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_targets.R
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_targets.R
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#### Setup #############################################################
library(targets)
library(tarchetypes)
package_list <- c(
"qs",
"tidyverse",
"scales",
"sf",
"omxr",
"ggspatial",
"ggalluvial",
"ggh4x",
"ggnewscale",
"ggrepel",
"DiagrammeR",
"od",
"magrittr",
"khroma"
)
tar_option_set(
packages = package_list,
memory = "transient",
garbage_collection = TRUE,
# format = "qs",
)
tar_source("R")
tar_seed_set(34985723)
ggplot2::theme_set(
ggplot2::theme_bw() +
ggplot2::theme(
text = ggplot2::element_text(
family = "Alte Haas Grotesk",
size = 11),
axis.title = ggplot2::element_text(size = ggplot2::rel(1)),
axis.text = ggplot2::element_text(size = ggplot2::rel(0.8)),
strip.text = ggplot2::element_text(size = ggplot2::rel(0.9))
)
)
ggplot2::update_geom_defaults(
"text",
list(
colour = ggplot2::theme_get()$text$colour,
family = ggplot2::theme_get()$text$family,
size = ggplot2::theme_get()$text$size/ggplot2::.pt * 0.8
)
)
library(ggrepel)
ggplot2::update_geom_defaults(
"text_repel",
list(
colour = ggplot2::theme_get()$text$colour,
family = ggplot2::theme_get()$text$family,
size = ggplot2::theme_get()$text$size/ggplot2::.pt * 0.8
)
)
detach("package:ggrepel", unload = TRUE)
#### List targets ######################################################
# FrontRunner ####
frontrunner_targets <- tar_plan(
tar_file(frontrunner_line_file, "data/frontrunner_line.geojson"),
tar_file(frontrunner_stops_file, "data/frontrunner_stops.geojson"),
frontrunner_line = sf::st_read(frontrunner_line_file),
frontrunner_stops = sf::st_read(frontrunner_stops_file),
frontrunner_plot = plot_frontrunner(frontrunner_line, frontrunner_stops),
)
# Shared data targets ####
shared_data_targets <- tar_plan(
tar_file(taz_file, "data/WFRC_TAZ.geojson"),
taz = read_taz_file(taz_file),
distsml = get_sml_districts_from_taz_file(taz_file),
distmed = get_med_districts_from_taz_file(taz_file),
taz_distsml_transl = make_taz_distsml_transl(taz_file),
taz_distmed_transl = make_taz_distmed_transl(taz_file),
taz_centroids = sf::st_centroid(taz),
distsml_centroids = sf::st_centroid(distsml),
distmed_centroids = sf::st_centroid(distmed),
tar_file(income_groups_file, "data/income_groups.csv"),
income_groups = read_income_groups(income_groups_file),
tar_file(distance_skims_file, "data/cube/skm_DY_Dist.omx"),
distances = read_distance_skims(distance_skims_file),
tar_file(job_code_translation_file, "data/job_code_translation.csv"),
job_code_translation = readr::read_csv(job_code_translation_file),
tar_file(cube_time_spent_table_file, "data/cube_time_spent.csv"),
cube_time_spent_table = readr::read_csv(cube_time_spent_table_file),
tar_file(asim_time_spent_table_file, "data/asim_time_spent.csv"),
asim_time_spent_table = readr::read_csv(asim_time_spent_table_file),
tar_file(time_spent_table_file, "data/time_spent.csv"),
time_spent_table = readr::read_csv(time_spent_table_file),
)
# CUBE data targets ####
cube_data_targets <- tar_plan(
# SE
tar_file(cube_by_taz_se_file, "data/cube/TAZ_SE_2019_WFRC.csv"),
cube_by_taz_se = read_cube_taz_se_file(cube_by_taz_se_file),
tar_file(cube_lu_taz_se_file, "data/cube/SE_prison.csv"),
cube_lu_taz_se = read_cube_taz_se_file(cube_lu_taz_se_file),
tar_file(cube_by_taz_inc_groups_file, "data/cube/Marginal_Income.csv"),
cube_by_taz_inc_groups = read_cube_taz_inc_groups_file(cube_by_taz_inc_groups_file),
cube_by_combined_se = combine_cube_se(cube_by_taz_se, cube_by_taz_inc_groups),
tar_file(cube_remote_work_totals_file, "data/cube/ControlTotal_WorkAtHome.csv"),
cube_remote_work_totals = read_cube_remote_work_totals(
cube_remote_work_totals_file),
cube_remote_work_totals_plot = plot_cube_remote_work_totals(
cube_remote_work_totals),
# Calibration
tar_file(
cube_remote_work_percentages_file,
"data/cube/remote_work_jobtype.csv"),
cube_remote_work_percentages = read_cube_rw_percentages(
cube_remote_work_percentages_file, job_code_translation),
cube_by_telecommute_percentages = dplyr::filter(
cube_remote_work_percentages, year == 2019, type == "tc"),
cube_wfh_telecommute_percentages = dplyr::filter(
cube_remote_work_percentages, year == 2050, type == "tc"),
# Base year
tar_file(cube_by_hbw_omx, "data/cube/output/base_2019/HBW_trips_allsegs_pkok.omx"),
cube_by_hbw = read_trip_matrix(cube_by_hbw_omx),
tar_file(cube_by_hbo_omx, "data/cube/output/base_2019/HBO_trips_allsegs_pkok.omx"),
cube_by_hbo = read_trip_matrix(cube_by_hbo_omx),
tar_file(cube_by_nhb_omx, "data/cube/output/base_2019/NHB_trips_allsegs_pkok.omx"),
cube_by_nhb = read_trip_matrix(cube_by_nhb_omx),
cube_by_trips = dplyr::bind_rows(
list(
hbw = cube_by_hbw,
hbo = cube_by_hbo,
nhb = cube_by_nhb),
.id = "purpose"),
# Land Use
tar_file(cube_lu_hbw_omx, "data/cube/output/land_use/HBW_trips_allsegs_pkok.omx"),
cube_lu_hbw = read_trip_matrix(cube_lu_hbw_omx),
tar_file(cube_lu_hbo_omx, "data/cube/output/land_use/HBO_trips_allsegs_pkok.omx"),
cube_lu_hbo = read_trip_matrix(cube_lu_hbo_omx),
tar_file(cube_lu_nhb_omx, "data/cube/output/land_use/NHB_trips_allsegs_pkok.omx"),
cube_lu_nhb = read_trip_matrix(cube_lu_nhb_omx),
cube_lu_trips = dplyr::bind_rows(
list(
hbw = cube_lu_hbw,
hbo = cube_lu_hbo,
nhb = cube_lu_nhb),
.id = "purpose"),
# Transit
tar_file(cube_tr_hbw_omx, "data/cube/output/transit/HBW_trips_allsegs_pkok.omx"),
cube_tr_hbw = read_trip_matrix(cube_tr_hbw_omx),
tar_file(cube_tr_hbo_omx, "data/cube/output/transit/HBO_trips_allsegs_pkok.omx"),
cube_tr_hbo = read_trip_matrix(cube_tr_hbo_omx),
tar_file(cube_tr_nhb_omx, "data/cube/output/transit/NHB_trips_allsegs_pkok.omx"),
cube_tr_nhb = read_trip_matrix(cube_tr_nhb_omx),
cube_tr_trips = dplyr::bind_rows(
list(
hbw = cube_tr_hbw,
hbo = cube_tr_hbo,
nhb = cube_tr_nhb),
.id = "purpose"),
# WFH
tar_file(cube_wfh_hbw_omx, "data/cube/output/wfh/HBW_trips_allsegs_pkok.omx"),
cube_wfh_hbw = read_trip_matrix(cube_wfh_hbw_omx),
tar_file(cube_wfh_hbo_omx, "data/cube/output/wfh/HBO_trips_allsegs_pkok.omx"),
cube_wfh_hbo = read_trip_matrix(cube_wfh_hbo_omx),
tar_file(cube_wfh_nhb_omx, "data/cube/output/wfh/NHB_trips_allsegs_pkok.omx"),
cube_wfh_nhb = read_trip_matrix(cube_wfh_nhb_omx),
cube_wfh_trips = dplyr::bind_rows(
list(
hbw = cube_wfh_hbw,
hbo = cube_wfh_hbo,
nhb = cube_wfh_nhb),
.id = "purpose"),
)
# ASIM data targets ####
asim_data_targets <- tar_plan(
# Calibration
# tar_files(
# asim_mode_choice_calibration_iters_files,
# list.files(
# "data/calibration/mode_choice",
# full.names = TRUE, pattern = ".*\\.csv")),
# tar_target(
# asim_mode_choice_calibration_iters,
# combine_asim_mode_choice_calibration_iters(
# asim_mode_choice_calibration_iters_files),
# pattern = map(asim_mode_choice_calibration_iters_files)),
tar_file(
asim_by_telecommute_coefficients_file,
"data/asim/asim_tc_coeffs_2019.csv"),
asim_by_telecommute_coefficients = read_asim_telecommute_coefficients(
asim_by_telecommute_coefficients_file, job_code_translation),
tar_file(
asim_wfh_telecommute_coefficients_file,
"data/asim/asim_tc_coeffs_2050.csv"),
asim_wfh_telecommute_coefficients = read_asim_telecommute_coefficients(
asim_wfh_telecommute_coefficients_file, job_code_translation),
# Remote work model coefficients
tar_file(asim_wfh_model_coeffs_file, "data/asim/work_from_home_coeffs.csv"),
asim_wfh_model_coeffs = readr::read_csv(asim_wfh_model_coeffs_file),
tar_file(asim_telecommute_model_coeffs_file, "data/asim/telecommute_frequency_coeffs.csv"),
asim_telecommute_model_coeffs = readr::read_csv(asim_telecommute_model_coeffs_file),
tar_file(asim_tc_dap_coeffs_file, "data/asim/remote_work_DAP_coeffs.csv"),
asim_tc_dap_coeffs = readr::read_csv(asim_tc_dap_coeffs_file),
# Base year
tar_file(asim_by_trips_file, "data/asim/output/base_2019/final_trips.csv.gz"),
tar_file(asim_by_tours_file, "data/asim/output/base_2019/final_tours.csv.gz"),
tar_file(asim_by_per_file, "data/asim/output/base_2019/final_persons.csv.gz"),
tar_file(asim_by_hh_file, "data/asim/output/base_2019/final_households.csv.gz"),
asim_by_raw_trips = read_asim_trips_file(asim_by_trips_file),
asim_by_trips = count_asim_trips(asim_by_raw_trips),
asim_by_per = readr::read_csv(asim_by_per_file),
asim_by_raw_hh = readr::read_csv(asim_by_hh_file),
asim_by_hh_taz = summarise_asim_hh(asim_by_raw_hh, income_groups),
# Land use
tar_file(asim_lu_trips_file, "data/asim/output/landuse/final_trips.csv.gz"),
tar_file(asim_lu_tours_file, "data/asim/output/landuse/final_tours.csv.gz"),
tar_file(asim_lu_per_file, "data/asim/output/landuse/final_persons.csv.gz"),
tar_file(asim_lu_hh_file, "data/asim/output/landuse/final_households.csv.gz"),
asim_lu_raw_trips = read_asim_trips_file(asim_lu_trips_file),
asim_lu_trips = count_asim_trips(asim_lu_raw_trips),
asim_lu_per = readr::read_csv(asim_lu_per_file),
# Transit
tar_file(asim_tr_trips_file, "data/asim/output/transit/final_trips.csv.gz"),
tar_file(asim_tr_tours_file, "data/asim/output/transit/final_tours.csv.gz"),
tar_file(asim_tr_per_file, "data/asim/output/transit/final_persons.csv.gz"),
tar_file(asim_tr_hh_file, "data/asim/output/transit/final_households.csv.gz"),
asim_tr_raw_trips = read_asim_trips_file(asim_tr_trips_file),
asim_tr_trips = count_asim_trips(asim_tr_raw_trips),
asim_tr_per = readr::read_csv(asim_tr_per_file),
asim_tr_hh = readr::read_csv(asim_tr_hh_file),
# WFH
tar_file(asim_wfh_trips_file, "data/asim/output/wfh/final_trips.csv.gz"),
tar_file(asim_wfh_tours_file, "data/asim/output/wfh/final_tours.csv.gz"),
tar_file(asim_wfh_per_file, "data/asim/output/wfh/final_persons.csv.gz"),
tar_file(asim_wfh_hh_file, "data/asim/output/wfh/final_households.csv.gz"),
asim_wfh_raw_trips = read_asim_trips_file(asim_wfh_trips_file),
asim_wfh_trips = count_asim_trips(asim_wfh_raw_trips),
)
# MCC targets ####
mc_calibration_targets <- tar_plan(
tar_files(mcc_by_trips_files, list_mcc_trips_iters("calibrate_mc")),
tar_target(
mcc_by_trips_raw,
read_asim_trips_file(mcc_by_trips_files),
pattern = map(mcc_by_trips_files),
iteration = "list"
),
tar_target(
mcc_by_trips,
count_asim_trips(mcc_by_trips_raw),
pattern = map(mcc_by_trips_raw),
# pattern = head(map(mcc_by_trips_raw)),
iteration = "list"
),
tar_files(mcc_tr_trips_files, list_mcc_trips_iters("transit")),
tar_target(
mcc_tr_trips_raw,
read_asim_trips_file(mcc_tr_trips_files),
pattern = map(mcc_tr_trips_files),
iteration = "list"
),
tar_target(
mcc_tr_trips,
count_asim_trips(mcc_tr_trips_raw),
pattern = map(mcc_tr_trips_raw),
# pattern = head(map(mcc_tr_trips_raw)),
iteration = "list"
),
tar_target(
mcc_tr_trip_diff,
get_trip_diff(list(tr = mcc_tr_trips, by = mcc_by_trips)),
pattern = map(mcc_tr_trips, mcc_by_trips),
iteration = "list"
),
tar_target(
mcc_tr_diff_plot,
plot_trips_diff_by_district(
mcc_tr_trip_diff, taz_distsml_transl, distsml,
frontrunner_line, frontrunner_stops),
pattern = map(mcc_tr_trip_diff),
iteration = "list"
),
tar_file(mode_crosswalk_file, "data/calibration/mode_crosswalk.csv"),
mode_crosswalk = readr::read_csv(mode_crosswalk_file),
)
# Base year ####
base_year_targets <- tar_plan(
# Data
combined_by_trips = dplyr::bind_rows(
list(asim = asim_by_trips, cube = cube_by_trips),
.id = "model"),
combined_by_trips_sampled = sample_trips(
combined_by_trips, prop = 0.1, weight = FALSE),
combined_by_se_data = combine_se_data(
list(cube = cube_by_combined_se, asim = asim_by_hh_taz)),
combined_by_se_data_distsml = summarise_combined_se_data(
combined_by_se_data, taz_distsml_transl, distsml),
# Population
comparison_pop_map = make_pop_comparison_map(
combined_by_se_data_distsml),
comparison_med_income_map = make_med_income_comparison_map(
combined_by_se_data_distsml),
comparison_inc_groups_map = make_inc_groups_comparison_map(
combined_by_se_data_distsml, income_groups),
comparison_inc_density_plot = make_inc_density_comparison_plot(
combined_by_se_data_distsml, income_groups),
# Mode choice
tar_file(mode_crosswalk_file, "data/calibration/mode_crosswalk.csv"),
mode_crosswalk = readr::read_csv(mode_crosswalk_file),
tar_files(
mcc_adjustments_files,
list.files(
"data/calibration/mode_choice/coeffs",
full.names = TRUE, pattern = "\\d+.*adjustments\\.csv")),
tar_target(
mcc_adjustments,
read_mcc_adjustments_files(mcc_adjustments_files),
pattern = map(mcc_adjustments_files)
),
mcc_adjustments_plot = plot_mcc_adjustments(mcc_adjustments),
## Mode choice coefficients
tar_files(
mcc_tour_coeffs_files,
list.files(
"data/calibration/mode_choice/coeffs",
full.names = TRUE, pattern = "\\d+_tour_mode_choice_coefficients.csv")
),
tar_target(
mcc_tour_coeffs,
read_mcc_coeffs(mcc_tour_coeffs_files),
pattern = map(mcc_tour_coeffs_files)
),
tar_target(
mcc_tour_coeffs_combined,
tidyr::pivot_wider(mcc_tour_coeffs, names_from = iter, values_from = value)
),
tar_files(
mcc_trip_coeffs_files,
list.files(
"data/calibration/mode_choice/coeffs",
full.names = TRUE, pattern = "\\d+_trip_mode_choice_coefficients.csv")
),
tar_target(
mcc_trip_coeffs,
read_mcc_coeffs(mcc_trip_coeffs_files),
pattern = map(mcc_trip_coeffs_files)
),
tar_target(
mcc_trip_coeffs_combined,
tidyr::pivot_wider(mcc_trip_coeffs, names_from = iter, values_from = value)
),
mc_comparison = get_mode_split_table(mcc_adjustments, 4),
# TLFD
combined_by_tlfd_plot = plot_combined_tlfd(
combined_by_trips_sampled, distances),
# WFH
comparison_by_telecommute_coeffs = compare_telecommute(
cube_by_telecommute_percentages,
asim_by_telecommute_coefficients),
)
# Land use ####
land_use_targets <- tar_plan(
draper_prison_site_map = make_draper_prison_site_map(taz, lu_tazs),
gateway_and_prison_map = plot_gateway_and_prison(),
# draper_prison_site_inset = get_draper_prison_site_inset(taz, lu_tazs),
# Data
lu_tazs = c(2138, 2140, 2141, 2149, 2170),
lu_distsml = get_dist_from_tazs(lu_tazs, taz_distsml_transl),
lu_distmed = get_dist_from_tazs(lu_tazs, taz_distmed_transl),
lu_plot_new_tazs = plot_lu_new_tazs(taz, lu_tazs),
lu_se_by_table = make_lu_se_table(cube_by_taz_se, lu_tazs),
lu_se_lu_table = make_lu_se_table(cube_lu_taz_se, lu_tazs),
lu_combined_se_table = combine_se_tables(list(by = lu_se_by_table, lu = lu_se_lu_table)),
# cube_lu_by_se_diff = get_cube_se_diff(list(lu = cube_lu_taz_se, by = cube_by_taz_se)),
# lu_se_diff_table = make_lu_se_table(cube_lu_by_se_diff, lu_tazs),
cube_lu_all_diff = get_trip_diff(list(lu = cube_lu_trips, by = cube_by_trips)),
cube_lu_all_diff_distsml = get_trip_diff(list(
lu = sum_trips_by_district(cube_lu_trips, taz_distsml_transl),
by = sum_trips_by_district(cube_by_trips, taz_distsml_transl))),
cube_lu_nhb_diff_distsml = dplyr::filter(
cube_lu_all_diff_distsml, purpose == "nhb"),
cube_lu_new_productions_distsml = dplyr::filter(
cube_lu_all_diff_distsml, origin %in% lu_distsml, purpose != "nhb"),
asim_lu_new_persons = get_asim_new_persons(asim_lu_per, asim_by_per, lu_tazs),
asim_lu_new_trips = get_asim_lu_new_trips(asim_lu_raw_trips, asim_lu_new_persons),
asim_lu_new_trips_distsml = sum_trips_by_district(
asim_lu_new_trips, taz_distsml_transl),
# PMT/VMT
cube_lu_new_pmt_plot = plot_cube_lu_new_pmt(
cube_lu_all_diff, distances, lu_tazs),
asim_lu_new_pmt_plot = plot_asim_lu_pmt(
asim_lu_raw_trips, asim_lu_new_persons, distances, lu_tazs),
# Desire lines
cube_lu_new_productions_desire = od::od_to_sf(
cube_lu_new_productions_distsml, distsml_centroids),
cube_lu_new_productions_desire_map = plot_cube_lu_desire_lines(
cube_lu_new_productions_desire, distsml),
cube_lu_nhb_diff_desire = od::od_to_sf(
cube_lu_nhb_diff_distsml, distsml_centroids),
cube_lu_nhb_diff_desire_map = plot_cube_lu_nhb_desire_lines(
cube_lu_nhb_diff_desire, distsml),
asim_lu_new_desire_lines = od::od_to_sf(
asim_lu_new_trips_distsml, distsml_centroids),
asim_lu_new_desire_map = plot_asim_lu_desire_lines(
asim_lu_new_desire_lines, lu_distsml, distsml),
)
# Transit ####
transit_targets <- tar_plan(
# Data
cube_tr_by_hbw = read_tr_trip_matrix(cube_by_hbw_omx),
cube_tr_by_hbo = read_tr_trip_matrix(cube_by_hbo_omx),
cube_tr_by_nhb = read_tr_trip_matrix(cube_by_nhb_omx),
cube_tr_by_trips = dplyr::bind_rows(
list(
hbw = cube_tr_by_hbw,
hbo = cube_tr_by_hbo,
nhb = cube_tr_by_nhb),
.id = "purpose"),
cube_tr_tr_hbw = read_tr_trip_matrix(cube_tr_hbw_omx),
cube_tr_tr_hbo = read_tr_trip_matrix(cube_tr_hbo_omx),
cube_tr_tr_nhb = read_tr_trip_matrix(cube_tr_nhb_omx),
cube_tr_tr_trips = dplyr::bind_rows(
list(
hbw = cube_tr_tr_hbw,
hbo = cube_tr_tr_hbo,
nhb = cube_tr_tr_nhb),
.id = "purpose"),
asim_by_tr_trips = count_asim_tr_trips(asim_by_raw_trips),
asim_tr_tr_trips = count_asim_tr_trips(asim_tr_raw_trips),
cube_tr_all_trips_diff = get_trip_diff(
list(tr = cube_tr_tr_trips, by = cube_tr_by_trips)),
asim_tr_all_trips_diff = get_trip_diff(
list(tr = asim_tr_tr_trips, by = asim_by_tr_trips)),
# Mode split
combined_tr_mode_split_diff = dplyr::full_join(
calculate_mode_split_diff_pct(cube_tr_all_trips_diff, "cube"),
calculate_mode_split_diff_pct(asim_tr_all_trips_diff, "asim"),
join_by(purpose, mode)),
combined_tr_mode_split_table = make_combined_mode_split_table(
combined_tr_mode_split_diff),
# Mode switching
asim_tr_mode_switching_raw = get_asim_mode_switching(list(
tr = asim_tr_raw_trips, by = asim_by_raw_trips)),
asim_tr_mode_switching = dplyr::filter(asim_tr_mode_switching_raw, mode_tr != mode_by),
asim_tr_mode_switching_plot = plot_asim_mode_switching(asim_tr_mode_switching),
asim_tr_work_switchers = get_asim_work_switchers(asim_tr_mode_switching),
asim_tr_atwork_switching = get_asim_atwork_mode_switching(
list(tr = asim_tr_raw_trips, by = asim_by_raw_trips),
asim_tr_work_switchers),
asim_tr_atwork_switching_plot = plot_asim_mode_switching(asim_tr_atwork_switching),
# SE comparison
cube_tr_productions_se = get_cube_production_se(cube_tr_tr_trips, cube_by_taz_se),
cube_tr_productions_se_summary = summarise_cube_transit_se(
cube_tr_productions_se),
asim_tr_trips_se = get_asim_trips_se(
asim_tr_raw_trips, asim_tr_per, asim_tr_hh),
asim_tr_trips_se_summary = summarise_asim_transit_se(
asim_tr_trips_se),
combined_tr_trips_se_for_income_plot = combine_tr_trips_se_for_income_plot(
cube = cube_tr_productions_se, asim = asim_tr_trips_se),
combined_tr_new_transit_income_plot = plot_tr_new_transit_income_dist(
combined_tr_trips_se_for_income_plot),
# New transit trip productions
cube_tr_diff_by_district_plot = plot_trips_diff_by_district(
cube_tr_all_trips_diff, taz_distsml_transl, distsml,
frontrunner_line, frontrunner_stops),
asim_tr_diff_by_district_plot = plot_trips_diff_by_district(
asim_tr_all_trips_diff, taz_distsml_transl, distsml,
frontrunner_line, frontrunner_stops),
)
# WFH ####
wfh_targets <- tar_plan(
# Data
cube_wfh_all_trips_diff = get_trip_diff(list(
wfh = cube_wfh_trips, by = cube_by_trips)),
asim_wfh_all_trips_diff = get_trip_diff(list(
wfh = asim_wfh_trips, by = asim_by_trips)),
combined_wfh_trips_diff = dplyr::bind_rows(list(
cube = cube_wfh_all_trips_diff, asim = asim_wfh_all_trips_diff),
.id = "model"),
cube_wfh_by_remote_work_pct_comparison = tidyr::pivot_wider(
cube_remote_work_percentages,
names_from = c(year, type), values_from = pct),
comparison_wfh_telecommute_coeffs = compare_telecommute(
cube_wfh_telecommute_percentages,
asim_wfh_telecommute_coefficients),
# Mode split
combined_wfh_mode_split_diff = dplyr::full_join(
calculate_mode_split_diff_pct(cube_wfh_all_trips_diff, "cube"),
calculate_mode_split_diff_pct(asim_wfh_all_trips_diff, "asim"),
join_by(purpose, mode)),
# Trip and PMT diff
cube_wfh_trip_pmt_diff = calculate_trip_and_pmt_diff(
cube_wfh_all_trips_diff, distances),
asim_wfh_trip_pmt_diff = calculate_trip_and_pmt_diff(
asim_wfh_all_trips_diff, distances),
# TLFD
combined_tlfd_diff = calculate_tlfd_diff(
combined_wfh_trips_diff, distances),
combined_tlfd_diff_plot = plot_tlfd_diff(combined_tlfd_diff),
# cube_wfh_tlfd_diff_comp_plot = plot_wfh_by_tlfd_diff_comp(
# combined_wfh_trips_diff, which_model = "cube", distances),
# asim_wfh_tlfd_diff_comp_plot = plot_wfh_by_tlfd_diff_comp(
# combined_wfh_trips_diff, which_model = "asim", distances),
)
#### Run all targets ###################################################
tar_plan(
frontrunner_targets,
shared_data_targets,
cube_data_targets,
asim_data_targets,
# mc_calibration_targets,
base_year_targets,
land_use_targets,
transit_targets,
wfh_targets,
)