RiP Post Twitter Followers 10.31.18

twitter_followers.R

follow <- read_csv("D://evantwitter/update_followers.csv") 

follow <- follow %>% 
  rename(old = Followers, new = `Followers in 2018`) %>% 
  mutate(n_change = new - old) %>% 
  mutate(pct_change = new/old) %>% 
  mutate(pct_change = 1 - pct_change) %>% 
  mutate(pct_change = -1*pct_change)

font_add_google("Abel", "font")
showtext_auto()

follow %>% 
  filter(pct_change > .5) %>% 
  ggplot(., aes(x = reorder(Username, pct_change), y = pct_change, fill = pct_change)) +
  geom_col(color = "black") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  theme_minimal() + 
  scale_fill_gradient(low = "#33001b", high = "#ff0084") +
  theme(legend.position = "none") +
  theme(text=element_text(size=44, family="font")) +
  labs(x = "Twitter Username", y = "Percent Change in Followers", title = "Percent Change in Followers from 2016 to 2018") +
  ggsave("D://follow_change_positive.png")


follow %>% 
  filter(pct_change < 0) %>% 
  ggplot(., aes(x = reorder(Username, pct_change), y = pct_change, fill = pct_change)) +
  geom_col(color = "black") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  theme_minimal() + 
  scale_fill_gradient(low = "#ff0084", high =  "#33001b") +
  theme(legend.position = "none") +
  theme(text=element_text(size=44, family="font")) +
  labs(x = "Twitter Username", y = "Percent Change in Followers", title = "Percent Change in Followers from 2016 to 2018") +
  ggsave("D://follow_change_negative.png")


positive <- follow %>%
  filter(n_change >  0) %>% 
  mutate(type = "Positive")

negative <- follow %>%
  filter(n_change <  0) %>% 
  mutate(type = "Negative")

follow <- bind_rows(positive, negative)

follow %>% 
  ggplot(., aes(x = reorder(Username, pct_change), y = pct_change, fill = type)) +
  geom_col(color = "black") +
  coord_flip() +
  geom_hline(yintercept = .162, linetype = "dashed") +
  scale_y_continuous(labels = scales::percent) +
  theme_minimal() + 
  scale_fill_manual(values = c("darkorchid", "firebrick3")) +
  theme(legend.position = "none") +
  theme(text=element_text(size=44, family="font")) +
  theme(axis.text.y = element_text(size = 24)) +
  labs(x = "Twitter Username", y = "Percent Change in Followers", title = "Percent Change in Followers from 2016 to 2018") +
  ggsave("D://follow_change_overall.png", height = 10)

top <- follow %>% 
  filter(n_change > 1000000)  %>% 
  mutate(group  = "top") %>% 
  select(Username, n_change) %>% 
  add_row(Username = "Everyone Else", n_change = 3897959) %>% 
  mutate(n_change = n_change/1000000) %>% 
  mutate(n_change = round(n_change, 2)) %>% 
  mutate(million = "M") %>% 
  mutate(new = paste0(n_change, million))

top %>% 
  ggplot(., aes(x = reorder(Username, -n_change), y = n_change, fill = Username)) +
  geom_col(color = "black") +
  geom_text(aes(y = n_change + .15, label = new), position = position_dodge(width = .9), size = 16, family = "font") +
  theme_minimal()  +
  theme(text=element_text(size=44, family="font")) +
  scale_fill_npg() +
  theme(legend.position = "none") +
  labs(x = "Twitter Username", y = "New Followers (in Millions)", title = "Growth in Followers from 2016 to 2018") +
  ggsave("D://follow_growth.png")