将JSON转换为R数据框

Question

I have a very messy JSON file (lists inside of lists) that I'm trying to convert to an R dataframe (part of the reason to convert the file is that I need to export it into a .csv file). Here is a sample of the data ( https://www.dropbox.com/s/ikb4znhpaavyc9z/20140909-20141010_10zdfxhqf0_2014_10_09_23_50_activities.json?dl=0 ). I tried this solution ( Parse nested JSON to Data Frame in R ), but that got rid of many of my columns. Below is the code I have so far:

    library("twitteR")
    library ("streamR")
    library("rjson")
    json_file <- "20140909-20141010_10zdfxhqf0_2014_09_09_01_00_activities.json"
    json_data <- fromJSON(file=json_file) #convert to r list 
    str (json_data) #list of 16 objects 

    #unlist elements
    tweets.i <- lapply(json_data, function(x){ unlist(x)})
    tweets <- do.call("rbind", tweets.i)
    tweets <- as.data.frame(tweets)

    library(plyr)
    tweets <- rbind.fill(lapply(tweets.i, 
                           function(x) do.call("data.frame", as.list(x))
    ))

Anyone have a way to convert the file to an R dataframe without losing all the info? I'm open to using Python to do this work to, I just don't have the expertise to figure out how to code it.

Answer 1

This is not very efficient, but it may work for you:

download.file("https://www.dropbox.com/s/ikb4znhpaavyc9z/20140909-20141010_10zdfxhqf0_2014_10_09_23_50_activities.json?dl=1", destfile = tf <- tempfile(fileext = ".json"))
txt <- readLines(tf)
library(jsonlite)
library(plyr)
df <- do.call(plyr::rbind.fill, lapply(txt[txt != ""], function(x) as.data.frame(t(unlist(fromJSON(x))))))

Answer 2

I like the answer provided above as a really quick way to get everything. You could try tidyjson , but it also will not be efficient since it requires pre-knowledge of the structure. listviewer::jsonedit might help visualize what you are working with.

#devtools::install_github("timelyportfolio/listviewer")
library(listviewer)
jsonedit(readLines(
   "https://www.dropbox.com/s/ikb4znhpaavyc9z/20140909-20141010_10zdfxhqf0_2014_10_09_23_50_activities.json?dl=1"
 )[2])

Perhaps a data.frame really isn't the best structure, but it really depends on what you are trying to accomplish.

This is just a sample to hopefully show you how it might look.

library(tidyjson)
library(dplyr)

json <- readLines(
  "https://www.dropbox.com/s/ikb4znhpaavyc9z/20140909-20141010_10zdfxhqf0_2014_10_09_23_50_activities.json?dl=1"
)

json %>%
  {
    Filter(
      function(x){return (nchar(x) != 0)}
      ,.
    )
  } %>%  
  as.tbl_json() %>%
  spread_values(
    id = jstring("id")
    ,objectType = jstring("objectType")
    ,link = jstring("link")
    ,body = jstring("body")
    ,favoritesCount = jstring("favoritesCount")
    ,twitter_filter_level = jstring("twitter_filter_level")
    ,twitter_lang = jstring("twitter_lang")
    ,retweetCount = jnumber("retweetCount")
    ,verb = jstring("verb")
    ,postedTime = jstring("postedTime")    
    # from actor object in the JSON
    ,actor_objectType = jstring("actor","objectType")
    ,actor_id = jstring("actor","id")
    ,actor_link = jstring("actor","link")
    ,actor_displayName = jstring("actor","displayName")
    ,actor_image = jstring("actor","image")
    ,actor_summary = jstring("actor","summary")
    ,actor_friendsCount = jnumber("actor","friendsCount")
    ,actor_followersCount = jnumber("actor","followersCount")
  ) %>%
  # careful once you enter you can't go back up
  enter_object("actor","links") %>%
  gather_array( ) %>%
  spread_values(
    actor_href = jstring("href")
  )