In paezha/discrtr: A Companion Package for the Book "Discrete Choice Analysis with 'R'"

Introduction

In this notebook we create an object with boundaries by type for Hamilton.

Preliminaries

Clear environment:

rm(list = ls())

Load packages used in the notebook:

#library(cancensus)
#library(disk.frame)
#library(pycno)
#library(readr)
#library(r5r) # the r5r package requires Java Development Kit version 11, which can be downloaded from https://www.oracle.com/java/technologies/javase-jdk11-downloads.html
library(sf)
library(smoothr)
library(tidyverse)
#library(tmap)
#library(tmaptools)

Hamilton Ward boundaries

Open Data Hamilton. Read:

wards <- st_read("input-data-files/Ward_Boundaries.shp")

Add label for type of ward (the urban/suburban/rural classification is by the planning teams; see https://www.hamilton.ca/develop-property/planning-applications/development-applications-mapping)

wards$Type <- c("Urban",
                "Urban",
                "Urban",
                "Urban",
                "Urban",
                "Suburban",
                "Suburban",
                "Suburban",
                "Suburban",
                "Suburban",
                "Suburban",
                "Suburban",
                "Suburban",
                "Suburban",
                "Suburban")

Merge boundaries by type:

wards <- wards %>% 
  group_by(Type) %>%
  summarize()

ggplot(wards) + 
  geom_sf(aes(fill = Type))

Read rural boundary:

rural <- st_read("input-data-files/Rural_Boundary.shp")
rural <- rural %>%
  transmute(Type = "Rural")

Plot rural boundary:

ggplot(rural) +
  geom_sf()

Obtain difference between urban and suburban regions and rural boundary:

st_difference(wards, rural) %>%
  ggplot() +
  geom_sf(aes(fill = Type))

Notice the detritus after the difference. Drop crumbs to obtain a cleaner version of the boundaries:

sub_urban <- st_difference(wards, rural) %>%
  transmute(Type) %>%
  smoothr::drop_crumbs(threshold = 5000)

ggplot(sub_urban) +
  geom_sf(aes(fill = Type))

Bind the boundaries with all three urban types:

urban_types <- rbind(sub_urban,
                     rural)

ggplot(urban_types) +
  geom_sf(aes(fill = Type))

Save data:

usethis::use_data(urban_types)

paezha/discrtr documentation built on March 1, 2023, 5:25 p.m.