Description
gtfsr is an R package for easily importing, validating, and mapping transit data that follows the General Transit Feed Specification (GTFS) format.
The 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 leaflet. The 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.
Installation
You can install this package from GitHub using the devtools package:
if (!require(devtools)) {
install.packages('devtools')
}
devtools::install_github('ropensci/gtfsr')
If you'd like to build the accompanying vignette, then run
devtools::install_github('ropensci/gtfsr', build_vignettes = TRUE)
If you have already installed gtfsr, you can get the latest version by running
remove.packages('gtfsr')
devtools::install_github('ropensci/gtfsr')
Example Usage
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$url_d)
#> [1] "agency.txt" "calendar_dates.txt" "calendar.txt"
#> [4] "routes.txt" "shapes.txt" "stop_times.txt"
#> [7] "stops.txt" "transfers.txt" "trips.txt"
# get line (routes) A and B
routes <- NYC[['routes_df']] %>%
slice(which(grepl('a|b', route_id, ignore.case=TRUE))) %>%
'$'('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$trips_df %>%
select(route_id, service_id, shape_id) %>%
distinct() %>%
filter(route_id %in% routes)
ids %>% head(5) # see all unique combos of ids
#> # A tibble: 5 x 3
#> route_id service_id shape_id
#> <chr> <chr> <chr>
#> 1 A B20170625WKD A..N43R
#> 2 A B20170625WKD A..S43R
#> 3 A B20170625WKD A..N85R
#> 4 A B20170625WKD A..N54R
#> 5 A B20170625WKD A..N65R
# lets map just the the first row
route_ids <- ids$route_id[1]
service_ids <- ids$service_id[1]
shape_ids <- ids$shape_id[1]
# 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
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