**R – What You're Doing Is Rather Desperate**, and kindly contributed to R-bloggers)

“Sydney stations where commuters fall through gaps, get stuck in lifts” blares the headline. The story tells us that:

Central Station, the city’s busiest, topped the list last year with about 54 people falling through gaps

Wow! Wait a minute…

Central Station,

the city’s busiest

Some poking around in the NSW Transport Open Data portal reveals how many people enter every Sydney train station on a “typical” day in 2016, 2017 and 2018. We could manipulate those numbers in various ways to estimate total, unique passengers for FY 2017-18 but I’m going to argue that the value as-is serves as a proxy variable for “station busyness”.

Grabbing the numbers for 2017:

library(tidyverse) tibble(station = c("Central", "Circular Quay", "Redfern"), falls = c(54, 34, 18), entries = c(118960, 27870, 30570)) %>% mutate(falls_per_entry = falls/entries) %>% select(-entries) %>% gather(Variable, Value, -station) %>% ggplot(aes(station, Value)) + geom_col() + facet_wrap(~Variable, scales = "free_y")

Looks like Circular Quay has the bigger problem. Now we have a data story. More tourists? Maybe improve the signage.

Deep in the comment thread, amidst the “only themselves to blame” crowd, one person gets it:

**leave a comment**for the author, please follow the link and comment on their blog:

**R – What You're Doing Is Rather Desperate**.

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