library(tidyverse)

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library(DT)
library(plotly)               # This package does interactive graphs

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library(rtweet)               # This package accesses Twitter data

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library(lubridate)  

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token <- create_token(
  app = "Research_psych_mc",
  consumer_key = "Fzi8hs7FKkINEKJVd90af6GH6",
  consumer_secret = "fgnL1l1M5P5tq3oumFj9G6y04NHrSFzm3w984fEXNXVWhCjVEv",
  access_token = "222284483-KzmytYfsNVMTvYbmNuYiyotLJ9Brpffa2A9a6evw",
  access_secret = "GAwhb9cDukKkFGyP9kVOqan0Wv8YbONdSuPB5PboNvpfY")

get_token()        # this shows the token. make sure key is the same as consumer_key above

## <Token>
## <oauth_endpoint>
##  request:   https://api.twitter.com/oauth/request_token
##  authorize: https://api.twitter.com/oauth/authenticate
##  access:    https://api.twitter.com/oauth/access_token
## <oauth_app> Research_psych_mc
##   key:    Fzi8hs7FKkINEKJVd90af6GH6
##   secret: <hidden>
## <credentials> oauth_token, oauth_token_secret
## ---

Get a sample of 10,000 tweets by Donald Trump

trump_tweets <- get_timeline("realDonaldTrump", n = 10000)

trump_tweets %>% 
  select(hashtags) %>%                   # Focus on the hashtags
  unnest() %>%                           # Separate multiple hashtags
  mutate(hashtags = tolower(hashtags)) %>%      # make all hashtags lowercase
  count(hashtags, sort=TRUE) %>%                # count how often they appear
  datatable()                                   # create an interactive table

trump_tweets %>% 
group_by(Day = date(created_at)) %>%    # extract the date, group by it
  summarize(tweets_per_day = n()) %>% 
  datatable()

# count the number of tweets each day

Mean(tweets per day) by Trump

trump_tweets %>% 
  group_by(Day = date(created_at)) %>%    # extract the date, group by it
  summarize(tweets_per_day = n()) %>% 
  summarize(mean(tweets_per_day))

Histogram of tweets by Trump- number of tweets per day Hover mouse over various parts of graph to obtain more detailed information on timeline of tweets as they fluctuate.

trump_tweets %>%
  mutate(day = date(created_at)) %>% 
  plot_ly(x = ~day) %>%                                        
  add_histogram() %>% 
layout(title = "When Does @realDonaldTrump?tweet", 
         xaxis = list(title = "Day"),
         yaxis = list(title = "Number of Tweets"))

When does Trump find the time to tweet? Timeline is equal to 24 hrs

trump_tweets %>% 
  mutate(Time = hour(with_tz(created_at, "America/New_York"))) %>% 
  count(Time) %>% 
  datatable(options = (list(pageLength = 24)), rownames = F)

Histogram of when Trump likes to tweet. Causing controversery from 6 AM- 11 PM but mostly over breakfast.

trump_tweets %>% 
  mutate(Time = hour(with_tz(created_at, "America/New_York"))) %>% 
  plot_ly(x = ~Time) %>% 
  add_histogram() %>% 
  layout(title = "When does Trump find the time to tweet?" ,
         xaxis = list(title = "Time of Day (0 = midnight)"))

Days of the weekday that Trump tweets

trump_tweets %>% 
  mutate(Day = wday(created_at,           # find the weekday that the tweet was created
                    label = T)) %>%       # use labels (Sun, Mon, etc) rather than number
  count(Day) %>%                          # count the number of tweets each day
  datatable(rownames = F)

trump_tweets %>%
  mutate(day = date(created_at)) %>% 
  plot_ly(x = ~day) %>%                                        
  add_histogram() %>% 
layout(title = "When @realDonaldTrump?tweet")

Contour graph of the time and day Trump tweets. Calm before incoming storm.

trump_tweets %>% 
  mutate(Time = hour(with_tz(created_at, "America/New_York"))) %>% 
  mutate(day = wday(created_at, label = T)) %>% 
  plot_ly(x = ~day, y = ~Time) %>% 
  add_histogram2dcontour() %>% 
  layout(title = "When Trump Tweets", 
         yaxis = list(title = "time of day (0 = midnight)"))