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May 4, 2022 19:55
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Plot number of smartphone users over time, data from a tweet
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library(dplyr) | |
library(stringr) | |
library(ggplot2) | |
d <- rtweet::search_tweets("from:stats_feed", n = 30) %>% | |
filter(status_id == "1521871339908263937") %>% | |
pull(text) %>% | |
str_split("\n") %>% | |
as_tibble(.name_repair = "universal") %>% | |
rename("data" = "...1") %>% | |
filter(str_detect(data, "billion")) %>% | |
mutate(year = as.numeric(str_extract(data, "20[0-9]{2}")), | |
billions = as.numeric(str_extract(data, "[0-9]\\.[0-9]"))) | |
d %>% | |
ggplot(aes(x = year, y = billions)) + | |
geom_col() + | |
scale_x_continuous(breaks = seq(2012, 2022, 2)) + | |
theme_minimal() + | |
labs(x = "Year", y = "Smartphone users (billions)") | |
ggsave("smartphone_users.png", width = 5, height = 5, scale = 0.6, bg = "white") |
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