Reference: https://jsta.rbind.io/blog/making-a-twitter-dashboard-with-r/

## Registered S3 method overwritten by 'openssl':
##   method      from
##   print.bytes Rcpp

Twitter likes history

# display data table: 
my_likes %>% 
    datatable(extensions = 'Buttons', 
              options = list(dom = 'Bfrtip', 
                             buttons = c('excel', "csv"), 
                             pageLength = 20)) %>% 
    formatStyle(columns = colnames(.), fontSize = '20%')
## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

## Whose tweets do I like most? ```r top_accounts <- my_likes %>% count(screen_name, followers_count, sort = TRUE) %>% mutate(proportion = n/sum(n)) %>% rename(likes_since_dec_2018 = n) top_accounts %>% datatable() %>% formatRound(4, 3) ```
```r # graph the relationship p1 <- top_accounts %>% ggplot(aes(x = followers_count, y = likes_since_dec_2018, group = screen_name)) + geom_point() + scale_x_log10(breaks = trans_breaks(trans = "log10", inv = function(x){10^x}, n = 10), labels = trans_format(trans = "log10", math_format(10^.x))) + scale_y_log10() + labs(title = "My likes on Twitter", subtitle = "The accounts I like most are neither unknown nor too popular, mostly focusing on statistics, Rn 1K followers is probably too niche/inactive; more than 100K probably doesn’t focus on interest areas”) +

theme_light() +
theme(panel.grid.minor = element_line(colour = "grey95"), 
      panel.grid.major = element_line(colour = "grey95")); p1

<img src="twitter-dashboard_files/figure-html/graph-1.png" width="672" />

```r
# ggplotly(p1)