rTweet

1. For this assignment, the well-known person I chose was the Cristine Rotenberg, a business owner, crime statistics analyst, and a YouTuber who goes by Simply Nailogical. The code below gathers retweets about her.

simply <- search_tweets("@nailogical", n = 1000, include_rts = F)

This code shows the top ten tweets about Cristine by the number of retweets and organizes them into a data table.

simply %>%
  select(text, retweet_count) %>%
  top_n(10) %>%
  arrange(-retweet_count) %>%
  datatable()

Selecting by retweet_count

2. This code gathers up to 5000 of Cristine’s tweets.

simply <- get_timeline("nailogical", n = 5000)

This will go through those tweets and find her most used hashtags and organizes them into a data table.

simply %>% 
  select(hashtags) %>% 
  unnest() %>% 
  mutate(hashtags = tolower(hashtags)) %>%      
  count(hashtags, sort=TRUE) %>%                
  datatable()                                   

`cols` is now required when using unnest().
Please use `cols = c(hashtags)`

3. This code lists how many times Cristine tweeted per day starting 01/05/2020.

simply %>% 
  group_by(day = date(created_at)) %>%    
  summarize(tweets_per_day = n())        

This code finds the average amount of tweets per day from the data listed above.

simply %>% 
  group_by(day = date(created_at)) %>%   
  summarize(tweets_per_day = n()) %>%  
  summarize(mean(tweets_per_day))

4. This code takes the tweets per day data and graphs it.

simply %>%
  mutate(day = date(created_at)) %>% 
  plot_ly(x = ~day) %>%                                        
  add_histogram() %>%
  layout(title = "Simply Nailogical's Tweets per Day")

5. This code makes a table of the number of tweets Cristine makes at each hour.

simply %>%
  mutate(time = hour(created_at)) %>%
  count(time) %>%
  datatable(options = (list(pageLength = 24)), rownames = F)

This code takes the data from the table above and puts it into a histogram.

simply %>%
  mutate(time = hour(created_at)) %>% 
  plot_ly(x = ~time) %>%                                        
  add_histogram() %>%                                           
  layout(title = "What Times Does @nailogical Tweet?", xaxis = list(title = "Time of Day (0 = midnight)"), yaxis = list(title = "Number of Tweets"))

6. This code shows how often Cristine tweets by days of the week.

simply %>%
  mutate(Day = wday(created_at, label = T)) %>%
  count(Day) %>%
  datatable(rownames = F)

This code takes the days of the week data and organizes it into a histogram.

simply %>%
  mutate(Day = wday(created_at, label = T)) %>%
  plot_ly(x = ~Day) %>%
  add_histogram() %>%
  layout(title = "Which Days Does @nailogical Tweet?", xaxis = list(title = "Days of the Week"), yaxis = list(title = "Number of Tweets"))

7. This code takes the days of the week data and plots it on a heatmap.

simply %>%
  mutate(day = wday(created_at, label = T)) %>%
  mutate(hour = hour(with_tz(created_at, "Canada/Eastern"))) %>%
  plot_ly(x = ~day, y = ~hour) %>%
  add_histogram2d(nbinsx = 7, nbinsy = 24) %>%
  layout(title = "What Days Does @nailogical Tweet?", xaxis = list(title = "Days of the Week"), yaxis = list(title = "Number of Tweets"))