) %>% 
    filter(x < y) %>% 
    mutate(x_plus_y = x + y) %>% 
    mutate(i = i)
}
df <- 1:5 %>% map_df(~combine_dfs(.))
df <- map_df(1:5, ~combine_dfs(.)) # both give the same results
> df %>% head()
# A tibble: 6 x 4
       x      y x_plus_y     i
   <dbl>  <dbl>    <dbl> <int>
1 -0.973 0.673    -0.300     1
2 -0.553 0.0463   -0.507     1
3  0.250 0.716     0.967     2
4 -0.745 0.0640   -0.681     2
5 -0.736 0.228    -0.508     2
6 -0.365 0.496     0.131     3
You could do something similar if you had a directory of files that needed to be combined:
dir_path <- '/path/to/data/test_directory/'
list.files(dir_path)
combine_files <- function(path, file){
  read_csv(paste0(path, file)) %>% 
    filter(a < b) %>% 
    mutate(a_plus_b = a + b) %>% 
    mutate(file_name = file) 
}
df <- list.files(dir_path, '\\.csv$') %>% 
  map_df(~combine_files(dir_path, .))
# or if you have Excel files, using the readxl package:
combine_xl_files <- function(path, file){
  readxl::read_xlsx(paste0(path, file)) %>% 
    filter(a < b) %>% 
    mutate(a_plus_b = a + b) %>% 
    mutate(file_name = file) 
}
df <- list.files(dir_path, '\\.xlsx$') %>% 
  map_df(~combine_xl_files(dir_path, .))