downloads user timelines and calculates favorites and retweets for last X tweets

tweet_activity.R

# some retweet analysis 

# load necessary packages
library("twitteR")
library("dplyr")
library("lubridate")
library("ggplot2")

# make a twitter app at https://apps.twitter.com/
# and get the api key, secret, and access token info

api_key <- "yourAPIkey"

api_secret <- "yourAPIsecret"

access_token <- "yourAccesstoken"

access_token_secret <- "yourAccesstokenSecret"

setup_twitter_oauth(api_key, api_secret, access_token, access_token_secret)

# download user timeline for a single account
# in this case to get last 500 tweets of @asianturfgrass
 tweets <- userTimeline("asianturfgrass", n = 500, includeRts = TRUE)

# convert to data frame
 tweetsDF <- twListToDF(tweets)

# look only at original tweets, not retweets
 original <- filter(tweetsDF, isRetweet == FALSE)

# mean and media of various tweet activity
 meanFav <- mean(original$favoriteCount)
 meanRT <- mean(original$retweetCount)
 medFav <- median(tweetsDF$favoriteCount)
 medRT <- median(tweetsDF$retweetCount)
 meanIsRT <- mean(tweetsDF$isRetweet)

# date of last tweet, useful to identify inactive accounts
 lastTweet <- ymd_hms(tweetsDF[[1, 5]])

# find which tweets are replies, calculate activity on those
 reply <- filter(original, replyToSN != "NA")
 notReply <- dplyr::anti_join(original, reply, by = "id")
 nRFav <- mean(notReply$favoriteCount)
 nRRT <- mean(notReply$retweetCount)
 nRMedFav <- median(notReply$favoriteCount)
 nRMedRT <- median(notReply$retweetCount)

# for a loop to get this information for follower and following accounts
# choose the account to work with, in this case I use @asianturfgrass
atc <- getUser("asianturfgrass") 

# gets all following and followers
atc.following <- lookupUsers(atc$getFriendIDs())
atc.follower <- lookupUsers(atc$getFollowerIDs())

# converts lists to data frames
followers <- twListToDF(atc.follower)
following <- twListToDF(atc.following)

# filter followers to be only open accounts, if not these requests are denied
# for following, access to the account already granted
followers2 <- filter(followers, protected == FALSE)
 
# combine the accounts, all following, and open followers
accounts <- dplyr::union(following, followers2)

accountsD <- unique(accounts)
   
# analyze only those accounts with >= than 500 tweets
accountsBusy <- filter(accountsD, statusesCount >= 500)

# set a blank data frame to fill in the loop with all calculations
atcAdd <- data.frame()

# set this if one wants to save the downloaded user timelines
# tweetText <- "data/TweetsDFX.csv"

j <- length(accountsBusy$id)

# this loop will download the timeline for all the accounts, calculate various
# measures of tweet activity, and write to a file for later analysis. 
# the timelines can be written to file for string analysis 

for (i in 1:j) {
  tweets <- userTimeline(accountsBusy[[i, 14]], n = 500, includeRts = TRUE)
  tweetsDF <- twListToDF(tweets)
  allFav <- mean(tweetsDF$favoriteCount)
  allRT <- mean(tweetsDF$retweetCount)
  medFav <- median(tweetsDF$favoriteCount)
  medRT <- median(tweetsDF$retweetCount)
  original <- filter(tweetsDF, isRetweet == FALSE)
  meanFav <- mean(original$favoriteCount)
  meanRT <- mean(original$retweetCount)
  medOFav <- median(original$favoriteCount)
  medORT <- median(original$retweetCount)
  meanIsRT <- mean(tweetsDF$isRetweet)
  lastTweet <- ymd_hms(tweetsDF[[1, 5]])
  reply <- filter(original, replyToSN != "NA")
  notReply <- dplyr::anti_join(original, reply, by = "id")
  nRFav <- mean(notReply$favoriteCount)
  nRRT <- mean(notReply$retweetCount)
  nRMedFav <- median(notReply$favoriteCount)
  nRMedRT <- median(notReply$retweetCount)
  orgTweet <- length(original$id)
  nRTweet <- length(notReply$id)
  newline <- cbind.data.frame(accountsBusy[[i, 2]],
                              accountsBusy[[i, 3]],
                              accountsBusy[[i, 5]],
                              accountsBusy[[i, 11]],
                              accountsBusy[[i, 14]],
                   meanFav, meanRT, meanIsRT, lastTweet,
                   allFav, allRT, medFav, medRT, medOFav, medORT,
                   nRFav, nRRT, nRMedFav, nRMedRT, orgTweet, nRTweet)
  atcAdd <-rbind.data.frame(atcAdd, newline)
  write.table(atcAdd, "data/atcAdd.csv", sep = ",", row.names = FALSE)
 # outputFile <- gsub("X", accountsBusy[[i, 14]], tweetText)
 # write.table(tweetsDF, outputFile, sep = ",", row.names = FALSE)
  Sys.sleep(15)
}