Inspect and plot
The print method previews tables, summary() reports
system-level properties, and plot() draws the network.
Analytical functions return ordinary tibbles or sf objects
so they remain compatible with standard R workflows.
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GTFSwizard creates, reads, validates, explores, edits, and exports
General Transit Feed Specification (GTFS) Schedule feeds. Its functions
work with a wizardgtfs object: a named list of GTFS tables
plus a dates_services table that connects calendar dates,
services, and service patterns.
The package includes two real, reduced examples from Fortaleza,
Brazil. for_rail_gtfs is small enough for learning and
examples; for_bus_gtfs is useful for checking workflows on
a larger bus network.
library(GTFSwizard)
gtfs <- for_rail_gtfs
summary(gtfs)
#> <summary.wizardgtfs>
#> Agency: METROFOR
#> Service: 2020-01-02 to 2021-12-31 (614 active dates)
#> 3 routes; 215 trips; 39 stops; 6 shapes
#> Median consecutive-stop spacing: 1144.6 m
#>
#> Tables:
#> agency calendar calendar_dates routes stops
#> 1 1 26 3 39
#> stop_times trips shapes
#> 3420 215 80Access an individual GTFS table with the usual list syntax.
head(gtfs$routes)
#> # A tibble: 3 × 9
#> route_id route_short_name route_long_name route_desc route_type route_url
#> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 8 "" VLT Parangaba Papicu "" 1 ""
#> 2 6 "" Linha Sul "" 1 ""
#> 3 7 "" Linha Oeste "" 1 ""
#> # ℹ 3 more variables: route_color <chr>, route_text_color <chr>,
#> # agency_id <chr>
head(gtfs$stops)
#> # A tibble: 6 × 10
#> stop_id stop_code stop_name stop_desc stop_lat stop_lon zone_id stop_url
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 66 "" Papicu "" -3.74 -38.5 "" ""
#> 2 65 "" Antônio Sales "" -3.75 -38.5 "" ""
#> 3 64 "" Pontes Vieira "" -3.75 -38.5 "" ""
#> 4 63 "" São João do Ta… "" -3.76 -38.5 "" ""
#> 5 41 "" Borges de Melo "" -3.76 -38.5 "" ""
#> 6 40 "" Vila União "" -3.77 -38.5 "" ""
#> # ℹ 2 more variables: location_type <int>, stop_timezone <chr>read_gtfs() reads a GTFS zip archive and validates its
required tables, fields, identifiers, sequences, dates, and times.
Supply the archive path explicitly. To choose a file interactively, call
explore_gtfs() without a feed in an interactive R
session.
Use as_wizardgtfs() when the GTFS tables are already
available as a named list. If shapes.txt is absent, the
default behavior infers straight lines from ordered stop coordinates for
analysis and visualization.
create_gtfs() validates the supplied tables using the
same package rules. A feed must define service using
calendar, calendar_dates, or both.
created <- create_gtfs(
agency = data.frame(
agency_id = "A",
agency_name = "Demo Transit",
agency_url = "https://example.com",
agency_timezone = "America/Fortaleza"
),
routes = data.frame(
route_id = "R1", agency_id = "A", route_short_name = "1",
route_long_name = "Central", route_type = 3
),
trips = data.frame(
route_id = "R1", service_id = "WK", trip_id = "T1"
),
stop_times = data.frame(
trip_id = "T1",
arrival_time = c("08:00:00", "08:10:00"),
departure_time = c("08:00:00", "08:10:00"),
stop_id = c("S1", "S2"),
stop_sequence = 1:2
),
stops = data.frame(
stop_id = c("S1", "S2"),
stop_name = c("First", "Second"),
stop_lat = c(-3.73, -3.74),
stop_lon = c(-38.52, -38.53)
),
calendar = data.frame(
service_id = "WK",
monday = 1, tuesday = 1, wednesday = 1, thursday = 1,
friday = 1, saturday = 0, sunday = 0,
start_date = "20260101", end_date = "20261231"
)
)
#> GTFSwizard: building straight-line shapes from ordered stop coordinates.
created
#> <wizardgtfs>
#> Agency: Demo Transit
#> 1 routes; 1 trips; 2 stops
#>
#> $agency [1 rows]
#> # A tibble: 1 × 4
#> agency_id agency_name agency_url agency_timezone
#> <chr> <chr> <chr> <chr>
#> 1 A Demo Transit https://example.com America/Fortaleza
#>
#> $routes [1 rows]
#> # A tibble: 1 × 5
#> route_id agency_id route_short_name route_long_name route_type
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 R1 A 1 Central 3
#>
#> $trips [1 rows]
#> # A tibble: 1 × 4
#> route_id service_id trip_id shape_id
#> <chr> <chr> <chr> <chr>
#> 1 R1 WK T1 shape-1
#>
#> $stop_times [2 rows]
#> # A tibble: 2 × 5
#> trip_id arrival_time departure_time stop_id stop_sequence
#> <chr> <chr> <chr> <chr> <int>
#> 1 T1 08:00:00 08:00:00 S1 1
#> 2 T1 08:10:00 08:10:00 S2 2
#>
#> $stops [2 rows]
#> # A tibble: 2 × 4
#> stop_id stop_name stop_lat stop_lon
#> <chr> <chr> <dbl> <dbl>
#> 1 S1 First -3.73 -38.5
#> 2 S2 Second -3.74 -38.5
#>
#> $calendar [1 rows]
#> # A tibble: 1 × 10
#> service_id monday tuesday wednesday thursday friday saturday sunday start_date
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <date>
#> 1 WK 1 1 1 1 1 0 0 2026-01-01
#> # ℹ 1 more variable: end_date <date>
#>
#> $shapes [2 rows]
#> # A tibble: 2 × 5
#> shape_id shape_pt_lat shape_pt_lon shape_pt_sequence shape_dist_traveled
#> <chr> <dbl> <dbl> <int> <dbl>
#> 1 shape-1 -3.73 -38.5 1 0
#> 2 shape-1 -3.74 -38.5 2 1571.The print method previews tables, summary() reports
system-level properties, and plot() draws the network.
Analytical functions return ordinary tibbles or sf objects
so they remain compatible with standard R workflows.
write_gtfs() removes the internal
dates_services table, restores standard GTFS date and
spatial columns, and writes a zip archive.
output <- tempfile(fileext = ".zip")
write_gtfs(created, output)
file.exists(output)
#> [1] TRUE
unlink(output)Continue with service analysis, or learn how to filter and edit feeds.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.