gtfsr is an R package for easily importing, validating, and mapping transit data that follows the General Transit Feed Specification (GTFS) format.
gtfsr package provides functions for converting files following the GTFS format into a single
gtfs data objects. A
gtfs object can then be validated for proper data formatting (i.e. if the source data is properly structured and formatted as a GTFS feed) or have any spatial data for stops and routes mapped using
gtfsr package also provides API wrappers for the popular public GTFS feed sharing site TransitFeeds, allowing users quick, easy access to hundreds of GTFS feeds from within R.
You can install this package from GitHub using the devtools package:
If you have already installed
gtfsr, you can get the latest version by running
If you’d like to build the accompanying vignette, then run
library(gtfsr) library(magrittr) library(dplyr) # set the API key # set_api_key() # uncomment to set api key # get the feedlist dataframe and filter out NYC subway feedlist_df <- get_feedlist() %>% filter(grepl('NYC Subway GTFS', t, ignore.case= TRUE)) # import NYC gtfs feed by sending the url to `import_gtfs` NYC <- import_gtfs(feedlist_df$url_d) #>  "agency.txt" "calendar_dates.txt" "calendar.txt" #>  "routes.txt" "shapes.txt" "stop_times.txt" #>  "stops.txt" "transfers.txt" "trips.txt" # get line (routes) A and B routes <- NYC[['routes_df']] %>% slice(which(grepl('a|b', route_id, ignore.case=TRUE))) %>% '$'('route_id') # take the NYC `gtfs` object and map routes. includes stops by default. NYC %>% map_gtfs(route_ids = routes)
# gtfs will plot ALL shapes for a given route_ids. These can be reduced using the `service_ids` option. ids <- NYC$trips_df %>% select(route_id, service_id, shape_id) %>% distinct() %>% filter(route_id %in% routes) ids %>% head(5) # see all unique combos of ids #> # A tibble: 5 x 3 #> route_id service_id shape_id #> <chr> <chr> <chr> #> 1 A B20171105WKD A..N43R #> 2 A B20171105WKD A..S43R #> 3 A B20171105WKD A..N85R #> 4 A B20171105WKD A..N54R #> 5 A B20171105WKD A..N65R # lets map just the the first row route_ids <- ids$route_id service_ids <- ids$service_id shape_ids <- ids$shape_id # lets map the specific data with some other options enabled. NYC %>% map_gtfs(route_ids = route_ids, service_ids = service_ids, shape_ids = shape_ids, route_colors = 'green', # set the route color stop_details = TRUE, # get more stop details on click route_opacity = .5) # change the route opacity