# Intro to rtweet: Collecting Twitter Data

#### Michael W. Kearney

This vignette provides a quick tour of the R package `rtweet: Collecting Twitter Data`.

### Search tweets

Search for up to 18,000 (non-retweeted) tweets containing the rstats hashtag.

    ## search for 5000 tweets using the rstats hashtag
    rt <- search_tweets(
      "#rstats", n = 18000, include_rts = FALSE
    )

    ## preview tweets data
    rt

    ## preview users data
    users_data(rt)

    ## plot time series (if ggplot2 is installed)
    ts_plot(rt)

Quickly visualize frequency of tweets over time using `ts_plot()`.

    ## plot time series of tweets
    ts_plot(rt, "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"
      )

Twitter rate limits cap the number of search results returned to 18,000 every 15 minutes. To request more than that, simply set `retryonratelimit = TRUE` and rtweet will wait for rate limit resets for you.

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

Search by geo-location—for example, find 10,000 tweets in the English language sent from the United States.

    ## 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)))

### Stream tweets

Randomly sample (approximately 1%) from the live stream of all tweets.

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

Stream all geo enabled tweets from London for 60 seconds.

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

Stream all tweets mentioning realDonaldTrump or Trump for a week.

    ## 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 friends

Retrieve a list of all the accounts a user follows.

    ## 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 followers

Retrieve a list of the accounts following a user.

    ## 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)

Or if you really want ALL of their followers:

    ## 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 timelines

Get the most recent 3,200 tweets from cnn, BBCWorld, and foxnews.

    ## 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"
      )

### Get favorites

Get the 3,000 most recently favorited statuses by JK Rowling.

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

### Search users

Search for 1,000 users with the rstats hashtag in their profile bios.

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

### Get trends

Discover what’s currently trending in San Francisco.

    sf <- get_trends("san francisco")

### Lookup users

    ## lookup users by screen_name or user_id
    users <- c("KimKardashian", "justinbieber", "taylorswift13",
               "espn", "JoelEmbiid", "cstonehoops", "KUHoops",
               "upshotnyt", "fivethirtyeight", "hadleywickham",
               "cnn", "foxnews", "msnbc", "maddow", "seanhannity",
               "potus", "epa", "hillaryclinton", "realdonaldtrump",
               "natesilver538", "ezraklein", "annecoulter")
    famous_tweeters <- lookup_users(users)

    ## preview users data
    famous_tweeters

    # extract most recent tweets data from the famous tweeters
    tweets_data(famous_tweeters)

### Posting statuses

    post_tweet("my first rtweet #rstats")

### Following users

    ## ty for the follow ;)
    post_follow("kearneymw")