get_ntd()
get_ntd() is the main function of the package. It
doesn’t have any required parameters:
ntd_data <- get_ntd()
#> New names:
#> • `22526` -> `22526...34`
#> • `22523` -> `22523...71`
#> • `22526` -> `22526...95`
#> • `23344` -> `23344...100`
#> • `22523` -> `22523...102`
#> • `23344` -> `23344...139`
#> • `19423` -> `19423...147`
#> • `19423` -> `19423...187`
ntd_data
#> # A tibble: 576,300 × 12
#> ntd_id_5 ntd_id_4 agency active reporter_type uza uza_name modes tos
#> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 2 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 3 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 4 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 5 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 6 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 7 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 8 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 9 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> 10 00001 0001 King Count… Active Full Reporte… 14 Seattle… FB DO
#> # ℹ 576,290 more rows
#> # ℹ 3 more variables: month <date>, value <dbl>, ntd_variable <chr>
colnames(ntd_data)
#> [1] "ntd_id_5" "ntd_id_4" "agency" "active"
#> [5] "reporter_type" "uza" "uza_name" "modes"
#> [9] "tos" "month" "value" "ntd_variable"
By default, the package downloads what the NTD calls “Complete
Monthly Ridership (with adjustments and estimates).” Alternatively you
can request raw data (“Raw Monthly Ridership (No
Adjustments or Estimates)”). For a more detailed discussion of the
difference between the two data types, see this
blog post.
You see that the package downloads a fairly large xlsx
file from the web and returns a tibble with 576300 rows and 12 columns.
The first two columns are identifiers for the transit agency; followed
by a human-readable agency name. Note that the agency name may not be
what you expect. For example, the name of our local agency in Madison
(Wisconsin) is “Metro
Transit”. But in the NTD data it is listed as “City of Madison”. So
if you cannot find your agency, use the uza_name variable
described below.
NTD data go back as far as 2002, and some agencies no longer actively
report data, report them under a different ID, or don’t even exist
anymore. This is reflected in the active column.
reporter_type most commonly takes on the
Full reporter value, but especially smaller or rural
systems may have a different value for this variable. For agencies that
aren’t full reporters, the NTD data may include projections rather than
actually reported data.
uza is an identifier for urbanized
areas and uza_name has the name of that area (this will
usually be how you will find your local agency).
modes denotes the type of transit reported on.
ntd_data |>
count(modes)
#> # A tibble: 22 × 2
#> modes n
#> <chr> <int>
#> 1 AG 1275
#> 2 AR 255
#> 3 CB 35700
#> 4 CC 255
#> 5 CR 9690
#> 6 DR 237150
#> 7 FB 11985
#> 8 HR 3825
#> 9 IP 1020
#> 10 LR 8925
#> # ℹ 12 more rows
There are a lot of different modes, including rather obscure ones
like “Inclined Plane” (IP) or “Alaska Railroad”
(AR). You can find documentation of the different modes here.
The tos variable represents the “type of service”:
ntd_data |>
count(tos, sort = TRUE)
#> # A tibble: 13 × 2
#> tos n
#> <chr> <int>
#> 1 DO 294270
#> 2 PT 244545
#> 3 TX 31365
#> 4 TN 3060
#> 5 Reduced Reporters 510
#> 6 Rural Reporters 510
#> 7 <NA> 510
#> 8 Rolling 12-Month Sum 255
#> 9 Rolling 12-Month Sum with Reduced Reporters 255
#> 10 Rolling 12-Month Sum with Reduced and Rural Reporter Estimates 255
#> 11 Subtotal with Reduced Reporters 255
#> 12 Subtotal with Reduced and Rural Reporter Estimates 255
#> 13 Total 255
The most common values are DO, which is directly
operated service, i.e. a transit agency running their own service; and
PT for “purchased transportation”, i.e. a transit agency
contracting out services. Often agencies will have an entry for both of
these, with DO being the regular, fixed route service and
PT being paratransit or other more specialized forms of
transit.
Finally, the month and value variables
provide the actual transit data for a given month. What variable is
presented by value is in the ntd_variable. If
you call get_ntd() without any additional parameters, it
will return the “unlinked passenger trips” (UPT) metric for all
agencies, modes, and types of service.
