4E Capstone Housing Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7RAYUevLDm1mJgYnDhxAv/++y+srKyy5BjZVamaLva+9NJLqF69enYd8oXHiY2N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YHPRUwWIZP4ICI/Hyh1vpBinLdfJzVUtg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\njY0T3VAz13AUfNHU7gFvXL0chY0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Np3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\ngCggCogCooAoIAqIAqKAKCAKiALWKvB/ieLdfmIdIIMAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display logos\n",
    "library(\"IRdisplay\")\n",
    "display_png(file=\"logos.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>  \n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       "  if (code_show){\n",
       "    $('div.input').hide();\n",
       "  } else {\n",
       "    $('div.input').show();\n",
       "  }\n",
       "  code_show = !code_show\n",
       "}  \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "  <form action=\"javascript:code_toggle()\">\n",
       "    <input type=\"submit\" value=\"Click to toggle on/off the raw code.\">\n",
       " </form>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add \"Toggle code\" button\n",
    "library(IRdisplay)\n",
    "\n",
    "display_html(\n",
    "'<script>  \n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    "  if (code_show){\n",
    "    $(\\'div.input\\').hide();\n",
    "  } else {\n",
    "    $(\\'div.input\\').show();\n",
    "  }\n",
    "  code_show = !code_show\n",
    "}  \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "  <form action=\"javascript:code_toggle()\">\n",
    "    <input type=\"submit\" value=\"Click to toggle on/off the raw code.\">\n",
    " </form>'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US Affordable Housing Shortage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Natalee Morris, Gayatri Pai, and Mason Putt**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Covid-19 global pandemic has brought about a wave of joblessness and housing insecurity for millions of American families. This analysis aims to explore the extent to which this negative unemployment shock causes housing displacement among renters in all US counties, specifically in counties that were already facing housing shortages prior to the pandemic. We use data ranging from 2008-2017 to show how higher unemployment and housing affordability gaps leads to higher rates of evictions, and how this effect is different across counties with different rurality or racial breakdowns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Studying evictions in the US is still in its infancy.  Most impoverished people rent their households, and affordable housing in the US is scarce.  Launched in 2017, Princeton University's Eviction Lab became the first resource for data to study evictions at a national scale.  In order to tackle these issues and create equitable communities, we must effectively examine eviction rates and the conditions that lead households to get evicted in the first place.  Doing so will allow our local and national officials to make data-driven decisions that combat the equitability gap that plagues this country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an academic project completed by members of the [University of Pittsburgh's MS in Quantitative Economics program](https://www.mqe.pitt.edu/), advised by Pittsburgh consulting firm [Fourth Economy](https://www.fourtheconomy.com/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define housing affordability statistics using data from the **[Comprehensive Housing Affordability Strategy](https://www.huduser.gov/portal/datasets/cp.html#2006-2017_data)**, or CHAS, in the form of 5-year estimates from 2008-2012 and 2013-2017. The 2013-2017 period shows the state of a given county's housing market prior to the onset of Covid-19, while the 2008-2012 period gives some insight into how that county responded to the 2008 recession. The latter period gives some intuition as to how a county will respond to an unemployment shock such as 2020's.\n",
    "\n",
    "This dataset is then merged with data from the following sources, also aggregated into 5-year averages:\n",
    "\n",
    "- **[Princeton University's Eviction Lab](https://evictionlab.org/map/#/2016?geography=states&type=er)**: Estimates of evictions and demographic information\n",
    "- **[Economic Research Service](https://www.ers.usda.gov/)** 2015 Database: Binaries to indicate a county's economic typology\n",
    "- **[ACS School Enrollment Survey](https://www.census.gov/topics/education/school-enrollment.html)**: Estimates of college enrollment in a given county\n",
    "\n",
    "The following variables are used to define housing markets and local economies:\n",
    "\n",
    "- *population*: A raw count of the total population\n",
    "- *unemp_rate*: Unemployed population divided by the total labor force\n",
    "- *renter_rate*: The proportion of a county's population that rents (rather than owns) housing\n",
    "- *evict_rate*: Number of evictions divided by number of renters\n",
    "- *nonwhite_rate*: Proportion of a county's population that is nonwhite\n",
    "- *r_totalgap_rate*: The proportion of a county's population that does not have affordable rental housing available to them\n",
    "- *college_rate*: The proportion of a county's population attending college\n",
    "- *costbur_rate*: The proportion of renters facing cost burden or severe cost burden - spending at least 30% of their income on housing\n",
    "- *metro*: A binary indicating counties constaining one or more urbanized areas\n",
    "\n",
    "The full dataset and data dictionary of sources / variable deifnitions can be found at https://github.com/MasonPutt/4E-Capstone-Project. Here, we also provide an identical dataset that forces the data into 1-year estimates rather than 5-year averages. To avoid making false assumptions about the state of housing markets (i.e. assuming identicality in housing markets between 2008 and 2012), our analysis utilizes 5-year averages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following gives an example of the data format for the first three counties in the US alphabetically:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Table 1: Data Format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       " <thead>\n",
       "  <tr>\n",
       "   <th style=\"text-align:left;\"> county_state </th>\n",
       "   <th style=\"text-align:left;\"> period </th>\n",
       "   <th style=\"text-align:right;\"> population </th>\n",
       "   <th style=\"text-align:right;\"> unemp_rate </th>\n",
       "   <th style=\"text-align:right;\"> renter_rate </th>\n",
       "   <th style=\"text-align:right;\"> evict_rate </th>\n",
       "   <th style=\"text-align:right;\"> nonwhite_rate </th>\n",
       "   <th style=\"text-align:right;\"> r_totalgap_rate </th>\n",
       "   <th style=\"text-align:right;\"> college_rate </th>\n",
       "   <th style=\"text-align:right;\"> costbur_rate </th>\n",
       "   <th style=\"text-align:right;\"> metro </th>\n",
       "  </tr>\n",
       " </thead>\n",
       "<tbody>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> Abbeville County, SC </td>\n",
       "   <td style=\"text-align:left;\"> 2008-2012 </td>\n",
       "   <td style=\"text-align:right;\"> 25221.0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.1178 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2232 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0095 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3090 </td>\n",
       "   <td style=\"text-align:right;\"> 0.1542 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0611 </td>\n",
       "   <td style=\"text-align:right;\"> 0.4286 </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> Abbeville County, SC </td>\n",
       "   <td style=\"text-align:left;\"> 2013-2017 </td>\n",
       "   <td style=\"text-align:right;\"> 24997.0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0688 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2295 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0418 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3108 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2923 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0616 </td>\n",
       "   <td style=\"text-align:right;\"> 0.4288 </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> Acadia Parish, LA </td>\n",
       "   <td style=\"text-align:left;\"> 2008-2012 </td>\n",
       "   <td style=\"text-align:right;\"> 61066.2 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0610 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3067 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0079 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2168 </td>\n",
       "   <td style=\"text-align:right;\"> 0.1735 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0350 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3390 </td>\n",
       "   <td style=\"text-align:right;\"> 1 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> Acadia Parish, LA </td>\n",
       "   <td style=\"text-align:left;\"> 2013-2017 </td>\n",
       "   <td style=\"text-align:right;\"> 62163.0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0636 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2867 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0220 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2230 </td>\n",
       "   <td style=\"text-align:right;\"> 0.1938 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0344 </td>\n",
       "   <td style=\"text-align:right;\"> 0.4192 </td>\n",
       "   <td style=\"text-align:right;\"> 1 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> Accomack County, VA </td>\n",
       "   <td style=\"text-align:left;\"> 2008-2012 </td>\n",
       "   <td style=\"text-align:right;\"> 35287.6 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0705 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2951 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0270 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3900 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2214 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0332 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2918 </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> Accomack County, VA </td>\n",
       "   <td style=\"text-align:left;\"> 2013-2017 </td>\n",
       "   <td style=\"text-align:right;\"> 33115.0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0577 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3001 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0230 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3908 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2783 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0354 </td>\n",
       "   <td style=\"text-align:right;\"> 0.4039 </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "  </tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(warn=-1)\n",
    "options(message=-1)\n",
    "\n",
    "library(kableExtra)\n",
    "library(IRdisplay)\n",
    "library(tidyverse)\n",
    "\n",
    "# Load data\n",
    "alldata5yr <-read.csv(\"https://raw.githubusercontent.com/MasonPutt/4E-Capstone-Project/main/alldata5yr.csv\")\n",
    "kable(head(alldata5yr[c(3,6,22,7,8,9,10,15,17,18,112)],6), format='html') %>%\n",
    "    as.character() %>%\n",
    "    display_html() \n",
    "\n",
    "# Define options\n",
    "Pitt.Blue<- \"#003594\"\n",
    "Pitt.Gold<-\"#FFB81C\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main features of the data are described below for all 3,141 geographies (3,006 counties, 14 boroughs, and 11 census areas) that make up the United States in the 2013-2017 period. The median and mean of each variable give us insight into what the average county looks like and provides a starting point for considering the normality assumptions in the modeling process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Table 2: Summary Statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       " <thead>\n",
       "  <tr>\n",
       "   <th style=\"text-align:left;\"> Variable </th>\n",
       "   <th style=\"text-align:right;\"> Number Missing </th>\n",
       "   <th style=\"text-align:right;\"> Min </th>\n",
       "   <th style=\"text-align:right;\"> 1st Quartile </th>\n",
       "   <th style=\"text-align:right;\"> Median </th>\n",
       "   <th style=\"text-align:right;\"> Mean </th>\n",
       "   <th style=\"text-align:right;\"> 3rd Quartile </th>\n",
       "   <th style=\"text-align:left;\"> Max </th>\n",
       "   <th style=\"text-align:left;\"> Distribution </th>\n",
       "  </tr>\n",
       " </thead>\n",
       "<tbody>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> population </td>\n",
       "   <td style=\"text-align:right;\"> 1 </td>\n",
       "   <td style=\"text-align:right;\"> 85.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 11030.000000 </td>\n",
       "   <td style=\"text-align:right;\"> 25768.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 100776.6108280 </td>\n",
       "   <td style=\"text-align:right;\"> 67492.75000 </td>\n",
       "   <td style=\"text-align:left;\"> 10038388.0000 </td>\n",
       "   <td style=\"text-align:left;\"> ▇▁▁▁▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> unemp_rate </td>\n",
       "   <td style=\"text-align:right;\"> 1 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0179 </td>\n",
       "   <td style=\"text-align:right;\"> 0.044100 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0559 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0580238 </td>\n",
       "   <td style=\"text-align:right;\"> 0.06880 </td>\n",
       "   <td style=\"text-align:left;\"> 0.2350 </td>\n",
       "   <td style=\"text-align:left;\"> ▇▅▁▁▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> renter_rate </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0588 </td>\n",
       "   <td style=\"text-align:right;\"> 0.231500 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2741 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2865895 </td>\n",
       "   <td style=\"text-align:right;\"> 0.32480 </td>\n",
       "   <td style=\"text-align:left;\"> 0.9091 </td>\n",
       "   <td style=\"text-align:left;\"> ▃▇▁▁▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> evict_rate </td>\n",
       "   <td style=\"text-align:right;\"> 542 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 0.004800 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0121 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0172846 </td>\n",
       "   <td style=\"text-align:right;\"> 0.02430 </td>\n",
       "   <td style=\"text-align:left;\"> 0.1737 </td>\n",
       "   <td style=\"text-align:left;\"> ▇▁▁▁▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> nonwhite_rate </td>\n",
       "   <td style=\"text-align:right;\"> 1 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0024 </td>\n",
       "   <td style=\"text-align:right;\"> 0.066475 </td>\n",
       "   <td style=\"text-align:right;\"> 0.1536 </td>\n",
       "   <td style=\"text-align:right;\"> 0.2271571 </td>\n",
       "   <td style=\"text-align:right;\"> 0.34375 </td>\n",
       "   <td style=\"text-align:left;\"> 0.9905 </td>\n",
       "   <td style=\"text-align:left;\"> ▇▃▂▁▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> r_totalgap_rate </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 0.107100 </td>\n",
       "   <td style=\"text-align:right;\"> 0.1631 </td>\n",
       "   <td style=\"text-align:right;\"> 0.1611107 </td>\n",
       "   <td style=\"text-align:right;\"> 0.21250 </td>\n",
       "   <td style=\"text-align:left;\"> 0.4609 </td>\n",
       "   <td style=\"text-align:left;\"> ▃▇▆▁▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> college_rate </td>\n",
       "   <td style=\"text-align:right;\"> 3 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 0.030600 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0406 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0509359 </td>\n",
       "   <td style=\"text-align:right;\"> 0.05800 </td>\n",
       "   <td style=\"text-align:left;\"> 0.5749 </td>\n",
       "   <td style=\"text-align:left;\"> ▇▁▁▁▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> costbur_rate </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 0.305600 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3685 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3593675 </td>\n",
       "   <td style=\"text-align:right;\"> 0.42380 </td>\n",
       "   <td style=\"text-align:left;\"> 0.6766 </td>\n",
       "   <td style=\"text-align:left;\"> ▁▂▇▅▁ </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\"> metro </td>\n",
       "   <td style=\"text-align:right;\"> 0 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 0.000000 </td>\n",
       "   <td style=\"text-align:right;\"> 0.0000 </td>\n",
       "   <td style=\"text-align:right;\"> 0.3712194 </td>\n",
       "   <td style=\"text-align:right;\"> 1.00000 </td>\n",
       "   <td style=\"text-align:left;\"> 1.0000 </td>\n",
       "   <td style=\"text-align:left;\"> ▇▁▁▁▅ </td>\n",
       "  </tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(skimr)\n",
    "options(scipen = 999)\n",
    "\n",
    "# Extract only 2013-17 data\n",
    "recent <- filter(alldata5yr, period=='2013-2017')\n",
    "\n",
    "# Creates summary table\n",
    "sumtab <- t(t(skim(recent[c(22,7,8,9,10,15,17,18,112)])))\n",
    "sumtab <- sumtab[, c(2:3, 7:9, 5, 10:12)]\n",
    "sumtab <- as.data.frame(sumtab) %>%\n",
    "    select('Variable'=1,'Number Missing'=2,'Min'=3,'1st Quartile'=4,'Median'=5,'Mean'=6,'3rd Quartile'=7,'Max'=8, 'Distribution'=9)\n",
    "sumtab[2:7] <- lapply(sumtab[2:7],as.numeric)\n",
    "kable(sumtab, format = \"html\") %>%\n",
    "    as.character() %>%\n",
    "    display_html() \n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A note on missing data:**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the 6,282 observations (3,141 geographies over two periods), we find 1,066 missing observations for eviction rates. 542 (50.8%) of the missing observations occured in the 2013-2017 time period. \n",
    "\n",
    "For simplicity in the modeling process, we assume that the distribution of missing observations is essentialy random. The table below describes the main features of the missing observations from the 2013-2017 period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ymcEnPVFU7RpwiIgAiIgAiIgAgMOwKs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8QbQRhvXebLUEMEEPAEFP7qmmuuofHDA+EZ\nAQQQCLhAwg0gL7zwgj377LNZuBQXUT9MNBExc4BkoWEhBwKaKJmEQKICo0ePtieeeMJtrpiu\nU6ZMsVNOOSXR3dkuiQJq4KhQoYILg6XGkMg0fvx49wMlMp/lzBZo0KCB6XtErMbvU089NbMr\nT+2SIlCyZMmY59HIZVJ6CHTr1s2uvvpqF6ZEczvo/UINHyNGjDA1glx00UXpURFKmacCmivK\nL6mDHgkBBBA4//zzbfbs2UAggAACCCDgBBJuAHnmmWfs73//exY2/YBkorksJCwggEASBKZP\nn+4mQA0/lUYWfPLJJ1ajRo3wbF6ngMBf/vIXW758uelZ8XZ10zs8nXnmmeGLvA6IgEZ5xGr8\nUPXjxVEOCA/VzAOBMmXKmBpguSGaB5gFdAiNHnz00Udt0KBB5t30/uijj1wHrFdffdXFdS+g\nonHaAhQoX7687/9rQt8V4IXh1AgUsMDcuXNt2bJlrhTVq1e3zz//3M1Tq7lAIucHatWqFb8b\nC/h6cXoEEEAgmQIJN4CosUMx3NXjWh8s69atc5MPXnrppXbTTTcls8ycCwEEAi7Qp0+fmAID\nBw50k6TGXElmgQl8//33dvvtt1vfvn0LrAycOPUE1IvbL82aNcv1+vZbTz4CiQgoPNLgwYOj\nNr3yyiuj8shIXYF+/fpZjx49bN68ebZ27VqrVauWtWjRwjVu7du3z4XBTN3SU7L8EPjDH/5g\nDz30UMxD9+7dO2Y+mQggkPkC//znP01zf4Snd9991/SITBqBTse5SBWWEUAAgcwVKJxo1RRD\nsVmzZjZ8+HA3EaF6W2v0hyYmvOOOO3x7cSZ6fLZDAAEEEhXQDY9Y6ccff4yVTV4BC5x33nm2\ndOnSAi4Fp081AfXM90t+oYv8ticfgVgCuuERa34ojRYkpYfAfffdZxqFrp68+u2h3xzqfKUR\nAE2aNLHXXnstPSpCKfNUQKHRYo300KgvhUwjIYBAMAWef/55U2jkRB5du3YNJhK1RgABBAIq\nkPAIkDFjxrgfkZs3b84SmmLRokXuB4h+kDRt2jSgjFQbAQRSQcAvnE4qlC3IZejSpYu9/PLL\n7qaE4vFWq1YtSxgshqAH869jyZIlvhXXpMeXXXaZ73pWIJCIgEYL/Pbbb1Gb7ty5MyqPjNQR\n0HXzRu7MmDHD3ejWSMLwpGuohvXIkCbh2/A6swUUkeD666+3BQsWuIo2atTIJk6cmNmVpnYI\nIJCwwJAhQ2zDhg2m0aCtW7e2okUTvvWV8DnYEAEEEEAgfQQS/hT48ssv7e67787S+KFqNm7c\n2E1AqJj8NICkz4WnpAiks4BC8h06dCiqClWqVInKI6PgBTR/lOLx6vHOO+9EFYgh6FEkgcjY\ntm2bbz23bt3qu44VCCQqcODAgUQ3ZbsUEjj++OPdKHM1hOq9QKM+IxtANLLnnnvuMc0RQgqu\nwLhx41woZkUlGD16dHAhqDkCCEQJnHXWWS56SceOHU0jiy+//HLXGHLJJZdY6dKlo7YnAwEE\nEEAgswUSbgDRMHM1gvz+97/PIrJ9+3abM2eOaYg6CQEEEEiGgF8DCF9mk6Gf/XOMHDnS9CAh\nEC6gecX80u7du/1WkY9AwgInnXSS6/F58ODBLPsUL148yzILqScwYsQIVyiF3lX4q2uuuSb1\nCkmJEEAAAQRSVkDhEvXYs2ePKfTle++9Z5ojaMuWLda+fXs3t22DBg1StvwUDAEEEEAgbwUS\nbgC57rrrTK3lClWisBRqEPniiy9s8uTJVrVqVfdBol44Ss2bNzf96CQhkB2BYsWKZWdztg2w\ngHrxxOrZe9xxxwVYhaojkF4C8d7z461Lr1pS2oIU0PfWJ554wsIbQPS3xRwBBXlVsnfuPn36\nZG8HtkYAAQQQQCBMQHPOqcFDzxUrVjRNlD5lyhQ3eowGkDAoXiKAAAIZLpBwA8izzz5r69at\nc4/Zs2dHsXTv3j2Up/lCaAAJcfAiQYFKlSoluCWbBV0gVvgrmaiHDyk1BXbs2GHDhg2zWbNm\nWZ06dWzo0KH24osvutGDxORNzWuW36VS2Ez9AI2Vzj777FjZ5CGQLQHNDzFp0iQXJkk3PjQf\nSOfOnW3QoEHZOg4bF6zA6tWr7eOPPzZ9jkTO9aWQJieffHLBFpCzI4AAAgiknIDCKL711lv2\n6aefuo67Cp14wQUX2AMPPOBCuDds2DDlykyBEEAAAQTyTyDhBhBCmOTfReDI/xPYvHkzFAgk\nJKAvsLFS2bJlY2WTV8ACCpWom92atLZ27dqmm1lqrFIjiEYRqlGEFDyBFStW+FZa88WQEMgL\ngbp169rixYttzZo1Lua3Ri2T0kdg/vz51rJlSzfqUw1akZ//avygASR9riclRQABBJIloPtX\n6sRbr149e/75500ddiM/Q5JVFs6DAAIIIFDwAoULvgiUAIH/Cfj16scHgUiB8HAm4esYARKu\nkTqvNfJDPa/V8HHHHXe4gtWsWdOWLFniJrb97rvvUqewlCRpAvFG/cVbl7QCcqKMEVDYq6lT\np9ovv/ySMXUKSkV0A0thdxWzfePGja4hS41Z3kMjQEgIIIAAAghECtx7770uDKY6X915552u\nIeS2226z119/3davXx+5OcsIIIAAAhkukK0GkF9//dXuuusu90NEIa6uvPJK+8tf/mKHDx/O\ncCaqlwwBGkCSoZwZ5/BrAIk1L0hm1Di9a7FgwQL3wyNyXocTTzzR2rRpY9OmTUvvClL6HAnE\na7CMty5HJ2OnwAs888wzNnfu3MA7pBuAfntcddVVduyxx6Zb0SkvAggggEABCmjkR//+/d3v\nDDWijxo1ytTBZuDAgVa9enU3Cr0Ai8epEUAAAQSSLJBwA8jWrVtdCJOXX37Z1HNXk6Kr5XzA\ngAFuAqnImLxJrgenQwCBAAns3bs3Zm01ooCUegJVqlQxxeGNTPv377eZM2dahQoVIlexHACB\neKHP9HdBQgABBM4//3z76KOPgEAAAQQQQCBHAvrdOH36dPvggw/caNBVq1ZZs2bNrEaNGjk6\nHjshgAACCKSnQMJzgPz1r3819dBXT6zSpUuHaquevWeddZb17dvXjQwJreAFAgggkGQB3VAn\npZ5At27d7Oqrr7b69etb4cKF3SS2usE9YsQI0zW76KKLUq/QlCjfBTQnjF/atWuX3yryEUAg\nQAKatH706NGu45Umr40cCdKqVStuYgXo74GqIoAAAokKTJkyxYYPH26ff/65FSpUyNq1a2d3\n3323KXSiRoCQEEAAAQSCJZBwA8jChQutZ8+eWRo/RKWJbdu2bWszZsygASRYfzvUFoGUEyhS\npEjKlYkCmXXo0MEeffRRGzRokHmjd9SjVyNDXn31VTv++ONhCqCAfoz6pXjr/PYhHwEEMk/g\n6aefth9++ME9xo8fH1VB5dGLN4qFDAQQQCDwAgp7qXC7b7zxhrVv3z7qPlbggQBAAAEEAiaQ\ncANI+fLlbfHixVE8mthWP0y6d+8etY4MBBBAIJkCep8ipaZAv379rEePHjZv3jxbu3at1apV\ny1q0aGFcs9S8XskoVeScMOHnLFGiRPgirxFAIKACmgRdDxICCCCAAALZEXjkkUds9erV9vHH\nH7v5PyJDtmskyMknn5ydQ7ItAgjkkYDuIyssXRAieChM+4QJE/JILjUP06hRIzv11FNTs3Bh\npUq4AUThSzTvx9/+9je74oorrE6dOm4yyZdeesm2bdtGCJMwVF4igED+CiiM0uHDh6NOUrJk\nyag8MlJDwPsBsmPHDhcC6/vvvzc9lPgBkhrXKNml0ESUfol5YfxkyEcgeAL63Bg2bJhp3iD9\n/hg6dKi9+OKLdt9991nRogn/lAkeHDVGAAEEAiwwf/58a9mypR04cMAqV65sxxxzTBYNNX7Q\nAJKFhAUEkiagEVrqIBmE9Mknn5gemZyaNTvHxo59NeWrmPCvhq5du1r//v3dXB/33nuvG0K4\ne/du13tXjSCaGJ2EAAIIJEPArwEk8ottMsrCOY4uwA+QoxsFcYvw+cT+H3v3AW9FcS9w/H8b\nl96kFyWAD1QURQNcQQkg0YBBEAUVW2xYwNgCaOLjBUUjsRDL01hRNEQQSCAQjF3RBCwgIkgU\nVJR26b2zb/6Tt+vp99x7T9lzzm8+n8vZOjvz3eXM2Z2dmdD816xZM3QR8wggkIMC27Zts93t\n6phBrVq1sm/z7t6921aCzJw501aK5CALWUYAAQQQKENAWw+eeOKJMnfu3LDxo8rYldUIIJBk\nAR1fWsNTl+2Sdk3DX2xN8uGJPoECt71cTQ4ePJjAGJMXVdwVIJqEe+65Ry6//HKvCxO9EdEB\nCbUfdwICCCCQKoFo4wPw0DRVZ6B8x+EGpHxeubK1jg0T7W2Yn/3sZ7nCQD4RQCCGgLb80G4S\ntBXhpEmT5JVXXrEvXWl3AieccIIsXbpUjj322BgxsAoBBBBAIBcFNmzYIAMGDKDyIxdPPnnO\nGIG61R1pWMvJmPSS0HCBIlOrkBnVHyL54cmPvkTfwpo4caI899xz9nP69Om237boe7AGgfgF\neHs/fqtc3zJalxfRlue6V7rzn4gbEH3Ypd2e/OY3v5EFCxbEzJL28avbrly5Mmi78sQRtCMz\nSRHo16+f1KpVKyxuLQu0coSAAAIIfPLJJ3LddddJ6JhBOrDtT37yE3nttddAQgABBBBAIEzg\ntNNOk1dffTVsOQsQQAABBHJTIO4KkO+++046d+4sDz/8sL0J0XFAtBsavSkZNmyY7dM9NwnJ\ndXkF6tatG3GXSy65JOJyFiIQKhCtsowWIKFS/piv7A3I+vXrZcyYMdKxY0c7cPq4ceNk+fLl\nETO3Z88eue+++2TOnDmi024oTxzuPnwmX+Bf//qX1K9f3zvQj370I1m4cKE3zwQCCOS2gLYy\n//zzz8MQdNDMefPmCeMFhdGwAAEEEEDACAwcOFD097+OY/v444/L5MmTg/70+RYBAQQQQCB3\nBOLuAuv555+3A0dt2rRJiouLPaHFixfbvhW1EqRTp07eciYQiCZwxx13yKhRo8IqzX7xi19E\n24XlCAQJaF/gkcIXX3wRaTHL0iygNyDaelBvQLTbxNBK0O7du0vLli2jplJbG/bs2dNrFaAt\nSmbMmCGjR48O2+eyyy6zZVKjRo2C1pUnjqAdmUmqgLba+uc//ylaAa5d273wwgtJPR6RI4BA\nZgkMHjxYzj33XGnXrp198Upb+GnFx6OPPipaCXLGGWdkVoZILQIIIIBASgQmTJggem+of1Om\nTAk7pi6Ldf8RtgMLEEAAAQQyWiDuChB9S/Omm24KqvzQnGv/u3rz8c4771ABktGXQuoSf845\n54g+wPz9739vD3rEEUfI008/LaEPLFOXIo6UaQL6ACRS2LhxY6TFLEuzQGVvQFavXi1aSeKG\ntm3bRu0Ga+zYsdK+fXu58MIL3c3tZzxx/Pvf/xat1Nfw2Wef2Up/O8M/SRfQyg9tVUpAAAEE\nAgW0O7y77rrLtgJ0W/VplybaMuTFF1+UZs2aBW7ONAIIIIAAAlZAxyDUPwICCCCAAAIqEHcF\nyIknnihaCXLVVVcFyem4INof+6233hq0nBkEYgnodVS9enX57W9/K1OnTpXmzZvH2px1CMQl\nUFBQENd2bJRagcregGjz9cCxIrSrs61bt0bMhFZ+RArxxPHee+/J+PHjvd2rVq3qTTOBAAII\nIJB6Af3uvuGGG+Tiiy+Wjz76SNasWSNHHXWU7Q6xTp06qU8QR0QAAQQQQAABBBBAAIGME4i7\nAkS7LjnzzDPtW7g/+9nPbBcj2m3FzJkz7Zv7mzdvtn0qqkDXrl1F+/EmIBBLwO1KLdp4DrH2\nZV1uC+jb4pFagdCKKDuvi2rVqsn+/fu9zO3du1dq167tzcczEU8cF110kfTv399G99JLL8kt\nt9wST9RsgwACCCCQJAFt1bdo0SJ5//33RccfJCCAAAIIIBCvgPY68T//8z+2DNEXd7X3Em1V\nri/v0vI4XkW2QwABBLJDIO4KEB04au3atfbvgw8+CMv90KFDvWU6XggVIB4HEwggkGCBGjVq\nyM6dO8NibdWqVdgyFqRfQPtrX7p0adSEaDeKrVu3jrpeK7ZKS0u99Xoz07RpU28+nol44tBK\nEv3ToK1MIlWyxXMstkEAAQQQSIyAjvnlvjCTmBiJBQEEEEAgFwS2bNliu2jXMWz1XuO4446T\nN998044h+Omnn8qkSZPs+HO5YEEeEUAAAQTK0QVWZbswARsBBBBIlECVKlUiRhXYTVLEDViY\nFgEdgPyZZ57xjn348GGvAuvoo4+2lR+xKkB69eolTz31lPTp00e0m7PZs2fL+eefb+Nbt26d\nbR1y5JFHevFHmogVR6TtWYYAAgggkH4BbYmn3/eDBg2SHj16SJMmTYIeWJWUlEiLFi3Sn1BS\ngAACCCDgK4EHHnhADh06ZMce1Zfn3PDJJ5/IySefLLfddpvt1cRdzicCCCCAQHYLxN0CJLsZ\nyB0CCGSSQIMGDUS73QsMhYWFdlDUwGVM+0PgwQcfFP0LDDqGx3PPPSfTpk2Tbt26Ba4Km9YH\nXNqKRFsaagsNbbreu3dvu52OIaQtQrSblFghVhyx9mMdAggggED6BJ588kn56quv7J9WpoeG\nKVOmeBXioeuYRwABBBDIXYGFCxfKtddeK4GVH6rRqVMn0Rej3n33XSpAcvfyIOcIIJCDAlSA\n5OBJJ8sIZLqAPuzWcYncoH24Hjx4UG688UZ3EZ8+F6hbt67cfPPNduyouXPnysCBA6OmWMd8\nGTlypAwfPty2AAnsDmXEiBER95s8eXLQ8lhxBG3IDAIIIICAbwTKaoGu3+0EBBBAAAEEQgXq\n1Kkjn332WehiOXDggHzxxRf2xaqwlSxAAAEEEMhagfyszRkZQwCBrBU46aSTZObMmaIP0bVF\ngDZj1gFSq1atmrV5ztaMaZdWK1eujCt71atXr3Rf8ImII67EshECCCCAQKUFtIIj1l+lD0AE\nCCCAAAJZKXDuuefKjBkz5KGHHrKtCLUL3vnz59sXqrQluo4LQkAAAQQQyB0BWoDkzrkmpwhk\nlUC7du3kjjvusC0Dxo8fL9otFsGfAosWLZKvv/7aS5wOLr57927brZXeiNx///3eOiYQQAAB\nBBBAAAEEEEAAgcoInHfeefY+Ucf60PGktCusXbt2ibYMefbZZ6Ws8QMrc2z2RQABBBDwn0DK\nK0D0wdfvfvc7GTJkiB341iWZNWuWfRimDzF/8YtfeA8ztZ9/rblftmyZ6FvfF154oWh3Nxpi\nrVuyZIlonFrI9e/fXzp37uweSmKt8zZiAgEEEEAgIQLah/vjjz8eFJe+0duwYUO55557yhwD\nJGhHZhBAAAEEEEAAAQTSLqADTL/zzjuyf//+tKcl2Qn48ssvRbtszebQvn17adWqVVZlUe8z\nLr/8cvnoo49kzZo1Nn89evRg3MisOstkBgEEEIhPIKUVIHv27JE//OEPMmfOHFsp4SZRKyr0\nTwep0sGobrrpJnnhhRdsRcfdd98tLVq0kIsuukj++Mc/2reGr776artrtHXr16+XMWPGyJVX\nXila4TJu3DjRN8T1jfFY69z08IkAAgggkDiBRx55xH73B8aoXV+5ldmBy5lGAAEEEEAAAQQQ\n8L/AggUL5LrrrvN/QhOQwtmzZ4v+ZXPo0qWzeQYzKauy+P3339uXbLdv327HEfzuu+/kxRdf\ntHk8++yz5eijj86q/JIZBBBAAIHoAimtALnsssvkxBNPlEaNGgWlaNKkSbbC4rjjjpNOnTqJ\nbqe19LVr15Zvv/1WHnjgAdv/769//WtbSXLFFVeIvoURbd306dOlZ8+e0rdvX3ucDRs22FYk\no0ePlljrghLFDAIIIIBAQgS0skP/CAgggAACCCCAAALZIXDw4EGbkT9ctFuObnw4OzKVo7m4\nc3pVOzh4NmX/448/tq3MtYWS9jJSVFQUlD2t/KACJIiEGQQQQCCrBVJaATJ27FjRppXajZUb\n9IeTtspo27atu0jatGljKzfq169vl2tXKRq0n0ZtRbJlyxZZvXp1zHXdu3f34tO49Q0VDbpf\ntHXuDq+99ppMmzbNzq5atcoOsuyu4xMBBBBAAAEEEEAAAQQQQAABBESa1TksP2pABUgmXwtV\nq4jsyeQMREj7Y489Zl++1a7L6tatG2ELFiGAAAII5JJASitAtPIjNGzcuFEKCwuluLjYW1Wz\nZk3ZunWrfQuhVq1a3nKdcNdppUm869x9dP/Q/QLX6XoNWsmyadMmO60D9boVMHYB/yCAAAII\nIIAAAggggAACCCCAAAII+FJAewEZMGAAlR8VPDvXX3+DHTelgrtnxG6rTJdo+qr1gAEDMyK9\nFU1k8+bN5bHHHq3o7uyHQNYIpLQCJJJatWrVbEXH4cOHvf7g9+3bJ/qfVCtFdDow6HydOnWk\nevXqUddpnIGDse3du9d2p6XxxFrnHkcHTdc/DU8//bQd3M1dxycCCCCAAAIIIIAAAggggAAC\nCCCAgD8FTjvtNPn73/8u2g06oXwC+iztjTdel31OHTkgwS8kly8mv29dxyZw3dLtfk9ohdNX\nJNtl2bKloj3v6IvnBARyWSDt/wO0MkP7Y9SWIO7YIFpb361bN9tnvE67YefOnfY/7hFHHCEN\nGzaUaOs0ntLSUnc3u13Tpk3tfKx13g5MIIAAAggggAACCCCAAAIIIIAAAghknMDAgQNl4sSJ\ncsEFF0iPHj3CWoJot+gtW7bMuHylIsFuDyjbnday1Tk2FYfkGEkSqJf3uRTnLUpS7ESLQGYJ\n5PshuTpg+ZQpU+TQoUOmdnKZrFixQk444QQ5+eST7ZgdixYtEm0h8uc//1lOPfVU21Ik1rpe\nvXqJ9vW4efNm2bZtm8yePdsb9yPWOj9YkAYEEEAAAQQQQAABBBBAAAEEEEAAgYoJTJgwQb74\n4gv7nOmGG26QoUOHBv3Nnz+/YhGzFwIIIIBARgqkvQWIql199dUyatQoGTx4sB1v45ZbbvFq\n6G+77Ta7rnbt2qKDoo8bN85CV61aVaKtKykpkXnz5tkCTru80tr93r172/1irbMb8A8CCCCA\nAAIIIIAAAggggAACCCCAQEYK6CDo+kdAAAEEEEBABdJSATJ58uQg/caNG9vmiVu2bLEVH26T\nO91ImytqBYZ2f6XdZQWGaOt0/5EjR8rw4cNtN1qBA6zHWhcYN9MIIIAAAggggAACCCCAAAII\nIIAAAggggAACCCCQuQJpqQCJxlWvXr2IqwoKCsIqP9wNY63TgdKjhVjrou3DcgQQQAABBBBA\nAAEEEEAgEwRWrVpluwTOhLRWNI07duyw3SNrl8nZHHQMTMYryOYzTN4QQAABBBBAIJkCvqoA\nSWZGiRsBBBBAAAEEEEAAAQQQyAUBHVuxb9++cuDAgVzIrgwZMiSr81lUVEUWL/7UVvZkdUbJ\nHAIIIIAAAgggkAQBKkCSgEqUCCCAAAIIIIAAAggggEC6BLQCRCs/th1uKzuco9KVDI6bAIFa\ned9KnQNfyeHDh6kASYAnUSCAAAIIIIBA7glQAZJ755wcI4AAAggggAACCCCAQBYLuGMq7pda\nskeaZHFOsz9rVWVT9meSHCKAAAIIIIAAAkkUyE9i3ESNAAIIIIAAAggggAACCCCAAAIIIIAA\nAggggAACCKRFgAqQtLBzUAQQQAABBBBAAAEEEEAAAQQQQAA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BBAAIFKCMR6bjRi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AJBFPAZANF2vPzyy6Y5kyZNMut/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\nQIAAgeQE/gNl2Fg5oF7TZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 800
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggpubr)\n",
    "\n",
    "# Creates binary for missing data\n",
    "recent$missing <- as.integer(complete.cases(recent))\n",
    "recent$missing <- ifelse(recent$missing==0,1,0)\n",
    "\n",
    "# Pulls proportion of counties with missing data by state\n",
    "#aggregate(recent['missing'], list(recent$state), mean)\n",
    "\n",
    "# Creates boxplots, coloring by missing binary\n",
    "pop <- ggplot(data=recent, aes(y=population, x=as.factor(missing), fill=as.factor(missing))) + \n",
    "        geom_boxplot() + theme_classic() + ggtitle(\"Distribution of Population\")+ theme(legend.position = \"none\", axis.title.x=element_blank()) +\n",
    "        ylim(c(0,300000))+ scale_fill_manual(values=c(Pitt.Blue, Pitt.Gold))\n",
    "un <- ggplot(data=recent, aes(y=unemp_rate, x=as.factor(missing), fill=as.factor(missing))) + \n",
    "        geom_boxplot() + theme_classic() + ggtitle(\"Distribution of Unemployment Rate\")+ theme(legend.position = \"none\", axis.title.x=element_blank())+ \n",
    "        scale_fill_manual(values=c(Pitt.Blue, Pitt.Gold))\n",
    "rr <- ggplot(data=recent, aes(y=renter_rate, x=as.factor(missing), fill=as.factor(missing))) + \n",
    "        geom_boxplot() + theme_classic() + ggtitle(\"Distribution of Renter Rate\")+ theme(legend.position = \"none\", axis.title.x=element_blank())+ \n",
    "        scale_fill_manual(values=c(Pitt.Blue, Pitt.Gold))\n",
    "nw <- ggplot(data=recent, aes(y=nonwhite_rate, x=as.factor(missing), fill=as.factor(missing))) + \n",
    "        geom_boxplot() + theme_classic() + ggtitle(\"Distribution of Nonwhite Rate\")+ theme(legend.position = \"none\", axis.title.x=element_blank())+ \n",
    "        scale_fill_manual(values=c(Pitt.Blue, Pitt.Gold))\n",
    "tgr <- ggplot(data=recent, aes(y=r_totalgap_rate, x=as.factor(missing), fill=as.factor(missing))) + \n",
    "        geom_boxplot() + theme_classic() + ggtitle(\"Distribution of Shortage Rate\")+ theme(legend.position = \"none\", axis.title.x=element_blank())+ \n",
    "        scale_fill_manual(values=c(Pitt.Blue, Pitt.Gold))\n",
    "col <- ggplot(data=recent, aes(y=college_rate, x=as.factor(missing), fill=as.factor(missing))) + \n",
    "        geom_boxplot() + theme_classic() + ggtitle(\"Distribution of College Rate\")+ theme(legend.position = \"none\", axis.title.x=element_blank())+ \n",
    "        scale_fill_manual(values=c(Pitt.Blue, Pitt.Gold))\n",
    "cb <- ggplot(data=recent, aes(y=costbur_rate, x=as.factor(missing), fill=as.factor(missing))) + \n",
    "        geom_boxplot() + theme_classic() + ggtitle(\"Distribution of Cost Burden Rate\")+ theme(legend.position = \"none\", axis.title.x=element_blank()) + \n",
    "        scale_fill_manual(values=c(Pitt.Blue, Pitt.Gold))\n",
    "\n",
    "options(repr.plot.width = 16, repr.plot.height = 8, repr.plot.res = 100)\n",
    "\n",
    "# Display boxplots in grid form\n",
    "bplots <- ggarrange(pop,un,rr,nw,tgr,col,cb, ncol=4, nrow=2)\n",
    "annotate_figure(bplots,\n",
    "               top = text_grob(\"Comparing County Characteristics \\nAll Counties (Blue) vs Counties with Missing data (Gold)\\n\", face = \"bold\", size = 20)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see very similar measures of spread and central tendency for the characteristics between counties with and without missing eviction data. That being said, the there are geographic trends that exist between counties with missing data. The following are a few disclaimers regarding missing observations:\n",
    "\n",
    "- The following states have missing data for every county: Alaska, Arkansas, North Dakota, South Dakota\n",
    "- The following states have missing data for over 50% of counties: Arizona, Maryland, New York\n",
    "- Eviction Lab has reported that not all records were collectible.  Eviction record policies are strict in certain areas of the country which makes acquiring data impossible.\n",
    "- Eviction Lab purchased some of their data from LexisNexis which was last updated in 2017 and American Information Research Services (AIRS) which last updated its records in 2018.  It is possible that by now the records have been updated to include more backdated 2008-2017 eviction data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graph shows the distribution of actual eviction rates recorded in the 3,141 geographies over the two time periods. The figure below shows a positive skew of the eviction rates, indicating that a normal curve fit by a linear regression is inaccurate. We also show a beta distribution model, which we believe is a better fit for the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ggggAACCCDgCtQlYPLCCy/4+Uc04k18Ujed8ePHG7VA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\nQAIkQAIkQAKRI/D/Adg4CiwOXOthAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 325,
       "width": 550
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(EnvStats)\n",
    "# Pull coefficients to graph beta distribution\n",
    "betacoefs <- ebeta(na.omit(alldata5yr)$evict_rate, method = \"mle\")\n",
    "\n",
    "# Create plot\n",
    "options(repr.plot.width = 11, repr.plot.height = 6.5)\n",
    "ggplot(alldata5yr, aes(x=evict_rate)) + \n",
    "    geom_histogram(aes(y = ..density..), bins = 100, fill='grey', col='black') + xlim(c(-0.05, 0.15)) +\n",
    "    stat_function(fun = dnorm, args = list(mean(na.omit(alldata5yr)$evict_rate), sd(na.omit(alldata5yr)$evict_rate)), aes(colour = Pitt.Gold), size=1.5) +\n",
    "    stat_function(fun = dbeta, args = list(betacoefs$parameters[1], betacoefs$parameters[2]), aes(colour = Pitt.Blue), size=1.5) + ylab(\"Density\")+ xlab(\"Eviction Rate\")+\n",
    "    theme_classic() + ggtitle(\"Distribution of Eviction Rates\") + theme(axis.text=element_text(size=12), plot.title=element_text(size=20,face=\"bold\"),\n",
    "        axis.title=element_text(size=14)) + \n",
    "    scale_colour_manual(\"\", values = c(Pitt.Blue, Pitt.Gold), labels = c(\"Beta Distribution\", \"Normal Distribution\")) + \n",
    "    theme(legend.text = element_text(size = 17), legend.position=\"top\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use two types of models to estimate eviction rates within US counties.  Our initial model was a standard OLS model.  We are most interested in the effects of cost burden rate and unemployment rate with respect to predicting eviction rates.\n",
    "\n",
    "We select controls from an array of variables that are held constant at the county level.  These controls include dummy variables that account for the time period and the region that the county is in. We also control for county-level demographic characteristics.\n",
    "\n",
    "The OLS model provides us with a preliminary view of how certain factors helped predict eviction rates.  Unfortunately, the model was not an optimal fit to our data.  We then decided to pursue a beta regression model that works best for observations that lie within a 0 to 1 interval.  This model produced similar coefficients as the OLS but with a better fit to our data.\n",
    "\n",
    "We included an interaction term between our two main effects (cost burden and unemployment) to account for the combined effect of these two factors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: In our beta regression model, we first transform the dependent variable (eviction rates) to make them abide to the standard unit interval (0,1). This is necessary step mathematically, as the beta distribution bounds estimates to the open (0,1) interval. The transformation we use follows the methods of [Smithson and Verkuilen, 2006](https://pubmed.ncbi.nlm.nih.gov/16594767/): \n",
    "\n",
    "$$alt\\_evictrate = \\frac{(evictrate*(n-1)+0.5)}{n}$$\n",
    "\n",
    "In the context of our data, this would transform an eviction rate of 0 to 0.00008, or an eviction rate of 0.1 would become 0.10006. This transformation allows the beta model to run without changing the interpretation of the dependent variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Table 3: Eviction Rate Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we create regions to be used as fixed effects in modeling\n",
    "\n",
    "NE.name <- c(\"Connecticut\",\"Maine\",\"Massachusetts\",\"New Hampshire\",\n",
    "              \"Rhode Island\",\"Vermont\",\"New Jersey\",\"New York\",\n",
    "              \"Pennsylvania\")\n",
    " \n",
    "MW.name <- c(\"Indiana\",\"Illinois\",\"Michigan\",\"Ohio\",\"Wisconsin\",\n",
    "              \"Iowa\",\"Kansas\",\"Minnesota\",\"Missouri\",\"Nebraska\",\n",
    "              \"North Dakota\",\"South Dakota\")\n",
    " \n",
    "S.name <- c(\"Delaware\",\"District of Columbia\",\"Florida\",\"Georgia\",\n",
    "             \"Maryland\",\"North Carolina\",\"South Carolina\",\"Virginia\",\n",
    "             \"West Virginia\",\"Alabama\",\"Kentucky\",\"Mississippi\",\n",
    "             \"Tennessee\",\"Arkansas\",\"Louisiana\",\"Oklahoma\",\"Texas\")\n",
    " \n",
    "W.name <- c(\"Arizona\",\"Colorado\",\"Idaho\",\"New Mexico\",\"Montana\",\n",
    "             \"Utah\",\"Nevada\",\"Wyoming\",\"Alaska\",\"California\",\n",
    "             \"Hawaii\",\"Oregon\",\"Washington\")\n",
    "\n",
    "region.list <- list(\n",
    "     Northeast=NE.name,\n",
    "     Midwest=MW.name,\n",
    "     South=S.name,\n",
    "     West=W.name)\n",
    "\n",
    "alldata5yr$region <- sapply(alldata5yr$state, \n",
    "                      function(x) names(region.list)[grep(x,region.list)])\n",
    "alldata5yr$region <- as.character(alldata5yr$region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==========================================================================================================\n",
      "                                        OLS: Eviction Rate               Beta Reg: Alt Eviction Rate      \n",
      "                               ___________________________________________________________________________\n",
      "                                   OLS     Interaction  Controls       Beta     Interaction  Controls     \n",
      "__________________________________________________________________________________________________________\n",
      "Main Effects                                                                                              \n",
      "                                                                                                          \n",
      "    Proportion Cost Burdened      0.077 ***   0.059 ***   0.028 ***    0.060 ***    0.071 ***    0.057 ***\n",
      "                                 (0.003)     (0.007)     (0.007)      (0.002)      (0.005)      (0.005)   \n",
      "    Unemployment Rate             0.029 **   -0.070      -0.133 ***    0.011        0.075 **     0.053    \n",
      "                                 (0.010)     (0.037)     (0.035)      (0.007)      (0.029)      (0.027)   \n",
      "    Interaction (Unemp*CostBur)               0.268 **    0.484 ***                -0.164 *     -0.042    \n",
      "                                             (0.096)     (0.091)                   (0.073)      (0.070)   \n",
      "Controls                                                                                                  \n",
      "                                                                                                          \n",
      "    Metropolitan                                          0.010 ***                              0.008 ***\n",
      "                                                         (0.001)                                (0.000)   \n",
      "    Proportion Nonwhite                                   0.008 ***                             -0.002    \n",
      "                                                         (0.001)                                (0.001)   \n",
      "    Proportion in College                                -0.004                                 -0.001    \n",
      "                                                         (0.007)                                (0.004)   \n",
      "    2013-17 Period                                        0.001                                  0.001 ** \n",
      "                                                         (0.001)                                (0.000)   \n",
      "__________________________________________________________________________________________________________\n",
      "Region Fixed Effects             No          No         Yes           No           No          Yes        \n",
      "Num. obs.                      5214        5214        5214         5214         5214         5214        \n",
      "Adj. R^2                          0.148       0.149       0.266                                           \n",
      "Log Likelihood                                                     16333.802    16336.843    16675.275    \n",
      "==========================================================================================================\n",
      "*** p < 0.001; ** p < 0.01; * p < 0.05\n"
     ]
    }
   ],
   "source": [
    "library(lfe)\n",
    "library(xtable)\n",
    "library(IRdisplay)\n",
    "library(repr)\n",
    "library(mfx)\n",
    "library(betareg)\n",
    "library(texreg)\n",
    "\n",
    "alldata5yr$alt_evrate <- (alldata5yr$evict_rate * (6281) + 0.5) / 6282\n",
    "\n",
    "# Run models\n",
    "ols <- lm(evict_rate ~ costbur_rate+unemp_rate, data=alldata5yr)\n",
    "ols_int <- lm(evict_rate ~ costbur_rate+unemp_rate+costbur_rate*unemp_rate, data=alldata5yr)\n",
    "ols_controls <- lm(evict_rate ~ costbur_rate+unemp_rate+costbur_rate*unemp_rate+metro+nonwhite_rate+college_rate+as.factor(period)+as.factor(region), data=alldata5yr)\n",
    "betam <- betamfx(alt_evrate ~ costbur_rate+unemp_rate, data=alldata5yr)\n",
    "betam_int <- betamfx(alt_evrate ~ costbur_rate+unemp_rate+costbur_rate*unemp_rate, data=alldata5yr)\n",
    "betam_controls <- betamfx(alt_evrate ~ costbur_rate+unemp_rate+costbur_rate*unemp_rate+metro+nonwhite_rate+college_rate+as.factor(period)+as.factor(region), data=alldata5yr)\n",
    "\n",
    "# Extracts predictions (using same beta model, different formatting to use predict command)\n",
    "beta_controls <- betareg(alt_evrate ~ costbur_rate+unemp_rate+unemp_rate*costbur_rate+metro+nonwhite_rate+college_rate+as.factor(period)+as.factor(region), data=alldata5yr)\n",
    "alldata5yr$pred_evrate <- predict(beta_controls, alldata5yr)\n",
    "alldata5yr$pred_error <- alldata5yr$pred_evrate-alldata5yr$evict_rate\n",
    "\n",
    "# Creates results table\n",
    "a <- screenreg(list(ols, ols_int, ols_controls, betam, betam_int, betam_controls), custom.header = list(\"OLS: Eviction Rate\"=1:3, \"Beta Reg: Alt Eviction Rate\"=4:6),\n",
    "             custom.model.names = c(\"    OLS\", \"Interaction\", \" Controls\", \"    Beta\", \"Interaction\", \"Controls\"), digits=3,\n",
    "             custom.coef.map = list(\"costbur_rate\" = \"Proportion Cost Burdened\", \"unemp_rate\" = \"Unemployment Rate\", \"costbur_rate:unemp_rate\" = \"Interaction (Unemp*CostBur)\", \"metro\" = \"Metropolitan\", \"nonwhite_rate\" = \"Proportion Nonwhite\", \"college_rate\"=\"Proportion in College\", \"as.factor(period)2013-2017\"=\"2013-17 Period\"),\n",
    "             groups = list(\"Main Effects\" = 1:3, \"Controls\" = 4:7), inner.rule = \"_\", outer.rule = \"=\",\n",
    "             include.rsquared = F, include.pseudors = F, custom.gof.rows=list(\"Region Fixed Effects\" = c(\"No\", \"No\", \"Yes\", \"No\", \"No\", \"Yes\")), reorder.gof = c(1, 3, 2,4), column.spacing=0)\n",
    "write.table(a, quote = FALSE, eol='', row.names=FALSE, col.names=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe below is comprised of summary statistics of the actual eviction rates, predicted eviction rates, and their difference. The means of the actual and predicted eviction rates are nearly identical.  The table also displays a small error that is normally distributed around mean 0. Overall, the model gives a mean squared error of 0.0002."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate MSE:\n",
    "#recent_nona <- na.omit(recent)\n",
    "#mean((recent_nona$evict_rate - recent_nona$pred_evrate)^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       " <thead>\n",
       "  <tr>\n",
       "   <th style=\"text-align:left;\">   </th>\n",
       "   <th style=\"text-align:left;\"> Actual Eviction Rates </th>\n",
       "   <th style=\"text-align:left;\"> Predicted Eviction Rates </th>\n",
       "   <th style=\"text-align:left;\"> Prediction Error </th>\n",
       "  </tr>\n",
       " </thead>\n",
       "<tbody>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\">  </td>\n",
       "   <td style=\"text-align:left;\"> Min.   :0.0000 </td>\n",
       "   <td style=\"text-align:left;\"> Min.   :0.002753 </td>\n",
       "   <td style=\"text-align:left;\"> Min.   :-0.1585 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\">  </td>\n",
       "   <td style=\"text-align:left;\"> 1st Qu.:0.0048 </td>\n",
       "   <td style=\"text-align:left;\"> 1st Qu.:0.011660 </td>\n",
       "   <td style=\"text-align:left;\"> 1st Qu.:-0.0054 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\">  </td>\n",
       "   <td style=\"text-align:left;\"> Median :0.0121 </td>\n",
       "   <td style=\"text-align:left;\"> Median :0.015989 </td>\n",
       "   <td style=\"text-align:left;\"> Median : 0.0041 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\">  </td>\n",
       "   <td style=\"text-align:left;\"> Mean   :0.0173 </td>\n",
       "   <td style=\"text-align:left;\"> Mean   :0.017536 </td>\n",
       "   <td style=\"text-align:left;\"> Mean   : 0.0004 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\">  </td>\n",
       "   <td style=\"text-align:left;\"> 3rd Qu.:0.0243 </td>\n",
       "   <td style=\"text-align:left;\"> 3rd Qu.:0.022811 </td>\n",
       "   <td style=\"text-align:left;\"> 3rd Qu.: 0.0096 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\">  </td>\n",
       "   <td style=\"text-align:left;\"> Max.   :0.1737 </td>\n",
       "   <td style=\"text-align:left;\"> Max.   :0.047190 </td>\n",
       "   <td style=\"text-align:left;\"> Max.   : 0.0345 </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "   <td style=\"text-align:left;\">  </td>\n",
       "   <td style=\"text-align:left;\"> NA's   :542 </td>\n",
       "   <td style=\"text-align:left;\"> NA's   :4 </td>\n",
       "   <td style=\"text-align:left;\"> NA's   :543 </td>\n",
       "  </tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent <- filter(alldata5yr, period=='2013-2017')\n",
    "df <- data.frame(recent$county_state, recent$evict_rate, recent$pred_evrate, recent$pred_error)\n",
    "\n",
    "kable(summary(df[2:4]), col.names=c('Actual Eviction Rates','Predicted Eviction Rates','Prediction Error'), format = \"html\") %>%\n",
    "    as.character() %>%\n",
    "    display_html() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict eviction rates in each county in the 2013-2017 period, we decided to use the beta regression model that incorporates the interaction term, controls, and region fixed effects. The following graph visualizes the inteaction between cost burden and unemployment in this model. We see that in counties with higher rates of cost burden, a negative unemployment shock increases predicted evictions by a wider margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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dVyb5vOs2qLrjWVjlM9zpvR9jdcrSvzIYAAAgggMJgAAcXBhErTdUNrzXn9KwUQ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\nQaTs+nUTePHFFyf1xk/qdQMa11fuMU9AUcsreyu7/fqbios1N+4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eS61/feey+Boms1TkAAAQQQQAABBBBAAIF4FdhXtt+3VmHTSMLA0LDM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\nBYGGhkYzddgeLRg6mrDKGmGoFY0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BBAIL4EtLCHP/gLs+6fXSXYnv6roaA9etB+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\ncA4CC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rZA1PEf/cu///1v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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 325,
       "width": 650
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(warn=-1)\n",
    "\n",
    "library(sjPlot)\n",
    "options(repr.plot.width = 13, repr.plot.height = 6.5)\n",
    "suppressMessages(plot_model(beta_controls, type = \"pred\", terms = c(\"unemp_rate\",\"costbur_rate[0,0.2,0.4,0.6]\"), ci.lvl = NA, line.size=1.5)+ylim(c(0,0.035)) + \n",
    "    theme_classic() + ggtitle(\"Predicted Eviction Rates by Unemployment and Cost Burden\") + xlab('Unemployment Rate') + ylab('Predicted Eviction Rate') +\n",
    "    theme(axis.text=element_text(size=12), plot.title=element_text(size=20,face=\"bold\"), axis.title=element_text(size=14), legend.title=element_text(size=16)) + \n",
    "    scale_colour_manual(\"Proportion \\nCost Burdened\", values = c(\"red\", \"darkgreen\", Pitt.Blue, Pitt.Gold), labels = c(\"0\", \"0.2\", \"0.4\", \"0.6\")) + \n",
    "    theme(legend.text = element_text(size = 14)) + theme(panel.grid.major = element_line(colour = \"grey\")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Allegheny <- filter(recent, county_state==\"Allegheny County, PA\")\n",
    "Allegheny$unemp_rate <- 0.0535\n",
    "Allegheny2 <- Allegheny\n",
    "Allegheny2$unemp_rate <- 0.1535\n",
    "\n",
    "#predict(beta_controls, Allegheny)\n",
    "#predict(beta_controls, Allegheny2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict the effect of an unemployment shock, the unemployment rate variable must be updated, and the model can be run accordingly. In Allegheny County, PA, the uenmployment rate for 2013-2017 was 0.0535. If this were to increase by 10% to 0.1535, the predicted eviction rate would increase from 1.75% to 2.19%, an increase of 8,290 rental households. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One key component of this project is visualizing shortages in housing affordability. Affordability is defined in the CHAS dataset relative to the HUD Area Median Family Income (HAMFI). A rental unit is considered affordable if the renter is not spending more than 30% of their income on housing. \n",
    "\n",
    "We calculate shortage by the number of renters who “up-buy” (rent above what is considered affordable), minus affordable vacancies. Therefore, the shortage only captures the lack of availability of rental housing units.\n",
    "\n",
    "The housing affordability map for 2013-2017 can be viewed [here](https://htmlpreview.github.io/?https://github.com/MasonPutt/4E-Capstone-Project/blob/main/Shortage_map.html). This map shows that there are high concentrations of housing shortages on both the western and northeastern coasts.\n",
    "\n",
    "For more specific state and county characteristics, you can view our [interactive dashboard](https://datastudio.google.com/embed/reporting/ba1b4842-a802-43b2-accb-c09e67854e96/page/jhJFC)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Reflection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data**\n",
    "\n",
    "Through data cleaning and analysis we arrived at a model that best fits and estimates eviction rates in US counties. We completed the data cleaning process on RStudio, documented [here](https://github.com/MasonPutt/4E-Capstone-Project/blob/main/Capstone%20Data%20Cleaning%20Counties.Rmd). This involved accumulating data from multiple sources, manipulating them into a merged format, and aggregating them to assure data compatibility. This process tested our coding skills, and forced us to find unique solutions to incombatibility issues that came up. \n",
    "\n",
    "**Modeling**\n",
    "\n",
    "Finding a model that really fit our data proved to be challenging.  Starting with the OLS model proved to be a good first step but it was clear that the approach was not optimal.  After some trial and error we settled on a Tobit model which censored the data between 0 and 1, fitting the proportional nature of our variables.  It was not until creating predictions that problems arose.  This caused us to shift to a beta regression close to the deadline.  Ultimately beta regression was the best model choice but it was enlightening how quickly things can change in a project.  This project has shown that it is always useful to revisit different aspects of the project even if they are considered “complete”.  You can always go back and improve on thing and it may illuminate some underlying problem that can be fixed soon.\n",
    "\n",
    "**Visualization**\n",
    "\n",
    "The data visualization portion of the projected presented an opportunity to learn more about Python and Google Data Studio. The interactive map of the US is an example of using Python’s Plotly package, which includes a choropleth map structure. We additionally used Google Data Studio in order to make an intuitive dropdown menu to view housing affordability at different income levels. There are several ways to go about visualization and this project was a great opportunity to learn about different tools.\n",
    "\n",
    "**Putting it Together**\n",
    "\n",
    "As a whole this project was very informative and stimulating.  What surprised me the most was how effective group work was.  We each had our roles to play in this project but meeting as a group helped work through some of the bumps in the roads we hit individually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suggestions for Further Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Aggregation at a city or tract level\n",
    "- Further exploration of potential controls in the prediction of eviction rates\n",
    "- Accounting for more localized policy differences and effects\n",
    "- Considering distance to employment and affordable vacancies\n",
    "- Searching for more complete measures of eviction rates"
   ]
  }
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