rtweet

## install rtweet from CRAN
install.packages("rtweet")

## load rtweet package
library(rtweet)

## install remotes package if it's not already
if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}

## install dev version of rtweet from github
remotes::install_github("ropensci/rtweet")

## load rtweet package
library(rtweet)

## quick overview of rtweet functions
vignette("auth", package = "rtweet")

## quick overview of rtweet functions
vignette("intro", package = "rtweet")

## working with the stream
vignette("stream", package = "rtweet")

## working with the stream
vignette("FAQ", package = "rtweet")

## search for 18000 tweets using the rstats hashtag
rt <- search_tweets(
  "#rstats", n = 18000, include_rts = FALSE
)

## plot time series of tweets
rt %>%
  ts_plot("3 hours") +
  ggplot2::theme_minimal() +
  ggplot2::theme(plot.title = ggplot2::element_text(face = "bold")) +
  ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of #rstats Twitter statuses from past 9 days",
    subtitle = "Twitter status (tweet) counts aggregated using three-hour intervals",
    caption = "\nSource: Data collected from Twitter's REST API via rtweet"
  )

## search for 250,000 tweets containing the word data
rt <- search_tweets(
  "data", n = 250000, retryonratelimit = TRUE
)

## search for 10,000 tweets sent from the US
rt <- search_tweets(
  "lang:en", geocode = lookup_coords("usa"), n = 10000
)

## create lat/lng variables using all available tweet and profile geo-location data
rt <- lat_lng(rt)

## plot state boundaries
par(mar = c(0, 0, 0, 0))
maps::map("state", lwd = .25)

## plot lat and lng points onto state map
with(rt, points(lng, lat, pch = 20, cex = .75, col = rgb(0, .3, .7, .75)))

## random sample for 30 seconds (default)
rt <- stream_tweets("")

## stream tweets from london for 60 seconds
rt <- stream_tweets(lookup_coords("london, uk"), timeout = 60)

## stream london tweets for a week (60 secs x 60 mins * 24 hours *  7 days)
stream_tweets(
  "realdonaldtrump,trump",
  timeout = 60 * 60 * 24 * 7,
  file_name = "tweetsabouttrump.json",
  parse = FALSE
)

## read in the data as a tidy tbl data frame
djt <- parse_stream("tweetsabouttrump.json")

## get user IDs of accounts followed by CNN
cnn_fds <- get_friends("cnn")

## lookup data on those accounts
cnn_fds_data <- lookup_users(cnn_fds$user_id)

## get user IDs of accounts following CNN
cnn_flw <- get_followers("cnn", n = 75000)

## lookup data on those accounts
cnn_flw_data <- lookup_users(cnn_flw$user_id)

## how many total follows does cnn have?
cnn <- lookup_users("cnn")

## get them all (this would take a little over 5 days)
cnn_flw <- get_followers(
  "cnn", n = cnn$followers_count, retryonratelimit = TRUE
)

## get user IDs of accounts followed by CNN
tmls <- get_timelines(c("cnn", "BBCWorld", "foxnews"), n = 3200)

## plot the frequency of tweets for each user over time
tmls %>%
  dplyr::filter(created_at > "2017-10-29") %>%
  dplyr::group_by(screen_name) %>%
  ts_plot("days", trim = 1L) +
  ggplot2::geom_point() +
  ggplot2::theme_minimal() +
  ggplot2::theme(
    legend.title = ggplot2::element_blank(),
    legend.position = "bottom",
    plot.title = ggplot2::element_text(face = "bold")) +
  ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of Twitter statuses posted by news organization",
    subtitle = "Twitter status (tweet) counts aggregated by day from October/November 2017",
    caption = "\nSource: Data collected from Twitter's REST API via rtweet"
  )

jkr <- get_favorites("jk_rowling", n = 3000)

## search for users with #rstats in their profiles
usrs <- search_users("#rstats", n = 1000)

sf <- get_trends("san francisco")