Tidier reshape for list of data.frames (n x 2 data.frames) to single data.frame (n x 3 columns)

Question

I read small text files from many folders into a list. Hence, I have a list of length n with 2 data.frames.

Here's an example of element 3 of the list (dput at the end of question)

ip_list[[3]]
$`dc:a6:32:2d:b6:c4`
# A tibble: 2 x 1
  X1                               
  <chr>                            
1 MAC address is: dc:a6:32:2d:b6:c4
2 IP is: 18.21.162.74              

$`dc:a6:32:2d:b6:c4_running`
# A tibble: 1 x 1
  datetime           
  <dttm>             
1 2020-03-13 19:11:07

My goal is to reshape the list into a data frame with n machines and 3 columns (mac, ip, datetime). I have done so in what I believe is somewhat cumbersome way using:

n_machines <- length(ip_list)

# first element will be the mac and ip
df  <- lapply(1:n_machines,
 function (xx) as.data.frame(t(ip_list[[xx]][[1]]),
    stringsAsFactors = FALSE)) %>%
    bind_rows() %>%
    # now clean
    rename(mac = V1, ip = V2) %>% 
    mutate(mac = str_remove(mac, "MAC address is: "),
           ip = str_remove(ip, "IP is: "))

# second element will be running time

running_time <- lapply(1:n_machines,
 function (xx) as.data.frame(t(ip_list[[xx]][[2]]),
  stringsAsFactors = FALSE)) %>%
    bind_rows() %>%
    rename(datetime = V1)


# join stuff (order should be kept)
df <- bind_cols(df, running_time)

Which produces the expected outcome:

df
                 mac            ip            datetime
1  dc:a6:32:21:59:2b  18.21.129.94                    
2  dc:a6:32:2d:8c:ca 18.21.171.210                    
3  dc:a6:32:2d:b6:c4  18.21.162.74 2020-03-13 19:11:07
4  dc:a6:32:2d:b8:62  18.21.178.96                                    

Question: Is there a better way? I feel like there should be a way to do this. Particularly:

• Relying on the order of elements might hide a problem (would much rather have a way to merge by mac address than rely on 1:n order)
• The whole lapply thing does the job but I get a feeling that it's difficult to debug (and will definitely explode if n_machines = 0)
• Using as.data.frame(t(...)) is cumbersome and I lose the names (which I have to reasign later) but I couldn't figure out a way to pivot_wider or similar.

Here's a small dput

dput(ip_list[1:4])
list(list(`dc:a6:32:21:59:2b` = structure(list(X1 = c("MAC address is: dc:a6:32:21:59:2b", 
"IP is: 18.21.129.94")), class = c("spec_tbl_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -2L), spec = structure(list(
    cols = list(X1 = structure(list(), class = c("collector_character", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 0), class = "col_spec")), `dc:a6:32:21:59:2b_running` = structure(list(
    datetime = ""), class = "data.frame", row.names = c(NA, -1L
))), list(`dc:a6:32:2d:8c:ca` = structure(list(X1 = c("MAC address is: dc:a6:32:2d:8c:ca", 
"IP is: 18.21.171.210")), class = c("spec_tbl_df", "tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -2L), spec = structure(list(
    cols = list(X1 = structure(list(), class = c("collector_character", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 0), class = "col_spec")), `dc:a6:32:2d:8c:ca_running` = structure(list(
    datetime = ""), class = "data.frame", row.names = c(NA, -1L
))), list(`dc:a6:32:2d:b6:c4` = structure(list(X1 = c("MAC address is: dc:a6:32:2d:b6:c4", 
"IP is: 18.21.162.74")), class = c("spec_tbl_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -2L), spec = structure(list(
    cols = list(X1 = structure(list(), class = c("collector_character", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 0), class = "col_spec")), `dc:a6:32:2d:b6:c4_running` = structure(list(
    datetime = structure(1584126667.65542, class = c("POSIXct", 
    "POSIXt"), tzone = "UTC")), class = c("spec_tbl_df", "tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -1L), spec = structure(list(
    cols = list(datetime = structure(list(format = ""), class = c("collector_datetime", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 0), class = "col_spec"))), list(`dc:a6:32:2d:b8:62` = structure(list(
    X1 = c("MAC address is: dc:a6:32:2d:b8:62", "IP is: 18.21.178.96"
    )), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -2L), spec = structure(list(cols = list(
    X1 = structure(list(), class = c("collector_character", "collector"
    ))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 0), class = "col_spec")), `dc:a6:32:2d:b8:62_running` = structure(list(
    datetime = ""), class = "data.frame", row.names = c(NA, -1L
))))

Update

It was a dplyr issue that got resolved when I went for development version (currently 0.8.99.9002).

Both answers provide what's expected and I think are overall improvements. I think the accepted answer is easier on readability but this is highly subjective. My only worry is that my previous lapply option would be quite stable overtime while purrr/dplyr get cannibalized quite often.

Answer 1

One option would be to loop over the 'ip_list' with map, bind the columns (bind_cols), convert the 'datetime' column to DateTime class as some elements have blank only values, then separate the 'X1' column (after converting to 'data.frame' with map_dfr), into two, and reshape into 'wide' format with pivot_wider

library(dplyr)
library(purrr)
library(stringr)
library(lubridate)
map_dfr(ip_list,  ~ 
       .x %>%
           bind_cols %>%
           mutate(datetime = ymd_hms(datetime)), .id = 'grp')  %>% 
   separate(X1, into = c('grp1','val'), sep=" is: ") %>%
   mutate(grp1 = word(grp1, 1)) %>%
   pivot_wider(names_from = grp1, values_from = val) %>%
   select(mac = MAC, ip = IP, datetime) 
# A tibble: 4 x 3
#  mac               ip            datetime           
#  <chr>             <chr>         <dttm>             
#1 dc:a6:32:21:59:2b 18.21.129.94  NA                 
#2 dc:a6:32:2d:8c:ca 18.21.171.210 NA                 
#3 dc:a6:32:2d:b6:c4 18.21.162.74  2020-03-13 19:11:07
#4 dc:a6:32:2d:b8:62 18.21.178.96  NA            

Answer 2

One option involving dplyr, tidyr, purrr and stringr could be:

map_dfr(.x = ip_list, ~ .x %>% 
         bind_rows() %>%
         mutate_all(as.character) %>%
         pivot_longer(everything(), values_drop_na = TRUE), .id = "ID") %>%
 mutate(name = if_else(name == "datetime", name, str_extract(value, "^MAC|^IP")),
        value = str_remove(value, ".*: ")) %>%
 pivot_wider(names_from = "name", values_from = "value") 

  ID    MAC               IP            datetime           
  <chr> <chr>             <chr>         <chr>              
1 1     dc:a6:32:21:59:2b 18.21.129.94  ""                 
2 2     dc:a6:32:2d:8c:ca 18.21.171.210 ""                 
3 3     dc:a6:32:2d:b6:c4 18.21.162.74  2020-03-13 19:11:07
4 4     dc:a6:32:2d:b8:62 18.21.178.96  ""        

Then, if you need datetime as an actual date-time, with the addition of lubridate:

map_dfr(.x = ip_list, ~ .x %>% 
         bind_rows() %>%
         mutate_all(as.character) %>%
         pivot_longer(everything(), values_drop_na = TRUE), .id = "ID") %>%
 mutate(name = if_else(name == "datetime", name, str_extract(value, "^MAC|^IP")),
        value = str_remove(value, ".*: ")) %>%
 pivot_wider(names_from = "name", values_from = "value") %>%
 mutate(datetime = ymd_hms(datetime))