This analysis of the pop’s twitter timeline focuses on .. wikipedia.
First, we need to load our packages:
library(rtweet)
library(tidyverse)
library(tidytext)
library(rtweet)
library(wordcloud2)Now, let’s get the Pope’s timeline:
gates <- get_timeline('billgates', n = 3200)The next step is to remove stop words and count word frequency. We’ll remove the extra words ‘t.co,’ ‘http,’ etc., and generate a wordcloud
gates %>%
unnest_tokens(word, text) %>%
anti_join(stop_words) %>%
filter(!word %in% c('t.co', 'https', 'http', 'amp')) %>%
count(word, sort = TRUE) %>%
head(20) %>%
knitr::kable()## Joining, by = "word"| word | n |
|---|---|
| world | 318 |
| people | 268 |
| i’m | 218 |
| progress | 174 |
| lives | 167 |
| health | 160 |
| change | 152 |
| polio | 152 |
| book | 149 |
| fight | 144 |
| energy | 143 |
| global | 140 |
| climate | 138 |
| it’s | 132 |
| here’s | 125 |
| read | 124 |
| time | 113 |
| life | 111 |
| malaria | 101 |
| students | 99 |
gates %>%
unnest_tokens(word, text) %>%
anti_join(stop_words) %>%
filter(!word %in% c('t.co', 'https', 'http', 'amp')) %>%
count(word, sort = TRUE) %>%
wordcloud2()## Joining, by = "word"