Source tweets

Now we will extract our first set of Twitter data.

The get_timeline function contains 90 fields for each tweet. The BVB account posts frequently so we will limit to the last 500 tweets.

   tweets_raw <- get_timeline (twitter_user, n = 500)

Transform Tweets

Many fields are not needed for this exercise so we can cull the dataset. I have also renamed several fields for brevity and more standard Twitter notation (i.e. likes instead of favorites).

We are focusing on original tweets so have filtered out retweets.

I have extracted year from the creation date to enable summarizing tweet volume by year. Also, I have truncated the Tweet text field from 280 to 60 characters so more cleanly fits in a formatted table.

   library (lubridate)

   tweets <- tweets_raw %>%
      filter (is_retweet == FALSE) %>%
      rename (
         likes = favorite_count,
         retweets = retweet_count,
         created = created_at
         ) %>%
      mutate (
         text = str_sub(text,1,60),
         created = as.Date(created),
         year = year(created)
         ) %>%
      rename(date = created) %>%
      select (text, date, year, likes, retweets)

Visualize most liked tweets

We will sort by most liked tweets over time and rank via the dplyr row_to_column formula.

The GT package by Richard Iannone formats tables in a easy-to-read manner. The core gt::gt function adds lines between rows. Many other table formatting enhancements are available but out of scope for this blog.

The tweet with the most likes was BVB’s tribute to football legend Diego Maradona, who recently passed away.

   library (gt)

   tweets %>%
      arrange (-likes, -retweets) %>%
      head (10) %>%
      rowid_to_column("rank") %>%
      select (-year) %>%
      relocate (rank) %>%
      gt () 

Visualize most retweeted tweets

Next we will re-sort and re-rank by retweets, again using the GT package to cleanly format.

A tweet celebrating player striker Giovanni Reyna signing a contract extension produced the most retweets. The tribute to Argentine football legend Diego Maradona (most liked tweet) was #2 when ranking by retweets.

   tweets %>%
      arrange (-retweets, -likes) %>%
      head (10) %>%
      rowid_to_column("rank") %>%
      select (rank, text, date, retweets, likes) %>%
      gt ()

Visualize number of tweets trends

Another area of interest is how tweet activity has evolved over time. Using dplyr we can easily summarize by number of tweets by day.

The volume of tweets was smallest during the international break in early to mid November. BVB does not play games during international breaks because the players are away representing their respective national football teams.

   tweets %>%
      group_by (date) %>%
      count () %>%
      ggplot (aes (x = date, y = n)) +
         geom_col () +
         geom_label (aes(label = n)) +
         theme_minimal() +
         labs (x="", y = "Number of tweets")

Source followers

Learning about followers for a Twitter handle is also frequently useful.

Two rtweet package functions are helpful here. First, get_followers returns a vector of Twitter user_ids of followers. The lookup_users function then extracts user attributes such as screen name, physical location (if supplied in profile) and number of followers.

The BVB account enjoys 583,000 followers so we will limit to 500 users for this blog entry. This is not a representative sample and is provided for illustrating R’s visualization capabilities.

We will also add a row ID ranking each follower by largest number of followers via dplyr’s rowid_to_column function.

   library(skimr)

  followers <- twitter_user %>%
      get_followers() %>%
      head (500) %>%
      pull (user_id) %>%
      lookup_users () %>%
      select (screen_name, name, location, followers_count) %>%
      arrange (-followers_count) %>%
      rowid_to_column("rank")

   skim (followers)

Variable type: character

Variable type: numeric

Top followers

We will first list the 10 largest followers by respective number of followers.

The follower from the sample with the most followers is winnie.

   followers %>%
      head (10) %>%
      select (rank, name, location, followers_count) %>%
      gt () %>%
         fmt_number (columns = vars(followers_count), use_seps = T, decimals = 0)

Obtain follower geographic attributes

Another interesting item to research on followers is geographic distribution.

The provided location in Twitter profile is a good starting point. However, locations are neither standardized by level of detail (city, country) nor spelling. Standardization is necessary to group in table or map.

We will standardize at the highest level of detail, which is country, via the Google Maps API and accessed via the ggmaps package. If this is the first time you have accessed the Google Maps API, you will need to complete a one-time step of obtaining a free Google maps API key (instructions here).

   library (ggmap)

   register_google(key = Sys.getenv("GOOGLE_MAPS_API"))

   followers_geo <- followers %>%
      mutate_geocode (location, output = "more") %>%
      mutate (
         country = word (address, -1, -1, sep = ","),
         country = str_to_upper (country),
         country = str_trim(country),
         country = ifelse(is.na(country), "NOT LISTED", country)
         ) %>%
      select (rank:followers_count, country, address, lon, lat) 
   
   followers_geo
## # A tibble: 500 x 9
##     rank screen_name name  location followers_count country address      lon
##    <int> <chr>       <chr> <chr>              <int> <chr>   <chr>      <dbl>
##  1     1 spencechec… Spen… "Salt L…           30252 USA     salt l… -112.   
##  2     2 shawkiii14  شوقي  ""                 16916 NOT LI… <NA>      NA    
##  3     3 James_Fiel… Jame… "London"            9751 UK      london…   -0.128
##  4     4 MalomaPale… 11.0… "Pretor…            3386 SOUTH … pretor…   28.2  
##  5     5 feyyyaz     feyy… "Sanliu…            2802 TURKEY  şanlıu…   38.8  
##  6     6 biyanrizal… Biya… "Indone…            2800 INDONE… indone…  114.   
##  7     7 ashwinappi… Ashw… "Seattl…            2592 USA     seattl… -122.   
##  8     8 TheGreekMa… TheG… "Toront…            2358 CANADA  toront…  -79.4  
##  9     9 folamisegun Like… "Nigeri…            2009 NIGERIA nigeria    8.68 
## 10    10 DeejayIrak… Dj I… "Kampal…            1951 UGANDA  kampal…   32.6  
## # … with 490 more rows, and 1 more variable: lat <dbl>

Visualize followers by country

We will first identify the top follower countries and summarize in a GT table.

The largest countries of followers in the sample are US, UK and Nigeria. The wider population for BVB followers of the English language account may be significantly different as the first 500 accounts are pulled, which is not a representative sample. Additionally, many followers of BVB’s tweets follow sister accounts in different languages, such as the primary German language Twitter handle.

   followers_agg <- followers_geo %>%
      count (country) %>%
      filter (country != "NOT LISTED", n > 1) %>%
      arrange (-n)
   
   ggplot (followers_agg, aes (x = reorder(country, n), y = n)) +
      geom_col () +
      coord_flip () +
      theme_minimal() +
      labs (x="", y = "Followers")

Visualize followers on a map

Next we will convert to a map to enable viewing the location of all users along with countries with most users.

   library (maps)

   world <- map_data ("world") %>%
      mutate (region = str_to_upper (region)) %>%
      left_join (followers_agg, by = c("region" = "country"), keep = T) 
   
   world %>%
      ggplot () +
         geom_map (map = world, aes (x = long, y = lat, map_id = region, fill = n)) +
         scale_fill_viridis_c () +
         theme_grey () +
         labs (title = "Distribution of BVB Football Club English Account Followers") +
         ggsave ("bvb_map.png", width = 11)