Run purrr::map_dfr on dataframe rows?

Question

Given a dataframe, say iris default, how to configure purrr::map_dfr() function to run on each row of the dataframe and perform function foo.

Here is one row of my df, please take into account that value is always a large JSON:

structure(list(Key = "2019/01/04/14/kuku@pupu.com_2ed026cb-8e9f-4392-9cc4-9f580b9d3aab_1345a5a4-3d5b-48a0-a678-67ed09a6f487_2019-01-04-14-52-43-537", 
    LastModified = "2019-01-04T14:52:44.000Z", ETag = "\"1c6269ab8b7baa85f0d2567de417f0d0\"", 
    Size = 35280, Owner = "e7c0d260939d15d18866126da3376642e2d4497f18ed762b608ed2307778bdf1", 
    StorageClass = "STANDARD", Bucket = "comp-kukupupu-streamed-data", 
    user_name = "kuku@pupu.com", value = list(---here goes a large json), 
    obs_id = 1137L), row.names = 1L, class = "data.frame")

and my function is:

extract_scroll_data <- function(df) {

  tryCatch({

    j <- fromJSON(unlist(df$value))

    if (is_empty(fromJSON(j$sensorsData)) | is_empty(fromJSON(j$eventList))) {

      return(tibble())

    } else {

      return(set_names(as_tibble(fromJSON(j$eventList, bigint_as_char = TRUE), 
                                 .name_repair = "unique"), 
                       nm = c("time_stamp", 
                              "x", "y", "size", 
                              "pressure", "scroll", "state")) %>%
               dplyr::mutate("user_name" = df$user_name,
                             "obs_id" = df$obs_id))
    }

  }, warning = function(war) {

    # Warning handler picks up where error was generated:
    print(paste0("Warning: occured at ", df$obs_id, war))

  }, error = function(err) {

    # error handler picks up where error was generated
    print(paste0("Error: occured at ", df$obs_id, err))

  }, finally = {

    gc()

  })

}

Please advise why it doesn't use the dataframe rows?

Answer 1

map_dfr(), as any other member of map family iterates over a list and data.frame is really a list of columns. You can check that out with typeof(iris) and as.list(iris). To make map_dfr() iterate over rows instead, you have to transform your data.frame into a list of rows with split() function.

iris %>%
  split(1:nrow(.)) %>%
  purrr::map_dfr(do_stuff)