fromJSON returns a list, you can use the *apply functions to go through each element.It's fairly straightforward (once you know what to do!) to convert it to a "table" (data frame is the correct R terminology).

library(rjson)# You can pass directly the filenamemy.JSON <- fromJSON(file="test.json")df <- lapply(my.JSON, function(play) # Loop through each "play"  {  # Convert each group to a data frame.  # This assumes you have 6 elements each time  data.frame(matrix(unlist(play), ncol=6, byrow=T))  })# Now you have a list of data frames, connect them together in# one single dataframedf <- do.call(rbind, df)# Make column names nicer, remove row namescolnames(df) <- names(my.JSON[[1]][[1]])rownames(df) <- NULLdf  wins losses max_killed battles plane_id max_ground_object_destroyed1  118     40          7     158     4401                           32  100     58          7     158     2401                           33  120     38          7     158     2403                           34   12    450          7     158     4401                           35  150      8          7     158     2401                           36  120    328          7     158     2403                           3

I find jsonlite to be a little more user friendly for this task. Here is a comparison of three JSON parsing packages (biased in favor of jsonlite)

library(jsonlite)data <- fromJSON('path/to/file.json')data#> $play1#   wins losses max_killed battles plane_id max_ground_object_destroyed# 1  118     40          7     158     4401                           3# 2  100     58          7     158     2401                           3# 3  120     38          7     158     2403                           3# # $play2#   wins losses max_killed battles plane_id max_ground_object_destroyed# 1   12    450          7     158     4401                           3# 2  150      8          7     158     2401                           3# 3  120    328          7     158     2403                           3

If you want to collapse those list names into a new column, I recommend dplyr::bind_rows rather than do.call(rbind, data)

library(dplyr)data <- bind_rows(data, .id = 'play')# Source: local data frame [6 x 7]#    play  wins losses max_killed battles plane_id max_ground_object_destroyed#   (chr) (chr)  (chr)      (chr)   (chr)    (chr)                       (chr)# 1 play1   118     40          7     158     4401                           3# 2 play1   100     58          7     158     2401                           3# 3 play1   120     38          7     158     2403                           3# 4 play2    12    450          7     158     4401                           3# 5 play2   150      8          7     158     2401                           3# 6 play2   120    328          7     158     2403                           3

Beware that the columns may not have the type you expect (notice the columns are all characters since all of the numbers were quoted in the provided JSON data)!

Edit Nov. 2017: One approach to type conversion would be to use mutate_if to guess the intended type of character columns.

data <- mutate_if(data, is.character, type.convert, as.is = TRUE)

I prefer tidyjson over rjson and jsonlite as it has a easy workflow for converting multilevel nested json objects to 2 dimensional tables. Your problem can be easily solved using this package from github.

devtools::install_github("sailthru/tidyjson")library(tidyjson)library(dplyr)> json %>%  as.tbl_json %>% gather_keys %>% gather_array %>%  +   spread_values(+     wins = jstring("wins"),+     losses = jstring("losses"),+     max_killed = jstring("max_killed"),+     battles = jstring("battles"),+     plane_id = jstring("plane_id"),+     max_ground_object_destroyed = jstring("max_ground_object_destroyed")+    )

Output

  document.id   key array.index wins losses max_killed battles plane_id max_ground_object_destroyed1           1 play1           1  118     40          7     158     4401                           32           1 play1           2  100     58          7     158     2401                           33           1 play1           3  120     38          7     158     2403                           34           1 play2           1   12    450          7     158     4401                           35           1 play2           2  150      8          7     158     2401                           36           1 play2           3  120    328          7     158     2403                           3