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## # A tibble: 6 x 90
## user_id status_id created_at screen_name text source
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 230285… 12030668… 2019-12-06 21:41:06 Ruffitup "htt… Twitt…
## 2 5741722 12030668… 2019-12-06 21:41:06 nprpolitics NEW:… Socia…
## 3 5741722 12030653… 2019-12-06 21:35:06 nprpolitics The … Socia…
## 4 214939… 12030668… 2019-12-06 21:41:05 roblynnm @Spe… Twitt…
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I’m analyzing the word “impeachment” on Twitter. I listed the percentage of the sources of the tweets, and put the top 5 sources in a column chart. I also listed the top 6 of each sentiment in the tweets, and made a chart showing the positive and negative words associated with “impeachment”.
## Selecting by percent_of_tweets
## # A tibble: 5 x 3
## source n percent_of_tweets
## <chr> <int> <dbl>
## 1 Twitter for iPhone 279 0.279
## 2 Twitter Web App 240 0.24
## 3 Twitter for Android 195 0.195
## 4 Twitter Web Client 127 0.127
## 5 Twitter for iPad 38 0.038
## Joining, by = "word"
## Joining, by = "word"
## # A tibble: 6 x 3
## word n sentiment
## <chr> <int> <chr>
## 1 support 21 Positive
## 2 top 17 Positive
## 3 won 17 Positive
## 4 win 10 Positive
## 5 intelligence 7 Positive
## 6 protect 7 Positive
## # A tibble: 6 x 3
## word n sentiment
## <chr> <int> <chr>
## 1 sham 29 Negative
## 2 concern 23 Negative
## 3 abuse 18 Negative
## 4 breaking 16 Negative
## 5 baseless 12 Negative
## 6 hoax 12 Negative