There are bunch of files in a directory that has json formatted entries in each line. The size of the files varies from 5k to 200MB. I have this code to go though each file, parse the data I am looking for in the json and finally form a data frame. This script is taking a very long time to finish, in fact it never finishes.
Is there any way to speed it up so that I can read the files faster?
Code:
library(jsonlite)
library(data.table)
setwd("C:/Files/")
#data <- lapply(readLines("test.txt"), fromJSON)
df<-data.frame(Timestamp=factor(),Source=factor(),Host=factor(),Status=factor())
filenames <- list.files("Json_files", pattern="*.txt", full.names=TRUE)
for(i in filenames){
print(i)
data <- lapply(readLines(i), fromJSON)
myDf <- do.call("rbind", lapply(data, function(d) {
data.frame(TimeStamp = d$payloadData$timestamp,
Source = d$payloadData$source,
Host = d$payloadData$host,
Status = d$payloadData$status)}))
df<-rbind(df,myDf)
}
This is a sample entry but there are thousands of entries like this in the file:
{"senderDateTimeStamp":"2016/04/08 10:53:18","senderHost":null,"senderAppcode":"app","senderUsecase":"appinternalstats_prod","destinationTopic":"app_appinternalstats_realtimedata_topic","correlatedRecord":false,"needCorrelationCacheCleanup":false,"needCorrelation":false,"correlationAttributes":null,"correlationRecordCount":0,"correlateTimeWindowInMills":0,"lastCorrelationRecord":false,"realtimeESStorage":true,"receiverDateTimeStamp":1460127623591,"payloadData":{"timestamp":"2016-04-08T10:53:18.169","status":"get","source":"STREAM","fund":"JVV","client":"","region":"","evetid":"","osareqid":"","basis":"","pricingdate":"","content":"","msgname":"","recipient":"","objid":"","idlreqno":"","host":"WEB01","servermember":"test"},"payloadDataText":"","key":"app:appinternalstats_prod","destinationTopicName":"app_appinternalstats_realtimedata_topic","hdfsPath":"app/appinternalstats_prod","esindex":"app","estype":"appinternalstats_prod","useCase":"appinternalstats_prod","appCode":"app"}
{"senderDateTimeStamp":"2016/04/08 10:54:18","senderHost":null,"senderAppcode":"app","senderUsecase":"appinternalstats_prod","destinationTopic":"app_appinternalstats_realtimedata_topic","correlatedRecord":false,"needCorrelationCacheCleanup":false,"needCorrelation":false,"correlationAttributes":null,"correlationRecordCount":0,"correlateTimeWindowInMills":0,"lastCorrelationRecord":false,"realtimeESStorage":true,"receiverDateTimeStamp":1460127623591,"payloadData":{"timestamp":"2016-04-08T10:53:18.169","status":"get","source":"STREAM","fund":"JVV","client":"","region":"","evetid":"","osareqid":"","basis":"","pricingdate":"","content":"","msgname":"","recipient":"","objid":"","idlreqno":"","host":"WEB02","servermember":""},"payloadDataText":"","key":"app:appinternalstats_prod","destinationTopicName":"app_appinternalstats_realtimedata_topic","hdfsPath":"app/appinternalstats_prod","esindex":"app","estype":"appinternalstats_prod","useCase":"appinternalstats_prod","appCode":"app"}
{"senderDateTimeStamp":"2016/04/08 10:55:18","senderHost":null,"senderAppcode":"app","senderUsecase":"appinternalstats_prod","destinationTopic":"app_appinternalstats_realtimedata_topic","correlatedRecord":false,"needCorrelationCacheCleanup":false,"needCorrelation":false,"correlationAttributes":null,"correlationRecordCount":0,"correlateTimeWindowInMills":0,"lastCorrelationRecord":false,"realtimeESStorage":true,"receiverDateTimeStamp":1460127623591,"payloadData":{"timestamp":"2016-04-08T10:53:18.169","status":"get","source":"STREAM","fund":"JVV","client":"","region":"","evetid":"","osareqid":"","basis":"","pricingdate":"","content":"","msgname":"","recipient":"","objid":"","idlreqno":"","host":"WEB02","servermember":""},"payloadDataText":"","key":"app:appinternalstats_prod","destinationTopicName":"app_appinternalstats_realtimedata_topic","hdfsPath":"app/appinternalstats_prod","esindex":"app","estype":"appinternalstats_prod","useCase":"appinternalstats_prod","appCode":"app"}
With your example data in "c:/tmp.txt":
> df <- jsonlite::fromJSON(paste0("[",paste0(readLines("c:/tmp.txt"),collapse=","),"]"))$payloadData[c("timestamp","source","host","status")]
> df
timestamp source host status
1 2016-04-08T10:53:18.169 STREAM WEB01 get
2 2016-04-08T10:53:18.169 STREAM WEB02 get
3 2016-04-08T10:53:18.169 STREAM WEB02 get
So to adapt your code to get a list of dataframes:
dflist <- lapply(filenames, function(i) {
jsonlite::fromJSON(
paste0("[",
paste0(readLines(i),collapse=","),
"]")
)$payloadData[c("timestamp","source","host","status")]
})
The idea is to transform your lines (from readLines) into a big json array and then create the dataframe by parsing it as json.
As lmo already showcased, using lapply on your filenmaes list procide you with a list of dataframes, if you really want only one dataframe at end you can load the data.table packages and then use rbindlist on dflist to get only one dataframe.
Or if you're short in memory this thread may help you.
One speed up is to replace your for loop with lapply Then drop the final rbind. the speed up here would be that R would not have to repeatedly copy an increasingly large file, df over your "bunch" of files. The result would be stored in a convenient list that you could either use as is or convert to a data.frame in one go:
# create processing function
getData <- function(i) {
print(i)
data <- lapply(readLines(i), fromJSON)
myDf <- do.call("rbind", lapply(data, function(d) {
data.frame(TimeStamp = d$payloadData$timestamp,
Source = d$payloadData$source,
Host = d$payloadData$host,
Status = d$payloadData$status)}))
}
# lapply over files
myDataList <- lapply(filenames, getData)
I'm having a heck of a time trying to convert a JSON file to a data frame. I have searched and tried to use others' code to my example but none seem to fit. The output is always still a list instead of a data frame.
library(jsonlite)
URL <- getURL("http://scores.nbcsports.msnbc.com/ticker/data/gamesMSNBC.js.asp?xml=true&sport=NBA&period=20160104")
URLP <- fromJSON(URL, simplifyDataFrame = TRUE, flatten = FALSE)
URLP
Here is what format the answer always ends up in.
$games
[1] "<ticker-entry gamecode=\"2016010405\" gametype=\"Regular Season\"><visiting-team display_name=\"Toronto\" alias=\"Tor\" nickname=\"Raptors\" id=\"28\" division=\"ECA\" conference=\"EC\" score=\"\"><score heading=\"\" value=\"0\" team-fouls=\"0\"></score><team-record wins=\"21\" losses=\"14\"></team-record><team-logo link=\"http://hosted.stats.com/nba/logos/nba_50x33/Toronto_Raptors.png\" gz-image=\"http://hosted.stats.com/GZ/images/NBAlogos/TorontoRaptors.png\"></team-logo></visiting-team><home-team display_name=\"Cleveland\" alias=\"Cle\" nickname=\"Cavaliers\" id=\"5\" division=\"ECC\" conference=\"EC\" score=\"\"><score heading=\"\" value=\"0\" team-fouls=\"0\"></score><team-record wins=\"22\" losses=\"9\" ties=\"\"></team-record><team-logo link=\"http://hosted.stats.com/nba/logos/nba_50x33/Cleveland_Cavaliers.png\" gz-image=\"http://hosted.stats.com/GZ/images/NBAlogos/ClevelandCavaliers.png\"></team-logo></home-team><gamestate status=\"Pre-Game\" display_status1=\"7:00 PM\" display_status2=\"\" href=\"http://scores.nbcsports.msnbc.com/nba/preview.asp?g=2016010405\" tv=\"FSOH/SNT\" gametime=\"7:00 PM\" gamedate=\"1/4\" is-dst=\"0\" is-world-dst=\"0\"></gamestate></ticker-entry>"
With regards to #jbaums comment, you could try
library(jsonlite)
library(RCurl)
library(dplyr)
library(XML)
URL <- getURL("http://scores.nbcsports.msnbc.com/ticker/data/gamesMSNBC.js.asp?xml=true&sport=NBA&period=20160104")
lst <- lapply(fromJSON(URL)$games, function(x) as.data.frame(t(unlist(xmlToList(xmlParse(x)))), stringsAsFactors=FALSE))
df <- bind_rows(lst)
View(df)
... in theory. However, as #hrbrmstr pointed out: practically, this would violate the website owner's terms of service.
I was trying to use readHTMLTable to store some data in a dataframe in R Studio, but it just keeps telling me could not find function "ReadHTMLTable". I don't understand where I did wrong. Can someone take a lot at this and tell me how I can fix this? or if it works in your R studio.
url <- 'http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html'
ebola <- getURL(url)
ebola <- readHTMLTable(ebola, stringAsFactors = F)
Error: could not find function "readHTMLTable"
You are reading the table in with R default which converts characters to factors. You can use stringsAsFactors = FALSE in readHTMLTable and this will be passed to data.frame. Also the table uses commas for thousand seperators which you will need to remove :
library(XML)
url1 <-'http://en.wikipedia.org/wiki/List_of_Ebola_outbreaks'
df1<- readHTMLTable(url1, which = 2, stringsAsFactors = FALSE)
df1$"Human death"
mySum <- sum(as.integer(gsub(",", "", df1$"Human death")))
> mySum
[1] 6910
The problem is that you dont initialize de XML library
library(XML)
I am trying to use R's rjson library to upload a 200MB JSON file into R , but I got the Cannot fit vector over 1KB error.
Here's the code I used to load the JSON file into R:
UnpackJSON <- function(filePath)
{
con <- file(filePath, "r")
input <- readLines(con, -1L)
# jsonData <- fromJSON(paste(input, collapse=""))
jsonData <- sapply(input, fromJSON)
close(con)
df <- data.frame(jsonData)
temp <- rownames(df)
df <- as.data.frame(t(df))
colnames(df) <- temp
rownames(df) <- NULL
return(df)
}
Is there a way to optimize this code or another way to load such a large file into R? I appreciate any input.
Why would you process the JSON data line per line using sapply? Couldn't you just say fromJSON(input)? I suspect a problem could be that the garbage collector waits for the entire sapply loop to finish before deleting any copies that are made in the analysis process (see also the comments here). So when vectorization does not work (which is greatly preferred), you could try and change the sapply loop to a for loop and see if that helps.
I am trying to grab tweets using the Topsy Otter api, so I can perform some data mining on it for my dissertation.
So far, I have got:
library(RJSONIO)
library(RCurl)
tweet_data <- getURL("http://otter.topsy.com/search.json?q=PSN&mintime=1301634000&perpage=10&maxtime=1304226000&apikey=xxx")
fromJSON(tweet_data)
Which works fine. Now however, I want to return just a couple details from this file, 'content' and 'trackback_date'. I cannot seem to figure out how - I have tried cobbling a couple of examples together, but unable to extract what I want.
Here is what I've tried so far:
trackback_date <- lapply(tweet_data$result, function(x){x$trackback_date})
content <- lapply(tweet_data$result, function(x){x$content})
Any help would be greatly appreciated, thank you.
edit
I have also tried:
library("rjson")
# use rjson
tweet_data <- fromJSON(paste(readLines("http://otter.topsy.com/search.json?q=PSN&mintime=1301634000&perpage=10&maxtime=1304226000&apikey=xxx"), collapse=""))
# get a data from Topsy Otter API
# convert JSON data into R object using fromJSON()
trackback_date <- lapply(tweet_data$result, function(x){x$trackback_date})
content <- lapply(tweet_data$result, function(x){x$content})
Basic processing of Topsy Otter API response:
library(RJSONIO)
library(RCurl)
tweet_data <- getURL("http://otter.topsy.com/search.json?q=PSN&mintime=1301634000&perpage=10&maxtime=1304226000&apikey=xxx")
#
# Addition to your code
#
tweets <- fromJSON(tweet_data)$response$list
content <- sapply(tweets, function(x) x$content)
trackback_date <- sapply(tweets, function(x) x$trackback_date)
EDIT: Processing multiple pages
Function gets 100 items from specified page:
pagetweets <- function(page){
url <- paste("http://otter.topsy.com/search.json?q=PSN&mintime=1301634000&page=",page,
"&perpage=100&maxtime=1304226000&apikey=xxx",
collapse="", sep="")
tweet_data <- getURL(url)
fromJSON(tweet_data)$response$list
}
Now we can apply it to multiple pages:
tweets <- unlist(lapply(1:10, pagetweets), recursive=F)
And, voila, this code:
content <- sapply(tweets, function(x) x$content)
trackback_date <- sapply(tweets, function(x) x$trackback_date)
returns you 1000 records.