knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(dplyr) library(trread)
Source: Wikimedia, user -stk.
In addition to the tables described above, trread attempts to calculate the following tables when one uses read_gtfs():
trread prints a message regarding these tables on reading any GTFS file.
# Read in GTFS feed # here we use a feed included in the package, but note that you can read directly from the New York City Metropolitan Transit Authority using the following URL: # nyc <- read_gtfs("http://web.mta.info/developers/data/nyct/subway/google_transit.zip") local_gtfs_path <- system.file("extdata", "google_transit_nyc_subway.zip", package = "trread") nyc <- read_gtfs(local_gtfs_path, local=TRUE, frequency=TRUE)
For example, joining the standard routes table, with the 'route_shortname' variable to routes_frequencies_df.
routes_df_frequencies <- nyc$routes_df %>% inner_join(nyc$routes_frequency_df, by = "route_id") %>% select(route_long_name, median_headways, mean_headways, st_dev_headways, stop_count) head(routes_df_frequencies)
A more complex example of cross-table joins is to pull the stops and their headways for a given route.
This simple question is a great way to begin to understand a lot about the GTFS data model.
First, we'll need to find a 'service_id', which will tell us which stops a route passes through on a given day of the week and year.
When calculating frequencies, trread tries to guess which service_id is representative of a standard weekday by walking through a set of steps. Below we'll just do some of this manually.
First, lets look at the calendar_df.
Then we'll pull a random route_id and set of service_ids that run on Mondays.
select_service_id <- filter(nyc$calendar_df,monday==1) %>% pull(service_id) select_route_id <- sample_n(nyc$routes_df,1) %>% pull(route_id)
Now we'll filter down through the data model to just stops for that route and service_ids.
some_trips <- nyc$trips_df %>% filter(route_id %in% select_route_id & service_id %in% select_service_id) some_stop_times <- nyc$stop_times_df %>% filter(trip_id %in% some_trips$trip_id) some_stops <- nyc$stops_df %>% filter(stop_id %in% some_stop_times$stop_id)
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