A collection of helper functions for twitter analysis using the {rtweet} package. NB: these functions have MANY dependencies… no warranty is offered, but please feel free to log issues. NB: This branch is now in development mode for the upcoming {rtweet version 1.0}. For compatibility with rtweet 0.7, please rather use the legacy version branch. Things will break here.
To install the package, use the {remotes} or {devtools} package:
remotes::install_github("arf9999/rtweetXtras")
hashtagcloud(rtweet_timeline_df, num_words = 200)
Delivers a wordcloud of hashtag terms in an rtweet tibble.
df <- rtweet::get_timeline("jack", n=1000)
rtweetXtras::hashtagcloud(df, n=200)
profilecloud(rtweet_timeline_df, num_words = 200)
Delivers a wordcloud of terms in unique twitter user profiles in an
rtweet tibble.
df1 <- rtweet::search_tweets("rstats",lang = "en", n= 2000)
rtweetXtras::profilecloud(df1, 100)
bar_plot_mentions(rtweet_df, no_of_bars = 20, title = NULL)
Delivers a bar plot of the count of user mentions in an rtweet tibble.
Defaults to top twenty mentioned accounts.
df1 <- rtweet::search_tweets("#rstats", lang = "en", n = 2000)
rtweetXtras::bar_plot_mentions(df1, 20, title = paste("Barplot of user mentions in twitter search for \"#rstats\"", Sys.Date()))
common_follower_analysis (user_list, follower_depth = 200, no_of_sets = 7, token = NULL)
This function creates an UpSetR graph of common followers Code cribbed
from Bob Rudis’ 21 Recipes for Mining Twitter with Rtweet
https://rud.is/books/21-recipes/visualizing-intersecting-follower-sets-with-upsetr.html
rstats_list <- c("hadleywickham", "dataandme", "juliasilge", "statgarrett","thomasp85")
rtweetXtras::common_follower_analysis(rstats_list, follower_depth = 1000, no_of_sets = 5, token = NULL)
common_follower_matrix (user_list, follower_depth = 200, token = NULL)
This function creates a matrix of followers of a list of twitter users,
sums the number of common followers, and then ranks them in descending
order.
rstats_list <- c("hadleywickham", "dataandme", "juliasilge", "statgarrett","thomasp85")
fm <- rtweetXtras::common_follower_matrix(rstats_list, follower_depth = 200, token = NULL)
dplyr::glimpse(fm)
## Rows: 902
## Columns: 9
## $ screen_name <chr> "statistician_dr", "ntthong", "Sahar62195425", "TimF…
## $ user_id <chr> "1537438062564626433", "133186653", "124793356659759…
## $ hadleywickham <dbl> 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dataandme <dbl> 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ juliasilge <dbl> 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0…
## $ statgarrett <dbl> 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1…
## $ thomasp85 <dbl> 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ sum_intersections <dbl> 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2…
## $ ranking <int> 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3…
account_activity(account_name, depth = 3200, time_zone = "Africa/Johannesburg", no_of_weeks = 4, token = NULL)
This function creates a bubble plot of account activity by hour of a
single twitter screen_name
(inspired by python script by twitter user “[@Conspirator0]”)
rtweetXtras::account_activity("arfness", depth = 1000, time_zone = "Africa/Johannesburg", no_of_weeks = 5, token = NULL)
follower_dot_plot(follower_df, point_colour = "statuses_count", show_legend = TRUE, suppress_warnings = TRUE, include_loess_smooth = FALSE, include_lm = FALSE, print_immediately = TRUE, log_transform = FALSE, viridis_option = "magma")
This builds a ggplot2 scatter plot with the creation date of twitter followers mapped to the order in which they followed. Various options of dot colouring are available to examine the follower’s attributes. Optionally, a loess smoothed curve and/or a linear model can be overlaid to analyse the change in follower creation dates over time.
arfness_followers <- rtweetXtras::get_followers_fast("arfness")
## [1] "arfness follower count = 6859"
## [1] "followers captured: 6859 out of 6859"
rtweetXtras::follower_dot_plot(arfness_followers, point_colour = "earliest_follow")
scatter_ts_interactive(df, title = "", print_immediately = TRUE)
Builds an interactive timeline of tweets indicating time of tweet, author, type (tweet, retweet, quote, reply), and rate of tweets (posts per second) - posts per second is calculated as moving average over 10 tweets. Posts per second are calculated separately for content tweets (tweets, quotes and replies) and copy tweets (retweets). Output is an Apache echarts HTML widget.
mickey <- rtweet::search_tweets("Mickey Mouse", n = 100)
rtweetXtras::scatter_ts_interactive(mickey, title = "100 tweets about Mickey Mouse")
The package includes some additional tools and wrappers for rtweet functions:
get_followers_fast and get_friends_fast wrap rtweet functions to
deliver a tibble of followers/friends that includes user details. The
order of friendship and following is added as separate column, the
account name being followed, befriended is added as a column, and the
earliest following or befriending date is added as a column.
In addition it is possible to pass a list of tokens to the function to
manage ratelimiting when querying accounts with large
following/friendship.
rtweet_net and save_csv_edgelist are functions to create an igraph
network and to save that as an edgelist for use in external
visualisation software. This is not being maintained - replaced by
create_gexf
create_gexf creates a gexf file for export to Gephi for visualisation.
write_csv_compatible saves a csv file of an rtweet tibble with a
modified “text” column to include “RT [@retweet_screen_name]:” for
all Retweets. Original text column is saved as additional column “text2”
snscrape_search is a function that uses the Python snscrape library to
search historical twitter. Python 3.8 and snscrape need to be installed.
See https://github.com/JustAnotherArchivist/snscrape for more
information.
snscrape_get_timeline is a function that uses Python snscrape library
to pull a twitter user timeline, and rtweet to rehydrate it. Note:
Currently, unlike rtweet::get_timeline(), no retweets are captured,
and there is no API limitation of 3200 statuses as snscrape uses the web
search facility of twitter.
get_perspective is a function to query the Google Perspective API to
classify toxicity in text. More information here:
https://www.perspectiveapi.com/ NB: A Google Cloud API key is required
to use this function. Instructions on how to set it up are here:
https://developers.perspectiveapi.com/s/docs-get-started
perspective_rtweet Queries the text from an rtweet dataframe,
sequentially by row, using the get_perspective function. A Perspective
API key is required. In addition, the number of queries per second can
be set if this has been negotiated with the Perspective team.
check_shadowban is a function to check whether an account has been
temporarily suppressed from search or display results by Twitter.
check_shadowban_list allows a list of twitter handles to be passed to
check_shadowban