In tlumip/swimr: Functions for Looking at Swim

library(swimr, warn.conflicts = FALSE)
library(knitr)
library(dplyr)
library(ggplot2)

opts_chunk$set(
  echo = FALSE, message = FALSE, warning = FALSE,
  fig.keep = TRUE, fig.path = params$fig.path,
  fig.width=8, fig.height=6
)

ref <- dbConnect(SQLite(), dbname=params$ref_db) # reference
db1 <- dbConnect(SQLite(), dbname=params$current_db) # current
db3 <- dbConnect(SQLite(), dbname=params$compare_db) # current

dbset <- list(ref, db1, db3)
db_names <- c(
  params$ref_name, params$current_name, params$compare_name
)

zones_shp <- extract_zones(db = ref)
zones_data <- zones_shp@data
zones <- fortify(zones_shp) %>%
  left_join(zones_data, by='id')

ODOT Regions

In this report we group figures by ODOT regions.^[As a note, ODOT's region definitions divide counties (and TLUMIP model zones). These are approximate definitions that keep counties in a single region.]

ggplot(zones, aes(x = long, y = lat, fill = factor(DOT_REGION), group = group)) +
  geom_polygon() +
  coord_map("conic", lat0 = 43)

Population

regions = zones_data$DOT_REGION %>%
  unique() %>%
  sort() 

for(r in regions){
  counties <- zones_data %>% filter(DOT_REGION == r)

  p <- multiple_sevar(dbset, db_names, variable = "population",    
                      facet_var = "COUNTY",
                      facet_levels = unique(counties$COUNTY)) +
    ggtitle(paste("Region", r)) + theme(legend.position = "bottom")

  print(p)

  if(r < 6){
    p <- plot_history(ref, counties = counties$COUNTY) +
      ggtitle(paste("Historical Region (Reference)", r))
    print(p)
  } 
}

Employment

for(r in regions){
  counties <- zones_data %>% filter(DOT_REGION == r)

  p <- multiple_sevar(dbset, db_names, variable = "employment",   
                      facet_var = "COUNTY",  facet_levels = counties$COUNTY)

  print(p + ggtitle(paste("Region", r)) + theme(legend.position = "bottom"))
}

By Sector

for(r in regions){
  counties <- zones_data %>% filter(DOT_REGION == r)

  p <- multiple_employment(dbset, db_names,
                           facet_var = "COUNTY", facet_levels = counties$COUNTY)

  print(p + ggtitle(paste("Region", r)) + theme(legend.position = "bottom"))
}

WAPR

for(r in regions){
  counties <- zones_data %>% filter(DOT_REGION == r)

  p <- multiple_wapr(dbset, db_names, 
                     facet_var = "COUNTY", facet_levels = counties$COUNTY)

  print(p + ggtitle(paste("Region", r)) + theme(legend.position = "bottom"))
}

Floorspace

for(r in regions){
  counties <- zones_data %>% filter(DOT_REGION == r)

  p <- multiple_floorspace(dbset = dbset, db_names = db_names,
                           variable = 'floorspace',
                           facet_var = "COUNTY", facet_levels = counties$COUNTY)

  print(p + ggtitle(paste("Region", r)) + theme(legend.position = "bottom"))
}