## ---- include=FALSE------------------------------------------------------
knitr::opts_chunk$set(
echo = TRUE, eval = FALSE, comment = "#>", collapse = TRUE)
## ---- eval=FALSE---------------------------------------------------------
# ## search for 18000 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)
## ---- eval=FALSE---------------------------------------------------------
# ## 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"
# )
## ---- eval=FALSE---------------------------------------------------------
# ## search for 250,000 tweets containing the word data
# rt <- search_tweets(
# "data", n = 250000, retryonratelimit = TRUE
# )
## ---- eval=FALSE---------------------------------------------------------
# ## 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)))
## ---- eval=FALSE---------------------------------------------------------
# ## random sample for 30 seconds (default)
# rt <- stream_tweets("")
## ---- eval=FALSE---------------------------------------------------------
# ## stream tweets from london for 60 seconds
# rt <- stream_tweets(lookup_coords("london, uk"), timeout = 60)
## ---- eval=FALSE---------------------------------------------------------
# ## 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")
## ---- eval=FALSE---------------------------------------------------------
# ## 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)
## ---- eval=FALSE---------------------------------------------------------
# ## 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$user_id)
## ---- eval=FALSE---------------------------------------------------------
# ## how many total follows does cnn have?
# cnn <- lookup_users("cnn")
#
# ## get them all (this would take a little over 5 days)
# cnn_flw <- get_followers(
# "cnn", n = cnn$followers_count, retryonratelimit = TRUE
# )
## ---- eval=FALSE---------------------------------------------------------
# ## 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"
# )
## ---- eval=FALSE---------------------------------------------------------
# jkr <- get_favorites("jk_rowling", n = 3000)
## ---- eval=FALSE---------------------------------------------------------
# ## search for users with #rstats in their profiles
# usrs <- search_users("#rstats", n = 1000)
## ---- eval=FALSE---------------------------------------------------------
# sf <- get_trends("san francisco")
## ---- eval=FALSE---------------------------------------------------------
# ## 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)
## ---- eval=FALSE---------------------------------------------------------
# post_tweet("my first rtweet #rstats")
## ---- eval=FALSE---------------------------------------------------------
# ## ty for the follow ;)
# post_follow("kearneymw")
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