In tlumip/swimr: Functions for Looking at Swim

library(knitr)
opts_chunk$set(echo = TRUE, fig.keep = TRUE, fig.path = params$fig.path)
library(methods)
library(tidyverse)
library(DT)
library(RSQLite)
library(swimr)

# This is the path to the scenario SWIM databases; direct these to your
# local paths.
# ref_db <- "J:/swim2/ALD_1_Region_test/outputs/ALD_1_Region_test.db"
ref_db <- params$ref_db
db <- dbConnect(SQLite(), dbname=ref_db)

# scenario_name <- "Reference"
scenario_name <- params$scenario_name

# Update to reflect scope of analysis.
# If you wish to focus on the Metro and Bend MPOs, for instance, change `facet`
# to "MPO" and facet_levels to `c("Metro", "Bend")`
# facet <- "COUNTY"
facet <- params$facet
facet_levels <-  c("Multnomah", "Washington", "Clackamas")

# The tables will only show data from these years.
years <- c(2010, 2025, 2040)

# The list MPOs to plot - note that in this version "Longview/Kelos/Rainier" and "Walla Walla Valley" are dropped
MPOs <- c("Albany","Bend", "Corvallis", "Eugene/Springfield","Medford", "METRO", "METRO_CLARK","Middle Rogue", "NonMPO", "Halo", "Salem/Keizer")

# The leaflet plots show a comparison between the scenarios in a specific year.
# Set this to the year you wish to study.
# diff_year <- 2040
diff_year <- params$diff_year

# show leaflet plots; FALSE will skip them (saving disk space and time)
# use_leaflet <- FALSE
use_leaflet <- params$use_leaflet

Scenario Description

# update to reflect current scenario
scen_info <- tibble(
  Name = basename(ref_db),
  Scenario = scenario_name,
  `File date` = file.info(ref_db)$mtime
)

kable(scen_info, caption = "Scenario Information")

Socioeconomics {.tabset}

Population

if(use_leaflet){
  change_leaflet(db, year1 = 2010, year2 = diff_year)
} else {
  print("Leaflet plots skipped with `use_leaflet` option")
}

se <- extract_se(db, "MPO") %>%
  filter(year %in% years)

pop <- se %>% filter(var == "population") %>%
  yearly_summary(group = "color_var", var = "y")

kable(pop, caption = "Population by MPO", digits = 2)

Employment

emp <- se %>% filter(var == "employment") %>%
  yearly_summary(group = "color_var", var = "y")

kable(emp, caption = "Employment by MPO", digits = 2)

plot_sevar(db, color_var = facet, color_levels = facet_levels, controls = FALSE, index = TRUE)

plot_sevar(db, color_var = facet, color_levels = facet_levels, index = TRUE, controls = TRUE)

plot_sevar(db, color_var = "MPO")

plot_sevar(db, color_var = "MPO", index = TRUE)

Historical Trends

plot_history(db, counties = facet_levels)

Transportation {.tabset}

Link Validation

plot_countcomparison(db, 2010)

plot_countcomparison(db, 2013, TRUE)

plot_countcomparison(db, 2010) + facet_wrap(~ FacType, scales = "free")

plot_traffic_count(db, atr =
                     c("03-011", "10-006", "03-013", "34-009", "24-022",
                       "15-020", "03-018", "10-008"))

VMT

vmt <- extract_vmt(db, "MPO", index = FALSE) %>%
  filter(year %in% years) %>%
  group_by(year, MPO) %>%
  summarise(vmt = sum(vmt)) %>%
  yearly_summary("MPO", "vmt")

kable(vmt, caption = "VMT by MPO", digits = 2)

plot_vmt(db, facet, facet_levels, index = FALSE)

plot_vmt(db, facet, facet_levels)

plot_vmt(db, "MPO")

Mode Split

trips <- extract_trips(db, "MPO") %>%
  filter(year %in% years) %>%
  group_by(year, facet_var) %>%
  summarise(trips = sum(trips)) %>%
  yearly_summary("facet_var", "trips")

kable(trips, caption = "Total Trips by MPO", digits = 2)

tab_mode <- extract_trips(db, "MPO", facet_levels = c("METRO", "METRO_CLARK")) %>%
  filter(year %in% years) %>%
  filter(facet_var == "METRO") %>%
  group_by(year, mode) %>%
  summarise(trips = sum(trips)) %>%
  yearly_summary("mode", "trips")

kable(tab_mode, caption = "Metro MPO Trips by Mode", digits = 2)

plot_trips(db, facet, facet_levels, share = TRUE)

plot_trips(db,  share = TRUE)

plot_trips(db, facet, facet_levels, share = FALSE, index = FALSE)

plot_trips(db, share = FALSE)

TLFD

plot_tlfd(db, facet, facet_levels)

plot_tlfd(db, facet, facet_levels, cumulative = TRUE)

plot_tlfd(db, "MPO", MPOs)

Log Sums

plot_logsums(db, facet, facet_levels)

plot_logsums(db, "MPO", c("Bend", "Corvallis", "EugeneSpringfield",
               "Metro", "NonMPO", "RogueValley", "SalemKeizer"))

Economics{.tabset}

troubleshoot_aa(db)

Built Floorspace

plot_floorspace(db, facet, facet_levels)

plot_floorspace(db)

Rent and Occupancy Rate

plot_floorspace(db, facet, facet_levels, price = TRUE)

plot_floorspace(db, price = TRUE)

plot_occupancy(db, facet, facet_levels)

plot_occupancy(db)

Employment

plot_employment(db, facet, facet_levels)

plot_employment(db)

Labor Output

plot_gdp(db, facet, facet_levels)

plot_gdp(db)

Labor Force Participation

Specifically, the ratio of workers to individuals 15 or older.

plot_wapr(db, facet, facet_levels)

plot_wapr(db, "MPO")

tlumip/swimr documentation built on Dec. 14, 2020, 3:16 a.m.