README.md
In kmeakinmbta/opmitools: Functions and Data for OPMI
opmitools
opmitools serves 3 primary purposes:
- Functions for cleaning and merging GTFS files (building on
tidytransit)
- Presenting data consistent with OPMI’s Office Theme (building on
ggplot2)
- This part of the package draws on “Creating corporate colour
palettes for
ggplot2”
- Access to common reference tables
Installation
You can install the development version of opmitools from
GitHub with:
# install.packages("devtools")
devtools::install_github("MassDOT-OPMI/opmitools")
And load/attach opmitools as you would any other package with:
library(opmitools)
Usage
GTFS Files
Read the current MBTA GTFS from mbta.com with:
mbta_gtfs <- read_mbta_gtfs()
read_mbta_gtfs() is a wrapper for tidytransit::read_gtfs(). It
stores the individual tables in a list and enforces column types as
specified in the GTFS standard.
For some downstream trip planners and other applications, it is
useful/necessary to remove all extraneous stops, shapes, routes, and
service_ids (i.e., those that are not used in the either trips.txt
or stop_times.txt). You can check for extraneous components with
gtfs_removal_check():
gtfs_removal_check(mbta_gtfs)
#> $routes_unused
#> # A tibble: 31 × 14
#> route_id agenc…¹ route…² route…³ route…⁴ route…⁵ route…⁶ route…⁷ route…⁸
#> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 195 1 195 "Lemue… Supple… 3 "https… FFC72C 000000
#> 2 Shuttle-Gene… 1 Shuttle "" Rail R… 3 "" FFC72C 000000
#> 3 Shuttle-Gene… 1 Blue L… "" Rail R… 3 "" FFC72C 000000
#> 4 Shuttle-Gene… 1 Commut… "" Rail R… 3 "" FFC72C 000000
#> 5 Shuttle-Gene… 1 North … "" Rail R… 3 "" FFC72C 000000
#> 6 Shuttle-Gene… 1 South … "" Rail R… 3 "" FFC72C 000000
#> 7 Shuttle-Gene… 1 Elevat… "" Rail R… 3 "" FFC72C 000000
#> 8 Shuttle-Gene… 1 Fairmo… "" Rail R… 3 "" FFC72C 000000
#> 9 Shuttle-Gene… 1 Fitchb… "" Rail R… 3 "" FFC72C 000000
#> 10 Shuttle-Gene… 1 Frankl… "" Rail R… 3 "" FFC72C 000000
#> # … with 21 more rows, 5 more variables: route_sort_order <int>,
#> # route_fare_class <chr>, line_id <chr>, listed_route <chr>,
#> # network_id <chr>, and abbreviated variable names ¹agency_id,
#> # ²route_short_name, ³route_long_name, ⁴route_desc, ⁵route_type, ⁶route_url,
#> # ⁷route_color, ⁸route_text_color
#>
#> $shapes_unused
#> # A tibble: 2,053 × 5
#> shape_id shape_pt_lat shape_pt_lon shape_pt_sequence shape_dist_traveled
#> <chr> <dbl> <dbl> <int> <dbl>
#> 1 pull0141 42.3 -71.1 10001 NA
#> 2 pull0141 42.3 -71.1 10002 NA
#> 3 pull0141 42.3 -71.1 10003 NA
#> 4 pull0141 42.3 -71.1 10004 NA
#> 5 pull0141 42.3 -71.1 10005 NA
#> 6 pull0141 42.3 -71.1 10006 NA
#> 7 pull0141 42.3 -71.1 10007 NA
#> 8 pull0141 42.3 -71.1 10008 NA
#> 9 pull0141 42.3 -71.1 10009 NA
#> 10 pull0141 42.3 -71.1 10010 NA
#> # … with 2,043 more rows
#>
#> $stops_unused
#> # A tibble: 2,349 × 19
#> stop_id stop_…¹ stop_…² stop_…³ platf…⁴ stop_…⁵ stop_…⁶ zone_id stop_…⁷
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 Boat-George "" George… "Georg… "" 42.3 -70.9 Boat-G… https:…
#> 2 Boat-Long-So… "" Long W… "Long … "4" 42.4 -71.0 Boat-L… https:…
#> 3 place-CM-0493 "" Wareha… "" "" 41.8 -70.7 CF-zon… https:…
#> 4 place-CM-0547 "" Buzzar… "" "" 41.7 -70.6 CF-zon… https:…
#> 5 place-CM-0564 "" Bourne "" "" 41.7 -70.6 CF-zon… https:…
#> 6 place-CM-0790 "" Hyannis "" "" 41.7 -70.3 CF-zon… https:…
#> 7 place-DB-0095 "" Readvi… "" "" 42.2 -71.1 CR-zon… https:…
#> 8 DB-0095 "" Readvi… "Readv… "" 42.2 -71.1 CR-zon… https:…
#> 9 place-DB-2205 "" Fairmo… "" "" 42.3 -71.1 CR-zon… https:…
#> 10 DB-2205 "" Fairmo… "Fairm… "" 42.3 -71.1 CR-zon… https:…
#> # … with 2,339 more rows, 10 more variables: level_id <chr>,
#> # location_type <int>, parent_station <chr>, wheelchair_boarding <int>,
#> # platform_name <chr>, stop_address <chr>, municipality <chr>,
#> # on_street <chr>, at_street <chr>, vehicle_type <chr>, and abbreviated
#> # variable names ¹stop_code, ²stop_name, ³stop_desc, ⁴platform_code,
#> # ⁵stop_lat, ⁶stop_lon, ⁷stop_url
And remove those components with gtfs_remove_all():
route_count_pre <- length(mbta_gtfs$routes$route_id)
mbta_gtfs <- gtfs_remove_all(mbta_gtfs)
route_count_post <- length(mbta_gtfs$routes$route_id)
paste0("Route count prior to removal: ", route_count_pre)
#> [1] "Route count prior to removal: 220"
paste0("Route count after removal: ", route_count_post)
#> [1] "Route count after removal: 189"
Other use cases might include getting some useful information out of the
calendar.txt file:
mbta_calendar_info <- gtfs_calendar_info(mbta_gtfs)
head(mbta_calendar_info)
#> # A tibble: 6 × 5
#> service_id day_type start_date end_date totdays
#> <chr> <chr> <date> <date> <drtn>
#> 1 BUS423-hbs43sf1-Wdy-02 <NA> 2023-11-24 2023-11-24 1 days
#> 2 BUS423-hbs43sn1-Wdy-02 <NA> 2023-11-10 2023-11-10 1 days
#> 3 BUS423-hbs43sp1-Wdy-02 <NA> 2023-11-20 2023-11-22 3 days
#> 4 BUS423-hbs43sp6-Sa-02 Saturday 2023-11-18 2023-11-25 8 days
#> 5 BUS423-hbs43sp7-Su-02 Sunday 2023-11-19 2023-11-26 8 days
#> 6 BUS423-hbs43sw1-Wdy-02 Weekday 2023-10-11 2023-12-15 66 days
Note that opmitools is not yet capable of handling GTFS-Pathways – it
is only set up to handle the required GTFS files used by the MBTA:
agency, calendar, routes, shapes, stop_times, stops, and trips. While
some functions will work without tidytransit, it is recommended to
install and attach tidytransit in conjunction with opmitools.
Plotting
opmitools contains several convenience functions for making
“OPMI-themed” plots. As an example, we can plot information from
rt_ridership, which is included in the package.
The following code creates a simple ggplot showing fall average weekday
ridership on rapid transit.
library(tidyverse) # load & attach tidyverse for data wrangling with dplyr and plotting with ggplot2
(ridership_plot <-
rt_ridership %>%
filter(day_type_name == "weekday") %>%
ggplot(aes(x = rating, y = tot_ons, fill = route_id)) +
geom_col(position = position_dodge()))
Then, we can add the OPMI theme with theme_opmi():
(ridership_plot <-
ridership_plot +
labs(x = "Rating", y = "Average\nWeekday\nBoardings") +
theme_opmi())
And use colors that make sense with scale_fill_opmi():
ridership_plot +
scale_fill_opmi(palette = "rt", guide = "none")
Included Data
opmitools contains several reference tables that may be useful,
including a table describing the MBTA service area:
head(mbta_service_area)
#> # A tibble: 6 × 3
#> muni_name mbta65 mbta175
#> <chr> <lgl> <lgl>
#> 1 Arlington TRUE TRUE
#> 2 Bedford TRUE TRUE
#> 3 Belmont TRUE TRUE
#> 4 Beverly TRUE TRUE
#> 5 Boston TRUE TRUE
#> 6 Braintree TRUE TRUE
And frequently used demographic information by MA block group:
head(mabgs_19)
#> # A tibble: 6 × 14
#> GEOID pop minopop tothh li45hh li50hh limen…¹ MHHI lowno…² forei…³ minopct
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2501… 571 231 228 38 46 0 73000 17.7 64.9 0.405
#> 2 2501… 1270 610 327 73 73 0 132750 227. 349. 0.480
#> 3 2501… 2605 290 1064 254 281 98 113800 149. 425. 0.111
#> 4 2502… 1655 578 560 88 88 11 133636 220. 569. 0.349
#> 5 2502… 659 501 234 113 156 30 45370 92.0 227. 0.760
#> 6 2502… 1452 246 700 177 193 26 130645 227. 71.1 0.169
#> # … with 3 more variables: li45pct <dbl>, li50pct <dbl>, lownocarpct <dbl>, and
#> # abbreviated variable names ¹limenghh, ²lownocarhh, ³foreignbornres
kmeakinmbta/opmitools documentation built on Oct. 25, 2023, 10:34 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
opmitools
opmitools serves 3 primary purposes:
- Functions for cleaning and merging GTFS files (building on
tidytransit) - Presenting data consistent with OPMI’s Office Theme (building on
ggplot2)- This part of the package draws on “Creating corporate colour palettes for ggplot2”
- Access to common reference tables
Installation
You can install the development version of opmitools from GitHub with:
# install.packages("devtools")
devtools::install_github("MassDOT-OPMI/opmitools")
And load/attach opmitools as you would any other package with:
library(opmitools)
Usage
GTFS Files
Read the current MBTA GTFS from mbta.com with:
mbta_gtfs <- read_mbta_gtfs()
read_mbta_gtfs() is a wrapper for tidytransit::read_gtfs(). It
stores the individual tables in a list and enforces column types as
specified in the GTFS standard.
For some downstream trip planners and other applications, it is
useful/necessary to remove all extraneous stops, shapes, routes, and
service_ids (i.e., those that are not used in the either trips.txt
or stop_times.txt). You can check for extraneous components with
gtfs_removal_check():
gtfs_removal_check(mbta_gtfs)
#> $routes_unused
#> # A tibble: 31 × 14
#> route_id agenc…¹ route…² route…³ route…⁴ route…⁵ route…⁶ route…⁷ route…⁸
#> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 195 1 195 "Lemue… Supple… 3 "https… FFC72C 000000
#> 2 Shuttle-Gene… 1 Shuttle "" Rail R… 3 "" FFC72C 000000
#> 3 Shuttle-Gene… 1 Blue L… "" Rail R… 3 "" FFC72C 000000
#> 4 Shuttle-Gene… 1 Commut… "" Rail R… 3 "" FFC72C 000000
#> 5 Shuttle-Gene… 1 North … "" Rail R… 3 "" FFC72C 000000
#> 6 Shuttle-Gene… 1 South … "" Rail R… 3 "" FFC72C 000000
#> 7 Shuttle-Gene… 1 Elevat… "" Rail R… 3 "" FFC72C 000000
#> 8 Shuttle-Gene… 1 Fairmo… "" Rail R… 3 "" FFC72C 000000
#> 9 Shuttle-Gene… 1 Fitchb… "" Rail R… 3 "" FFC72C 000000
#> 10 Shuttle-Gene… 1 Frankl… "" Rail R… 3 "" FFC72C 000000
#> # … with 21 more rows, 5 more variables: route_sort_order <int>,
#> # route_fare_class <chr>, line_id <chr>, listed_route <chr>,
#> # network_id <chr>, and abbreviated variable names ¹agency_id,
#> # ²route_short_name, ³route_long_name, ⁴route_desc, ⁵route_type, ⁶route_url,
#> # ⁷route_color, ⁸route_text_color
#>
#> $shapes_unused
#> # A tibble: 2,053 × 5
#> shape_id shape_pt_lat shape_pt_lon shape_pt_sequence shape_dist_traveled
#> <chr> <dbl> <dbl> <int> <dbl>
#> 1 pull0141 42.3 -71.1 10001 NA
#> 2 pull0141 42.3 -71.1 10002 NA
#> 3 pull0141 42.3 -71.1 10003 NA
#> 4 pull0141 42.3 -71.1 10004 NA
#> 5 pull0141 42.3 -71.1 10005 NA
#> 6 pull0141 42.3 -71.1 10006 NA
#> 7 pull0141 42.3 -71.1 10007 NA
#> 8 pull0141 42.3 -71.1 10008 NA
#> 9 pull0141 42.3 -71.1 10009 NA
#> 10 pull0141 42.3 -71.1 10010 NA
#> # … with 2,043 more rows
#>
#> $stops_unused
#> # A tibble: 2,349 × 19
#> stop_id stop_…¹ stop_…² stop_…³ platf…⁴ stop_…⁵ stop_…⁶ zone_id stop_…⁷
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 Boat-George "" George… "Georg… "" 42.3 -70.9 Boat-G… https:…
#> 2 Boat-Long-So… "" Long W… "Long … "4" 42.4 -71.0 Boat-L… https:…
#> 3 place-CM-0493 "" Wareha… "" "" 41.8 -70.7 CF-zon… https:…
#> 4 place-CM-0547 "" Buzzar… "" "" 41.7 -70.6 CF-zon… https:…
#> 5 place-CM-0564 "" Bourne "" "" 41.7 -70.6 CF-zon… https:…
#> 6 place-CM-0790 "" Hyannis "" "" 41.7 -70.3 CF-zon… https:…
#> 7 place-DB-0095 "" Readvi… "" "" 42.2 -71.1 CR-zon… https:…
#> 8 DB-0095 "" Readvi… "Readv… "" 42.2 -71.1 CR-zon… https:…
#> 9 place-DB-2205 "" Fairmo… "" "" 42.3 -71.1 CR-zon… https:…
#> 10 DB-2205 "" Fairmo… "Fairm… "" 42.3 -71.1 CR-zon… https:…
#> # … with 2,339 more rows, 10 more variables: level_id <chr>,
#> # location_type <int>, parent_station <chr>, wheelchair_boarding <int>,
#> # platform_name <chr>, stop_address <chr>, municipality <chr>,
#> # on_street <chr>, at_street <chr>, vehicle_type <chr>, and abbreviated
#> # variable names ¹stop_code, ²stop_name, ³stop_desc, ⁴platform_code,
#> # ⁵stop_lat, ⁶stop_lon, ⁷stop_url
And remove those components with gtfs_remove_all():
route_count_pre <- length(mbta_gtfs$routes$route_id)
mbta_gtfs <- gtfs_remove_all(mbta_gtfs)
route_count_post <- length(mbta_gtfs$routes$route_id)
paste0("Route count prior to removal: ", route_count_pre)
#> [1] "Route count prior to removal: 220"
paste0("Route count after removal: ", route_count_post)
#> [1] "Route count after removal: 189"
Other use cases might include getting some useful information out of the
calendar.txt file:
mbta_calendar_info <- gtfs_calendar_info(mbta_gtfs)
head(mbta_calendar_info)
#> # A tibble: 6 × 5
#> service_id day_type start_date end_date totdays
#> <chr> <chr> <date> <date> <drtn>
#> 1 BUS423-hbs43sf1-Wdy-02 <NA> 2023-11-24 2023-11-24 1 days
#> 2 BUS423-hbs43sn1-Wdy-02 <NA> 2023-11-10 2023-11-10 1 days
#> 3 BUS423-hbs43sp1-Wdy-02 <NA> 2023-11-20 2023-11-22 3 days
#> 4 BUS423-hbs43sp6-Sa-02 Saturday 2023-11-18 2023-11-25 8 days
#> 5 BUS423-hbs43sp7-Su-02 Sunday 2023-11-19 2023-11-26 8 days
#> 6 BUS423-hbs43sw1-Wdy-02 Weekday 2023-10-11 2023-12-15 66 days
Note that opmitools is not yet capable of handling GTFS-Pathways – it
is only set up to handle the required GTFS files used by the MBTA:
agency, calendar, routes, shapes, stop_times, stops, and trips. While
some functions will work without tidytransit, it is recommended to
install and attach tidytransit in conjunction with opmitools.
Plotting
opmitools contains several convenience functions for making
“OPMI-themed” plots. As an example, we can plot information from
rt_ridership, which is included in the package.
The following code creates a simple ggplot showing fall average weekday ridership on rapid transit.
library(tidyverse) # load & attach tidyverse for data wrangling with dplyr and plotting with ggplot2
(ridership_plot <-
rt_ridership %>%
filter(day_type_name == "weekday") %>%
ggplot(aes(x = rating, y = tot_ons, fill = route_id)) +
geom_col(position = position_dodge()))
Then, we can add the OPMI theme with theme_opmi():
(ridership_plot <-
ridership_plot +
labs(x = "Rating", y = "Average\nWeekday\nBoardings") +
theme_opmi())
And use colors that make sense with scale_fill_opmi():
ridership_plot +
scale_fill_opmi(palette = "rt", guide = "none")
Included Data
opmitools contains several reference tables that may be useful,
including a table describing the MBTA service area:
head(mbta_service_area)
#> # A tibble: 6 × 3
#> muni_name mbta65 mbta175
#> <chr> <lgl> <lgl>
#> 1 Arlington TRUE TRUE
#> 2 Bedford TRUE TRUE
#> 3 Belmont TRUE TRUE
#> 4 Beverly TRUE TRUE
#> 5 Boston TRUE TRUE
#> 6 Braintree TRUE TRUE
And frequently used demographic information by MA block group:
head(mabgs_19)
#> # A tibble: 6 × 14
#> GEOID pop minopop tothh li45hh li50hh limen…¹ MHHI lowno…² forei…³ minopct
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2501… 571 231 228 38 46 0 73000 17.7 64.9 0.405
#> 2 2501… 1270 610 327 73 73 0 132750 227. 349. 0.480
#> 3 2501… 2605 290 1064 254 281 98 113800 149. 425. 0.111
#> 4 2502… 1655 578 560 88 88 11 133636 220. 569. 0.349
#> 5 2502… 659 501 234 113 156 30 45370 92.0 227. 0.760
#> 6 2502… 1452 246 700 177 193 26 130645 227. 71.1 0.169
#> # … with 3 more variables: li45pct <dbl>, li50pct <dbl>, lownocarpct <dbl>, and
#> # abbreviated variable names ¹limenghh, ²lownocarhh, ³foreignbornres
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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