clustered_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script explores \n",
    "1. the characteristics of the **Ku-ring-gai** LGA with those of other LGAs in the same cluster\n",
    "2. the characteristics of the clusters that contain the **Ku-ring-gai** LGA, with another cluster. The variables will be aggregated at a cluster level and the same analyses as (1) followed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(MASS)\n",
    "library(tidyverse)\n",
    "library(magrittr)\n",
    "library(reshape2)\n",
    "library(ggplot2)\n",
    "library(scales)\n",
    "library(ggrepel)\n",
    "library(readxl)\n",
    "library(corrplot)\n",
    "library(purrr)\n",
    "library(rlang)\n",
    "library(ggcorrplot)\n",
    "library(ggfortify)\n",
    "library(AER)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Theme for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_theme <- function(base_size = 12, base_family = \"sans\"){\n",
    " theme_grey(base_size = base_size, base_family = base_family) +\n",
    "   theme(\n",
    "     axis.text = element_text(size = 12),\n",
    "     axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),\n",
    "     axis.ticks.length = unit(.25, \"cm\"),\n",
    "     axis.title = element_text(size = 14),\n",
    "     panel.grid.major = element_line(color = \"grey\"),\n",
    "     panel.grid.minor = element_blank(),\n",
    "     panel.background = element_rect(fill = \"aliceblue\"), # alice blue could be replaced with NA as well,\n",
    "     panel.border = element_rect(linetype = \"solid\", fill = NA),\n",
    "     strip.background = element_rect(fill = \"lightgrey\", color = \"grey\", size = 1),\n",
    "     strip.text = element_text(face = \"bold\", size = 12, color = \"navy\"),\n",
    "     legend.position = \"right\",\n",
    "     legend.background = element_blank(),\n",
    "     legend.title = element_text(face = \"bold\"),\n",
    "     panel.spacing  = unit(.5, \"lines\")\n",
    "     #panel.border = element_rect(color = \"grey\", fill = NA, size = 0.5)\n",
    "   )\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path <- \"D:/OneDrive - UTS/36103/AT2B/data2\"\n",
    "cluster_path <- \"D:/OneDrive - UTS/36103/AT2/Git/STDS-AT2-Transport/LGA clustering/KMClustGroups2_cosine.csv\"\n",
    "chart_dir <- \"D:/OneDrive - UTS/36103/AT2B/charts\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  LGA = \u001b[31mcol_character()\u001b[39m,\n",
      "  crime_tot_per_lga = \u001b[32mcol_double()\u001b[39m,\n",
      "  crime_rate_per_lga = \u001b[32mcol_double()\u001b[39m,\n",
      "  Sex = \u001b[31mcol_character()\u001b[39m,\n",
      "  Age = \u001b[31mcol_character()\u001b[39m,\n",
      "  pop_age_lga = \u001b[32mcol_double()\u001b[39m,\n",
      "  pop_total = \u001b[32mcol_double()\u001b[39m,\n",
      "  birthplace_of_person = \u001b[31mcol_character()\u001b[39m,\n",
      "  birth_value = \u001b[32mcol_double()\u001b[39m,\n",
      "  `Industry of Employment` = \u001b[31mcol_character()\u001b[39m,\n",
      "  emp_value = \u001b[32mcol_double()\u001b[39m,\n",
      "  labour_type = \u001b[31mcol_character()\u001b[39m,\n",
      "  labour_value = \u001b[32mcol_double()\u001b[39m,\n",
      "  Pop_Dens_km2 = \u001b[32mcol_double()\u001b[39m,\n",
      "  seifa_value = \u001b[32mcol_double()\u001b[39m,\n",
      "  num_service = \u001b[32mcol_double()\u001b[39m\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>LGA</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>pop_age_lga</th><th scope=col>pop_total</th><th scope=col>birthplace_of_person</th><th scope=col>birth_value</th><th scope=col>Industry of Employment</th><th scope=col>emp_value</th><th scope=col>labour_type</th><th scope=col>labour_value</th><th scope=col>Pop_Dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Mining                           </td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Mining                           </td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Manufacturing                    </td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Manufacturing                    </td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllll}\n",
       " LGA & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & Sex & Age & pop\\_age\\_lga & pop\\_total & birthplace\\_of\\_person & birth\\_value & Industry of Employment & emp\\_value & labour\\_type & labour\\_value & Pop\\_Dens\\_km2 & seifa\\_value & num\\_service\\\\\n",
       "\\hline\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Agriculture, Forestry and Fishing & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Agriculture, Forestry and Fishing & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Mining                            & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Mining                            & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Manufacturing                     & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Manufacturing                     & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| LGA | crime_tot_per_lga | crime_rate_per_lga | Sex | Age | pop_age_lga | pop_total | birthplace_of_person | birth_value | Industry of Employment | emp_value | labour_type | labour_value | Pop_Dens_km2 | seifa_value | num_service |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Agriculture, Forestry and Fishing | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Agriculture, Forestry and Fishing | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Mining                            | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Mining                            | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Manufacturing                     | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Manufacturing                     | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              |\n",
       "\n"
      ],
      "text/plain": [
       "  LGA    crime_tot_per_lga crime_rate_per_lga Sex     Age         pop_age_lga\n",
       "1 albury 6233              0.1220219          Females 85 and over 845        \n",
       "2 albury 6233              0.1220219          Females 85 and over 845        \n",
       "3 albury 6233              0.1220219          Females 85 and over 845        \n",
       "4 albury 6233              0.1220219          Females 85 and over 845        \n",
       "5 albury 6233              0.1220219          Females 85 and over 845        \n",
       "6 albury 6233              0.1220219          Females 85 and over 845        \n",
       "  pop_total birthplace_of_person birth_value Industry of Employment           \n",
       "1 51081     New Zealand          2510        Agriculture, Forestry and Fishing\n",
       "2 51081     New Zealand          2510        Agriculture, Forestry and Fishing\n",
       "3 51081     New Zealand          2510        Mining                           \n",
       "4 51081     New Zealand          2510        Mining                           \n",
       "5 51081     New Zealand          2510        Manufacturing                    \n",
       "6 51081     New Zealand          2510        Manufacturing                    \n",
       "  emp_value labour_type labour_value Pop_Dens_km2 seifa_value num_service\n",
       "1 0         employed      0          170.5        964         1769       \n",
       "2 0         unemployed  834          170.5        964         1769       \n",
       "3 0         employed      0          170.5        964         1769       \n",
       "4 0         unemployed  834          170.5        964         1769       \n",
       "5 0         employed      0          170.5        964         1769       \n",
       "6 0         unemployed  834          170.5        964         1769       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 11,616,480\n",
      "Variables: 16\n",
      "$ LGA                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"albury\", \"albury\", \"albury\", \"a...\n",
      "$ crime_tot_per_lga        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 6233, 6233, 6233, 6233, 6233, 6233, 6233, ...\n",
      "$ crime_rate_per_lga       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.1220219, 0.1220219, 0.1220219, 0.1220219...\n",
      "$ Sex                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Females\", \"Females\", \"Females\", \"Females\"...\n",
      "$ Age                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"85 and over\", \"85 and over\", \"85 and over...\n",
      "$ pop_age_lga              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 845, 845, 845, 845, 845, 845, 845, 845, 84...\n",
      "$ pop_total                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 51081, 51081, 51081, 51081, 51081, 51081, ...\n",
      "$ birthplace_of_person     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"New Zealand\", \"New Zealand\", \"New Zealand...\n",
      "$ birth_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 2510, 2510, 2510, 2510, 2510, 2510, 2510, ...\n",
      "$ `Industry of Employment` \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Agriculture, Forestry and Fishing\", \"Agri...\n",
      "$ emp_value                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
      "$ labour_type              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"employed\", \"unemployed\", \"employed\", \"une...\n",
      "$ labour_value             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 834, 0, 834, 0, 834, 0, 834, 0, 834, 0,...\n",
      "$ Pop_Dens_km2             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 170.5, 170.5, 170.5, 170.5, 170.5, 170.5, ...\n",
      "$ seifa_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 964, 964, 964, 964, 964, 964, 964, 964, 96...\n",
      "$ num_service              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1769, 1769, 1769, 1769, 1769, 1769, 1769, ...\n"
     ]
    }
   ],
   "source": [
    "data <- readr::read_csv(paste(data_path, \"unified4.csv\", sep = \"/\"))\n",
    "head(data)\n",
    "glimpse(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lga</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>pop_age_lga</th><th scope=col>pop_total</th><th scope=col>birthplace_of_person</th><th scope=col>birth_value</th><th scope=col>industry of employment</th><th scope=col>emp_value</th><th scope=col>labour_type</th><th scope=col>labour_value</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Mining                           </td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Mining                           </td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Manufacturing                    </td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Manufacturing                    </td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllll}\n",
       " lga & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & sex & age & pop\\_age\\_lga & pop\\_total & birthplace\\_of\\_person & birth\\_value & industry of employment & emp\\_value & labour\\_type & labour\\_value & pop\\_dens\\_km2 & seifa\\_value & num\\_service\\\\\n",
       "\\hline\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Agriculture, Forestry and Fishing & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Agriculture, Forestry and Fishing & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Mining                            & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Mining                            & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Manufacturing                     & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Manufacturing                     & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lga | crime_tot_per_lga | crime_rate_per_lga | sex | age | pop_age_lga | pop_total | birthplace_of_person | birth_value | industry of employment | emp_value | labour_type | labour_value | pop_dens_km2 | seifa_value | num_service |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Agriculture, Forestry and Fishing | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Agriculture, Forestry and Fishing | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Mining                            | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Mining                            | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Manufacturing                     | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Manufacturing                     | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              |\n",
       "\n"
      ],
      "text/plain": [
       "  lga    crime_tot_per_lga crime_rate_per_lga sex     age         pop_age_lga\n",
       "1 albury 6233              0.1220219          Females 85 and over 845        \n",
       "2 albury 6233              0.1220219          Females 85 and over 845        \n",
       "3 albury 6233              0.1220219          Females 85 and over 845        \n",
       "4 albury 6233              0.1220219          Females 85 and over 845        \n",
       "5 albury 6233              0.1220219          Females 85 and over 845        \n",
       "6 albury 6233              0.1220219          Females 85 and over 845        \n",
       "  pop_total birthplace_of_person birth_value industry of employment           \n",
       "1 51081     New Zealand          2510        Agriculture, Forestry and Fishing\n",
       "2 51081     New Zealand          2510        Agriculture, Forestry and Fishing\n",
       "3 51081     New Zealand          2510        Mining                           \n",
       "4 51081     New Zealand          2510        Mining                           \n",
       "5 51081     New Zealand          2510        Manufacturing                    \n",
       "6 51081     New Zealand          2510        Manufacturing                    \n",
       "  emp_value labour_type labour_value pop_dens_km2 seifa_value num_service\n",
       "1 0         employed      0          170.5        964         1769       \n",
       "2 0         unemployed  834          170.5        964         1769       \n",
       "3 0         employed      0          170.5        964         1769       \n",
       "4 0         unemployed  834          170.5        964         1769       \n",
       "5 0         employed      0          170.5        964         1769       \n",
       "6 0         unemployed  834          170.5        964         1769       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 11,616,480\n",
      "Variables: 16\n",
      "$ lga                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"albury\", \"albury\", \"albury\", \"a...\n",
      "$ crime_tot_per_lga        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 6233, 6233, 6233, 6233, 6233, 6233, 6233, ...\n",
      "$ crime_rate_per_lga       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.1220219, 0.1220219, 0.1220219, 0.1220219...\n",
      "$ sex                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Females\", \"Females\", \"Females\", \"Females\"...\n",
      "$ age                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"85 and over\", \"85 and over\", \"85 and over...\n",
      "$ pop_age_lga              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 845, 845, 845, 845, 845, 845, 845, 845, 84...\n",
      "$ pop_total                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 51081, 51081, 51081, 51081, 51081, 51081, ...\n",
      "$ birthplace_of_person     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"New Zealand\", \"New Zealand\", \"New Zealand...\n",
      "$ birth_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 2510, 2510, 2510, 2510, 2510, 2510, 2510, ...\n",
      "$ `industry of employment` \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Agriculture, Forestry and Fishing\", \"Agri...\n",
      "$ emp_value                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
      "$ labour_type              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"employed\", \"unemployed\", \"employed\", \"une...\n",
      "$ labour_value             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 834, 0, 834, 0, 834, 0, 834, 0, 834, 0,...\n",
      "$ pop_dens_km2             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 170.5, 170.5, 170.5, 170.5, 170.5, 170.5, ...\n",
      "$ seifa_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 964, 964, 964, 964, 964, 964, 964, 964, 96...\n",
      "$ num_service              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1769, 1769, 1769, 1769, 1769, 1769, 1769, ...\n"
     ]
    }
   ],
   "source": [
    "names(data) %<>% tolower\n",
    "\n",
    "head(data)\n",
    "glimpse(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "data %<>%\n",
    "    mutate(lga = as.factor(lga))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Cluster Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Missing column names filled in: 'X1' [1]\"Parsed with column specification:\n",
      "cols(\n",
      "  X1 = \u001b[31mcol_character()\u001b[39m,\n",
      "  x = \u001b[32mcol_double()\u001b[39m\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X1</th><th scope=col>x</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury           </td><td>1                </td></tr>\n",
       "\t<tr><td>ballina          </td><td>1                </td></tr>\n",
       "\t<tr><td>balranald        </td><td>4                </td></tr>\n",
       "\t<tr><td>bathurst regional</td><td>1                </td></tr>\n",
       "\t<tr><td>bega valley      </td><td>5                </td></tr>\n",
       "\t<tr><td>bellingen        </td><td>2                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " X1 & x\\\\\n",
       "\\hline\n",
       "\t albury            & 1                \\\\\n",
       "\t ballina           & 1                \\\\\n",
       "\t balranald         & 4                \\\\\n",
       "\t bathurst regional & 1                \\\\\n",
       "\t bega valley       & 5                \\\\\n",
       "\t bellingen         & 2                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| X1 | x |\n",
       "|---|---|\n",
       "| albury            | 1                 |\n",
       "| ballina           | 1                 |\n",
       "| balranald         | 4                 |\n",
       "| bathurst regional | 1                 |\n",
       "| bega valley       | 5                 |\n",
       "| bellingen         | 2                 |\n",
       "\n"
      ],
      "text/plain": [
       "  X1                x\n",
       "1 albury            1\n",
       "2 ballina           1\n",
       "3 balranald         4\n",
       "4 bathurst regional 1\n",
       "5 bega valley       5\n",
       "6 bellingen         2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 105\n",
      "Variables: 2\n",
      "$ X1 \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"ballina\", \"balranald\", \"bathurst regional\", \"bega val...\n",
      "$ x  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1, 1, 4, 1, 5, 2, 4, 3, 4, 2, 3, 4, 4, 4, 3, 5, 2, 3, 3, 3, 3, 4...\n"
     ]
    }
   ],
   "source": [
    "clusters <- readr::read_csv(cluster_path)\n",
    "head(clusters)\n",
    "glimpse(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lga</th><th scope=col>cluster</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury           </td><td>1                </td></tr>\n",
       "\t<tr><td>ballina          </td><td>1                </td></tr>\n",
       "\t<tr><td>balranald        </td><td>4                </td></tr>\n",
       "\t<tr><td>bathurst regional</td><td>1                </td></tr>\n",
       "\t<tr><td>bega valley      </td><td>5                </td></tr>\n",
       "\t<tr><td>bellingen        </td><td>2                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " lga & cluster\\\\\n",
       "\\hline\n",
       "\t albury            & 1                \\\\\n",
       "\t ballina           & 1                \\\\\n",
       "\t balranald         & 4                \\\\\n",
       "\t bathurst regional & 1                \\\\\n",
       "\t bega valley       & 5                \\\\\n",
       "\t bellingen         & 2                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lga | cluster |\n",
       "|---|---|\n",
       "| albury            | 1                 |\n",
       "| ballina           | 1                 |\n",
       "| balranald         | 4                 |\n",
       "| bathurst regional | 1                 |\n",
       "| bega valley       | 5                 |\n",
       "| bellingen         | 2                 |\n",
       "\n"
      ],
      "text/plain": [
       "  lga               cluster\n",
       "1 albury            1      \n",
       "2 ballina           1      \n",
       "3 balranald         4      \n",
       "4 bathurst regional 1      \n",
       "5 bega valley       5      \n",
       "6 bellingen         2      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 105\n",
      "Variables: 2\n",
      "$ lga     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"ballina\", \"balranald\", \"bathurst regional\", \"beg...\n",
      "$ cluster \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1, 1, 4, 1, 5, 2, 4, 3, 4, 2, 3, 4, 4, 4, 3, 5, 2, 3, 3, 3,...\n"
     ]
    }
   ],
   "source": [
    "clusters %<>%\n",
    "    rename(lga = X1, cluster = x)\n",
    "head(clusters)\n",
    "glimpse(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_clusters <- clusters %>%\n",
    "    dplyr::select(cluster) %>%\n",
    "    distinct %>%\n",
    "    count %>%\n",
    "    pull(n)\n",
    "\n",
    "n_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining Cluster to the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Column `lga` joining factor and character vector, coercing into character vector\""
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lga</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>pop_age_lga</th><th scope=col>pop_total</th><th scope=col>birthplace_of_person</th><th scope=col>birth_value</th><th scope=col>industry of employment</th><th scope=col>emp_value</th><th scope=col>labour_type</th><th scope=col>labour_value</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>cluster</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td><td>1                                </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td><td>1                                </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Mining                           </td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td><td>1                                </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Mining                           </td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td><td>1                                </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Manufacturing                    </td><td>0                                </td><td>employed                         </td><td>  0                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td><td>1                                </td></tr>\n",
       "\t<tr><td>albury                           </td><td>6233                             </td><td>0.1220219                        </td><td>Females                          </td><td>85 and over                      </td><td>845                              </td><td>51081                            </td><td>New Zealand                      </td><td>2510                             </td><td>Manufacturing                    </td><td>0                                </td><td>unemployed                       </td><td>834                              </td><td>170.5                            </td><td>964                              </td><td>1769                             </td><td>1                                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllll}\n",
       " lga & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & sex & age & pop\\_age\\_lga & pop\\_total & birthplace\\_of\\_person & birth\\_value & industry of employment & emp\\_value & labour\\_type & labour\\_value & pop\\_dens\\_km2 & seifa\\_value & num\\_service & cluster\\\\\n",
       "\\hline\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Agriculture, Forestry and Fishing & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                              & 1                                \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Agriculture, Forestry and Fishing & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                              & 1                                \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Mining                            & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                              & 1                                \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Mining                            & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                              & 1                                \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Manufacturing                     & 0                                 & employed                          &   0                               & 170.5                             & 964                               & 1769                              & 1                                \\\\\n",
       "\t albury                            & 6233                              & 0.1220219                         & Females                           & 85 and over                       & 845                               & 51081                             & New Zealand                       & 2510                              & Manufacturing                     & 0                                 & unemployed                        & 834                               & 170.5                             & 964                               & 1769                              & 1                                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lga | crime_tot_per_lga | crime_rate_per_lga | sex | age | pop_age_lga | pop_total | birthplace_of_person | birth_value | industry of employment | emp_value | labour_type | labour_value | pop_dens_km2 | seifa_value | num_service | cluster |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Agriculture, Forestry and Fishing | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              | 1                                 |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Agriculture, Forestry and Fishing | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              | 1                                 |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Mining                            | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              | 1                                 |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Mining                            | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              | 1                                 |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Manufacturing                     | 0                                 | employed                          |   0                               | 170.5                             | 964                               | 1769                              | 1                                 |\n",
       "| albury                            | 6233                              | 0.1220219                         | Females                           | 85 and over                       | 845                               | 51081                             | New Zealand                       | 2510                              | Manufacturing                     | 0                                 | unemployed                        | 834                               | 170.5                             | 964                               | 1769                              | 1                                 |\n",
       "\n"
      ],
      "text/plain": [
       "  lga    crime_tot_per_lga crime_rate_per_lga sex     age         pop_age_lga\n",
       "1 albury 6233              0.1220219          Females 85 and over 845        \n",
       "2 albury 6233              0.1220219          Females 85 and over 845        \n",
       "3 albury 6233              0.1220219          Females 85 and over 845        \n",
       "4 albury 6233              0.1220219          Females 85 and over 845        \n",
       "5 albury 6233              0.1220219          Females 85 and over 845        \n",
       "6 albury 6233              0.1220219          Females 85 and over 845        \n",
       "  pop_total birthplace_of_person birth_value industry of employment           \n",
       "1 51081     New Zealand          2510        Agriculture, Forestry and Fishing\n",
       "2 51081     New Zealand          2510        Agriculture, Forestry and Fishing\n",
       "3 51081     New Zealand          2510        Mining                           \n",
       "4 51081     New Zealand          2510        Mining                           \n",
       "5 51081     New Zealand          2510        Manufacturing                    \n",
       "6 51081     New Zealand          2510        Manufacturing                    \n",
       "  emp_value labour_type labour_value pop_dens_km2 seifa_value num_service\n",
       "1 0         employed      0          170.5        964         1769       \n",
       "2 0         unemployed  834          170.5        964         1769       \n",
       "3 0         employed      0          170.5        964         1769       \n",
       "4 0         unemployed  834          170.5        964         1769       \n",
       "5 0         employed      0          170.5        964         1769       \n",
       "6 0         unemployed  834          170.5        964         1769       \n",
       "  cluster\n",
       "1 1      \n",
       "2 1      \n",
       "3 1      \n",
       "4 1      \n",
       "5 1      \n",
       "6 1      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 9,639,360\n",
      "Variables: 17\n",
      "$ lga                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"albury\", \"albury\", \"albury\", \"a...\n",
      "$ crime_tot_per_lga        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 6233, 6233, 6233, 6233, 6233, 6233, 6233, ...\n",
      "$ crime_rate_per_lga       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.1220219, 0.1220219, 0.1220219, 0.1220219...\n",
      "$ sex                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Females\", \"Females\", \"Females\", \"Females\"...\n",
      "$ age                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"85 and over\", \"85 and over\", \"85 and over...\n",
      "$ pop_age_lga              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 845, 845, 845, 845, 845, 845, 845, 845, 84...\n",
      "$ pop_total                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 51081, 51081, 51081, 51081, 51081, 51081, ...\n",
      "$ birthplace_of_person     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"New Zealand\", \"New Zealand\", \"New Zealand...\n",
      "$ birth_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 2510, 2510, 2510, 2510, 2510, 2510, 2510, ...\n",
      "$ `industry of employment` \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Agriculture, Forestry and Fishing\", \"Agri...\n",
      "$ emp_value                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
      "$ labour_type              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"employed\", \"unemployed\", \"employed\", \"une...\n",
      "$ labour_value             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 834, 0, 834, 0, 834, 0, 834, 0, 834, 0,...\n",
      "$ pop_dens_km2             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 170.5, 170.5, 170.5, 170.5, 170.5, 170.5, ...\n",
      "$ seifa_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 964, 964, 964, 964, 964, 964, 964, 964, 96...\n",
      "$ num_service              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1769, 1769, 1769, 1769, 1769, 1769, 1769, ...\n",
      "$ cluster                  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n"
     ]
    }
   ],
   "source": [
    "data %<>%\n",
    "    inner_join(clusters, by = \"lga\")\n",
    "\n",
    "head(data)\n",
    "glimpse(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(table(clusters$cluster))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>cluster</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1 </td><td>10</td></tr>\n",
       "\t<tr><td>2 </td><td>16</td></tr>\n",
       "\t<tr><td>3 </td><td>42</td></tr>\n",
       "\t<tr><td>4 </td><td>28</td></tr>\n",
       "\t<tr><td>5 </td><td> 9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " cluster & count\\\\\n",
       "\\hline\n",
       "\t 1  & 10\\\\\n",
       "\t 2  & 16\\\\\n",
       "\t 3  & 42\\\\\n",
       "\t 4  & 28\\\\\n",
       "\t 5  &  9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| cluster | count |\n",
       "|---|---|\n",
       "| 1  | 10 |\n",
       "| 2  | 16 |\n",
       "| 3  | 42 |\n",
       "| 4  | 28 |\n",
       "| 5  |  9 |\n",
       "\n"
      ],
      "text/plain": [
       "  cluster count\n",
       "1 1       10   \n",
       "2 2       16   \n",
       "3 3       42   \n",
       "4 4       28   \n",
       "5 5        9   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_count <- tibble(cluster = seq(1, dim(table(clusters$cluster))), count = table(clusters$cluster))\n",
    "cluster_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Don't know how to automatically pick scale for object of type table. Defaulting to continuous.\n",
      "Saving 6.67 x 6.67 in image\n",
      "Don't know how to automatically pick scale for object of type table. Defaulting to continuous.\n",
      "Saving 6.67 x 6.67 in image\n",
      "Don't know how to automatically pick scale for object of type table. Defaulting to continuous.\n"
     ]
    },
    {
     "data": {
      "image/png": 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k4IyYaQaiyvkosTQrIhpBrLq+TihJBs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\nCAkgA/8P7A+rWfkuDT0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Cluster Count\"\n",
    "filename = paste(chart_dir, \"cluster_count\", sep = \"/\")\n",
    "\n",
    "ggplot(cluster_count, aes(x = cluster, y = count)) +\n",
    "    geom_bar(stat = \"identity\") + \n",
    "    ggtitle(title) +\n",
    "    my_theme() +\n",
    "    theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 1))\n",
    "\n",
    "ggsave(filename = paste(filename, \".svg\", sep = \"\"))\n",
    "ggsave(filename = paste(filename, \".png\", sep = \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3"
      ],
      "text/latex": [
       "3"
      ],
      "text/markdown": [
       "3"
      ],
      "text/plain": [
       "[1] 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters %>%\n",
    "    filter(lga == \"ku-ring-gai\") %>%\n",
    "    pull(cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ku-ring-gai** belongs to cluster three, which is the most numerous, at 42 counts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ku-ring-gai Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lga</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>pop_age_lga</th><th scope=col>pop_total</th><th scope=col>birthplace_of_person</th><th scope=col>birth_value</th><th scope=col>industry of employment</th><th scope=col>emp_value</th><th scope=col>labour_type</th><th scope=col>labour_value</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>cluster</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>blacktown                        </td><td>45643                            </td><td>0.1354464                        </td><td>Females                          </td><td>85 and over                      </td><td>2306                             </td><td>336982                           </td><td>New Zealand                      </td><td>35171                            </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>employed                         </td><td>  14                             </td><td>1449.8                           </td><td>995                              </td><td>248915                           </td><td>3                                </td></tr>\n",
       "\t<tr><td>blacktown                        </td><td>45643                            </td><td>0.1354464                        </td><td>Females                          </td><td>85 and over                      </td><td>2306                             </td><td>336982                           </td><td>New Zealand                      </td><td>35171                            </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>unemployed                       </td><td>2289                             </td><td>1449.8                           </td><td>995                              </td><td>248915                           </td><td>3                                </td></tr>\n",
       "\t<tr><td>blacktown                        </td><td>45643                            </td><td>0.1354464                        </td><td>Females                          </td><td>85 and over                      </td><td>2306                             </td><td>336982                           </td><td>New Zealand                      </td><td>35171                            </td><td>Mining                           </td><td>0                                </td><td>employed                         </td><td>  14                             </td><td>1449.8                           </td><td>995                              </td><td>248915                           </td><td>3                                </td></tr>\n",
       "\t<tr><td>blacktown                        </td><td>45643                            </td><td>0.1354464                        </td><td>Females                          </td><td>85 and over                      </td><td>2306                             </td><td>336982                           </td><td>New Zealand                      </td><td>35171                            </td><td>Mining                           </td><td>0                                </td><td>unemployed                       </td><td>2289                             </td><td>1449.8                           </td><td>995                              </td><td>248915                           </td><td>3                                </td></tr>\n",
       "\t<tr><td>blacktown                        </td><td>45643                            </td><td>0.1354464                        </td><td>Females                          </td><td>85 and over                      </td><td>2306                             </td><td>336982                           </td><td>New Zealand                      </td><td>35171                            </td><td>Manufacturing                    </td><td>0                                </td><td>employed                         </td><td>  14                             </td><td>1449.8                           </td><td>995                              </td><td>248915                           </td><td>3                                </td></tr>\n",
       "\t<tr><td>blacktown                        </td><td>45643                            </td><td>0.1354464                        </td><td>Females                          </td><td>85 and over                      </td><td>2306                             </td><td>336982                           </td><td>New Zealand                      </td><td>35171                            </td><td>Manufacturing                    </td><td>0                                </td><td>unemployed                       </td><td>2289                             </td><td>1449.8                           </td><td>995                              </td><td>248915                           </td><td>3                                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllll}\n",
       " lga & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & sex & age & pop\\_age\\_lga & pop\\_total & birthplace\\_of\\_person & birth\\_value & industry of employment & emp\\_value & labour\\_type & labour\\_value & pop\\_dens\\_km2 & seifa\\_value & num\\_service & cluster\\\\\n",
       "\\hline\n",
       "\t blacktown                         & 45643                             & 0.1354464                         & Females                           & 85 and over                       & 2306                              & 336982                            & New Zealand                       & 35171                             & Agriculture, Forestry and Fishing & 0                                 & employed                          &   14                              & 1449.8                            & 995                               & 248915                            & 3                                \\\\\n",
       "\t blacktown                         & 45643                             & 0.1354464                         & Females                           & 85 and over                       & 2306                              & 336982                            & New Zealand                       & 35171                             & Agriculture, Forestry and Fishing & 0                                 & unemployed                        & 2289                              & 1449.8                            & 995                               & 248915                            & 3                                \\\\\n",
       "\t blacktown                         & 45643                             & 0.1354464                         & Females                           & 85 and over                       & 2306                              & 336982                            & New Zealand                       & 35171                             & Mining                            & 0                                 & employed                          &   14                              & 1449.8                            & 995                               & 248915                            & 3                                \\\\\n",
       "\t blacktown                         & 45643                             & 0.1354464                         & Females                           & 85 and over                       & 2306                              & 336982                            & New Zealand                       & 35171                             & Mining                            & 0                                 & unemployed                        & 2289                              & 1449.8                            & 995                               & 248915                            & 3                                \\\\\n",
       "\t blacktown                         & 45643                             & 0.1354464                         & Females                           & 85 and over                       & 2306                              & 336982                            & New Zealand                       & 35171                             & Manufacturing                     & 0                                 & employed                          &   14                              & 1449.8                            & 995                               & 248915                            & 3                                \\\\\n",
       "\t blacktown                         & 45643                             & 0.1354464                         & Females                           & 85 and over                       & 2306                              & 336982                            & New Zealand                       & 35171                             & Manufacturing                     & 0                                 & unemployed                        & 2289                              & 1449.8                            & 995                               & 248915                            & 3                                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lga | crime_tot_per_lga | crime_rate_per_lga | sex | age | pop_age_lga | pop_total | birthplace_of_person | birth_value | industry of employment | emp_value | labour_type | labour_value | pop_dens_km2 | seifa_value | num_service | cluster |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| blacktown                         | 45643                             | 0.1354464                         | Females                           | 85 and over                       | 2306                              | 336982                            | New Zealand                       | 35171                             | Agriculture, Forestry and Fishing | 0                                 | employed                          |   14                              | 1449.8                            | 995                               | 248915                            | 3                                 |\n",
       "| blacktown                         | 45643                             | 0.1354464                         | Females                           | 85 and over                       | 2306                              | 336982                            | New Zealand                       | 35171                             | Agriculture, Forestry and Fishing | 0                                 | unemployed                        | 2289                              | 1449.8                            | 995                               | 248915                            | 3                                 |\n",
       "| blacktown                         | 45643                             | 0.1354464                         | Females                           | 85 and over                       | 2306                              | 336982                            | New Zealand                       | 35171                             | Mining                            | 0                                 | employed                          |   14                              | 1449.8                            | 995                               | 248915                            | 3                                 |\n",
       "| blacktown                         | 45643                             | 0.1354464                         | Females                           | 85 and over                       | 2306                              | 336982                            | New Zealand                       | 35171                             | Mining                            | 0                                 | unemployed                        | 2289                              | 1449.8                            | 995                               | 248915                            | 3                                 |\n",
       "| blacktown                         | 45643                             | 0.1354464                         | Females                           | 85 and over                       | 2306                              | 336982                            | New Zealand                       | 35171                             | Manufacturing                     | 0                                 | employed                          |   14                              | 1449.8                            | 995                               | 248915                            | 3                                 |\n",
       "| blacktown                         | 45643                             | 0.1354464                         | Females                           | 85 and over                       | 2306                              | 336982                            | New Zealand                       | 35171                             | Manufacturing                     | 0                                 | unemployed                        | 2289                              | 1449.8                            | 995                               | 248915                            | 3                                 |\n",
       "\n"
      ],
      "text/plain": [
       "  lga       crime_tot_per_lga crime_rate_per_lga sex     age        \n",
       "1 blacktown 45643             0.1354464          Females 85 and over\n",
       "2 blacktown 45643             0.1354464          Females 85 and over\n",
       "3 blacktown 45643             0.1354464          Females 85 and over\n",
       "4 blacktown 45643             0.1354464          Females 85 and over\n",
       "5 blacktown 45643             0.1354464          Females 85 and over\n",
       "6 blacktown 45643             0.1354464          Females 85 and over\n",
       "  pop_age_lga pop_total birthplace_of_person birth_value\n",
       "1 2306        336982    New Zealand          35171      \n",
       "2 2306        336982    New Zealand          35171      \n",
       "3 2306        336982    New Zealand          35171      \n",
       "4 2306        336982    New Zealand          35171      \n",
       "5 2306        336982    New Zealand          35171      \n",
       "6 2306        336982    New Zealand          35171      \n",
       "  industry of employment            emp_value labour_type labour_value\n",
       "1 Agriculture, Forestry and Fishing 0         employed      14        \n",
       "2 Agriculture, Forestry and Fishing 0         unemployed  2289        \n",
       "3 Mining                            0         employed      14        \n",
       "4 Mining                            0         unemployed  2289        \n",
       "5 Manufacturing                     0         employed      14        \n",
       "6 Manufacturing                     0         unemployed  2289        \n",
       "  pop_dens_km2 seifa_value num_service cluster\n",
       "1 1449.8       995         248915      3      \n",
       "2 1449.8       995         248915      3      \n",
       "3 1449.8       995         248915      3      \n",
       "4 1449.8       995         248915      3      \n",
       "5 1449.8       995         248915      3      \n",
       "6 1449.8       995         248915      3      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 6,861,600\n",
      "Variables: 17\n",
      "$ lga                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"blacktown\", \"blacktown\", \"blacktown\", \"bl...\n",
      "$ crime_tot_per_lga        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 45643, 45643, 45643, 45643, 45643, 45643, ...\n",
      "$ crime_rate_per_lga       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.1354464, 0.1354464, 0.1354464, 0.1354464...\n",
      "$ sex                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Females\", \"Females\", \"Females\", \"Females\"...\n",
      "$ age                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"85 and over\", \"85 and over\", \"85 and over...\n",
      "$ pop_age_lga              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 2306, 2306, 2306, 2306, 2306, 2306, 2306, ...\n",
      "$ pop_total                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 336982, 336982, 336982, 336982, 336982, 33...\n",
      "$ birthplace_of_person     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"New Zealand\", \"New Zealand\", \"New Zealand...\n",
      "$ birth_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 35171, 35171, 35171, 35171, 35171, 35171, ...\n",
      "$ `industry of employment` \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Agriculture, Forestry and Fishing\", \"Agri...\n",
      "$ emp_value                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
      "$ labour_type              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"employed\", \"unemployed\", \"employed\", \"une...\n",
      "$ labour_value             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 14, 2289, 14, 2289, 14, 2289, 14, 2289, 14...\n",
      "$ pop_dens_km2             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1449.8, 1449.8, 1449.8, 1449.8, 1449.8, 14...\n",
      "$ seifa_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 995, 995, 995, 995, 995, 995, 995, 995, 99...\n",
      "$ num_service              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 248915, 248915, 248915, 248915, 248915, 24...\n",
      "$ cluster                  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...\n"
     ]
    }
   ],
   "source": [
    "cluster <- 3\n",
    "kuringgai_cluster <- data %>%\n",
    "    filter(cluster == !!cluster)\n",
    "\n",
    "head(kuringgai_cluster)\n",
    "glimpse(kuringgai_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Density Distribution in the Ku-ring-gai Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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gOSW1AMIGUTQOqi2YDkFRSLgOTsD5A6CyB10WxAEh8XQPJif/LrESBdF0DqohmB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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = paste(\"Population Density: Cluster \", cluster, sep = \"\")\n",
    "type = \"bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"Density (per km squared)\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"pop_dens_km2\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "kuringgai_cluster %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var)) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        my_theme()\n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a wide difference in the population density within the cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transport Distribution in the Ku-ring-gai Cluster (cluster 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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l+GhiBrkiWE7AhSxATLHwIBJBaxANL90jSEWZMA0j2tgGEsgHS/hH4heJA1KQFS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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = paste(\"Public Transport: Cluster \", cluster, sep = \"\")\n",
    "type = \"bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"Level of Public Transportation\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"num_service\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "kuringgai_cluster %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var)) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        scale_y_continuous(labels = scales::comma) +\n",
    "        # to make the right x tick not get cut off\n",
    "        theme(plot.margin = margin(1, 15, 1, 1)) +\n",
    "        my_theme()\n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SEIFA Distribution in the Ku-ring-gai Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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3yQyceP9G2mbSCSXg15pHqBSAlIrIkEmWg6aaXrIQl/amcXSeSR6mSiJNJC8kgT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\nmACIBMAEQCQAJgAiATAB/x8oM77ptR0aNAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = paste(\"SEIFA: Cluster \", cluster, sep = \"\")\n",
    "type = \"bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"SEIFA\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"seifa_value\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "kuringgai_cluster %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var)) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        scale_y_continuous(labels = scales::comma) +\n",
    "        # to make the right x tick not get cut off\n",
    "        theme(plot.margin = margin(1, 15, 1, 1)) +\n",
    "        my_theme()\n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lga</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>pop_age_lga</th><th scope=col>pop_total</th><th scope=col>birthplace_of_person</th><th scope=col>birth_value</th><th scope=col>industry of employment</th><th scope=col>emp_value</th><th scope=col>labour_type</th><th scope=col>labour_value</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>cluster</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>balranald                        </td><td>192                              </td><td>0.08376963                       </td><td>Females                          </td><td>85 and over                      </td><td>24                               </td><td>2292                             </td><td>New Zealand                      </td><td>45                               </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>employed                         </td><td> 0                               </td><td>0.1                              </td><td>969                              </td><td>4                                </td><td>4                                </td></tr>\n",
       "\t<tr><td>balranald                        </td><td>192                              </td><td>0.08376963                       </td><td>Females                          </td><td>85 and over                      </td><td>24                               </td><td>2292                             </td><td>New Zealand                      </td><td>45                               </td><td>Agriculture, Forestry and Fishing</td><td>0                                </td><td>unemployed                       </td><td>21                               </td><td>0.1                              </td><td>969                              </td><td>4                                </td><td>4                                </td></tr>\n",
       "\t<tr><td>balranald                        </td><td>192                              </td><td>0.08376963                       </td><td>Females                          </td><td>85 and over                      </td><td>24                               </td><td>2292                             </td><td>New Zealand                      </td><td>45                               </td><td>Mining                           </td><td>0                                </td><td>employed                         </td><td> 0                               </td><td>0.1                              </td><td>969                              </td><td>4                                </td><td>4                                </td></tr>\n",
       "\t<tr><td>balranald                        </td><td>192                              </td><td>0.08376963                       </td><td>Females                          </td><td>85 and over                      </td><td>24                               </td><td>2292                             </td><td>New Zealand                      </td><td>45                               </td><td>Mining                           </td><td>0                                </td><td>unemployed                       </td><td>21                               </td><td>0.1                              </td><td>969                              </td><td>4                                </td><td>4                                </td></tr>\n",
       "\t<tr><td>balranald                        </td><td>192                              </td><td>0.08376963                       </td><td>Females                          </td><td>85 and over                      </td><td>24                               </td><td>2292                             </td><td>New Zealand                      </td><td>45                               </td><td>Manufacturing                    </td><td>0                                </td><td>employed                         </td><td> 0                               </td><td>0.1                              </td><td>969                              </td><td>4                                </td><td>4                                </td></tr>\n",
       "\t<tr><td>balranald                        </td><td>192                              </td><td>0.08376963                       </td><td>Females                          </td><td>85 and over                      </td><td>24                               </td><td>2292                             </td><td>New Zealand                      </td><td>45                               </td><td>Manufacturing                    </td><td>0                                </td><td>unemployed                       </td><td>21                               </td><td>0.1                              </td><td>969                              </td><td>4                                </td><td>4                                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllll}\n",
       " lga & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & sex & age & pop\\_age\\_lga & pop\\_total & birthplace\\_of\\_person & birth\\_value & industry of employment & emp\\_value & labour\\_type & labour\\_value & pop\\_dens\\_km2 & seifa\\_value & num\\_service & cluster\\\\\n",
       "\\hline\n",
       "\t balranald                         & 192                               & 0.08376963                        & Females                           & 85 and over                       & 24                                & 2292                              & New Zealand                       & 45                                & Agriculture, Forestry and Fishing & 0                                 & employed                          &  0                                & 0.1                               & 969                               & 4                                 & 4                                \\\\\n",
       "\t balranald                         & 192                               & 0.08376963                        & Females                           & 85 and over                       & 24                                & 2292                              & New Zealand                       & 45                                & Agriculture, Forestry and Fishing & 0                                 & unemployed                        & 21                                & 0.1                               & 969                               & 4                                 & 4                                \\\\\n",
       "\t balranald                         & 192                               & 0.08376963                        & Females                           & 85 and over                       & 24                                & 2292                              & New Zealand                       & 45                                & Mining                            & 0                                 & employed                          &  0                                & 0.1                               & 969                               & 4                                 & 4                                \\\\\n",
       "\t balranald                         & 192                               & 0.08376963                        & Females                           & 85 and over                       & 24                                & 2292                              & New Zealand                       & 45                                & Mining                            & 0                                 & unemployed                        & 21                                & 0.1                               & 969                               & 4                                 & 4                                \\\\\n",
       "\t balranald                         & 192                               & 0.08376963                        & Females                           & 85 and over                       & 24                                & 2292                              & New Zealand                       & 45                                & Manufacturing                     & 0                                 & employed                          &  0                                & 0.1                               & 969                               & 4                                 & 4                                \\\\\n",
       "\t balranald                         & 192                               & 0.08376963                        & Females                           & 85 and over                       & 24                                & 2292                              & New Zealand                       & 45                                & Manufacturing                     & 0                                 & unemployed                        & 21                                & 0.1                               & 969                               & 4                                 & 4                                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lga | crime_tot_per_lga | crime_rate_per_lga | sex | age | pop_age_lga | pop_total | birthplace_of_person | birth_value | industry of employment | emp_value | labour_type | labour_value | pop_dens_km2 | seifa_value | num_service | cluster |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| balranald                         | 192                               | 0.08376963                        | Females                           | 85 and over                       | 24                                | 2292                              | New Zealand                       | 45                                | Agriculture, Forestry and Fishing | 0                                 | employed                          |  0                                | 0.1                               | 969                               | 4                                 | 4                                 |\n",
       "| balranald                         | 192                               | 0.08376963                        | Females                           | 85 and over                       | 24                                | 2292                              | New Zealand                       | 45                                | Agriculture, Forestry and Fishing | 0                                 | unemployed                        | 21                                | 0.1                               | 969                               | 4                                 | 4                                 |\n",
       "| balranald                         | 192                               | 0.08376963                        | Females                           | 85 and over                       | 24                                | 2292                              | New Zealand                       | 45                                | Mining                            | 0                                 | employed                          |  0                                | 0.1                               | 969                               | 4                                 | 4                                 |\n",
       "| balranald                         | 192                               | 0.08376963                        | Females                           | 85 and over                       | 24                                | 2292                              | New Zealand                       | 45                                | Mining                            | 0                                 | unemployed                        | 21                                | 0.1                               | 969                               | 4                                 | 4                                 |\n",
       "| balranald                         | 192                               | 0.08376963                        | Females                           | 85 and over                       | 24                                | 2292                              | New Zealand                       | 45                                | Manufacturing                     | 0                                 | employed                          |  0                                | 0.1                               | 969                               | 4                                 | 4                                 |\n",
       "| balranald                         | 192                               | 0.08376963                        | Females                           | 85 and over                       | 24                                | 2292                              | New Zealand                       | 45                                | Manufacturing                     | 0                                 | unemployed                        | 21                                | 0.1                               | 969                               | 4                                 | 4                                 |\n",
       "\n"
      ],
      "text/plain": [
       "  lga       crime_tot_per_lga crime_rate_per_lga sex     age        \n",
       "1 balranald 192               0.08376963         Females 85 and over\n",
       "2 balranald 192               0.08376963         Females 85 and over\n",
       "3 balranald 192               0.08376963         Females 85 and over\n",
       "4 balranald 192               0.08376963         Females 85 and over\n",
       "5 balranald 192               0.08376963         Females 85 and over\n",
       "6 balranald 192               0.08376963         Females 85 and over\n",
       "  pop_age_lga pop_total birthplace_of_person birth_value\n",
       "1 24          2292      New Zealand          45         \n",
       "2 24          2292      New Zealand          45         \n",
       "3 24          2292      New Zealand          45         \n",
       "4 24          2292      New Zealand          45         \n",
       "5 24          2292      New Zealand          45         \n",
       "6 24          2292      New Zealand          45         \n",
       "  industry of employment            emp_value labour_type labour_value\n",
       "1 Agriculture, Forestry and Fishing 0         employed     0          \n",
       "2 Agriculture, Forestry and Fishing 0         unemployed  21          \n",
       "3 Mining                            0         employed     0          \n",
       "4 Mining                            0         unemployed  21          \n",
       "5 Manufacturing                     0         employed     0          \n",
       "6 Manufacturing                     0         unemployed  21          \n",
       "  pop_dens_km2 seifa_value num_service cluster\n",
       "1 0.1          969         4           4      \n",
       "2 0.1          969         4           4      \n",
       "3 0.1          969         4           4      \n",
       "4 0.1          969         4           4      \n",
       "5 0.1          969         4           4      \n",
       "6 0.1          969         4           4      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 548,640\n",
      "Variables: 17\n",
      "$ lga                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"balranald\", \"balranald\", \"balranald\", \"ba...\n",
      "$ crime_tot_per_lga        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 192, 192, 192, 192, 192, 192, 192, 192, 19...\n",
      "$ crime_rate_per_lga       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.08376963, 0.08376963, 0.08376963, 0.0837...\n",
      "$ sex                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Females\", \"Females\", \"Females\", \"Females\"...\n",
      "$ age                      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"85 and over\", \"85 and over\", \"85 and over...\n",
      "$ pop_age_lga              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24...\n",
      "$ pop_total                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 2292, 2292, 2292, 2292, 2292, 2292, 2292, ...\n",
      "$ birthplace_of_person     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"New Zealand\", \"New Zealand\", \"New Zealand...\n",
      "$ birth_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45...\n",
      "$ `industry of employment` \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Agriculture, Forestry and Fishing\", \"Agri...\n",
      "$ emp_value                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
      "$ labour_type              \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"employed\", \"unemployed\", \"employed\", \"une...\n",
      "$ labour_value             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 21, 0, 21, 0, 21, 0, 21, 0, 21, 0, 21, ...\n",
      "$ pop_dens_km2             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0....\n",
      "$ seifa_value              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 969, 969, 969, 969, 969, 969, 969, 969, 96...\n",
      "$ num_service              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...\n",
      "$ cluster                  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ...\n"
     ]
    }
   ],
   "source": [
    "cluster <- 4\n",
    "cluster_4 <- data %>%\n",
    "    filter(cluster == !!cluster)\n",
    "\n",
    "head(cluster_4)\n",
    "glimpse(cluster_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Density Distribution in the Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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K2v7TUl0sBAY1FzjXtlgzGr8sqGeXNl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\nmgsai5orEKsZlmlsGIq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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = paste(\"Population Density: Cluster \", cluster, sep = \"\")\n",
    "type = \"bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"Density (per km squared)\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"pop_dens_km2\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "cluster_4 %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var)) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        my_theme()\n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a wide difference in the population density within the cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transport Distribution in the Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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qLiliqm1WLjVCdvDtJiA+JTF2KlR4NsVXxp6ECQglotNs4wSEsNiC8L9a5BQ7bS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\ngvSYKB8aRZAe0/8Dp7J6OQHq4JwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = paste(\"Public Transport: Cluster \", cluster, sep = \"\")\n",
    "type = \"bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"Level of Public Transportation\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"num_service\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "cluster_4 %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var)) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        scale_y_continuous(labels = scales::comma) +\n",
    "        # to make the right x tick not get cut off\n",
    "        theme(plot.margin = margin(1, 15, 1, 1))\n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SEIFA Distribution in the Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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FBZFyFdZwRd5377IfmDhK4ZMbIYBqQH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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = paste(\"SEIFA: Cluster \", cluster, sep = \"\")\n",
    "type = \"bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"SEIFA\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"seifa_value\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "cluster_4 %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var)) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        scale_y_continuous(labels = scales::comma) +\n",
    "        # to make the right x tick not get cut off\n",
    "        theme(plot.margin = margin(1, 15, 1, 1))\n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facetting by Cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Density For All Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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oZMKzNoG5VNrUipBpke5lQppREpKcypUkojUlKYU6WURqSkMKdKKe3izgZRBwAL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\nAJEABCASgABEAhDwP7/Q9aHGQhLFAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Population Density\"\n",
    "type = \"facet_bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"Density (per km squared)\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"pop_dens_km2\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "data %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var), cluster) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        # remove the x labels\n",
    "        theme(axis.text.y = element_blank(),\n",
    "              axis.ticks.y = element_blank(),\n",
    "              axis.text.x = element_text(angle = 0, hjust = 1)) +\n",
    "        facet_wrap(cluster ~ ., nrow = n_clusters, scales = \"free\")\n",
    "        \n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a wide difference in the population density within the cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transport Distribution For All Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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5k+piEuSAFy7zygRRQEViR//oWcmEAm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\nMY53mQjSxv4PlNhAqhWS9TcAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Public Transport\"\n",
    "type = \"facet_bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"Level of Public Transportation\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"num_service\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "data %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var), cluster) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        # remove the x labels\n",
    "        theme(axis.text.y = element_blank(),\n",
    "              axis.ticks.y = element_blank(),\n",
    "              plot.margin = margin(1, 15, 1, 1)) +\n",
    "        scale_y_continuous(labels = scales::comma) +\n",
    "        facet_wrap(cluster ~ ., nrow = n_clusters, scales = \"free\")\n",
    "        \n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SEIFA Distribution in the Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"SEIFA\"\n",
    "type = \"facet_bar\"\n",
    "x_lab = \"LGA\"\n",
    "y_lab = \"SEIFA\"\n",
    "x_var = \"lga\"\n",
    "y_var = \"seifa_value\"\n",
    "\n",
    "filename = paste(type, y_var, \"vs\", x_var, \"cluster\", cluster, sep = \"_\")\n",
    "\n",
    "data %>%\n",
    "    dplyr::select(!!sym(x_var), !!sym(y_var), cluster) %>%\n",
    "    distinct %>%\n",
    "    ggplot(aes(x = reorder(!!sym(x_var), !!sym(y_var)), y = !!sym(y_var))) + \n",
    "        geom_bar(stat = \"identity\") +\n",
    "        xlab(x_lab) +\n",
    "        ylab(y_lab) +\n",
    "        ggtitle(title) +\n",
    "        #theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "        coord_flip() +\n",
    "        # remove the x labels\n",
    "        theme(axis.text.y = element_blank(),\n",
    "              axis.ticks.y = element_blank(),\n",
    "              plot.margin = margin(1, 15, 1, 1)) +\n",
    "        scale_y_continuous(labels = scales::comma) +\n",
    "        facet_wrap(cluster ~ ., nrow = n_clusters, scales = \"free\")\n",
    "        \n",
    "\n",
    "ggsave(paste(chart_dir, paste(filename, \".png\", sep = \"\"), sep = \"/\"))\n",
    "ggsave(paste(chart_dir, paste(filename, \".svg\", sep = \"\"), sep = \"/\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  .default = col_double(),\n",
      "  LGA = \u001b[31mcol_character()\u001b[39m,\n",
      "  Age = \u001b[31mcol_character()\u001b[39m\n",
      ")\n",
      "See spec(...) for full column specifications.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>LGA</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>Age</th><th scope=col>pop_total</th><th scope=col>Pop_Dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>female_lga_age</th><th scope=col>male_lga_age</th><th scope=col>...</th><th scope=col>female_lga_perc</th><th scope=col>male_lga_perc</th><th scope=col>employed_lga_age</th><th scope=col>unemployed_lga_age</th><th scope=col>employed_lga_age_perc</th><th scope=col>unemployed_lga_age_perc</th><th scope=col>employed_lga</th><th scope=col>unemployed_lga</th><th scope=col>employed_lga_perc</th><th scope=col>unemployed_lga_perc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>85 and over </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td> 845        </td><td> 489        </td><td>...         </td><td>0.4249525   </td><td>0.3869541   </td><td>   3        </td><td>1316        </td><td>5.873025e-05</td><td>0.02576300  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>75 - 84     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>1658        </td><td>1247        </td><td>...         </td><td>0.4249525   </td><td>0.3869541   </td><td>  90        </td><td>2832        </td><td>1.761908e-03</td><td>0.05544136  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>65 - 74     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>2572        </td><td>2335        </td><td>...         </td><td>0.4249525   </td><td>0.3869541   </td><td> 882        </td><td>4025        </td><td>1.726669e-02</td><td>0.07879642  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>55 - 64     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>3324        </td><td>3051        </td><td>...         </td><td>0.4249525   </td><td>0.3869541   </td><td>3832        </td><td>2540        </td><td>7.501811e-02</td><td>0.04972495  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>45 - 54     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>3331        </td><td>3169        </td><td>...         </td><td>0.4249525   </td><td>0.3869541   </td><td>4826        </td><td>1681        </td><td>9.447740e-02</td><td>0.03290852  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>35 - 44     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>3231        </td><td>2974        </td><td>...         </td><td>0.4249525   </td><td>0.3869541   </td><td>4619        </td><td>1580        </td><td>9.042501e-02</td><td>0.03093127  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllll}\n",
       " LGA & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & Age & pop\\_total & Pop\\_Dens\\_km2 & seifa\\_value & num\\_service & female\\_lga\\_age & male\\_lga\\_age & ... & female\\_lga\\_perc & male\\_lga\\_perc & employed\\_lga\\_age & unemployed\\_lga\\_age & employed\\_lga\\_age\\_perc & unemployed\\_lga\\_age\\_perc & employed\\_lga & unemployed\\_lga & employed\\_lga\\_perc & unemployed\\_lga\\_perc\\\\\n",
       "\\hline\n",
       "\t albury       & 6233         & 0.1220219    & 85 and over  & 51081        & 170.5        & 964          & 1769         &  845         &  489         & ...          & 0.4249525    & 0.3869541    &    3         & 1316         & 5.873025e-05 & 0.02576300   & 22913        & 18546        & 0.4485621    & 0.3630704   \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 75 - 84      & 51081        & 170.5        & 964          & 1769         & 1658         & 1247         & ...          & 0.4249525    & 0.3869541    &   90         & 2832         & 1.761908e-03 & 0.05544136   & 22913        & 18546        & 0.4485621    & 0.3630704   \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 65 - 74      & 51081        & 170.5        & 964          & 1769         & 2572         & 2335         & ...          & 0.4249525    & 0.3869541    &  882         & 4025         & 1.726669e-02 & 0.07879642   & 22913        & 18546        & 0.4485621    & 0.3630704   \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 55 - 64      & 51081        & 170.5        & 964          & 1769         & 3324         & 3051         & ...          & 0.4249525    & 0.3869541    & 3832         & 2540         & 7.501811e-02 & 0.04972495   & 22913        & 18546        & 0.4485621    & 0.3630704   \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 45 - 54      & 51081        & 170.5        & 964          & 1769         & 3331         & 3169         & ...          & 0.4249525    & 0.3869541    & 4826         & 1681         & 9.447740e-02 & 0.03290852   & 22913        & 18546        & 0.4485621    & 0.3630704   \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 35 - 44      & 51081        & 170.5        & 964          & 1769         & 3231         & 2974         & ...          & 0.4249525    & 0.3869541    & 4619         & 1580         & 9.042501e-02 & 0.03093127   & 22913        & 18546        & 0.4485621    & 0.3630704   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| LGA | crime_tot_per_lga | crime_rate_per_lga | Age | pop_total | Pop_Dens_km2 | seifa_value | num_service | female_lga_age | male_lga_age | ... | female_lga_perc | male_lga_perc | employed_lga_age | unemployed_lga_age | employed_lga_age_perc | unemployed_lga_age_perc | employed_lga | unemployed_lga | employed_lga_perc | unemployed_lga_perc |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| albury       | 6233         | 0.1220219    | 85 and over  | 51081        | 170.5        | 964          | 1769         |  845         |  489         | ...          | 0.4249525    | 0.3869541    |    3         | 1316         | 5.873025e-05 | 0.02576300   | 22913        | 18546        | 0.4485621    | 0.3630704    |\n",
       "| albury       | 6233         | 0.1220219    | 75 - 84      | 51081        | 170.5        | 964          | 1769         | 1658         | 1247         | ...          | 0.4249525    | 0.3869541    |   90         | 2832         | 1.761908e-03 | 0.05544136   | 22913        | 18546        | 0.4485621    | 0.3630704    |\n",
       "| albury       | 6233         | 0.1220219    | 65 - 74      | 51081        | 170.5        | 964          | 1769         | 2572         | 2335         | ...          | 0.4249525    | 0.3869541    |  882         | 4025         | 1.726669e-02 | 0.07879642   | 22913        | 18546        | 0.4485621    | 0.3630704    |\n",
       "| albury       | 6233         | 0.1220219    | 55 - 64      | 51081        | 170.5        | 964          | 1769         | 3324         | 3051         | ...          | 0.4249525    | 0.3869541    | 3832         | 2540         | 7.501811e-02 | 0.04972495   | 22913        | 18546        | 0.4485621    | 0.3630704    |\n",
       "| albury       | 6233         | 0.1220219    | 45 - 54      | 51081        | 170.5        | 964          | 1769         | 3331         | 3169         | ...          | 0.4249525    | 0.3869541    | 4826         | 1681         | 9.447740e-02 | 0.03290852   | 22913        | 18546        | 0.4485621    | 0.3630704    |\n",
       "| albury       | 6233         | 0.1220219    | 35 - 44      | 51081        | 170.5        | 964          | 1769         | 3231         | 2974         | ...          | 0.4249525    | 0.3869541    | 4619         | 1580         | 9.042501e-02 | 0.03093127   | 22913        | 18546        | 0.4485621    | 0.3630704    |\n",
       "\n"
      ],
      "text/plain": [
       "  LGA    crime_tot_per_lga crime_rate_per_lga Age         pop_total\n",
       "1 albury 6233              0.1220219          85 and over 51081    \n",
       "2 albury 6233              0.1220219          75 - 84     51081    \n",
       "3 albury 6233              0.1220219          65 - 74     51081    \n",
       "4 albury 6233              0.1220219          55 - 64     51081    \n",
       "5 albury 6233              0.1220219          45 - 54     51081    \n",
       "6 albury 6233              0.1220219          35 - 44     51081    \n",
       "  Pop_Dens_km2 seifa_value num_service female_lga_age male_lga_age ...\n",
       "1 170.5        964         1769         845            489         ...\n",
       "2 170.5        964         1769        1658           1247         ...\n",
       "3 170.5        964         1769        2572           2335         ...\n",
       "4 170.5        964         1769        3324           3051         ...\n",
       "5 170.5        964         1769        3331           3169         ...\n",
       "6 170.5        964         1769        3231           2974         ...\n",
       "  female_lga_perc male_lga_perc employed_lga_age unemployed_lga_age\n",
       "1 0.4249525       0.3869541        3             1316              \n",
       "2 0.4249525       0.3869541       90             2832              \n",
       "3 0.4249525       0.3869541      882             4025              \n",
       "4 0.4249525       0.3869541     3832             2540              \n",
       "5 0.4249525       0.3869541     4826             1681              \n",
       "6 0.4249525       0.3869541     4619             1580              \n",
       "  employed_lga_age_perc unemployed_lga_age_perc employed_lga unemployed_lga\n",
       "1 5.873025e-05          0.02576300              22913        18546         \n",
       "2 1.761908e-03          0.05544136              22913        18546         \n",
       "3 1.726669e-02          0.07879642              22913        18546         \n",
       "4 7.501811e-02          0.04972495              22913        18546         \n",
       "5 9.447740e-02          0.03290852              22913        18546         \n",
       "6 9.042501e-02          0.03093127              22913        18546         \n",
       "  employed_lga_perc unemployed_lga_perc\n",
       "1 0.4485621         0.3630704          \n",
       "2 0.4485621         0.3630704          \n",
       "3 0.4485621         0.3630704          \n",
       "4 0.4485621         0.3630704          \n",
       "5 0.4485621         0.3630704          \n",
       "6 0.4485621         0.3630704          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 1,125\n",
      "Variables: 24\n",
      "$ LGA                     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"albury\", \"albury\", \"albury\", \"al...\n",
      "$ crime_tot_per_lga       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 6233, 6233, 6233, 6233, 6233, 6233, 6233, 6...\n",
      "$ crime_rate_per_lga      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.12202189, 0.12202189, 0.12202189, 0.12202...\n",
      "$ Age                     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"85 and over\", \"75 - 84\", \"65 - 74\", \"55 - ...\n",
      "$ pop_total               \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 51081, 51081, 51081, 51081, 51081, 51081, 5...\n",
      "$ Pop_Dens_km2            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 170.5, 170.5, 170.5, 170.5, 170.5, 170.5, 1...\n",
      "$ seifa_value             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 964, 964, 964, 964, 964, 964, 964, 964, 964...\n",
      "$ num_service             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1769, 1769, 1769, 1769, 1769, 1769, 1769, 1...\n",
      "$ female_lga_age          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 845, 1658, 2572, 3324, 3331, 3231, 3301, 18...\n",
      "$ male_lga_age            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 489, 1247, 2335, 3051, 3169, 2974, 3195, 16...\n",
      "$ male_lga                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 19766, 19766, 19766, 19766, 19766, 19766, 1...\n",
      "$ female_lga              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 21707, 21707, 21707, 21707, 21707, 21707, 2...\n",
      "$ female_lga_age_perc     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.01654235, 0.03245825, 0.05035140, 0.06507...\n",
      "$ male_lga_age_perc       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.009573031, 0.024412208, 0.045711713, 0.05...\n",
      "$ female_lga_perc         \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.4249525, 0.4249525, 0.4249525, 0.4249525,...\n",
      "$ male_lga_perc           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3869541, 0.3869541, 0.3869541, 0.3869541,...\n",
      "$ employed_lga_age        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 3, 90, 882, 3832, 4826, 4619, 4832, 2306, 1...\n",
      "$ unemployed_lga_age      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1316, 2832, 4025, 2540, 1681, 1580, 1665, 1...\n",
      "$ employed_lga_age_perc   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 5.873025e-05, 1.761908e-03, 1.726669e-02, 7...\n",
      "$ unemployed_lga_age_perc \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.02576300, 0.05544136, 0.07879642, 0.04972...\n",
      "$ employed_lga            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 22913, 22913, 22913, 22913, 22913, 22913, 2...\n",
      "$ unemployed_lga          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 18546, 18546, 18546, 18546, 18546, 18546, 1...\n",
      "$ employed_lga_perc       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.4485621, 0.4485621, 0.4485621, 0.4485621,...\n",
      "$ unemployed_lga_perc     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3630704, 0.3630704, 0.3630704, 0.3630704,...\n"
     ]
    }
   ],
   "source": [
    "data_model <- readr::read_csv(paste(data_path, \"unified4_spread.csv\", sep = \"/\"))\n",
    "\n",
    "head(data_model)\n",
    "glimpse(data_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exclude some variables for modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "names(data_model) %<>% tolower"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join the Cluster Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lga</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>age</th><th scope=col>pop_total</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>female_lga_age</th><th scope=col>male_lga_age</th><th scope=col>...</th><th scope=col>male_lga_perc</th><th scope=col>employed_lga_age</th><th scope=col>unemployed_lga_age</th><th scope=col>employed_lga_age_perc</th><th scope=col>unemployed_lga_age_perc</th><th scope=col>employed_lga</th><th scope=col>unemployed_lga</th><th scope=col>employed_lga_perc</th><th scope=col>unemployed_lga_perc</th><th scope=col>cluster</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>85 and over </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td> 845        </td><td> 489        </td><td>...         </td><td>0.3869541   </td><td>   3        </td><td>1316        </td><td>5.873025e-05</td><td>0.02576300  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td><td>1           </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>75 - 84     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>1658        </td><td>1247        </td><td>...         </td><td>0.3869541   </td><td>  90        </td><td>2832        </td><td>1.761908e-03</td><td>0.05544136  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td><td>1           </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>65 - 74     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>2572        </td><td>2335        </td><td>...         </td><td>0.3869541   </td><td> 882        </td><td>4025        </td><td>1.726669e-02</td><td>0.07879642  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td><td>1           </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>55 - 64     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>3324        </td><td>3051        </td><td>...         </td><td>0.3869541   </td><td>3832        </td><td>2540        </td><td>7.501811e-02</td><td>0.04972495  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td><td>1           </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>45 - 54     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>3331        </td><td>3169        </td><td>...         </td><td>0.3869541   </td><td>4826        </td><td>1681        </td><td>9.447740e-02</td><td>0.03290852  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td><td>1           </td></tr>\n",
       "\t<tr><td>albury      </td><td>6233        </td><td>0.1220219   </td><td>35 - 44     </td><td>51081       </td><td>170.5       </td><td>964         </td><td>1769        </td><td>3231        </td><td>2974        </td><td>...         </td><td>0.3869541   </td><td>4619        </td><td>1580        </td><td>9.042501e-02</td><td>0.03093127  </td><td>22913       </td><td>18546       </td><td>0.4485621   </td><td>0.3630704   </td><td>1           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllll}\n",
       " lga & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & age & pop\\_total & pop\\_dens\\_km2 & seifa\\_value & num\\_service & female\\_lga\\_age & male\\_lga\\_age & ... & male\\_lga\\_perc & employed\\_lga\\_age & unemployed\\_lga\\_age & employed\\_lga\\_age\\_perc & unemployed\\_lga\\_age\\_perc & employed\\_lga & unemployed\\_lga & employed\\_lga\\_perc & unemployed\\_lga\\_perc & cluster\\\\\n",
       "\\hline\n",
       "\t albury       & 6233         & 0.1220219    & 85 and over  & 51081        & 170.5        & 964          & 1769         &  845         &  489         & ...          & 0.3869541    &    3         & 1316         & 5.873025e-05 & 0.02576300   & 22913        & 18546        & 0.4485621    & 0.3630704    & 1           \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 75 - 84      & 51081        & 170.5        & 964          & 1769         & 1658         & 1247         & ...          & 0.3869541    &   90         & 2832         & 1.761908e-03 & 0.05544136   & 22913        & 18546        & 0.4485621    & 0.3630704    & 1           \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 65 - 74      & 51081        & 170.5        & 964          & 1769         & 2572         & 2335         & ...          & 0.3869541    &  882         & 4025         & 1.726669e-02 & 0.07879642   & 22913        & 18546        & 0.4485621    & 0.3630704    & 1           \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 55 - 64      & 51081        & 170.5        & 964          & 1769         & 3324         & 3051         & ...          & 0.3869541    & 3832         & 2540         & 7.501811e-02 & 0.04972495   & 22913        & 18546        & 0.4485621    & 0.3630704    & 1           \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 45 - 54      & 51081        & 170.5        & 964          & 1769         & 3331         & 3169         & ...          & 0.3869541    & 4826         & 1681         & 9.447740e-02 & 0.03290852   & 22913        & 18546        & 0.4485621    & 0.3630704    & 1           \\\\\n",
       "\t albury       & 6233         & 0.1220219    & 35 - 44      & 51081        & 170.5        & 964          & 1769         & 3231         & 2974         & ...          & 0.3869541    & 4619         & 1580         & 9.042501e-02 & 0.03093127   & 22913        & 18546        & 0.4485621    & 0.3630704    & 1           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lga | crime_tot_per_lga | crime_rate_per_lga | age | pop_total | pop_dens_km2 | seifa_value | num_service | female_lga_age | male_lga_age | ... | male_lga_perc | employed_lga_age | unemployed_lga_age | employed_lga_age_perc | unemployed_lga_age_perc | employed_lga | unemployed_lga | employed_lga_perc | unemployed_lga_perc | cluster |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| albury       | 6233         | 0.1220219    | 85 and over  | 51081        | 170.5        | 964          | 1769         |  845         |  489         | ...          | 0.3869541    |    3         | 1316         | 5.873025e-05 | 0.02576300   | 22913        | 18546        | 0.4485621    | 0.3630704    | 1            |\n",
       "| albury       | 6233         | 0.1220219    | 75 - 84      | 51081        | 170.5        | 964          | 1769         | 1658         | 1247         | ...          | 0.3869541    |   90         | 2832         | 1.761908e-03 | 0.05544136   | 22913        | 18546        | 0.4485621    | 0.3630704    | 1            |\n",
       "| albury       | 6233         | 0.1220219    | 65 - 74      | 51081        | 170.5        | 964          | 1769         | 2572         | 2335         | ...          | 0.3869541    |  882         | 4025         | 1.726669e-02 | 0.07879642   | 22913        | 18546        | 0.4485621    | 0.3630704    | 1            |\n",
       "| albury       | 6233         | 0.1220219    | 55 - 64      | 51081        | 170.5        | 964          | 1769         | 3324         | 3051         | ...          | 0.3869541    | 3832         | 2540         | 7.501811e-02 | 0.04972495   | 22913        | 18546        | 0.4485621    | 0.3630704    | 1            |\n",
       "| albury       | 6233         | 0.1220219    | 45 - 54      | 51081        | 170.5        | 964          | 1769         | 3331         | 3169         | ...          | 0.3869541    | 4826         | 1681         | 9.447740e-02 | 0.03290852   | 22913        | 18546        | 0.4485621    | 0.3630704    | 1            |\n",
       "| albury       | 6233         | 0.1220219    | 35 - 44      | 51081        | 170.5        | 964          | 1769         | 3231         | 2974         | ...          | 0.3869541    | 4619         | 1580         | 9.042501e-02 | 0.03093127   | 22913        | 18546        | 0.4485621    | 0.3630704    | 1            |\n",
       "\n"
      ],
      "text/plain": [
       "  lga    crime_tot_per_lga crime_rate_per_lga age         pop_total\n",
       "1 albury 6233              0.1220219          85 and over 51081    \n",
       "2 albury 6233              0.1220219          75 - 84     51081    \n",
       "3 albury 6233              0.1220219          65 - 74     51081    \n",
       "4 albury 6233              0.1220219          55 - 64     51081    \n",
       "5 albury 6233              0.1220219          45 - 54     51081    \n",
       "6 albury 6233              0.1220219          35 - 44     51081    \n",
       "  pop_dens_km2 seifa_value num_service female_lga_age male_lga_age ...\n",
       "1 170.5        964         1769         845            489         ...\n",
       "2 170.5        964         1769        1658           1247         ...\n",
       "3 170.5        964         1769        2572           2335         ...\n",
       "4 170.5        964         1769        3324           3051         ...\n",
       "5 170.5        964         1769        3331           3169         ...\n",
       "6 170.5        964         1769        3231           2974         ...\n",
       "  male_lga_perc employed_lga_age unemployed_lga_age employed_lga_age_perc\n",
       "1 0.3869541        3             1316               5.873025e-05         \n",
       "2 0.3869541       90             2832               1.761908e-03         \n",
       "3 0.3869541      882             4025               1.726669e-02         \n",
       "4 0.3869541     3832             2540               7.501811e-02         \n",
       "5 0.3869541     4826             1681               9.447740e-02         \n",
       "6 0.3869541     4619             1580               9.042501e-02         \n",
       "  unemployed_lga_age_perc employed_lga unemployed_lga employed_lga_perc\n",
       "1 0.02576300              22913        18546          0.4485621        \n",
       "2 0.05544136              22913        18546          0.4485621        \n",
       "3 0.07879642              22913        18546          0.4485621        \n",
       "4 0.04972495              22913        18546          0.4485621        \n",
       "5 0.03290852              22913        18546          0.4485621        \n",
       "6 0.03093127              22913        18546          0.4485621        \n",
       "  unemployed_lga_perc cluster\n",
       "1 0.3630704           1      \n",
       "2 0.3630704           1      \n",
       "3 0.3630704           1      \n",
       "4 0.3630704           1      \n",
       "5 0.3630704           1      \n",
       "6 0.3630704           1      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 945\n",
      "Variables: 25\n",
      "$ lga                     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"albury\", \"albury\", \"albury\", \"al...\n",
      "$ crime_tot_per_lga       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 6233, 6233, 6233, 6233, 6233, 6233, 6233, 6...\n",
      "$ crime_rate_per_lga      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.12202189, 0.12202189, 0.12202189, 0.12202...\n",
      "$ age                     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"85 and over\", \"75 - 84\", \"65 - 74\", \"55 - ...\n",
      "$ pop_total               \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 51081, 51081, 51081, 51081, 51081, 51081, 5...\n",
      "$ pop_dens_km2            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 170.5, 170.5, 170.5, 170.5, 170.5, 170.5, 1...\n",
      "$ seifa_value             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 964, 964, 964, 964, 964, 964, 964, 964, 964...\n",
      "$ num_service             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1769, 1769, 1769, 1769, 1769, 1769, 1769, 1...\n",
      "$ female_lga_age          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 845, 1658, 2572, 3324, 3331, 3231, 3301, 18...\n",
      "$ male_lga_age            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 489, 1247, 2335, 3051, 3169, 2974, 3195, 16...\n",
      "$ male_lga                \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 19766, 19766, 19766, 19766, 19766, 19766, 1...\n",
      "$ female_lga              \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 21707, 21707, 21707, 21707, 21707, 21707, 2...\n",
      "$ female_lga_age_perc     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.01654235, 0.03245825, 0.05035140, 0.06507...\n",
      "$ male_lga_age_perc       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.009573031, 0.024412208, 0.045711713, 0.05...\n",
      "$ female_lga_perc         \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.4249525, 0.4249525, 0.4249525, 0.4249525,...\n",
      "$ male_lga_perc           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3869541, 0.3869541, 0.3869541, 0.3869541,...\n",
      "$ employed_lga_age        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 3, 90, 882, 3832, 4826, 4619, 4832, 2306, 1...\n",
      "$ unemployed_lga_age      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1316, 2832, 4025, 2540, 1681, 1580, 1665, 1...\n",
      "$ employed_lga_age_perc   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 5.873025e-05, 1.761908e-03, 1.726669e-02, 7...\n",
      "$ unemployed_lga_age_perc \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.02576300, 0.05544136, 0.07879642, 0.04972...\n",
      "$ employed_lga            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 22913, 22913, 22913, 22913, 22913, 22913, 2...\n",
      "$ unemployed_lga          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 18546, 18546, 18546, 18546, 18546, 18546, 1...\n",
      "$ employed_lga_perc       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.4485621, 0.4485621, 0.4485621, 0.4485621,...\n",
      "$ unemployed_lga_perc     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3630704, 0.3630704, 0.3630704, 0.3630704,...\n",
      "$ cluster                 \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...\n"
     ]
    }
   ],
   "source": [
    "data_model %<>%\n",
    "    inner_join(clusters, by = \"lga\")\n",
    "\n",
    "head(data_model)\n",
    "glimpse(data_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'age'</li>\n",
       "\t<li>'female_lga_age'</li>\n",
       "\t<li>'male_lga_age'</li>\n",
       "\t<li>'female_lga_age_perc'</li>\n",
       "\t<li>'male_lga_age_perc'</li>\n",
       "\t<li>'employed_lga_age'</li>\n",
       "\t<li>'unemployed_lga_age'</li>\n",
       "\t<li>'employed_lga_age_perc'</li>\n",
       "\t<li>'unemployed_lga_age_perc'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'age'\n",
       "\\item 'female\\_lga\\_age'\n",
       "\\item 'male\\_lga\\_age'\n",
       "\\item 'female\\_lga\\_age\\_perc'\n",
       "\\item 'male\\_lga\\_age\\_perc'\n",
       "\\item 'employed\\_lga\\_age'\n",
       "\\item 'unemployed\\_lga\\_age'\n",
       "\\item 'employed\\_lga\\_age\\_perc'\n",
       "\\item 'unemployed\\_lga\\_age\\_perc'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'age'\n",
       "2. 'female_lga_age'\n",
       "3. 'male_lga_age'\n",
       "4. 'female_lga_age_perc'\n",
       "5. 'male_lga_age_perc'\n",
       "6. 'employed_lga_age'\n",
       "7. 'unemployed_lga_age'\n",
       "8. 'employed_lga_age_perc'\n",
       "9. 'unemployed_lga_age_perc'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"age\"                     \"female_lga_age\"         \n",
       "[3] \"male_lga_age\"            \"female_lga_age_perc\"    \n",
       "[5] \"male_lga_age_perc\"       \"employed_lga_age\"       \n",
       "[7] \"unemployed_lga_age\"      \"employed_lga_age_perc\"  \n",
       "[9] \"unemployed_lga_age_perc\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns_containing_age <- names(data_model)[grepl(\"age\", names(data_model))]\n",
    "columns_containing_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lga</th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>pop_total</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>male_lga</th><th scope=col>female_lga</th><th scope=col>female_lga_perc</th><th scope=col>male_lga_perc</th><th scope=col>employed_lga</th><th scope=col>unemployed_lga</th><th scope=col>employed_lga_perc</th><th scope=col>unemployed_lga_perc</th><th scope=col>cluster</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>albury           </td><td>6233             </td><td>0.12202189       </td><td>51081            </td><td>170.5            </td><td>964              </td><td>1769             </td><td>19766            </td><td>21707            </td><td>0.4249525        </td><td>0.3869541        </td><td>22913            </td><td>18546            </td><td>0.4485621        </td><td>0.3630704        </td><td>1                </td></tr>\n",
       "\t<tr><td>ballina          </td><td>2690             </td><td>0.06439412       </td><td>41774            </td><td> 88.7            </td><td>986              </td><td>1353             </td><td>16488            </td><td>18355            </td><td>0.4393881        </td><td>0.3946953        </td><td>17483            </td><td>17354            </td><td>0.4185139        </td><td>0.4154259        </td><td>1                </td></tr>\n",
       "\t<tr><td>balranald        </td><td> 192             </td><td>0.08376963       </td><td> 2292            </td><td>  0.1            </td><td>969              </td><td>   4             </td><td>  926            </td><td>  877            </td><td>0.3826353        </td><td>0.4040140        </td><td>  990            </td><td>  807            </td><td>0.4319372        </td><td>0.3520942        </td><td>4                </td></tr>\n",
       "\t<tr><td>bathurst regional</td><td>4027             </td><td>0.09751314       </td><td>41297            </td><td> 11.1            </td><td>994              </td><td>2882             </td><td>16466            </td><td>16712            </td><td>0.4046783        </td><td>0.3987215        </td><td>18160            </td><td>14999            </td><td>0.4397414        </td><td>0.3631983        </td><td>1                </td></tr>\n",
       "\t<tr><td>bega valley      </td><td>2119             </td><td>0.06376960       </td><td>33229            </td><td>  5.4            </td><td>974              </td><td> 771             </td><td>13543            </td><td>14433            </td><td>0.4343495        </td><td>0.4075657        </td><td>13180            </td><td>14829            </td><td>0.3966415        </td><td>0.4462668        </td><td>5                </td></tr>\n",
       "\t<tr><td>bellingen        </td><td> 798             </td><td>0.06305310       </td><td>12656            </td><td>  8.1            </td><td>957              </td><td> 422             </td><td> 5018            </td><td> 5437            </td><td>0.4295986        </td><td>0.3964918        </td><td> 4871            </td><td> 5553            </td><td>0.3848767        </td><td>0.4387642        </td><td>2                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllll}\n",
       " lga & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & pop\\_total & pop\\_dens\\_km2 & seifa\\_value & num\\_service & male\\_lga & female\\_lga & female\\_lga\\_perc & male\\_lga\\_perc & employed\\_lga & unemployed\\_lga & employed\\_lga\\_perc & unemployed\\_lga\\_perc & cluster\\\\\n",
       "\\hline\n",
       "\t albury            & 6233              & 0.12202189        & 51081             & 170.5             & 964               & 1769              & 19766             & 21707             & 0.4249525         & 0.3869541         & 22913             & 18546             & 0.4485621         & 0.3630704         & 1                \\\\\n",
       "\t ballina           & 2690              & 0.06439412        & 41774             &  88.7             & 986               & 1353              & 16488             & 18355             & 0.4393881         & 0.3946953         & 17483             & 17354             & 0.4185139         & 0.4154259         & 1                \\\\\n",
       "\t balranald         &  192              & 0.08376963        &  2292             &   0.1             & 969               &    4              &   926             &   877             & 0.3826353         & 0.4040140         &   990             &   807             & 0.4319372         & 0.3520942         & 4                \\\\\n",
       "\t bathurst regional & 4027              & 0.09751314        & 41297             &  11.1             & 994               & 2882              & 16466             & 16712             & 0.4046783         & 0.3987215         & 18160             & 14999             & 0.4397414         & 0.3631983         & 1                \\\\\n",
       "\t bega valley       & 2119              & 0.06376960        & 33229             &   5.4             & 974               &  771              & 13543             & 14433             & 0.4343495         & 0.4075657         & 13180             & 14829             & 0.3966415         & 0.4462668         & 5                \\\\\n",
       "\t bellingen         &  798              & 0.06305310        & 12656             &   8.1             & 957               &  422              &  5018             &  5437             & 0.4295986         & 0.3964918         &  4871             &  5553             & 0.3848767         & 0.4387642         & 2                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lga | crime_tot_per_lga | crime_rate_per_lga | pop_total | pop_dens_km2 | seifa_value | num_service | male_lga | female_lga | female_lga_perc | male_lga_perc | employed_lga | unemployed_lga | employed_lga_perc | unemployed_lga_perc | cluster |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| albury            | 6233              | 0.12202189        | 51081             | 170.5             | 964               | 1769              | 19766             | 21707             | 0.4249525         | 0.3869541         | 22913             | 18546             | 0.4485621         | 0.3630704         | 1                 |\n",
       "| ballina           | 2690              | 0.06439412        | 41774             |  88.7             | 986               | 1353              | 16488             | 18355             | 0.4393881         | 0.3946953         | 17483             | 17354             | 0.4185139         | 0.4154259         | 1                 |\n",
       "| balranald         |  192              | 0.08376963        |  2292             |   0.1             | 969               |    4              |   926             |   877             | 0.3826353         | 0.4040140         |   990             |   807             | 0.4319372         | 0.3520942         | 4                 |\n",
       "| bathurst regional | 4027              | 0.09751314        | 41297             |  11.1             | 994               | 2882              | 16466             | 16712             | 0.4046783         | 0.3987215         | 18160             | 14999             | 0.4397414         | 0.3631983         | 1                 |\n",
       "| bega valley       | 2119              | 0.06376960        | 33229             |   5.4             | 974               |  771              | 13543             | 14433             | 0.4343495         | 0.4075657         | 13180             | 14829             | 0.3966415         | 0.4462668         | 5                 |\n",
       "| bellingen         |  798              | 0.06305310        | 12656             |   8.1             | 957               |  422              |  5018             |  5437             | 0.4295986         | 0.3964918         |  4871             |  5553             | 0.3848767         | 0.4387642         | 2                 |\n",
       "\n"
      ],
      "text/plain": [
       "  lga               crime_tot_per_lga crime_rate_per_lga pop_total pop_dens_km2\n",
       "1 albury            6233              0.12202189         51081     170.5       \n",
       "2 ballina           2690              0.06439412         41774      88.7       \n",
       "3 balranald          192              0.08376963          2292       0.1       \n",
       "4 bathurst regional 4027              0.09751314         41297      11.1       \n",
       "5 bega valley       2119              0.06376960         33229       5.4       \n",
       "6 bellingen          798              0.06305310         12656       8.1       \n",
       "  seifa_value num_service male_lga female_lga female_lga_perc male_lga_perc\n",
       "1 964         1769        19766    21707      0.4249525       0.3869541    \n",
       "2 986         1353        16488    18355      0.4393881       0.3946953    \n",
       "3 969            4          926      877      0.3826353       0.4040140    \n",
       "4 994         2882        16466    16712      0.4046783       0.3987215    \n",
       "5 974          771        13543    14433      0.4343495       0.4075657    \n",
       "6 957          422         5018     5437      0.4295986       0.3964918    \n",
       "  employed_lga unemployed_lga employed_lga_perc unemployed_lga_perc cluster\n",
       "1 22913        18546          0.4485621         0.3630704           1      \n",
       "2 17483        17354          0.4185139         0.4154259           1      \n",
       "3   990          807          0.4319372         0.3520942           4      \n",
       "4 18160        14999          0.4397414         0.3631983           1      \n",
       "5 13180        14829          0.3966415         0.4462668           5      \n",
       "6  4871         5553          0.3848767         0.4387642           2      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 105\n",
      "Variables: 16\n",
      "$ lga                 \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"albury\", \"ballina\", \"balranald\", \"bathurst reg...\n",
      "$ crime_tot_per_lga   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 6233, 2690, 192, 4027, 2119, 798, 482, 45643, 3...\n",
      "$ crime_rate_per_lga  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.12202189, 0.06439412, 0.08376963, 0.09751314,...\n",
      "$ pop_total           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 51081, 41774, 2292, 41297, 33229, 12656, 8456, ...\n",
      "$ pop_dens_km2        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 170.5, 88.7, 0.1, 11.1, 5.4, 8.1, 4.2, 1449.8, ...\n",
      "$ seifa_value         \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 964, 986, 969, 994, 974, 957, 965, 995, 997, 10...\n",
      "$ num_service         \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1769, 1353, 4, 2882, 771, 422, 10, 248915, 8, 2...\n",
      "$ male_lga            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 19766, 16488, 926, 16466, 13543, 5018, 3498, 12...\n",
      "$ female_lga          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 21707, 18355, 877, 16712, 14433, 5437, 3518, 13...\n",
      "$ female_lga_perc     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.4249525, 0.4393881, 0.3826353, 0.4046783, 0.4...\n",
      "$ male_lga_perc       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3869541, 0.3946953, 0.4040140, 0.3987215, 0.4...\n",
      "$ employed_lga        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 22913, 17483, 990, 18160, 13180, 4871, 3288, 15...\n",
      "$ unemployed_lga      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 18546, 17354, 807, 14999, 14829, 5553, 3380, 10...\n",
      "$ employed_lga_perc   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.4485621, 0.4185139, 0.4319372, 0.4397414, 0.3...\n",
      "$ unemployed_lga_perc \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3630704, 0.4154259, 0.3520942, 0.3631983, 0.4...\n",
      "$ cluster             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1, 1, 4, 1, 5, 2, 4, 3, 4, 2, 3, 4, 4, 4, 3, 5,...\n"
     ]
    }
   ],
   "source": [
    "data_model %<>%\n",
    "    dplyr::select(-columns_containing_age) %>%\n",
    "    distinct\n",
    "\n",
    "head(data_model)\n",
    "glimpse(data_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full Set Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_model_2 <- data_model %>%\n",
    "    dplyr::select(-lga, -cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poisson Regression on the full set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = crime_tot_per_lga ~ ., family = poisson, data = data_model_2, \n",
       "    offset = log(pop_total))\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-28.0915   -4.5384   -0.3036    3.6716   21.8784  \n",
       "\n",
       "Coefficients:\n",
       "                      Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)          2.617e+00  1.607e-01  16.291  < 2e-16 ***\n",
       "crime_rate_per_lga   5.020e+00  4.132e-02 121.511  < 2e-16 ***\n",
       "pop_total            7.054e-06  6.099e-07  11.566  < 2e-16 ***\n",
       "pop_dens_km2        -4.990e-05  1.837e-06 -27.170  < 2e-16 ***\n",
       "seifa_value         -5.629e-03  6.728e-05 -83.669  < 2e-16 ***\n",
       "num_service         -4.698e-07  6.976e-08  -6.735 1.64e-11 ***\n",
       "male_lga             2.717e-04  9.207e-06  29.510  < 2e-16 ***\n",
       "female_lga           2.513e-04  9.667e-06  25.991  < 2e-16 ***\n",
       "female_lga_perc     -1.470e+01  5.909e-01 -24.877  < 2e-16 ***\n",
       "male_lga_perc       -1.457e+01  5.847e-01 -24.926  < 2e-16 ***\n",
       "employed_lga        -2.671e-04  9.303e-06 -28.714  < 2e-16 ***\n",
       "unemployed_lga      -2.718e-04  9.367e-06 -29.016  < 2e-16 ***\n",
       "employed_lga_perc    1.579e+01  5.250e-01  30.079  < 2e-16 ***\n",
       "unemployed_lga_perc  1.352e+01  5.405e-01  25.013  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for poisson family taken to be 1)\n",
       "\n",
       "    Null deviance: 139260.8  on 104  degrees of freedom\n",
       "Residual deviance:   6588.4  on  91  degrees of freedom\n",
       "AIC: 7614.4\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"The five-percent critical value for a chi-squared with 91 degrees of freedom is 114.267867677194\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOverdispersion test\n",
       "\n",
       "data:  model\n",
       "z = 5.0478, p-value = 2.235e-07\n",
       "alternative hypothesis: true dispersion is greater than 1\n",
       "sample estimates:\n",
       "dispersion \n",
       "  61.28459 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eR2JFc6SpLoBT++NE9HaLVR4N1UlnJt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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- glm(crime_tot_per_lga ~., \n",
    "             offset = log(pop_total), \n",
    "             family = poisson, \n",
    "             data = data_model_2)\n",
    "\n",
    "summary(model)\n",
    "# The five-percent critical value for a chi-squared with __ degrees of freedom\n",
    "print(paste(\"The five-percent critical value for a chi-squared with\", model$df.residual, \"degrees of freedom is\", qchisq(0.95, model$df.residual)))\n",
    "dispersiontest(model)\n",
    "autoplot(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quasi-Poisson Regression on the full set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = crime_tot_per_lga ~ ., family = quasipoisson, data = data_model_2, \n",
       "    offset = log(pop_total))\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-28.0915   -4.5384   -0.3036    3.6716   21.8784  \n",
       "\n",
       "Coefficients:\n",
       "                      Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)          2.617e+00  1.351e+00   1.937 0.055785 .  \n",
       "crime_rate_per_lga   5.020e+00  3.474e-01  14.451  < 2e-16 ***\n",
       "pop_total            7.054e-06  5.128e-06   1.376 0.172349    \n",
       "pop_dens_km2        -4.990e-05  1.544e-05  -3.231 0.001716 ** \n",
       "seifa_value         -5.629e-03  5.657e-04  -9.951 3.23e-16 ***\n",
       "num_service         -4.698e-07  5.866e-07  -0.801 0.425213    \n",
       "male_lga             2.717e-04  7.741e-05   3.510 0.000700 ***\n",
       "female_lga           2.513e-04  8.128e-05   3.091 0.002646 ** \n",
       "female_lga_perc     -1.470e+01  4.968e+00  -2.959 0.003938 ** \n",
       "male_lga_perc       -1.457e+01  4.916e+00  -2.964 0.003871 ** \n",
       "employed_lga        -2.671e-04  7.822e-05  -3.415 0.000955 ***\n",
       "unemployed_lga      -2.718e-04  7.876e-05  -3.451 0.000849 ***\n",
       "employed_lga_perc    1.579e+01  4.414e+00   3.577 0.000559 ***\n",
       "unemployed_lga_perc  1.352e+01  4.545e+00   2.975 0.003753 ** \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for quasipoisson family taken to be 70.69873)\n",
       "\n",
       "    Null deviance: 139260.8  on 104  degrees of freedom\n",
       "Residual deviance:   6588.4  on  91  degrees of freedom\n",
       "AIC: NA\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"The five-percent critical value for a chi-squared with 91 degrees of freedom is 114.267867677194\"\n"
     ]
    },
    {
     "data": {
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Pzw3r+44hZpZxA2zjnViUAaJakeOEGx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\nQSAJBCBBIAnk/wOB8ihY6FwWMQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- glm(crime_tot_per_lga ~., \n",
    "             offset = log(pop_total), \n",
    "             family = quasipoisson, \n",
    "             data = data_model_2)\n",
    "\n",
    "summary(model)\n",
    "# The five-percent critical value for a chi-squared with __ degrees of freedom\n",
    "print(paste(\"The five-percent critical value for a chi-squared with\", model$df.residual, \"degrees of freedom is\", qchisq(0.95, model$df.residual)))\n",
    "autoplot(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Negative Binomial Regression on the full set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm.nb(formula = crime_tot_per_lga ~ ., data = data_model_2, \n",
       "    init.theta = 3.651906423, link = log)\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-2.13470  -0.95454  -0.09984   0.65881   2.15874  \n",
       "\n",
       "Coefficients:\n",
       "                      Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)          7.942e+00  4.053e+00   1.959 0.050076 .  \n",
       "crime_rate_per_lga   4.523e+00  1.050e+00   4.307 1.66e-05 ***\n",
       "pop_total           -3.541e-05  2.685e-05  -1.319 0.187298    \n",
       "pop_dens_km2         1.585e-04  6.481e-05   2.446 0.014449 *  \n",
       "seifa_value         -9.875e-04  2.041e-03  -0.484 0.628455    \n",
       "num_service         -1.616e-05  3.571e-06  -4.525 6.04e-06 ***\n",
       "male_lga             1.011e-03  2.283e-04   4.429 9.49e-06 ***\n",
       "female_lga           6.406e-04  2.469e-04   2.594 0.009489 ** \n",
       "female_lga_perc     -2.256e+01  9.742e+00  -2.315 0.020591 *  \n",
       "male_lga_perc       -4.017e+01  9.364e+00  -4.290 1.79e-05 ***\n",
       "employed_lga        -7.321e-04  2.317e-04  -3.160 0.001577 ** \n",
       "unemployed_lga      -7.605e-04  2.311e-04  -3.291 0.000999 ***\n",
       "employed_lga_perc    3.046e+01  7.743e+00   3.933 8.38e-05 ***\n",
       "unemployed_lga_perc  3.058e+01  7.852e+00   3.895 9.81e-05 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for Negative Binomial(3.6519) family taken to be 1)\n",
       "\n",
       "    Null deviance: 716.11  on 104  degrees of freedom\n",
       "Residual deviance: 109.79  on  91  degrees of freedom\n",
       "AIC: 1812\n",
       "\n",
       "Number of Fisher Scoring iterations: 1\n",
       "\n",
       "\n",
       "              Theta:  3.652 \n",
       "          Std. Err.:  0.485 \n",
       "\n",
       " 2 x log-likelihood:  -1782.006 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"The five-percent critical value for a chi-squared with 91 degrees of freedom is 114.267867677194\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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SNLsWaQZBgBSCoVfetCg2Tcq/OOK6KG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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- glm.nb(crime_tot_per_lga ~., \n",
    "             data = data_model_2)\n",
    "\n",
    "summary(model)\n",
    "# The five-percent critical value for a chi-squared with __ degrees of freedom\n",
    "print(paste(\"The five-percent critical value for a chi-squared with\", model$df.residual, \"degrees of freedom is\", qchisq(0.95, model$df.residual)))\n",
    "autoplot(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster 3 Poisson Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>pop_total</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>male_lga</th><th scope=col>female_lga</th><th scope=col>female_lga_perc</th><th scope=col>male_lga_perc</th><th scope=col>employed_lga</th><th scope=col>unemployed_lga</th><th scope=col>employed_lga_perc</th><th scope=col>unemployed_lga_perc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>45643     </td><td>0.13544640</td><td>336982    </td><td>1449.8    </td><td> 995      </td><td>248915    </td><td>128178    </td><td>132137    </td><td>0.3921189 </td><td>0.3803705 </td><td>151466    </td><td>108839    </td><td>0.4494780 </td><td>0.3229816 </td></tr>\n",
       "\t<tr><td> 4872     </td><td>0.06335583</td><td> 76899    </td><td>  55.1    </td><td>1030      </td><td> 42057    </td><td> 29902    </td><td> 32932    </td><td>0.4282500 </td><td>0.3888477 </td><td> 35853    </td><td> 26993    </td><td>0.4662349 </td><td>0.3510189 </td></tr>\n",
       "\t<tr><td> 3320     </td><td>0.09016839</td><td> 36820    </td><td>5406.2    </td><td> 956      </td><td> 52222    </td><td> 15368    </td><td> 16653    </td><td>0.4522814 </td><td>0.4173819 </td><td> 17158    </td><td> 14814    </td><td>0.4659967 </td><td>0.4023357 </td></tr>\n",
       "\t<tr><td> 4039     </td><td>0.05164037</td><td> 78214    </td><td> 399.3    </td><td>1094      </td><td> 45334    </td><td> 28794    </td><td> 30722    </td><td>0.3927941 </td><td>0.3681438 </td><td> 40011    </td><td> 19494    </td><td>0.5115580 </td><td>0.2492393 </td></tr>\n",
       "\t<tr><td>25367     </td><td>0.16158972</td><td>156984    </td><td> 518.0    </td><td> 969      </td><td>114888    </td><td> 59603    </td><td> 63526    </td><td>0.4046654 </td><td>0.3796756 </td><td> 68865    </td><td> 54288    </td><td>0.4386753 </td><td>0.3458187 </td></tr>\n",
       "\t<tr><td> 5140     </td><td>0.05838852</td><td> 88031    </td><td>4644.5    </td><td>1030      </td><td> 87730    </td><td> 35071    </td><td> 38667    </td><td>0.4392430 </td><td>0.3983937 </td><td> 44680    </td><td> 29020    </td><td>0.5075485 </td><td>0.3296566 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       " crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & pop\\_total & pop\\_dens\\_km2 & seifa\\_value & num\\_service & male\\_lga & female\\_lga & female\\_lga\\_perc & male\\_lga\\_perc & employed\\_lga & unemployed\\_lga & employed\\_lga\\_perc & unemployed\\_lga\\_perc\\\\\n",
       "\\hline\n",
       "\t 45643      & 0.13544640 & 336982     & 1449.8     &  995       & 248915     & 128178     & 132137     & 0.3921189  & 0.3803705  & 151466     & 108839     & 0.4494780  & 0.3229816 \\\\\n",
       "\t  4872      & 0.06335583 &  76899     &   55.1     & 1030       &  42057     &  29902     &  32932     & 0.4282500  & 0.3888477  &  35853     &  26993     & 0.4662349  & 0.3510189 \\\\\n",
       "\t  3320      & 0.09016839 &  36820     & 5406.2     &  956       &  52222     &  15368     &  16653     & 0.4522814  & 0.4173819  &  17158     &  14814     & 0.4659967  & 0.4023357 \\\\\n",
       "\t  4039      & 0.05164037 &  78214     &  399.3     & 1094       &  45334     &  28794     &  30722     & 0.3927941  & 0.3681438  &  40011     &  19494     & 0.5115580  & 0.2492393 \\\\\n",
       "\t 25367      & 0.16158972 & 156984     &  518.0     &  969       & 114888     &  59603     &  63526     & 0.4046654  & 0.3796756  &  68865     &  54288     & 0.4386753  & 0.3458187 \\\\\n",
       "\t  5140      & 0.05838852 &  88031     & 4644.5     & 1030       &  87730     &  35071     &  38667     & 0.4392430  & 0.3983937  &  44680     &  29020     & 0.5075485  & 0.3296566 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| crime_tot_per_lga | crime_rate_per_lga | pop_total | pop_dens_km2 | seifa_value | num_service | male_lga | female_lga | female_lga_perc | male_lga_perc | employed_lga | unemployed_lga | employed_lga_perc | unemployed_lga_perc |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 45643      | 0.13544640 | 336982     | 1449.8     |  995       | 248915     | 128178     | 132137     | 0.3921189  | 0.3803705  | 151466     | 108839     | 0.4494780  | 0.3229816  |\n",
       "|  4872      | 0.06335583 |  76899     |   55.1     | 1030       |  42057     |  29902     |  32932     | 0.4282500  | 0.3888477  |  35853     |  26993     | 0.4662349  | 0.3510189  |\n",
       "|  3320      | 0.09016839 |  36820     | 5406.2     |  956       |  52222     |  15368     |  16653     | 0.4522814  | 0.4173819  |  17158     |  14814     | 0.4659967  | 0.4023357  |\n",
       "|  4039      | 0.05164037 |  78214     |  399.3     | 1094       |  45334     |  28794     |  30722     | 0.3927941  | 0.3681438  |  40011     |  19494     | 0.5115580  | 0.2492393  |\n",
       "| 25367      | 0.16158972 | 156984     |  518.0     |  969       | 114888     |  59603     |  63526     | 0.4046654  | 0.3796756  |  68865     |  54288     | 0.4386753  | 0.3458187  |\n",
       "|  5140      | 0.05838852 |  88031     | 4644.5     | 1030       |  87730     |  35071     |  38667     | 0.4392430  | 0.3983937  |  44680     |  29020     | 0.5075485  | 0.3296566  |\n",
       "\n"
      ],
      "text/plain": [
       "  crime_tot_per_lga crime_rate_per_lga pop_total pop_dens_km2 seifa_value\n",
       "1 45643             0.13544640         336982    1449.8        995       \n",
       "2  4872             0.06335583          76899      55.1       1030       \n",
       "3  3320             0.09016839          36820    5406.2        956       \n",
       "4  4039             0.05164037          78214     399.3       1094       \n",
       "5 25367             0.16158972         156984     518.0        969       \n",
       "6  5140             0.05838852          88031    4644.5       1030       \n",
       "  num_service male_lga female_lga female_lga_perc male_lga_perc employed_lga\n",
       "1 248915      128178   132137     0.3921189       0.3803705     151466      \n",
       "2  42057       29902    32932     0.4282500       0.3888477      35853      \n",
       "3  52222       15368    16653     0.4522814       0.4173819      17158      \n",
       "4  45334       28794    30722     0.3927941       0.3681438      40011      \n",
       "5 114888       59603    63526     0.4046654       0.3796756      68865      \n",
       "6  87730       35071    38667     0.4392430       0.3983937      44680      \n",
       "  unemployed_lga employed_lga_perc unemployed_lga_perc\n",
       "1 108839         0.4494780         0.3229816          \n",
       "2  26993         0.4662349         0.3510189          \n",
       "3  14814         0.4659967         0.4023357          \n",
       "4  19494         0.5115580         0.2492393          \n",
       "5  54288         0.4386753         0.3458187          \n",
       "6  29020         0.5075485         0.3296566          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 42\n",
      "Variables: 14\n",
      "$ crime_tot_per_lga   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 45643, 4872, 3320, 4039, 25367, 5140, 32475, 59...\n",
      "$ crime_rate_per_lga  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.13544640, 0.06335583, 0.09016839, 0.05164037,...\n",
      "$ pop_total           \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 336982, 76899, 36820, 78214, 156984, 88031, 327...\n",
      "$ pop_dens_km2        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1449.8, 55.1, 5406.2, 399.3, 518.0, 4644.5, 200...\n",
      "$ seifa_value         \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 995, 1030, 956, 1094, 969, 1030, 969, 972, 945,...\n",
      "$ num_service         \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 248915, 42057, 52222, 45334, 114888, 87730, 198...\n",
      "$ male_lga            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 128178, 29902, 15368, 28794, 59603, 35071, 1276...\n",
      "$ female_lga          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 132137, 32932, 16653, 30722, 63526, 38667, 1397...\n",
      "$ female_lga_perc     \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3921189, 0.4282500, 0.4522814, 0.3927941, 0.4...\n",
      "$ male_lga_perc       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3803705, 0.3888477, 0.4173819, 0.3681438, 0.3...\n",
      "$ employed_lga        \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 151466, 35853, 17158, 40011, 68865, 44680, 1395...\n",
      "$ unemployed_lga      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 108839, 26993, 14814, 19494, 54288, 29020, 1276...\n",
      "$ employed_lga_perc   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.4494780, 0.4662349, 0.4659967, 0.5115580, 0.4...\n",
      "$ unemployed_lga_perc \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0.3229816, 0.3510189, 0.4023357, 0.2492393, 0.3...\n"
     ]
    }
   ],
   "source": [
    "cluster_3 <- data_model %>%\n",
    "    filter(cluster == 3) %>%\n",
    "    dplyr::select(-lga, -cluster)\n",
    "\n",
    "head(cluster_3)\n",
    "glimpse(cluster_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = crime_tot_per_lga ~ ., family = poisson, data = cluster_3, \n",
       "    offset = log(pop_total))\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-15.4126   -3.6860   -0.4609    3.5558   11.2557  \n",
       "\n",
       "Coefficients:\n",
       "                      Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)         -3.284e-01  2.176e-01  -1.509  0.13127    \n",
       "crime_rate_per_lga   9.314e+00  9.031e-02 103.129  < 2e-16 ***\n",
       "pop_total            7.795e-06  7.533e-07  10.349  < 2e-16 ***\n",
       "pop_dens_km2         2.658e-06  2.300e-06   1.156  0.24772    \n",
       "seifa_value         -3.423e-03  9.304e-05 -36.794  < 2e-16 ***\n",
       "num_service         -3.333e-06  9.048e-08 -36.831  < 2e-16 ***\n",
       "male_lga             9.483e-04  1.168e-04   8.121 4.63e-16 ***\n",
       "female_lga           9.502e-04  1.159e-04   8.198 2.45e-16 ***\n",
       "female_lga_perc     -2.607e+01  9.108e+00  -2.862  0.00421 ** \n",
       "male_lga_perc       -2.345e+01  9.186e+00  -2.552  0.01070 *  \n",
       "employed_lga        -9.550e-04  1.166e-04  -8.190 2.61e-16 ***\n",
       "unemployed_lga      -9.577e-04  1.164e-04  -8.229  < 2e-16 ***\n",
       "employed_lga_perc    2.616e+01  9.171e+00   2.852  0.00435 ** \n",
       "unemployed_lga_perc  2.428e+01  9.153e+00   2.653  0.00799 ** \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for poisson family taken to be 1)\n",
       "\n",
       "    Null deviance: 122513.6  on 41  degrees of freedom\n",
       "Residual deviance:   1414.5  on 28  degrees of freedom\n",
       "AIC: 1889\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IJp6hkgsXURipBDjizBHYwSzS4YAGmiJEXchHm4CEXIIUfDwR2MEt2+TABpoiRF\n3IR5uAhFyCFHg8EdjBLtToEAyc28B7IRN2EeLpYGySwBSLXCGyT/JfoRN2EeLgDSaAlfkEYs\n0WfehN23kQJIGCMZFb4gjbjYxxskj40NtwiS7Wl8mLUrFcEgRb5Ev+ycLB3GDDaquXRv4+PU\ndlkF09TzRs9ISi8fZ6TeO73bYStJ0MMIABJAmiiKYcVyIJkkYY/H2TZI45foswYJYySf4ErJ\n8IOmAJKbrQwkzNp5BAeQjAqAxN/FIiDpdwsCSCbFSJCwsmFGF0uAZNh1C2Mkk8IfJO8kRdyE\nebgIRcghR92iTbvXYdbOpKgNILF1EYqQQ44cg2PVdlkF09QzQGLrIhQhhxw5Bseq7bIKpqln\ngETtQtf9AUjzSgBS/C60A/IYQBra3ZtT22UVTFPPAInWhX6KOAKQBnfJ59R2WQXT1DNAAkjy\nx/DTJji1XVbBNPW8BpBOovjl+2fx+ftiURQWK0haqyt2169YDzfzSwDSGBcnUeT7W/6o7+99\nwSxRVBbtGKlvgxXr7mYBCUAa4UKmWbbf/xJf/nn8EJ+XiaKx1czaKRX77Fess5slJADJ38Wn\nT7+yfGet90/xY7kopnARipBDjnI76MdHasVyarsAyV/h4uL0eAghu1P/Lv57uSimcBGKkEOO\npB0MErViObVdgOSvcHRR5Dv7+VV8/rlYFOQuQhFyyNFj+OFHdcUe+xXr8g0WkwCkUS6qfP+7\nHBP/WioKchehCDnkyBLcUMV6uJlfApBGuSi68kJknZHv4stSUZC7CEXIIUeW4OqKTfsV6+Fm\nfglAGuWimFz6N1G9XiYKchehCDnkyBJcXbFpv2I93MwvAUijXBQ5/gqQfHN0sARXVyxAMilq\nWw9IP4qu3beloiB3EYqQNUcHW3B1xab9inX5BotJANIoF+XB8kt+Af7vpaIgdxGKkC1H1eUj\nG0j6inX5BotJANIoF1Wv4/RJfPm9WBTkLkIRcsiRJbi6YpN+xXq4mV8CkOBCeYvErCXH1nZZ\nBdPUM0Bi6yIUIYccOQbHqu2yCqapZ4DE1kUoQgM5ai2vi6ztsgqmqWeAxNZFKELmHLVXqUbW\ndlkF09QzQGLrIhQhY45sDz8aEz9AIk6SVxQEX2TFLkIRcsiRY3Cs2i6rYJp6BkhsXYQi5JAj\nx+BYtV1WwTT1DJDYughFyCFHjsGxarusgmnqGSCxdRGKkNYOk3iFASS+LmgS3Papu6s8spMA\nq2CaegZIbF2EIqTJkXZ3hsjaLqtgmnoGSGxdhCLkkCPH4Fi1XVbBNPUMkNi6CEXIIUeOwbFq\nu6yCaeoZILF1EYqQQ44cg2PVdlkF09QzQGLrIhShdo4Mu9c5Bceq7bIKpqlngMTWRShCrRwN\nbJJfF11u9f3rixBffuklft9gMQlAggvlLRIrfLk8/Kjc6vtnfj+s+NrQdQwAACAASURBVKmT\nhH1HgDScpPFREHyRFbsIRcghR2rR1Vbf/5I72P1qb/XNqe2yCqapZ4DE1kUoQg45Uoqut/oW\nmt2YOLVdVsE09QyQ2LoIRcghR0rR9Vbff4j/lGekr37xA6TJkhRxE+bhIhShKkfDD+NTii5A\nev1TDpFOnvEDpLAkhUVB8EVW7CIUoTJHNo56IP0hQfqqlwR9R4BkTFJgFARfZMUuQhEqzMqR\nClLxOKTvcqPNk1YS9B0BkslCoyD4Iit2EYqQQ47aRRezduVkwye/+AHSZEmKuAnzcBGKkEOO\n2kWr83WYtXOSNPUMkNi6CEXIIUftoqs983/0Ho/Dqe2yCqapZ4DE1kUoQg45ahddgPS7WNnw\nWyvx+waLSQASXChvkZi15C5Ij99fhfj6Wy/x+waLSWIDidjORTAfeyH2HwvHshqbq9EBpKmS\n5H0U7i+ajPh0QuAiFCGHHDkGx6rtsgqmqWc+IGkWTUZMAYGLUIS0OXp9ffUPjlXbZRVMU89s\nQNItmoyYAgIXoQjpcpQvXfAOjlXbZRVMU89sQNItmoyYAgIXoQhpcvT6qiEpsrbLKpimntmA\n9NAsmoyYAgIXoQhpcgSQiCVNPTMEqVk0GTEFBC5CEdLkCCARS5p65gVSZ9FkxBQQuAhFSJcj\njJFoJU09MwOpvWgyYgoIXIQipM0RZu1IJU09cwOp9TNiCghchCLkkCPH4Fi1XVbBNPXMDyRl\n0WTEFBC4CEXIIUeOwbFqu6yCaeqZH0jKosmIKSBwEYqQQ44cg2PVdlkF09QzP5CURZMRU0Dg\nIhQhhxw5Bseq7bIKpqlnTiDBRfstErOWHFvbZRVMU88Aia2LUIQccuQYHKu2yyqYpp4BElsX\noQg55MgxOFZtl1UwTT0DJLYuQhFyyJFjcKzaLqtgmnoGSGxdhCLkkCPH4Fi1XVbBNPUMkNi6\nCEXIIUeOwbFqu6yCaeoZILF1EYqQQ44cg2PVdlkF09Tzkns2/AUXlC4WLprGTbTBAKTVuFi4\naIC0mPFof6txsXDRAGkx49H+VuNi4aIBEgwGCzWABIMRGECCwQgMIMFgBAaQYDACA0gwGIEt\nBlKSW7gPgjDCHIS7STr/z2wEtVg6CvfAJxTfYJY9IxG04YVdJI2HsW6qdBG1Ie/i6x+hjoKd\nMArFO5hFQQo9IYX7CHWRpMEgJdVJbaEzElXrJYifUSgAaW4X4Wekpbt2ZEWzAYmuKuMAKXh8\nE+4EIBEWDZAWsvCKD5+uSEM71KsAickInx1IkUw2MDgjSRZxRgJI4W4WBIlNvW8VpPqEHtg9\npnGT8gPJx8vGQcIYibBcDglVPc3qBCBtHiSyYjkkVPU0q4+YQcLKBgqjWGJSegr3wGhlg2e9\nYK0dDEZgAAkGIzCABIMRGECCwQgMIMFgBAaQYDACA0gwGIEBJBiMwAASDEZg8YAkCjvdBhTF\nv9ouugvTYuArD/0N1jFRm2/F5XkxfER9+7IXYn/xcbhcAuNpOXXajCT1alJbrQCJyMaDlKut\nIN13hffk7u4QINmtrKOz2FsUA28Y33T4G0xjVYWNAMn6t53Yf6Tpx17YF7zVHwJIdlNzlp2W\nkoyn5ynr6j3lm/e9ODaHpPtRJOfikJlqVfJdscv/32VnuI+jyD+gHNQ6n3xLxM6lj7E1U5Jy\nLqpQrW756p720qWcxMpMpe0U5Hatjph7cWllRZG2El07XSRtsYK0F6c0TWQNSh6e8tWxqsn8\ntxyZ/DMalbSjkDm+Z3+5Fl2Icw+k+pPnXACSetYk5VhWYae6RfLspatp91WmOinI7Sg+ihc5\nUU1WVGlSvOqAtEjaYgMpO9qc5GuZsjf58ywrSvb3nvuqJs+Z5KOuVp1K2jX3cRbX7KT0nqa3\nVi7yn80ns2Nf5nHBzUm4WgPS/pnVV9Kt7ux0cu6nqx7S1Jlqp6DlupsVVZoVepGFtsZIy6Qt\nJpBKuxc1JKs0f/8oX93lyUVUVf2sPmJQ5bZrdj65X9/2fZCaTybidJ3zu0ZjDUj3tFtpZXXv\n+umq232dqXYKWq57/QRFelf/2qS/KmfOtMUGUpJPf1fV2p40aqOQ1vXbV+V2yfoOH+ItlYdN\n0ekdlK+q969Zb2FnnzzanrUnG9qV1k5K7y/tSbZ9+1Op+reCxfq9vaGAJoFLpC0mkLqvA0F6\nZv2KszwknrIR6fXe/nQ7I1lfYieSj1m+Z1RGBVI7BbnVY6SPZugkf3alAyDNmbaYQdrV79i7\ndm1VYSdxlx2AQvZsgXRv+giVXUQ8dTWb9UHqVfeun65+166dgtze81m7/fG+F081K12poWtX\n2Vxpi6dx9EE6y0NVXt9vctipTDaclYGrTlVYNszNj3ryZzNVkWSD2eK35pNJprhhsqFvfZCa\nSlMnG+Rfm7/U7V7JlJqCwvLrSPKq7HuqZqUr7YG0TNpiBqmYPJUrHdrT3/dmnjXRq0rbFdeS\nzq0+R/7bm3zVfLJQvM38jSOwPkid6i6nv+Vfm7+IaqatzlQ7BYUVfywPdk1WutLiVdKAtEza\nYgYpv+K3z7vA96N6Qfa2L64E5lOjOlVpl/xgJ/t4maA5hSVZ3RddifqT2XsJOOpbHyS1upsL\nsmmq/qWZsq4y1UlBaZfsdLS/5hc8lKx0pPLnRQVpmbTFAxJsq/Y0r1PmYwAJBiMwgASDERhA\ngsEIDCDBYAQGkGAwAgNIMBiBASQYjMAAEgxGYAAJBiMwgASDERhAgsEIDCDBYAQGkGAwAgNI\nMBiBASQYjMAAEgxGYAAJBiMwgASDERhAgsEIDCDBYAQGkGAwAgNIMBiBjQbpepI7lJ+fAxLT\nZrGem8g+TyM+BKut2gvbI1kDle2fh21kbuyXPFbpGXhuBhFIyuaDsBEmaksGSAJIYTbyS15E\nIgm6XwaejUwLEmysVfX3ke/D7fURvz+RfSJGG/klE1E+duaS7ydr8A2QOFhdf+77yQMkbxv5\nJZvKKV5d99Xzb9Pzrtp5ufhTdiAU+6v+w5ndsrFW8fQwg5v25uqKPHvnnIgdHqVnsW6u1Izc\nTvLZbR/N37IMJG+pUuHlkyTaSW0+Jq16rnXxmJb239JOuquy259RQhLiucsfttMUWUR1MbYn\nFjYSpE4vodj2Pz/eJc3QKf/i78Xvqlyt2as61NK6aYGkyuUjfi2jNFjaPyMpGfkQSjXLvxUZ\nOHVB6iRV+Vhup/KZYB/FI2E7WVHT3ZStfkYNKX9eyLlVZPpW/NXUnljYSJDu8rzwXg2PbnLE\n9MzhesufovGWPyRKfvFbflS57dXmrtTsTYi3Z/o8548DHXBTHQlVuZAPY3seq6fIwwxWj5GS\nvPmpGckfbJy1zV1Tv+/FEyNbIHWzoXwst1t+Cikestf9WyfdddnqZ9SQ8icst4tUo9K1JxY2\ntv96L6btjvn3OeWPYM/P1uUz2JRHSeW/P8tqS+s/FnYuDy2n4iBldFM9cUWVF4er5za64AHW\nzNrlhxw1I0rdlfV7yf+WtEHqZqNX5YXg2Tydr11+/VItW/lMO6SPxmMrqndTe2Jh41vhM3+2\ndJ4c5eHUmd3Kh06XOahMKbN5vSsnLe4KPTo35c+WvNWJhxmtvo6UN8dWRo6yX3EvVWmTgWN3\njNTOhvKxwq75Ae5Nnjh6f2unuylb+Yz6dqOui9xV8BjaEwsLjCcb+b212/Ilqb9nUS/1F2+9\nXxUvlBcDblonp7acXZVys7yCspPMtfq1aYrlE/Muaad+953q7WRD+VhpSVL96P+tlW4Fg+Yz\n6tuVutcAeu2Jl42Lp/kaz/rha4VdsnPU+f2u6wX4gNR1A5DGWznZVfaZOvV1PcmkvHXqt1O9\n/WzUHyvtnJ1YrmW/u/s3Xbrbn9F0V/pFGnqVXGxcXMpYT36zpOmT7UQzSVr8qX/B1tS1G3CD\nrt14KyvorVzY0MvI7VQfDEvpswNSPxv1x0qTQ529uKe6vykJapXdfEZ9u1SrRapdO117YmHj\nWmEzKZM/PfpUDvvrk9O1rvPiAaByskUpUx19Ftdzy8kGo5t62KnIAZKbVRW0KypPk5Gam2PR\nTC/FG7L5Xhumrm2Q2jV/FKfW+F8va5ddf0Z9u5XVa5n1Sx2Vrj2xsJGtcC+S96ye7+d86uwq\nkls5b72TX7qYqcwr45ZP+d8Sw/R3du4+F/PZtwE39QN9W3KA5GZVBd2K5VxqRoq56nM9gXfJ\n0lpcqpEpPj7LFHSTqnysMnn5KE9x/29Kgtqtof6M+nZ9RmqKLKa/i6h07YmFjWyF93015sv7\nwsV1PHmQuFTvf5R1Ul5CbV+QbcaLmguyXTc70Toq1nKA5GZ1BZ2KfoSSkfLqaXKvVEVa3+Qv\nxd/O5RiplVTlY7Xtyk5K/2+adJ/bn1HfbsZIdZHlBVmdBz42uhVej/ltFGWP9bITSfHdLvJS\n7ce1uUpxl+t4LupHWxMvrSVCWje3nTKh0V4ipPwHM1lTQUnRR1Iy8pGv57k3qvd6iVD6satf\ndpPafKy2d1HmuPe3VrpbraH+jPK2MmtXFVksHTt+mNoTC0MrhEVi/C7CqgaQYNwtHyLJS5b8\nzkONASQYd6uGSNwm6loGkGDs7SoXdu7flw5j0AASDEZgAAkGIzCABIMRGECCwQgMIMFgBAaQ\nYDACA0gwGIGNAukxbLa/x6RYMBCaBPvHsi7dtAUDJFcFQIpcB5B4KABS5DqAxEMBkCLXASQe\nCoAUuQ4g8VAApMh1AImHAiBFrgNIPBQAKXIdQOKhAEiR6wASDwVAilwHkHgoAFLkOoDEQwGQ\nItcBJB4KXyevr69UgSwNkvarOEXOSQeQeCg8nby+6ppflCDpv4pT5Jx0AImHws/J66u2+cUI\nkuGrOEXOSQeQeCgA0ojIOekAEg8FQBoROScdQOKhwBhpROScdACJhwKzdiMi56QDSDwUCway\nNEjr0AEkHgqAFLkOIPFQAKTIdQCJhwIgRa4DSDwUACly3bZAOon8v19fhPjya6SPaRQAyTJ7\nB5AYgXQSOUg/iwez/RzlYyIFQLJcTwJIviAlk4H0TUiQXl//JbKz0S/xeYyPqRSbB8m2wgEg\neYKUTAbSp0+/MpCyZBXnpeInQAJIJDpuICXTnZFOEh6ZrD/Ef8oz0tcRPiZTACSANKDzBimZ\nsmtXgfT6pxwinUb6mEaxeZAwRhrSBYD0V2Y0KVasBOkPCdKR3PumLby9YNbOrGvq2Q2kJJ36\njPTIT0jfH4/vxSkJZyQuZ6TIdaxASuof04H0qCcbPo3zMY0iKpCKFCXJ0MQQ94a/bpAKmxak\n9k+A5A9S0hzwzLni3vBXDVLazs1UIH0VP2TX7ss4H9MoIgIpSQHS3AXzBOl3sbLh9zgf0ygi\nAikFSLMXzBOkx++vQnz9PdLHNIqYQfprkhlWmNZYrLVjrIgZJG2uuJ9B1n9GAkgzKwAShQ4g\n8VAApMh1AImHAiBFrgNIPBQAKXIdQOKhiA8krGyYsWCA5KqICiSXXHFv+ADJJ6R4FAApch1A\n4qHYIEjFbRPcAQFIcSm2B1J5Ix93QABSXIrNgVTdWs4dEIAUlwIgBX+3ZXUAiYcCIAV/t2V1\nAImHYnMg2cdI7T0cABJAAkhas8zadXYVAkgACSCNiLm7zx1AAkgAaUTMAKn1y2pAKp9j8fj+\nWXz+PkEpAKljAKn1y1pAKp9jke/DL8R3jSKwFICUm4IOxkjqLysBqXqOxX+JL/88fhTPsaAt\nBSBJa8GDWTvll3WAVD/H4k+5kdckpQCkx/BG+gBpBSDVz7H4d/HfE5UCkB4AyaxbCUiPCqTs\n51fx+ecEpQCkB0Ay69YH0r/LyYZf9KUAJGkDj3ZJhx9WEVYugQ4guSmqZ/2d6s2OaUsBSLmZ\nabE8Pimw3HAdQHJTFLN2/6Zsv09bCkAaNtsD/aYqFyARK6rt95vXtKUApGEDSKsC6UfRtftG\nXwpAGjYykConAMknJGqQHl/ylQ1/05cCkCxGNEaq3QAkn5DIQXqcPokvvycoBSDZdCSzds2J\nDSD5hBSPAiDNogNI40KKRwGQZtEBpHEhESt+1PdQ/MoGS19+9RWjSwFI8+gwRhoVEq3iR30P\nxc/i4Zk/6UoBSDPpMGs3JiRaxWfxK0PoU5aLf8lVQr+q2ykAUkQgTaUDSJ6KfNGdaF2YBUgA\niTdIDO0q/sxA+kP8Z5p+iOPS0TCyWdsVQx1rkIJDIld8FV/z4eqfcoh0Iixlvi/Tux4DkCh0\nAMlLcfos/njNT0mZfSUsZbYv018hAJAodADJU/FD9u3+lHN336tTUkwgadasASSzOS6noC94\n/SDlsw3lZMMnulIAEked6wI/8oK7Oh1IHkgFh0Sr+CT+eRRzdfHO2q0XpAkutDovOacuuKcz\ngSQ6FgdIJ/E/5U0UX+VuQvWdsjGBtNox0hRLf/iDNDI53iHRKv75JKH/9Pfj8bvg/zddKZi1\nC9PVbX7LIMVyRnr8/U2Ib/nNSL+/CvH1d18xupSFBnz5ewBp0Culw7G6lYE0oQIghemmAYn1\nrF28XbspFQApUDfV7RGuOoA0oaI+ngGkgJgddRPdHgGQllc0PWyAFBBzHLrFQIpw+ttTocz5\nbAAk2DwGkJYLxKKgSbB/LOvSLdq1O+7vaXrfG+9GCA5pScUrQHKxteiWBOkonsU7JpKCQ1pQ\noXIUN0jGDgNAmq3gYZDKDD1X2bVrXRUHSAExD+g0l3m2CNJeFF27NZ+RXH0wBsnN/GMx6AzX\nQHX+dAsPtgjSPSmmGpL7CkFqZRkgOUdkWpWj8addCrdFkNLneSfE7u3pmhzvkJaetXP3AZAK\nM64TBUgDIPkmxzukeBTcQHK5OgGQZisYILkqtgpSFwQfkDBGKjKVWjMVHFI8Cm4gSbNd5iMA\nqY+CxxgJs3YAiVMgRo5sl/nCQdKdfzxm7XTizYE0JjneIcWjYAiS9TLfNCB5RK47fQEkgLSQ\nwlT71st8i4Ok+/gmQbokcuPf5A0gcQTJepkvHCSPe7kBkhmkS9ZpyLNlIik4pHgUDEGyXuYj\nAMn9Xm6AZAZpJz6yf5ebSAASR5C8zD8WAh3GSMUrkV7FDpufLBxIJCBpz12YtZOWiPtJ3OQo\nCSBxA8nl6gQJSK59O9fR1BZBepMjWZmzM0DaKkiufDjP720RpPQskmt2YjJxBJBmUbhgMhVI\nznwApCGQfJPjHVI8CoBEJARIAGkhhbH6bZf55gQJY6RBkC7HrAO+vwEkjiBZL/MRgOR+RVY/\na6fRuZW7KpCeu3wkK8QHQGIIkvUyHwVIzrN2SwESA0gncZazQ+9iX7+VZAaQ5lWY0LBe5iMB\naS26JUGSGar+lRzVPwDSbAoTGtbLfN4gvYyKOQ4dQOKhYAiS9TKfN0iPlxIlv+0sotAx6Nqd\nxamVEoA0r8LIhu0ynz9IJUrqDAN3QGIA6alfp1+A9FdmThmcwLI0z/ixdZhze3l5ac15cwck\nBpCy3oNcp39W1+lzmGxoT8k6+xiYyY3zjDQNSBKlHCbHmF/HPvdoeD5wXSBpbemuXecioauP\noWuLkYJkucw3FiRZTRlKbjFXlerd/ixXqNYE0r49NgJIyyhMaFgv840FqaiqcuLBEnNdq77t\nz7ZmYk0gJb2TE4tZO4BUmOYyHxFIZa/rxSFmgFT8MgjSbX/ubAfAAiSMkcpE9a5OkIFUmjwp\nASQX3TBImhteeKxsaCXA0cdg3gCSwV5eMEZy0XmDNJwc75DmUwQf/hyKsS5Lm6Rr173MRwpS\nc43WaJi1e4yctYsRJEtHggQk+0Jp4skG6u24tOGndpQcv9uyOoBEopgBJFun36kYH5B0l/lC\nQNKHL3UuKAEkgBQtSD5mLckQf6GzkwSQNgHSDGOk2UEyXOabBCT7SQkgbQOk4aFtlGOk/mW+\nCUHKFw6FfbdldQCJh4LhrF3/Ml8QSANjpMqGSAJIAIkKpIkUpton39fOMGun2MBZKSaQ6C9g\n2UDaxuYn1jPJXIEsDJJTRCaSIgJpgiUVwyBtZPMT+9hmpkAMChdM5gPJRFI8IIXP4PZ1wyBZ\nV0X6FMVW4TDbNk8gJsVYdIaXc41vqHqSGt1LYc7+5tEtCZJ1MZdPUWwVawXJssA4oKFqIUmr\nv73UKp0BJICkfmKKQEwKdiBpT0pSZ5khfzwavMaVG6BbcoxkXRXpUxRfhe8YyaTfEEiak5Ln\nmjwrTmuatbOuivQpirHCb9bOeAZjC5Lcp+avQ2byF1b/ZyyxiIPg/8pcNz9p2STtZgGF1xkp\nPpC0uQo+4rfOJ/b7lkz+TCemKc9I9DobSBYLDomHwm+MxAekczJ0HWlqkIqRUl4TDnfSDvrT\ndfMAkk9ILBRusw38xkjn4QuyE4JUff9yCzyXvR3s5XZoWhVIx/y12K17jOQLEpdZu0RcTH+a\nFqSmuuSuXeXGXTTttGFpTSCdi2Od2MKsnQ9I0yr682EGsywNmgwkpb7k6eh17K3mBnPbBszd\nn69wApCSYm3Qbd3XkXzHSNMqNCNvEylHMXxv7FQrG9ogvVKDVJ6W1gRSBdCqQNIxw2atnX7N\ngMHuyZ70NgrXmNUzePOKtp26XLL18echnACkozhlx7zneU1r7fRnH4fKmyFUwwIaExrLrP5+\ntOuwfkXeTp3WP8QB0r26IGu6jyI4pNkVhvEQB5DyZhMFSNoz+CTt1IGlGEDKTkb5BVljByI4\nJIBU2cAQ2wWTeUGaU2djKQqQfJPjHRJAKmxw0nfbID0sLAGkZRQcx0hKO/ECqewwUO1rN/Zb\nzaELv79pUZCG16BECZLDxgQzBVJZq4n4gHSn3ml15LeaSWeYfIgBJMsalDhBGqmYqphO4/AB\n6STk9Pd9P+3e36x0GpZiAMmyBgUgBSuclmqaat96mW99ID36LMUAkjVDwSHFo5iiGN0qBt2n\n5gap1fsla390KyBGLW5dEiTbGhSAFKJw7vGban+qrl17PkanGzNfU3mlac/NgCkGkKxrUIJD\nikdBXYxhForBZEPnCoFGN+YKQu2Vrj2/eK3JW7Zrh8mGSYrxm8w1sjHN9LcVJP21uPlBkua+\nJg8g8VAQFuN7edGJk42C9NCulw9zOEY3DBJsAnt5WaZc9/ZiGyONAol4jNTSMXikYFPPW1nZ\noDOXuyiIArHl3OuMdEnS9EMkb8QgWWftxq2yor9vqdEt/khBC0jnTXTtnO7rowhk5FpmI0dZ\nXvIJByeS/L+PWTfb6m93nb1uFwRpGysbDOtYyQNx2q7XB6Sd+Mj+XW4iMSmmAomnjmiZ+AQg\nJeK2F/fnft1Po5gHJOPdRnYfJjSy49tV7La2smFIN+vWrV6zdumbuKbPNd0h27cZQHoZuNvI\n7sOERiLuJ3GToySAVNkASt4OLY3CD6SrXG637q4d2RjJ4EXpvdOC9CYvxsrsnAFSY8bBkq9D\nW7PwAOko3u9Z3+FjrSAVNd48cSSsGF3Fd9zSgpSNYZNrdmJy4mgzID1MpyVPh9aOigdIkqC9\nnGtY5752L83mnuUc6hBStmL6FT9mG14vkLzM9/tErdOlcEGQ0utOLo00dx3cQmKqGDhV6Ihy\n6R8qFa/FkRakvdNi1U2CpH/ujJdDUpB8k+Md+oKKF6vi0SLKacai3hPbfUmqXWGq/cRrDYp/\nLJHr6iSMvBJMOEZaM0ied4cNd/taHA2MtmhBuu3N+zsBJGkv5fMyxq1NIpq1yx9nHvcF2aom\ndONR31JqiZammqPQp9D5gLTcvnbx6MrnZUyyWrb1y5pBqs/NfcWIh/G0JC+dM1SZreCZP4BU\nnwaoyi1RWg6kzEwbrMYBUtPZ6inGDEStwyiSXUF5z9oNdXZo2mkzMKFq9/Lw9rIwSGJ3XSVI\nblsl+Emykqb6MmxAGhx+k7RTZcaGrN0XXYXxz+Z00g2DtBMieRu6/zI4pEVAGneNYbkvY6x+\n2+J8YpCGJ4TZglSdRye9AXAYpPQud4g8mpasMgfJNEYaucsgP5Csi/MBUls44Q2AFpAy+8jS\ntXuPEiT9rN3Y5SP8QLIuzo8PpMmet1QKXbZ4mAik7LQU66ydVjH2SilDkKyL84lBmmGMNOHz\nlirhNDcA2kH6OGVnJNOGq8Ehza4YvzKYJUjDi/OpQZph1m46XSOc4gZAC0j5GOkU6xhJowh4\npAE/kKyL88lBilnXOpxSPCfGb9Zud4l21q6vCFm7wxAk6+L8WUFqzlYeS9Rm1HWnnELaQl83\nDJI4xnwdqasIPArxA8m6OH9OkJrxk8+i6fByx4Jkbg+p265SPiBZt/D0DH1ZReD9/AxB8jL/\nWHx0zYze8NweI5CMNwDaln3r/A2DlKaXo+w+GJcKeYe+nGJ4hAmQAmKWFiVI+lZh+wJaf8Mg\nPXf51T4R/y5C4atJWYJkOc4BJLuwP+8wAUinrPudZeo9+l2EKJ4xP0GomnT5gGQ9zs0IUoRj\npNo6KE0AkpxXrf5FDFLnTslxpUwQqi5fPiBZj3NzghTbrF3L2nsK0I+RVgFSvcHpQP14H4Ao\nFNojnw9I1vTMChJ33bCw9WB58lm78pB3jnkXoXqD04EzttMhKHaQYMP2QvSYEO1kg+2RcLQt\nawKFstmWGSS3TjE/kKzHucFccTmDmGp+3jNSbkpr8fU3DFKavg0/Ei449BCF/SGiLQU7kILH\nSNbjXAwgGat+AZCKFjMJSJ7J8Q49QPFiOYL0dgoe7tkFgzRinBU4a2c9zkUAkrnuFwFJNptt\ngfRSbzesVWj3dhyaa/AeI3U/MeE4yz0fAIlAOOpO2gGQhGrcQFK+q+Z7d9+yluJ/Nummf8ru\nIUCiKNfDodsTnlcAUvt7dp7Gq6kFijgsEwXzg3TbC3GKf4NIXmOkWud7J+0ASNKO+yxT9/3R\nNTneoY9RaM43zVvaClgjSLfiCOe2PIgCJNu3W8GsXUvndyftMEhHUYxjhYmk4ND9FYbzzeDO\ncjOANPsYSc59Zz/c99H3j6Wls34/Jmc4QoeWHp4HSGWX7ql09T66YQAADsJJREFU7ZLMFgSJ\n7hEPvgrr1PWIWTtHhZaLPCdPt4f1EYBkP+OuD6SHxz1swyDtRdG1a85ISf1jCZDGPkBqApBc\np64pFGaQfBY1+Mei6jYKkvOdtMMg3XtX/BYFafTGJVOANKMCIFHoRjt0epTmMEjp8yyv+HU3\nW10GJOOhYdDHq9uuzwDJFtEGx0i16QdLPiDprQDpr8ycsxhsI1cU5uknDoWHzQ3SZLN2c+nC\nHGpQCgZpgcmGsQ8dKjsk6zwj2S/zkYIUuy7U4fADgRcEyb3TNX5DP4AEkOgcdtphKEjqhGtA\n6EWf26FlhWwxu2KQvM0/lnXpKBy2BkuBILUuXIwPybWJ25dqDPbdARJAonXYvaeg+sUXpPYF\nwPEhOYKkX9vdc2Qrx+IDIAXFHIeOzKH2PjhfkJKktbRhfEhOIDncZmW7vuG2Bg4ghcQch47Q\noeY+ODeQplj9bR8jOd34C5AA0qD9+Cw+f89fvdevwguudwKpP7QYSLZZO/uN5JUXgASQjPYj\nn9X83no1RcFuIDknxzskk8IwpNOYjROMkTYN0mfx6/FTfMqawb/KV9MUzBMk4ySjzmyczDdr\nZyd2GpBwHcliQlSHUyEmKpgjSJ37X0f5oFc4EW0jCSAtofsp/iw7+D/FaaKCB0Ba6FZz7/0W\nuIDkMhpD124B3VfxtcxN9mqqgrmBNM1+CwBpyyCdPos/itxkrxxImqJrN/OeDcvd/wqQAmJm\nr/tR9O3yV/a+3QQgzbtnw5L3v8Y3RsKiVQ9dPtuQFq+mKXgYJM2eDVOBZNxoIh6Q5p21A0hO\nuk/in0eBT/NqioKHQerv2TARSANLUyMCaSqFkQ1bzxsgPU7ifx6P7+Kb+mqKgodB6u/ZMAlI\n427bq4//mwXJ2vOeCaQiEzxB+ueTbMCf/s5eJdWrKQoeBsmwZwMtSCNv22tGJJsFydrzngek\nMhM8QXr8/U2Ibzk99/rVBAVbQPJNjndIo2/bU+bINguStec9C0hVJpiCNFPBC4Pku8NyY14g\nUcwCMATJ2vMGSLMVPAxS1WdITPt5hoVkeCSLkw8fkEjmpRmCZO15TwVSqzoBUv6LGaRk4pUN\nL717Orx8uI+RaK6UcgTJy/xjMek61cl7jDRTwQMgXRSOLuQglZ26gLbnPGvHAKSweRFmIPXq\nk/Os3VwFD4CUOiwrHhtSPTaa4zC/PEiB8yKm2rf2vGcCKdDfTLr1TTaoixhm6S8tPUZqWh4h\nSC49b4A0W8FDID3P8tV7Io7GWaERIS1xt9HCs3aTgOTS854GJMOBCSCZQErkke4jn181zQv5\nhtRbUbfcCN5XwQ2k1Gvjb1KQ9AcmgGQA6SL2GT+7fZqe5cPhCECybUTu4mMxBcMxkp/5f591\n6RYDaS/kZXP5bEXzc+F8iprs6a78QZpk1s6h5w2QZit4AKS84/Cen4zCryNN+XTXCEAKU+jr\n3qHnDZBmK3gApET+f84fmx0KkuPjA1kruIHk0vMGSLMVPABSvkh/t0vlYW8fApLzA21ZK7iB\nNNjzLt4YfnA294a/GpAuWZKu4i1L1D5gZYPPI9ZZK7iBNNTzTppH/Zr3aefe8FcD0jMpu99C\n7AwcWUEKebYRMwU3kAZ63kkKkOYueACk9LYrJoQGuuDDJY2/SYKhghtIgz1vgDR3wUMgOdhA\nMS8hN0kwVHADabDn3QHpr3kfnL1towXJ6ZEsUSm4gTTY88YZae6CTSCJjnmB9OL4SJaoFNxA\nGux5A6S5CzaB5Gg6/3Ov7d4qSBqr57sB0twFk4PUmmDgAsFWQGqIAkgzF6wFyfEGF429vIz8\nIIzUANLcBTd132bH8X6x4JDiUcR3RsLKhhkLNoHkiFNwSOMVzT0xAMnJ/GNZl25JkBqc2IGk\n3KUJkOYEyXi3MUCyg+ScHO+QxirUfQMA0owgmfe/AEjDIPHs2gGkZUAa2JEJIA2BZJlwCA5p\nTpCGtkABSG4RASSDzgqSX3K8Q5pxjDS4KRdAcosIIBl0VmSYnpH8Z+2Gt4kESI4RYYyk17kS\n45acbh0v1/b6CoDkHYtWt/FZux+fxefvle5L8xxNUpB6LRUgkShYgRStjsThj/zKakFS+l1M\nA1K/qTICybIBOEAKiDkOHYnDz+LX46f4lJ+Xb4IJSK9OD9Vxn0oYVAAk/1jWpaNzKETelnaf\neICUi0k7bkMKgOQfy7p0ZA5/ij9lU/rf4sdEIPmNkSwt2/lruWJiRY0kkEkUAIlCR+Xwq/gq\nm9J/iT8eU4HkNWs3N0jWzh9JIJMoABKFjsrh6bP4I2tL//Zv/286kHxCmh2kwFIAUuQ6Ooc/\nsr7d/xL/kfIAaeYxUnApAClyHaFDIV7r3UyqDy0H0ryzdsGlAKTIdSQOP4l/HhKkByeQ4lIA\npMh1JA5P4n8ej+/iW6Hj0bWLTAGQIteROPznkzwLffq70AGkEQqAFLmOxuHf34T49nepA0gj\nFAApct20BQMkVwVAilwHkHgoAFLkOoDEQwGQItcBJB4KgDRgbpcE6cv10QEkHgqAZDbHRSrk\n5XrpABIPBUAymuuySepy/XQAiYcCIBkNIAEkdwVAMhpAAkjuCoBkNoyRAJKzAiANGGbtAJKr\nAiBFrgNIPBQAKXIdQOKhAEiR6wASDwVAilwHkHgoAFLkOnYgKc/3BUizKAAShY4bSEnUIJmu\nGwKkgJjj0DEDKYn6jGS8Ag+QAmKOQ8cMpKi7dua1LAApIOY4dGxB+iszmhTPZiVIS4exjM3a\nrhjq2IKkS453SDgjOSgAEoUOIBEqMEbyiWVdOoBEqdjwrB1sHtsGSOMV0YPkH8u6dDgj8VAA\npMh1AImHAiBFrmMH0lByvEOKRwGQItcBJB4KgBS5DiDxUACkyHUAiYcCIEWuA0g8FAApch1A\n4qHYFEjqdWvugACkuBRbAqm1koo7IAApLsWGQGqv7eUOCECKSwGQgr/bsjqAxEMBkIK/27I6\ngDRG0V7lDZC8QMIYyV+3UpA69x0BJD+QMGvnrVsnSN07YQGSJ0hr1AEkfwVAAkgzFwyQXBUA\nKXIdQBqhwBgJIM1b8EpBwqzdikESuXn7A0g8FACJie43QOKh0G4k5AvSSCcAKVz3U5w8++1E\nBQ/qtgeSfms7T5DGOgFI4brv4v96ziQRFTyo2xxIhs1W/UAa7QQgheu+if/4Q/zrv7xXXgAk\nUgVAco+Fp+5rMUb6b19/AIlUAZDcY+GpE+I/Xl//j/hfvv4AEq0CYyTnWLjqZOU303YAaSEF\nZu1cY2GrA0gRKxYMBCCp9ilHSHz29QeQeCgAEhPdN/FdzoF/9/UHkHgoABIT3d+f5KTdZ6uO\nvOBBHUByVQAkLrq/vwpx+sfbH0DioQBIkesAEg8FQIpcB5B4KABS5DqAxEMBkCLXASQeCoAU\nuQ4g8VAApMh1AImHAiBFrmMNUrD9tSIffAJZpCTu/uYKECCxcAKQpvIHkGLxwSeQRUri7g8g\nxeKDTyCLlMTd37pBgsFWZgAJBiMwgASDERhAgsEIDCDBYAQGkGAwAlsApCQ3AicUgYS6IHCU\ndP6nNzW+6nVIzCZ/Yx12/SWB8akfTlSHAR57vjvuljojBbaahIePpPER0Crb/9ObGl/1OiRm\nnb+QimzF0vUb7LB6HVS9Cujlj24ZC4FEciZYHKRE8TE+6Unr/wls8yAFZ1pJDkCa2Ed4ryFK\nkKo3wppp2o5PV8ZYh9VLikSrvnmAFD68IfACkChBChkiqfFVI5qA+LofTlTHIS5V3ysBiWqi\nINQJQEqVAz5hV5EepBCH6wWJ4mwiYcQZKdBf6yV1V5ESpM6rUS5V3yxAIjgh0bhB1y7Un+FV\niD9qkHTIj3Op+gZIE/nYLEiJ5r1l4yMPsOcNIE3nY6sgKX4p2n2i/CMHKaCG1woSVjZ4lZA0\nV44pVzYkaXvhAJf4Wg5rRAmSNBAg1trBYAQGkGAwAgNIMBiBASQYjMAAEgxGYAAJBiMwgASD\nERhAgsEIDCDBYAQWB0iisNNtQFH8q+2iu4wtBr7u0N9gBLbuCo7jy4nKjCT1QNKmDSAtaOuu\n4Di+XJmDs9hbFANvGN90+BuMwNZdwXF8uSoHxXnnlmQ8PU9ZV+8p37zvxbE5I92PIjkX57BU\nq5Lvil3+/y47w30cRf4B5ZzW+eRbInaXOb/tSk0BqaxcNQ91fZf5rfMiM7e7dnLCz2IEaS9O\naZpIVGQenvLVscIg/y1HJv+MRiXtKO7Zz3v2l2vRZTz3QKo/ec4FICnYFJCqym3y0NR3kd8m\nL0VG2zlhaDGBlB2QTvK1PE69yZ9n2b5lf++5rzA4Z5KPmgmdSto193EW1+xg+J6mt+oDDUjN\nJ0WW7A8x3T0Om7EGpLpymzyo9S3fa/LyVmeu0TC0WEAq7V40bFnR+ftH+eouD2olBjvxrD5i\nUOW2K+4skS/v17d9H6Tmk4k4Xef8rqu1BiQlLVUemreK/DZ5aTLXaBhaTCAl+fR31dirE37T\n+Fszd/kLnSq3i/jITjNv2au9qlB9Ve9fsw7F7j7r112nNSA1lVvnoZupdl66OWFoPKPqmlp5\nJCA9sx7gWZ68TmJ3ud6HQMp6GDuRfMzyPVdtOpDqPHQz1c4LQCKyPkg7pZ9g69q1VYWdxD3v\nIxSzQS2QlG5EZRem2YvKul273Ko87ERb1uSl27Vjapxja6wP0lkOPN/ldaVsMPpMlcmGczN3\noFUV9pEd2T5ybx/KVEWSDXGL35pPJpnihsmGcGsqv6ncOg/NWxVIH3Um9mnayQlDixWkclL0\n1p3+vldTpEK2fZ2qtF0xj3pudSny396aaXT5yULxNvM3XqEpHbq6cus8NG9VB8qOuPMxdhYr\nSOn9JMQ+H7ncj+oF2Vt28DrdZW8s0atKu8j51VT2LTJBcwpLMmSKDl79yey9BByFmzLEaSq3\nzkP9VpmjOi/5Bdn37sfYWRwgwbZu7PvWAAnG2/IxlLzOztsAEoy3laMl7lfyABKMuV12xaiX\ntwEkGIzAABIMRmAACQYjMIAEgxEYQILBCAwgwWAEBpBgMAIDSDAYgf1/Xzeu+wSzcB4AAAAA\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- glm(crime_tot_per_lga ~., offset = log(pop_total), family = poisson, data = cluster_3)\n",
    "\n",
    "summary(model)\n",
    "autoplot(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>pop_total</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>male_lga</th><th scope=col>female_lga</th><th scope=col>female_lga_perc</th><th scope=col>male_lga_perc</th><th scope=col>employed_lga</th><th scope=col>unemployed_lga</th><th scope=col>employed_lga_perc</th><th scope=col>unemployed_lga_perc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>crime_tot_per_lga</th><td>1          </td><td>0.1799817  </td><td> 0.85331519</td><td> 0.36634118</td><td>-0.05574115</td><td>0.90217259 </td><td> 0.87774177</td><td> 0.85621089</td><td> 0.1875020 </td><td> 0.11117483</td><td> 0.86588448</td><td> 0.8497058 </td><td> 0.2852929 </td><td>-0.15165114</td></tr>\n",
       "\t<tr><th scope=row>crime_rate_per_lga</th><td>0          </td><td>1.0000000  </td><td>-0.05880183</td><td>-0.05676528</td><td>-0.61943421</td><td>0.07488728 </td><td>-0.04210267</td><td>-0.05905993</td><td>-0.3225200 </td><td> 0.12461711</td><td>-0.05131925</td><td>-0.0495612 </td><td>-0.3172114 </td><td> 0.18803770</td></tr>\n",
       "\t<tr><th scope=row>pop_total</th><td>0          </td><td>0.0000000  </td><td> 1.00000000</td><td> 0.30227463</td><td> 0.20435198</td><td>0.82313208 </td><td> 0.99730212</td><td> 0.99875305</td><td> 0.2506011 </td><td>-0.14372733</td><td> 0.99112690</td><td> 0.9864222 </td><td> 0.3444699 </td><td>-0.25293818</td></tr>\n",
       "\t<tr><th scope=row>pop_dens_km2</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 1.00000000</td><td> 0.13569647</td><td>0.61474212 </td><td> 0.33489277</td><td> 0.32531817</td><td> 0.5327030 </td><td> 0.21970526</td><td> 0.36597913</td><td> 0.2774621 </td><td> 0.6210650 </td><td>-0.32632972</td></tr>\n",
       "\t<tr><th scope=row>seifa_value</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 1.00000000</td><td>0.10199514 </td><td> 0.18144741</td><td> 0.19912232</td><td> 0.1865389 </td><td>-0.42192315</td><td> 0.23545380</td><td> 0.1285134 </td><td> 0.6735699 </td><td>-0.68115948</td></tr>\n",
       "\t<tr><th scope=row>num_service</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>1.00000000 </td><td> 0.85392499</td><td> 0.83420421</td><td> 0.2967099 </td><td> 0.16415734</td><td> 0.86877257</td><td> 0.7947911 </td><td> 0.4928160 </td><td>-0.29593902</td></tr>\n",
       "\t<tr><th scope=row>male_lga</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 1.00000000</td><td> 0.99844728</td><td> 0.2679523 </td><td>-0.09496526</td><td> 0.99297527</td><td> 0.9867037 </td><td> 0.3542594 </td><td>-0.24128272</td></tr>\n",
       "\t<tr><th scope=row>female_lga</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 0.00000000</td><td> 1.00000000</td><td> 0.2798588 </td><td>-0.12576754</td><td> 0.99185071</td><td> 0.9882303 </td><td> 0.3549218 </td><td>-0.24679037</td></tr>\n",
       "\t<tr><th scope=row>female_lga_perc</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 1.0000000 </td><td>-0.04796597</td><td> 0.26592276</td><td> 0.2792150 </td><td> 0.3395610 </td><td> 0.05053036</td></tr>\n",
       "\t<tr><th scope=row>male_lga_perc</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.0000000 </td><td> 1.00000000</td><td>-0.11372358</td><td>-0.1036547 </td><td>-0.1617591 </td><td> 0.44211818</td></tr>\n",
       "\t<tr><th scope=row>employed_lga</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.00000000</td><td> 1.00000000</td><td> 0.9621733 </td><td> 0.4250518 </td><td>-0.31765536</td></tr>\n",
       "\t<tr><th scope=row>unemployed_lga</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 1.0000000 </td><td> 0.2566786 </td><td>-0.14284972</td></tr>\n",
       "\t<tr><th scope=row>employed_lga_perc</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.0000000 </td><td> 1.0000000 </td><td>-0.86475150</td></tr>\n",
       "\t<tr><th scope=row>unemployed_lga_perc</th><td>0          </td><td>0.0000000  </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td>0.00000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.00000000</td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.0000000 </td><td> 1.00000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & pop\\_total & pop\\_dens\\_km2 & seifa\\_value & num\\_service & male\\_lga & female\\_lga & female\\_lga\\_perc & male\\_lga\\_perc & employed\\_lga & unemployed\\_lga & employed\\_lga\\_perc & unemployed\\_lga\\_perc\\\\\n",
       "\\hline\n",
       "\tcrime\\_tot\\_per\\_lga & 1           & 0.1799817   &  0.85331519 &  0.36634118 & -0.05574115 & 0.90217259  &  0.87774177 &  0.85621089 &  0.1875020  &  0.11117483 &  0.86588448 &  0.8497058  &  0.2852929  & -0.15165114\\\\\n",
       "\tcrime\\_rate\\_per\\_lga & 0           & 1.0000000   & -0.05880183 & -0.05676528 & -0.61943421 & 0.07488728  & -0.04210267 & -0.05905993 & -0.3225200  &  0.12461711 & -0.05131925 & -0.0495612  & -0.3172114  &  0.18803770\\\\\n",
       "\tpop\\_total & 0           & 0.0000000   &  1.00000000 &  0.30227463 &  0.20435198 & 0.82313208  &  0.99730212 &  0.99875305 &  0.2506011  & -0.14372733 &  0.99112690 &  0.9864222  &  0.3444699  & -0.25293818\\\\\n",
       "\tpop\\_dens\\_km2 & 0           & 0.0000000   &  0.00000000 &  1.00000000 &  0.13569647 & 0.61474212  &  0.33489277 &  0.32531817 &  0.5327030  &  0.21970526 &  0.36597913 &  0.2774621  &  0.6210650  & -0.32632972\\\\\n",
       "\tseifa\\_value & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  1.00000000 & 0.10199514  &  0.18144741 &  0.19912232 &  0.1865389  & -0.42192315 &  0.23545380 &  0.1285134  &  0.6735699  & -0.68115948\\\\\n",
       "\tnum\\_service & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 1.00000000  &  0.85392499 &  0.83420421 &  0.2967099  &  0.16415734 &  0.86877257 &  0.7947911  &  0.4928160  & -0.29593902\\\\\n",
       "\tmale\\_lga & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  1.00000000 &  0.99844728 &  0.2679523  & -0.09496526 &  0.99297527 &  0.9867037  &  0.3542594  & -0.24128272\\\\\n",
       "\tfemale\\_lga & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  0.00000000 &  1.00000000 &  0.2798588  & -0.12576754 &  0.99185071 &  0.9882303  &  0.3549218  & -0.24679037\\\\\n",
       "\tfemale\\_lga\\_perc & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  0.00000000 &  0.00000000 &  1.0000000  & -0.04796597 &  0.26592276 &  0.2792150  &  0.3395610  &  0.05053036\\\\\n",
       "\tmale\\_lga\\_perc & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  0.00000000 &  0.00000000 &  0.0000000  &  1.00000000 & -0.11372358 & -0.1036547  & -0.1617591  &  0.44211818\\\\\n",
       "\temployed\\_lga & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  0.00000000 &  0.00000000 &  0.0000000  &  0.00000000 &  1.00000000 &  0.9621733  &  0.4250518  & -0.31765536\\\\\n",
       "\tunemployed\\_lga & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  0.00000000 &  0.00000000 &  0.0000000  &  0.00000000 &  0.00000000 &  1.0000000  &  0.2566786  & -0.14284972\\\\\n",
       "\temployed\\_lga\\_perc & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  0.00000000 &  0.00000000 &  0.0000000  &  0.00000000 &  0.00000000 &  0.0000000  &  1.0000000  & -0.86475150\\\\\n",
       "\tunemployed\\_lga\\_perc & 0           & 0.0000000   &  0.00000000 &  0.00000000 &  0.00000000 & 0.00000000  &  0.00000000 &  0.00000000 &  0.0000000  &  0.00000000 &  0.00000000 &  0.0000000  &  0.0000000  &  1.00000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | crime_tot_per_lga | crime_rate_per_lga | pop_total | pop_dens_km2 | seifa_value | num_service | male_lga | female_lga | female_lga_perc | male_lga_perc | employed_lga | unemployed_lga | employed_lga_perc | unemployed_lga_perc |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| crime_tot_per_lga | 1           | 0.1799817   |  0.85331519 |  0.36634118 | -0.05574115 | 0.90217259  |  0.87774177 |  0.85621089 |  0.1875020  |  0.11117483 |  0.86588448 |  0.8497058  |  0.2852929  | -0.15165114 |\n",
       "| crime_rate_per_lga | 0           | 1.0000000   | -0.05880183 | -0.05676528 | -0.61943421 | 0.07488728  | -0.04210267 | -0.05905993 | -0.3225200  |  0.12461711 | -0.05131925 | -0.0495612  | -0.3172114  |  0.18803770 |\n",
       "| pop_total | 0           | 0.0000000   |  1.00000000 |  0.30227463 |  0.20435198 | 0.82313208  |  0.99730212 |  0.99875305 |  0.2506011  | -0.14372733 |  0.99112690 |  0.9864222  |  0.3444699  | -0.25293818 |\n",
       "| pop_dens_km2 | 0           | 0.0000000   |  0.00000000 |  1.00000000 |  0.13569647 | 0.61474212  |  0.33489277 |  0.32531817 |  0.5327030  |  0.21970526 |  0.36597913 |  0.2774621  |  0.6210650  | -0.32632972 |\n",
       "| seifa_value | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  1.00000000 | 0.10199514  |  0.18144741 |  0.19912232 |  0.1865389  | -0.42192315 |  0.23545380 |  0.1285134  |  0.6735699  | -0.68115948 |\n",
       "| num_service | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 1.00000000  |  0.85392499 |  0.83420421 |  0.2967099  |  0.16415734 |  0.86877257 |  0.7947911  |  0.4928160  | -0.29593902 |\n",
       "| male_lga | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  1.00000000 |  0.99844728 |  0.2679523  | -0.09496526 |  0.99297527 |  0.9867037  |  0.3542594  | -0.24128272 |\n",
       "| female_lga | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  0.00000000 |  1.00000000 |  0.2798588  | -0.12576754 |  0.99185071 |  0.9882303  |  0.3549218  | -0.24679037 |\n",
       "| female_lga_perc | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  0.00000000 |  0.00000000 |  1.0000000  | -0.04796597 |  0.26592276 |  0.2792150  |  0.3395610  |  0.05053036 |\n",
       "| male_lga_perc | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  0.00000000 |  0.00000000 |  0.0000000  |  1.00000000 | -0.11372358 | -0.1036547  | -0.1617591  |  0.44211818 |\n",
       "| employed_lga | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  0.00000000 |  0.00000000 |  0.0000000  |  0.00000000 |  1.00000000 |  0.9621733  |  0.4250518  | -0.31765536 |\n",
       "| unemployed_lga | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  0.00000000 |  0.00000000 |  0.0000000  |  0.00000000 |  0.00000000 |  1.0000000  |  0.2566786  | -0.14284972 |\n",
       "| employed_lga_perc | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  0.00000000 |  0.00000000 |  0.0000000  |  0.00000000 |  0.00000000 |  0.0000000  |  1.0000000  | -0.86475150 |\n",
       "| unemployed_lga_perc | 0           | 0.0000000   |  0.00000000 |  0.00000000 |  0.00000000 | 0.00000000  |  0.00000000 |  0.00000000 |  0.0000000  |  0.00000000 |  0.00000000 |  0.0000000  |  0.0000000  |  1.00000000 |\n",
       "\n"
      ],
      "text/plain": [
       "                    crime_tot_per_lga crime_rate_per_lga pop_total  \n",
       "crime_tot_per_lga   1                 0.1799817           0.85331519\n",
       "crime_rate_per_lga  0                 1.0000000          -0.05880183\n",
       "pop_total           0                 0.0000000           1.00000000\n",
       "pop_dens_km2        0                 0.0000000           0.00000000\n",
       "seifa_value         0                 0.0000000           0.00000000\n",
       "num_service         0                 0.0000000           0.00000000\n",
       "male_lga            0                 0.0000000           0.00000000\n",
       "female_lga          0                 0.0000000           0.00000000\n",
       "female_lga_perc     0                 0.0000000           0.00000000\n",
       "male_lga_perc       0                 0.0000000           0.00000000\n",
       "employed_lga        0                 0.0000000           0.00000000\n",
       "unemployed_lga      0                 0.0000000           0.00000000\n",
       "employed_lga_perc   0                 0.0000000           0.00000000\n",
       "unemployed_lga_perc 0                 0.0000000           0.00000000\n",
       "                    pop_dens_km2 seifa_value num_service male_lga   \n",
       "crime_tot_per_lga    0.36634118  -0.05574115 0.90217259   0.87774177\n",
       "crime_rate_per_lga  -0.05676528  -0.61943421 0.07488728  -0.04210267\n",
       "pop_total            0.30227463   0.20435198 0.82313208   0.99730212\n",
       "pop_dens_km2         1.00000000   0.13569647 0.61474212   0.33489277\n",
       "seifa_value          0.00000000   1.00000000 0.10199514   0.18144741\n",
       "num_service          0.00000000   0.00000000 1.00000000   0.85392499\n",
       "male_lga             0.00000000   0.00000000 0.00000000   1.00000000\n",
       "female_lga           0.00000000   0.00000000 0.00000000   0.00000000\n",
       "female_lga_perc      0.00000000   0.00000000 0.00000000   0.00000000\n",
       "male_lga_perc        0.00000000   0.00000000 0.00000000   0.00000000\n",
       "employed_lga         0.00000000   0.00000000 0.00000000   0.00000000\n",
       "unemployed_lga       0.00000000   0.00000000 0.00000000   0.00000000\n",
       "employed_lga_perc    0.00000000   0.00000000 0.00000000   0.00000000\n",
       "unemployed_lga_perc  0.00000000   0.00000000 0.00000000   0.00000000\n",
       "                    female_lga  female_lga_perc male_lga_perc employed_lga\n",
       "crime_tot_per_lga    0.85621089  0.1875020       0.11117483    0.86588448 \n",
       "crime_rate_per_lga  -0.05905993 -0.3225200       0.12461711   -0.05131925 \n",
       "pop_total            0.99875305  0.2506011      -0.14372733    0.99112690 \n",
       "pop_dens_km2         0.32531817  0.5327030       0.21970526    0.36597913 \n",
       "seifa_value          0.19912232  0.1865389      -0.42192315    0.23545380 \n",
       "num_service          0.83420421  0.2967099       0.16415734    0.86877257 \n",
       "male_lga             0.99844728  0.2679523      -0.09496526    0.99297527 \n",
       "female_lga           1.00000000  0.2798588      -0.12576754    0.99185071 \n",
       "female_lga_perc      0.00000000  1.0000000      -0.04796597    0.26592276 \n",
       "male_lga_perc        0.00000000  0.0000000       1.00000000   -0.11372358 \n",
       "employed_lga         0.00000000  0.0000000       0.00000000    1.00000000 \n",
       "unemployed_lga       0.00000000  0.0000000       0.00000000    0.00000000 \n",
       "employed_lga_perc    0.00000000  0.0000000       0.00000000    0.00000000 \n",
       "unemployed_lga_perc  0.00000000  0.0000000       0.00000000    0.00000000 \n",
       "                    unemployed_lga employed_lga_perc unemployed_lga_perc\n",
       "crime_tot_per_lga    0.8497058      0.2852929        -0.15165114        \n",
       "crime_rate_per_lga  -0.0495612     -0.3172114         0.18803770        \n",
       "pop_total            0.9864222      0.3444699        -0.25293818        \n",
       "pop_dens_km2         0.2774621      0.6210650        -0.32632972        \n",
       "seifa_value          0.1285134      0.6735699        -0.68115948        \n",
       "num_service          0.7947911      0.4928160        -0.29593902        \n",
       "male_lga             0.9867037      0.3542594        -0.24128272        \n",
       "female_lga           0.9882303      0.3549218        -0.24679037        \n",
       "female_lga_perc      0.2792150      0.3395610         0.05053036        \n",
       "male_lga_perc       -0.1036547     -0.1617591         0.44211818        \n",
       "employed_lga         0.9621733      0.4250518        -0.31765536        \n",
       "unemployed_lga       1.0000000      0.2566786        -0.14284972        \n",
       "employed_lga_perc    0.0000000      1.0000000        -0.86475150        \n",
       "unemployed_lga_perc  0.0000000      0.0000000         1.00000000        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix <- data_model %>%\n",
    "    dplyr::select(-lga, -cluster) %>%\n",
    "    cor()\n",
    "\n",
    "\n",
    "correlation_matrix_print <- correlation_matrix\n",
    "\n",
    "correlation_matrix_print[lower.tri(correlation_matrix, diag = FALSE)] <- 0\n",
    "\n",
    "correlation_matrix_print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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QoQaUpMKiKZJnVPQKRlgUhTYpIRSTep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\nAxAJAAYgEgAMQCQAGPj/nCiyDPZg/cMAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_model %>%\n",
    "    dplyr::select(-cluster) %>%\n",
    "    cor() %>%\n",
    "    ggcorrplot(hc.order = TRUE, \n",
    "               type = \"lower\", \n",
    "               outline.col = \"white\",\n",
    "               #lab = TRUE,\n",
    "               p.mat = p.mat) +\n",
    "    ggtitle(\"Pearson Correlation\") +\n",
    "    my_theme() +\n",
    "    xlab(\"\") +\n",
    "    ylab(\"\")\n",
    "    \n",
    "ggsave(paste(chart_dir, \"correlation.png\", sep = \"/\"))\n",
    "ggsave(paste(chart_dir, \"correlation.svg\", sep = \"/\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the correlation p-values\n",
    "p.mat <- data_model %>%\n",
    "    dplyr::select(-cluster) %>%\n",
    "    cor_pmat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>crime_tot_per_lga</th><th scope=col>crime_rate_per_lga</th><th scope=col>pop_total</th><th scope=col>pop_dens_km2</th><th scope=col>seifa_value</th><th scope=col>num_service</th><th scope=col>male_lga</th><th scope=col>female_lga</th><th scope=col>female_lga_perc</th><th scope=col>male_lga_perc</th><th scope=col>employed_lga</th><th scope=col>unemployed_lga</th><th scope=col>employed_lga_perc</th><th scope=col>unemployed_lga_perc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>crime_tot_per_lga</th><td>0    </td><td>0.066</td><td>0.000</td><td>0.000</td><td>0.572</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.055</td><td>0.259</td><td>0.000</td><td>0.000</td><td>0.003</td><td>0.123</td></tr>\n",
       "\t<tr><th scope=row>crime_rate_per_lga</th><td>0    </td><td>0.000</td><td>0.551</td><td>0.565</td><td>0.000</td><td>0.448</td><td>0.670</td><td>0.550</td><td>0.001</td><td>0.205</td><td>0.603</td><td>0.616</td><td>0.001</td><td>0.055</td></tr>\n",
       "\t<tr><th scope=row>pop_total</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.037</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.010</td><td>0.144</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.009</td></tr>\n",
       "\t<tr><th scope=row>pop_dens_km2</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.168</td><td>0.000</td><td>0.000</td><td>0.001</td><td>0.000</td><td>0.024</td><td>0.000</td><td>0.004</td><td>0.000</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>seifa_value</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.301</td><td>0.064</td><td>0.042</td><td>0.057</td><td>0.000</td><td>0.016</td><td>0.191</td><td>0.000</td><td>0.000</td></tr>\n",
       "\t<tr><th scope=row>num_service</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.002</td><td>0.094</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.002</td></tr>\n",
       "\t<tr><th scope=row>male_lga</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.006</td><td>0.335</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.013</td></tr>\n",
       "\t<tr><th scope=row>female_lga</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.004</td><td>0.201</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.011</td></tr>\n",
       "\t<tr><th scope=row>female_lga_perc</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.627</td><td>0.006</td><td>0.004</td><td>0.000</td><td>0.609</td></tr>\n",
       "\t<tr><th scope=row>male_lga_perc</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.248</td><td>0.293</td><td>0.099</td><td>0.000</td></tr>\n",
       "\t<tr><th scope=row>employed_lga</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>unemployed_lga</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.008</td><td>0.146</td></tr>\n",
       "\t<tr><th scope=row>employed_lga_perc</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr>\n",
       "\t<tr><th scope=row>unemployed_lga_perc</th><td>0    </td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & crime\\_tot\\_per\\_lga & crime\\_rate\\_per\\_lga & pop\\_total & pop\\_dens\\_km2 & seifa\\_value & num\\_service & male\\_lga & female\\_lga & female\\_lga\\_perc & male\\_lga\\_perc & employed\\_lga & unemployed\\_lga & employed\\_lga\\_perc & unemployed\\_lga\\_perc\\\\\n",
       "\\hline\n",
       "\tcrime\\_tot\\_per\\_lga & 0     & 0.066 & 0.000 & 0.000 & 0.572 & 0.000 & 0.000 & 0.000 & 0.055 & 0.259 & 0.000 & 0.000 & 0.003 & 0.123\\\\\n",
       "\tcrime\\_rate\\_per\\_lga & 0     & 0.000 & 0.551 & 0.565 & 0.000 & 0.448 & 0.670 & 0.550 & 0.001 & 0.205 & 0.603 & 0.616 & 0.001 & 0.055\\\\\n",
       "\tpop\\_total & 0     & 0.000 & 0.000 & 0.002 & 0.037 & 0.000 & 0.000 & 0.000 & 0.010 & 0.144 & 0.000 & 0.000 & 0.000 & 0.009\\\\\n",
       "\tpop\\_dens\\_km2 & 0     & 0.000 & 0.000 & 0.000 & 0.168 & 0.000 & 0.000 & 0.001 & 0.000 & 0.024 & 0.000 & 0.004 & 0.000 & 0.001\\\\\n",
       "\tseifa\\_value & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.301 & 0.064 & 0.042 & 0.057 & 0.000 & 0.016 & 0.191 & 0.000 & 0.000\\\\\n",
       "\tnum\\_service & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.002 & 0.094 & 0.000 & 0.000 & 0.000 & 0.002\\\\\n",
       "\tmale\\_lga & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.006 & 0.335 & 0.000 & 0.000 & 0.000 & 0.013\\\\\n",
       "\tfemale\\_lga & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.004 & 0.201 & 0.000 & 0.000 & 0.000 & 0.011\\\\\n",
       "\tfemale\\_lga\\_perc & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.627 & 0.006 & 0.004 & 0.000 & 0.609\\\\\n",
       "\tmale\\_lga\\_perc & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.248 & 0.293 & 0.099 & 0.000\\\\\n",
       "\temployed\\_lga & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.001\\\\\n",
       "\tunemployed\\_lga & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.008 & 0.146\\\\\n",
       "\temployed\\_lga\\_perc & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000\\\\\n",
       "\tunemployed\\_lga\\_perc & 0     & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | crime_tot_per_lga | crime_rate_per_lga | pop_total | pop_dens_km2 | seifa_value | num_service | male_lga | female_lga | female_lga_perc | male_lga_perc | employed_lga | unemployed_lga | employed_lga_perc | unemployed_lga_perc |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| crime_tot_per_lga | 0     | 0.066 | 0.000 | 0.000 | 0.572 | 0.000 | 0.000 | 0.000 | 0.055 | 0.259 | 0.000 | 0.000 | 0.003 | 0.123 |\n",
       "| crime_rate_per_lga | 0     | 0.000 | 0.551 | 0.565 | 0.000 | 0.448 | 0.670 | 0.550 | 0.001 | 0.205 | 0.603 | 0.616 | 0.001 | 0.055 |\n",
       "| pop_total | 0     | 0.000 | 0.000 | 0.002 | 0.037 | 0.000 | 0.000 | 0.000 | 0.010 | 0.144 | 0.000 | 0.000 | 0.000 | 0.009 |\n",
       "| pop_dens_km2 | 0     | 0.000 | 0.000 | 0.000 | 0.168 | 0.000 | 0.000 | 0.001 | 0.000 | 0.024 | 0.000 | 0.004 | 0.000 | 0.001 |\n",
       "| seifa_value | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.301 | 0.064 | 0.042 | 0.057 | 0.000 | 0.016 | 0.191 | 0.000 | 0.000 |\n",
       "| num_service | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.002 | 0.094 | 0.000 | 0.000 | 0.000 | 0.002 |\n",
       "| male_lga | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.006 | 0.335 | 0.000 | 0.000 | 0.000 | 0.013 |\n",
       "| female_lga | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.004 | 0.201 | 0.000 | 0.000 | 0.000 | 0.011 |\n",
       "| female_lga_perc | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.627 | 0.006 | 0.004 | 0.000 | 0.609 |\n",
       "| male_lga_perc | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.248 | 0.293 | 0.099 | 0.000 |\n",
       "| employed_lga | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 |\n",
       "| unemployed_lga | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.008 | 0.146 |\n",
       "| employed_lga_perc | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |\n",
       "| unemployed_lga_perc | 0     | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |\n",
       "\n"
      ],
      "text/plain": [
       "                    crime_tot_per_lga crime_rate_per_lga pop_total pop_dens_km2\n",
       "crime_tot_per_lga   0                 0.066              0.000     0.000       \n",
       "crime_rate_per_lga  0                 0.000              0.551     0.565       \n",
       "pop_total           0                 0.000              0.000     0.002       \n",
       "pop_dens_km2        0                 0.000              0.000     0.000       \n",
       "seifa_value         0                 0.000              0.000     0.000       \n",
       "num_service         0                 0.000              0.000     0.000       \n",
       "male_lga            0                 0.000              0.000     0.000       \n",
       "female_lga          0                 0.000              0.000     0.000       \n",
       "female_lga_perc     0                 0.000              0.000     0.000       \n",
       "male_lga_perc       0                 0.000              0.000     0.000       \n",
       "employed_lga        0                 0.000              0.000     0.000       \n",
       "unemployed_lga      0                 0.000              0.000     0.000       \n",
       "employed_lga_perc   0                 0.000              0.000     0.000       \n",
       "unemployed_lga_perc 0                 0.000              0.000     0.000       \n",
       "                    seifa_value num_service male_lga female_lga female_lga_perc\n",
       "crime_tot_per_lga   0.572       0.000       0.000    0.000      0.055          \n",
       "crime_rate_per_lga  0.000       0.448       0.670    0.550      0.001          \n",
       "pop_total           0.037       0.000       0.000    0.000      0.010          \n",
       "pop_dens_km2        0.168       0.000       0.000    0.001      0.000          \n",
       "seifa_value         0.000       0.301       0.064    0.042      0.057          \n",
       "num_service         0.000       0.000       0.000    0.000      0.002          \n",
       "male_lga            0.000       0.000       0.000    0.000      0.006          \n",
       "female_lga          0.000       0.000       0.000    0.000      0.004          \n",
       "female_lga_perc     0.000       0.000       0.000    0.000      0.000          \n",
       "male_lga_perc       0.000       0.000       0.000    0.000      0.000          \n",
       "employed_lga        0.000       0.000       0.000    0.000      0.000          \n",
       "unemployed_lga      0.000       0.000       0.000    0.000      0.000          \n",
       "employed_lga_perc   0.000       0.000       0.000    0.000      0.000          \n",
       "unemployed_lga_perc 0.000       0.000       0.000    0.000      0.000          \n",
       "                    male_lga_perc employed_lga unemployed_lga employed_lga_perc\n",
       "crime_tot_per_lga   0.259         0.000        0.000          0.003            \n",
       "crime_rate_per_lga  0.205         0.603        0.616          0.001            \n",
       "pop_total           0.144         0.000        0.000          0.000            \n",
       "pop_dens_km2        0.024         0.000        0.004          0.000            \n",
       "seifa_value         0.000         0.016        0.191          0.000            \n",
       "num_service         0.094         0.000        0.000          0.000            \n",
       "male_lga            0.335         0.000        0.000          0.000            \n",
       "female_lga          0.201         0.000        0.000          0.000            \n",
       "female_lga_perc     0.627         0.006        0.004          0.000            \n",
       "male_lga_perc       0.000         0.248        0.293          0.099            \n",
       "employed_lga        0.000         0.000        0.000          0.000            \n",
       "unemployed_lga      0.000         0.000        0.000          0.008            \n",
       "employed_lga_perc   0.000         0.000        0.000          0.000            \n",
       "unemployed_lga_perc 0.000         0.000        0.000          0.000            \n",
       "                    unemployed_lga_perc\n",
       "crime_tot_per_lga   0.123              \n",
       "crime_rate_per_lga  0.055              \n",
       "pop_total           0.009              \n",
       "pop_dens_km2        0.001              \n",
       "seifa_value         0.000              \n",
       "num_service         0.002              \n",
       "male_lga            0.013              \n",
       "female_lga          0.011              \n",
       "female_lga_perc     0.609              \n",
       "male_lga_perc       0.000              \n",
       "employed_lga        0.001              \n",
       "unemployed_lga      0.146              \n",
       "employed_lga_perc   0.000              \n",
       "unemployed_lga_perc 0.000              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.mat.lower <- p.mat %>%\n",
    "    lower.tri(diag = FALSE)\n",
    "\n",
    "p.mat.print <- round(p.mat, 3)\n",
    "\n",
    "p.mat.print[p.mat.lower] <- 0\n",
    "\n",
    "p.mat.print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crime Rate vs Unemployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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xCJCiiQcD6oOg1AJCqgQML5oOo0AJGo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\nBIAHIBIAHoBIAHjg/wOn+JtMHcZTigAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var = \"unemployed_lga_perc\"\n",
    "y_var = \"crime_rate_per_lga\"\n",
    "title = \"Crime Rate vs Level of Unemployment\"\n",
    "\n",
    "ggplot(data_model, aes(!!sym(x_var), !!sym(y_var), size = pop_total)) +\n",
    "    geom_point(alpha = 0.2) +\n",
    "    scale_size_continuous(labels = scales::comma) +\n",
    "    scale_x_continuous(labels = number_format(accuracy = 0.01)) +\n",
    "    xlab(\"Level of Unemployment Rate\") +\n",
    "    ylab(\"Crime Rate Per Capita\") +\n",
    "    # label some points over a certain threshold\n",
    "    #geom_text(aes(label = ifelse((!!sym(x_var) > 1.1) & (!!sym(y_var) > 0), as.character(lga), '')), hjust = 1.3, vjust = 0, size = 3) +\n",
    "    #geom_text(aes(label = ifelse((!!sym(x_var) < 1) & (!!sym(y_var) > 0.2), as.character(lga), '')), hjust = -0.2, vjust = 0, size = 3) +\n",
    "    ggtitle(title) +\n",
    "    my_theme() +\n",
    "    theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 1))\n",
    "\n",
    "chart_name_svg = paste(\"scatter_\", y_var, \"_vs_\", x_var, \".svg\", sep = \"\")\n",
    "chart_name_png = paste(\"scatter_\", y_var, \"_vs_\", x_var, \".png\", sep = \"\")\n",
    "filename_svg = paste(chart_dir, chart_name_svg, sep = \"/\")\n",
    "filename_png = paste(chart_dir, chart_name_png, sep = \"/\")\n",
    "ggsave(file = filename_svg)\n",
    "ggsave(file = filename_png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crime Rate vs Female Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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CLfjotXXpCGPc88C0LLyGpqbS63AUgLMe0GqcZRTtKINk1q9/brwP4zNCAtI2up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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var = \"female_lga_perc\"\n",
    "y_var = \"crime_rate_per_lga\"\n",
    "title = \"Crime Rate vs Female Percentage\"\n",
    "\n",
    "ggplot(data_model, aes(!!sym(x_var), !!sym(y_var), size = pop_total)) +\n",
    "    geom_point(alpha = 0.2) +\n",
    "    scale_size_continuous(labels = scales::comma) +\n",
    "    scale_x_continuous(labels = number_format(accuracy = 0.01)) +\n",
    "    xlab(\"Percentage of Female Residents\") +\n",
    "    ylab(\"Crime Rate Per Capita\") +\n",
    "    # label some points over a certain threshold\n",
    "    #geom_text(aes(label = ifelse((!!sym(x_var) > 1.1) & (!!sym(y_var) > 0), as.character(lga), '')), hjust = 1.3, vjust = 0, size = 3) +\n",
    "    #geom_text(aes(label = ifelse((!!sym(x_var) < 1) & (!!sym(y_var) > 0.2), as.character(lga), '')), hjust = -0.2, vjust = 0, size = 3) +\n",
    "    ggtitle(title) +\n",
    "    my_theme() +\n",
    "    theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 1))\n",
    "\n",
    "chart_name_svg = paste(\"scatter_\", y_var, \"_vs_\", x_var, \".svg\", sep = \"\")\n",
    "chart_name_png = paste(\"scatter_\", y_var, \"_vs_\", x_var, \".png\", sep = \"\")\n",
    "filename_svg = paste(chart_dir, chart_name_svg, sep = \"/\")\n",
    "filename_png = paste(chart_dir, chart_name_png, sep = \"/\")\n",
    "ggsave(file = filename_svg)\n",
    "ggsave(file = filename_png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crime Rate vs Employment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "Saving 6.67 x 6.67 in image\n"
     ]
    },
    {
     "data": {
      "image/png": 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G4AIrH7ux9gecHiUiSKJBOwuAGINL5AvzUnFbC8YHEp0j1E2vi887UvYC0TLC6+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\niKdILkgAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_var = \"employed_lga_perc\"\n",
    "y_var = \"crime_rate_per_lga\"\n",
    "title = \"Crime Rate vs Employment Rate\"\n",
    "\n",
    "ggplot(data_model, aes(!!sym(x_var), !!sym(y_var), size = pop_total)) +\n",
    "    geom_point(alpha = 0.2) +\n",
    "    scale_size_continuous(labels = scales::comma) +\n",
    "    scale_x_continuous(labels = number_format(accuracy = 0.01)) +\n",
    "    xlab(\"Percentage of Employed\") +\n",
    "    ylab(\"Crime Rate Per Capita\") +\n",
    "    # label some points over a certain threshold\n",
    "    #geom_text(aes(label = ifelse((!!sym(x_var) > 1.1) & (!!sym(y_var) > 0), as.character(lga), '')), hjust = 1.3, vjust = 0, size = 3) +\n",
    "    #geom_text(aes(label = ifelse((!!sym(x_var) < 1) & (!!sym(y_var) > 0.2), as.character(lga), '')), hjust = -0.2, vjust = 0, size = 3) +\n",
    "    ggtitle(title) +\n",
    "    my_theme() +\n",
    "    theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust = 1))\n",
    "\n",
    "chart_name_svg = paste(\"scatter_\", y_var, \"_vs_\", x_var, \".svg\", sep = \"\")\n",
    "chart_name_png = paste(\"scatter_\", y_var, \"_vs_\", x_var, \".png\", sep = \"\")\n",
    "filename_svg = paste(chart_dir, chart_name_svg, sep = \"/\")\n",
    "filename_png = paste(chart_dir, chart_name_png, sep = \"/\")\n",
    "ggsave(file = filename_svg)\n",
    "ggsave(file = filename_png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "save.image(file = \"D:/OneDrive - UTS/36103/AT2/Git/STDS-AT2-Transport/clustered_eda.RData\")"
   ]
  }
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