{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Manipulation of kiva loans data and some additional graphs\n",
    "\n",
    "* In this notebook I join the externally downloaded Philippines economic indicator data with the philippines subset of kiva loans data on **REGION** field.\n",
    "\n",
    "\n",
    "* I downloaded the economic indicator data for philippines from [here](http://countrystat.psa.gov.ph/).\n",
    "\n",
    "\n",
    "\n",
    "* Lets first take a look at the Kiva loans data table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id</th><th scope=col>funded_amount</th><th scope=col>loan_amount</th><th scope=col>activity</th><th scope=col>sector</th><th scope=col>use</th><th scope=col>country_code</th><th scope=col>country</th><th scope=col>region</th><th scope=col>currency</th><th scope=col>partner_id</th><th scope=col>posted_time</th><th scope=col>disbursed_time</th><th scope=col>funded_time</th><th scope=col>term_in_months</th><th scope=col>lender_count</th><th scope=col>tags</th><th scope=col>borrower_genders</th><th scope=col>repayment_interval</th><th scope=col>date</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>653092                                    </td><td>175                                       </td><td>175                                       </td><td>Pigs                                      </td><td>Agriculture                               </td><td>to buy piglets and feed                   </td><td>PH                                        </td><td>Philippines                               </td><td>Liloy-Dela Paz                            </td><td>PHP                                       </td><td>126                                       </td><td>2014-01-02 00:25:40+00:00                 </td><td>2013-12-12 08:00:00+00:00                 </td><td>2014-01-02 01:42:51+00:00                 </td><td>8                                         </td><td>6                                         </td><td>                                          </td><td>female                                    </td><td>irregular                                 </td><td>2014-01-02                                </td></tr>\n",
       "\t<tr><td>653149                                    </td><td>175                                       </td><td>175                                       </td><td>Pigs                                      </td><td>Agriculture                               </td><td>to purchase feed and vitamins for her pigs</td><td>PH                                        </td><td>Philippines                               </td><td>Tanjay, Negros Oriental                   </td><td>PHP                                       </td><td>145                                       </td><td>2014-01-02 04:18:30+00:00                 </td><td>2013-12-06 08:00:00+00:00                 </td><td>2014-01-02 07:47:28+00:00                 </td><td>8                                         </td><td>7                                         </td><td>                                          </td><td>female                                    </td><td>irregular                                 </td><td>2014-01-02                                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllll}\n",
       " id & funded\\_amount & loan\\_amount & activity & sector & use & country\\_code & country & region & currency & partner\\_id & posted\\_time & disbursed\\_time & funded\\_time & term\\_in\\_months & lender\\_count & tags & borrower\\_genders & repayment\\_interval & date\\\\\n",
       "\\hline\n",
       "\t 653092                                     & 175                                        & 175                                        & Pigs                                       & Agriculture                                & to buy piglets and feed                    & PH                                         & Philippines                                & Liloy-Dela Paz                             & PHP                                        & 126                                        & 2014-01-02 00:25:40+00:00                  & 2013-12-12 08:00:00+00:00                  & 2014-01-02 01:42:51+00:00                  & 8                                          & 6                                          &                                            & female                                     & irregular                                  & 2014-01-02                                \\\\\n",
       "\t 653149                                     & 175                                        & 175                                        & Pigs                                       & Agriculture                                & to purchase feed and vitamins for her pigs & PH                                         & Philippines                                & Tanjay, Negros Oriental                    & PHP                                        & 145                                        & 2014-01-02 04:18:30+00:00                  & 2013-12-06 08:00:00+00:00                  & 2014-01-02 07:47:28+00:00                  & 8                                          & 7                                          &                                            & female                                     & irregular                                  & 2014-01-02                                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "id | funded_amount | loan_amount | activity | sector | use | country_code | country | region | currency | partner_id | posted_time | disbursed_time | funded_time | term_in_months | lender_count | tags | borrower_genders | repayment_interval | date | \n",
       "|---|---|\n",
       "| 653092                                     | 175                                        | 175                                        | Pigs                                       | Agriculture                                | to buy piglets and feed                    | PH                                         | Philippines                                | Liloy-Dela Paz                             | PHP                                        | 126                                        | 2014-01-02 00:25:40+00:00                  | 2013-12-12 08:00:00+00:00                  | 2014-01-02 01:42:51+00:00                  | 8                                          | 6                                          |                                            | female                                     | irregular                                  | 2014-01-02                                 | \n",
       "| 653149                                     | 175                                        | 175                                        | Pigs                                       | Agriculture                                | to purchase feed and vitamins for her pigs | PH                                         | Philippines                                | Tanjay, Negros Oriental                    | PHP                                        | 145                                        | 2014-01-02 04:18:30+00:00                  | 2013-12-06 08:00:00+00:00                  | 2014-01-02 07:47:28+00:00                  | 8                                          | 7                                          |                                            | female                                     | irregular                                  | 2014-01-02                                 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  id     funded_amount loan_amount activity sector     \n",
       "1 653092 175           175         Pigs     Agriculture\n",
       "2 653149 175           175         Pigs     Agriculture\n",
       "  use                                        country_code country    \n",
       "1 to buy piglets and feed                    PH           Philippines\n",
       "2 to purchase feed and vitamins for her pigs PH           Philippines\n",
       "  region                  currency partner_id posted_time              \n",
       "1 Liloy-Dela Paz          PHP      126        2014-01-02 00:25:40+00:00\n",
       "2 Tanjay, Negros Oriental PHP      145        2014-01-02 04:18:30+00:00\n",
       "  disbursed_time            funded_time               term_in_months\n",
       "1 2013-12-12 08:00:00+00:00 2014-01-02 01:42:51+00:00 8             \n",
       "2 2013-12-06 08:00:00+00:00 2014-01-02 07:47:28+00:00 8             \n",
       "  lender_count tags borrower_genders repayment_interval date      \n",
       "1 6                 female           irregular          2014-01-02\n",
       "2 7                 female           irregular          2014-01-02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# supress warnings globally - just for better vieweing of notebook. Warnings were causing too much unwanted clutter to be shown.\n",
    "options(warn=-1)\n",
    "\n",
    "suppressMessages(library(ggplot2))\n",
    "suppressMessages(library(dplyr))\n",
    "suppressMessages(library(repr))\n",
    "suppressMessages(library(corrplot))\n",
    "suppressMessages(library(gridExtra))\n",
    "\n",
    "kiva_loans <- read.csv(\"kiva_loans.csv\", header=T)\n",
    "kiva_regions <- read.csv(\"kiva_mpi_region_locations.csv\", header=T)\n",
    "\n",
    "philp_loans <- kiva_loans %>% filter(country == \"Philippines\")\n",
    "philp_regions <- kiva_regions %>% filter(country == \"Philippines\")\n",
    "\n",
    "head(philp_loans,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Each row represents a loan given out by Kiva **in Philippines**. It has regional details, loan receiver details etc.. \n",
    "* Kiva connects online lenders (like us) to needy people in the world through their partners.\n",
    "* **Goal of this notebook is to connect the external data to the Philippines subset of the above data frame**\n",
    "\n",
    "### Note the column 'REGION'. That is the column which we will use as the key to connect the external data.\n",
    "\n",
    "### Lets check the levels of REGION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "12696"
      ],
      "text/latex": [
       "12696"
      ],
      "text/markdown": [
       "12696"
      ],
      "text/plain": [
       "[1] 12696"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(levels(philp_loans$region))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are 12.5k+ levels for region which is huge. Lets take a look at a sample of those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>region</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>                                           </td><td>81                                         </td></tr>\n",
       "\t<tr><td>214 Bonifacio St. Calaocan, Alicia, Isabela</td><td> 1                                         </td></tr>\n",
       "\t<tr><td>26 barrientos st. oroquieta city           </td><td> 1                                         </td></tr>\n",
       "\t<tr><td>Abolo, Amulung, Cagayan                    </td><td>17                                         </td></tr>\n",
       "\t<tr><td>Abra, Santiago City, Isabela               </td><td> 1                                         </td></tr>\n",
       "\t<tr><td>Abuleg, Dinalungan, Aurora                 </td><td> 1                                         </td></tr>\n",
       "\t<tr><td>Abut Quezon, Isabela                       </td><td> 8                                         </td></tr>\n",
       "\t<tr><td>Abuyog, Leyte                              </td><td>43                                         </td></tr>\n",
       "\t<tr><td>Acad, Zamboanga del Sur                    </td><td> 9                                         </td></tr>\n",
       "\t<tr><td>Addalam Jones, Isabela                     </td><td> 1                                         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " region & count\\\\\n",
       "\\hline\n",
       "\t                                             & 81                                         \\\\\n",
       "\t 214 Bonifacio St. Calaocan, Alicia, Isabela &  1                                         \\\\\n",
       "\t 26 barrientos st. oroquieta city            &  1                                         \\\\\n",
       "\t Abolo, Amulung, Cagayan                     & 17                                         \\\\\n",
       "\t Abra, Santiago City, Isabela                &  1                                         \\\\\n",
       "\t Abuleg, Dinalungan, Aurora                  &  1                                         \\\\\n",
       "\t Abut Quezon, Isabela                        &  8                                         \\\\\n",
       "\t Abuyog, Leyte                               & 43                                         \\\\\n",
       "\t Acad, Zamboanga del Sur                     &  9                                         \\\\\n",
       "\t Addalam Jones, Isabela                      &  1                                         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "region | count | \n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "|                                             | 81                                          | \n",
       "| 214 Bonifacio St. Calaocan, Alicia, Isabela |  1                                          | \n",
       "| 26 barrientos st. oroquieta city            |  1                                          | \n",
       "| Abolo, Amulung, Cagayan                     | 17                                          | \n",
       "| Abra, Santiago City, Isabela                |  1                                          | \n",
       "| Abuleg, Dinalungan, Aurora                  |  1                                          | \n",
       "| Abut Quezon, Isabela                        |  8                                          | \n",
       "| Abuyog, Leyte                               | 43                                          | \n",
       "| Acad, Zamboanga del Sur                     |  9                                          | \n",
       "| Addalam Jones, Isabela                      |  1                                          | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   region                                      count\n",
       "1                                              81   \n",
       "2  214 Bonifacio St. Calaocan, Alicia, Isabela  1   \n",
       "3  26 barrientos st. oroquieta city             1   \n",
       "4  Abolo, Amulung, Cagayan                     17   \n",
       "5  Abra, Santiago City, Isabela                 1   \n",
       "6  Abuleg, Dinalungan, Aurora                   1   \n",
       "7  Abut Quezon, Isabela                         8   \n",
       "8  Abuyog, Leyte                               43   \n",
       "9  Acad, Zamboanga del Sur                      9   \n",
       "10 Addalam Jones, Isabela                       1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "philp_loans %>% group_by(region) %>% summarise(count = n()) %>% head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that the REGION field is actually a concatenation of multiple granular level region fields like County, City, State.\n",
    "\n",
    "### Let us extract the region field at the top most level of granularity. We will use that in our join because the key in the external table is at that level. I confirmed that the right most field in the concatenation represents such a field. \n",
    "\n",
    "\n",
    "### Extracting rightmost field from each REGION field above using RegExp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Liloy-Dela Paz'</li>\n",
       "\t<li>'Negros Oriental'</li>\n",
       "\t<li>'Palawan'</li>\n",
       "\t<li>'Negros Oriental'</li>\n",
       "\t<li>'Isabela'</li>\n",
       "\t<li>'Pagadian - Lower Bagong Silang Pagadian City'</li>\n",
       "\t<li>'Palawan'</li>\n",
       "\t<li>'Zamboanga del norte'</li>\n",
       "\t<li>'Misamis Occidental'</li>\n",
       "\t<li>'Zamboanga del Norte'</li>\n",
       "\t<li>'Negros Oriental'</li>\n",
       "\t<li>'Plaridel-Cebulin'</li>\n",
       "\t<li>'Misamis Occidental'</li>\n",
       "\t<li>'Misamis Occidental'</li>\n",
       "\t<li>'Misamis Occidental'</li>\n",
       "\t<li>'Misamis Occidental'</li>\n",
       "\t<li>'Siquijor'</li>\n",
       "\t<li>'Cebu'</li>\n",
       "\t<li>'Cebu'</li>\n",
       "\t<li>'Misamis Occidental'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Liloy-Dela Paz'\n",
       "\\item 'Negros Oriental'\n",
       "\\item 'Palawan'\n",
       "\\item 'Negros Oriental'\n",
       "\\item 'Isabela'\n",
       "\\item 'Pagadian - Lower Bagong Silang Pagadian City'\n",
       "\\item 'Palawan'\n",
       "\\item 'Zamboanga del norte'\n",
       "\\item 'Misamis Occidental'\n",
       "\\item 'Zamboanga del Norte'\n",
       "\\item 'Negros Oriental'\n",
       "\\item 'Plaridel-Cebulin'\n",
       "\\item 'Misamis Occidental'\n",
       "\\item 'Misamis Occidental'\n",
       "\\item 'Misamis Occidental'\n",
       "\\item 'Misamis Occidental'\n",
       "\\item 'Siquijor'\n",
       "\\item 'Cebu'\n",
       "\\item 'Cebu'\n",
       "\\item 'Misamis Occidental'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Liloy-Dela Paz'\n",
       "2. 'Negros Oriental'\n",
       "3. 'Palawan'\n",
       "4. 'Negros Oriental'\n",
       "5. 'Isabela'\n",
       "6. 'Pagadian - Lower Bagong Silang Pagadian City'\n",
       "7. 'Palawan'\n",
       "8. 'Zamboanga del norte'\n",
       "9. 'Misamis Occidental'\n",
       "10. 'Zamboanga del Norte'\n",
       "11. 'Negros Oriental'\n",
       "12. 'Plaridel-Cebulin'\n",
       "13. 'Misamis Occidental'\n",
       "14. 'Misamis Occidental'\n",
       "15. 'Misamis Occidental'\n",
       "16. 'Misamis Occidental'\n",
       "17. 'Siquijor'\n",
       "18. 'Cebu'\n",
       "19. 'Cebu'\n",
       "20. 'Misamis Occidental'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Liloy-Dela Paz\"                              \n",
       " [2] \"Negros Oriental\"                             \n",
       " [3] \"Palawan\"                                     \n",
       " [4] \"Negros Oriental\"                             \n",
       " [5] \"Isabela\"                                     \n",
       " [6] \"Pagadian - Lower Bagong Silang Pagadian City\"\n",
       " [7] \"Palawan\"                                     \n",
       " [8] \"Zamboanga del norte\"                         \n",
       " [9] \"Misamis Occidental\"                          \n",
       "[10] \"Zamboanga del Norte\"                         \n",
       "[11] \"Negros Oriental\"                             \n",
       "[12] \"Plaridel-Cebulin\"                            \n",
       "[13] \"Misamis Occidental\"                          \n",
       "[14] \"Misamis Occidental\"                          \n",
       "[15] \"Misamis Occidental\"                          \n",
       "[16] \"Misamis Occidental\"                          \n",
       "[17] \"Siquijor\"                                    \n",
       "[18] \"Cebu\"                                        \n",
       "[19] \"Cebu\"                                        \n",
       "[20] \"Misamis Occidental\"                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vec <- c()\n",
    "philp_loans$loan_region <- as.character(philp_loans$region)\n",
    "for(i in 1:nrow(philp_loans)){\n",
    "    vec <- append(vec, sub('.*,\\\\s*','', philp_loans$loan_region[i]))\n",
    "}\n",
    "philp_loans$sub_region <- vec\n",
    "philp_loans$sub_region[1:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let us see how the region field in the external table looks like. Our goal is to do data cleaning and achieve a respectable proportion of match on these keys. \n",
    "\n",
    "\n",
    "\n",
    "### To join external table there are two steps\n",
    "\n",
    "1. We create a 'philippines_regions' table having 2 important region keys. First key is 'provDesc' key which matches 83% with 'sub_region' key in 'philp_loans'. On making this join, the second key 'regDesc' is added to the 'philp_loans' table as a consequence of the join. This second key is very important because that is the common key between loans table and economic indicators external table.\n",
    "\n",
    "2. After we execute step 1, we simply join the loans data with economic indicators data on the key 'regDesc'\n",
    "\n",
    "\n",
    "### Step1\n",
    "\n",
    "#### **Pre-requisite**\n",
    "\n",
    "* Forming 1 file from 2 files for philippines regions. Files which will be used to bring in the appropriate keys which we will join.\n",
    "* This is A pre-requisite to step1. This was not been mentioned above so don't get confused. Actual step1 starts from the next to next cell of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id.x</th><th scope=col>psgcCode.x</th><th scope=col>provDesc</th><th scope=col>regCode</th><th scope=col>provCode</th><th scope=col>id.y</th><th scope=col>psgcCode.y</th><th scope=col>regDesc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1            </td><td>12800000     </td><td>Ilocos Norte </td><td>1            </td><td>128          </td><td>1            </td><td>10000000     </td><td>Ilocos Region</td></tr>\n",
       "\t<tr><td>2            </td><td>12900000     </td><td>Ilocos Sur   </td><td>1            </td><td>129          </td><td>1            </td><td>10000000     </td><td>Ilocos Region</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " id.x & psgcCode.x & provDesc & regCode & provCode & id.y & psgcCode.y & regDesc\\\\\n",
       "\\hline\n",
       "\t 1             & 12800000      & Ilocos Norte  & 1             & 128           & 1             & 10000000      & Ilocos Region\\\\\n",
       "\t 2             & 12900000      & Ilocos Sur    & 1             & 129           & 1             & 10000000      & Ilocos Region\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "id.x | psgcCode.x | provDesc | regCode | provCode | id.y | psgcCode.y | regDesc | \n",
       "|---|---|\n",
       "| 1             | 12800000      | Ilocos Norte  | 1             | 128           | 1             | 10000000      | Ilocos Region | \n",
       "| 2             | 12900000      | Ilocos Sur    | 1             | 129           | 1             | 10000000      | Ilocos Region | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  id.x psgcCode.x provDesc     regCode provCode id.y psgcCode.y regDesc      \n",
       "1 1    12800000   Ilocos Norte 1       128      1    10000000   Ilocos Region\n",
       "2 2    12900000   Ilocos Sur   1       129      1    10000000   Ilocos Region"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "province = read.csv(\"philippines_province.csv\", header=T)\n",
    "region = read.csv(\"philippines_region.csv\", header=T)\n",
    "\n",
    "philippines_regions <- inner_join(province, region, by = \"regCode\")\n",
    "philippines_regions$X <- NULL\n",
    "\n",
    "head(philippines_regions,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id</th><th scope=col>funded_amount</th><th scope=col>loan_amount</th><th scope=col>activity</th><th scope=col>sector</th><th scope=col>use</th><th scope=col>country_code</th><th scope=col>country</th><th scope=col>region</th><th scope=col>currency</th><th scope=col>...</th><th scope=col>date</th><th scope=col>loan_region</th><th scope=col>sub_region</th><th scope=col>id.x</th><th scope=col>psgcCode.x</th><th scope=col>regCode</th><th scope=col>provCode</th><th scope=col>id.y</th><th scope=col>psgcCode.y</th><th scope=col>regDesc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>653092                                    </td><td>175                                       </td><td>175                                       </td><td>Pigs                                      </td><td>Agriculture                               </td><td>to buy piglets and feed                   </td><td>PH                                        </td><td>Philippines                               </td><td>Liloy-Dela Paz                            </td><td>PHP                                       </td><td>...                                       </td><td>2014-01-02                                </td><td>Liloy-Dela Paz                            </td><td>Liloy-Dela Paz                            </td><td>NA                                        </td><td>      NA                                  </td><td>NA                                        </td><td> NA                                       </td><td>NA                                        </td><td>      NA                                  </td><td>NA                                        </td></tr>\n",
       "\t<tr><td>653149                                    </td><td>175                                       </td><td>175                                       </td><td>Pigs                                      </td><td>Agriculture                               </td><td>to purchase feed and vitamins for her pigs</td><td>PH                                        </td><td>Philippines                               </td><td>Tanjay, Negros Oriental                   </td><td>PHP                                       </td><td>...                                       </td><td>2014-01-02                                </td><td>Tanjay, Negros Oriental                   </td><td>Negros Oriental                           </td><td>41                                        </td><td>74600000                                  </td><td> 7                                        </td><td>746                                       </td><td> 8                                        </td><td>70000000                                  </td><td>Central Visayas                           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllll}\n",
       " id & funded\\_amount & loan\\_amount & activity & sector & use & country\\_code & country & region & currency & ... & date & loan\\_region & sub\\_region & id.x & psgcCode.x & regCode & provCode & id.y & psgcCode.y & regDesc\\\\\n",
       "\\hline\n",
       "\t 653092                                     & 175                                        & 175                                        & Pigs                                       & Agriculture                                & to buy piglets and feed                    & PH                                         & Philippines                                & Liloy-Dela Paz                             & PHP                                        & ...                                        & 2014-01-02                                 & Liloy-Dela Paz                             & Liloy-Dela Paz                             & NA                                         &       NA                                   & NA                                         &  NA                                        & NA                                         &       NA                                   & NA                                        \\\\\n",
       "\t 653149                                     & 175                                        & 175                                        & Pigs                                       & Agriculture                                & to purchase feed and vitamins for her pigs & PH                                         & Philippines                                & Tanjay, Negros Oriental                    & PHP                                        & ...                                        & 2014-01-02                                 & Tanjay, Negros Oriental                    & Negros Oriental                            & 41                                         & 74600000                                   &  7                                         & 746                                        &  8                                         & 70000000                                   & Central Visayas                           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "id | funded_amount | loan_amount | activity | sector | use | country_code | country | region | currency | ... | date | loan_region | sub_region | id.x | psgcCode.x | regCode | provCode | id.y | psgcCode.y | regDesc | \n",
       "|---|---|\n",
       "| 653092                                     | 175                                        | 175                                        | Pigs                                       | Agriculture                                | to buy piglets and feed                    | PH                                         | Philippines                                | Liloy-Dela Paz                             | PHP                                        | ...                                        | 2014-01-02                                 | Liloy-Dela Paz                             | Liloy-Dela Paz                             | NA                                         |       NA                                   | NA                                         |  NA                                        | NA                                         |       NA                                   | NA                                         | \n",
       "| 653149                                     | 175                                        | 175                                        | Pigs                                       | Agriculture                                | to purchase feed and vitamins for her pigs | PH                                         | Philippines                                | Tanjay, Negros Oriental                    | PHP                                        | ...                                        | 2014-01-02                                 | Tanjay, Negros Oriental                    | Negros Oriental                            | 41                                         | 74600000                                   |  7                                         | 746                                        |  8                                         | 70000000                                   | Central Visayas                            | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  id     funded_amount loan_amount activity sector     \n",
       "1 653092 175           175         Pigs     Agriculture\n",
       "2 653149 175           175         Pigs     Agriculture\n",
       "  use                                        country_code country    \n",
       "1 to buy piglets and feed                    PH           Philippines\n",
       "2 to purchase feed and vitamins for her pigs PH           Philippines\n",
       "  region                  currency ... date       loan_region            \n",
       "1 Liloy-Dela Paz          PHP      ... 2014-01-02 Liloy-Dela Paz         \n",
       "2 Tanjay, Negros Oriental PHP      ... 2014-01-02 Tanjay, Negros Oriental\n",
       "  sub_region      id.x psgcCode.x regCode provCode id.y psgcCode.y\n",
       "1 Liloy-Dela Paz  NA         NA   NA       NA      NA         NA  \n",
       "2 Negros Oriental 41   74600000    7      746       8   70000000  \n",
       "  regDesc        \n",
       "1 NA             \n",
       "2 Central Visayas"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Join philip_loans to philippines_regions on \"sub_region\" == \"provDesc\"\n",
    "# Changing name of provDesc to sub_region in the Philippines_regions file\n",
    "names(philippines_regions)[3] <- \"sub_region\"\n",
    "\n",
    "# Left joining to keep all the philip_loans (NAs whereever loan region has no match with external regions file)\n",
    "philp_loans_new <- left_join(philp_loans, philippines_regions, by = \"sub_region\")\n",
    "#prop.table(table(is.na(philp_loans_new$regDesc)))\n",
    "# currently missing ~ 17% values\n",
    "\n",
    "head(philp_loans_new,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We completed step1 here. New loans table is created with a join on 'sub_region'/'provDesc' (note that 'provDesc' has been renamed as 'sub_region' for the convinience of joining.\n",
    "\n",
    "### Step2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>regDesc</th><th scope=col>agr_wage_farm_workers_allgender_2015</th><th scope=col>agr_wage_farm_workers_male_2015</th><th scope=col>agr_wage_farm_workers_female_2015</th><th scope=col>avg_annual_total_incm_farm_households_02_03</th><th scope=col>avg_annual_farm_incm_farm_households_02_03</th><th scope=col>avg_annual_off_farm_incm_farm_households_02_03</th><th scope=col>avg_annual_non_farm_incm_farm_households_02_03</th><th scope=col>avg_annual_other_sources_incm_farm_households_02_03</th><th scope=col>avg_rural_income_2000</th><th scope=col>total_emply_2016</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Armm          </td><td>162.89        </td><td>163.65        </td><td>103.81        </td><td> 73356        </td><td>34026         </td><td>5737          </td><td>24936         </td><td> 8657         </td><td>73673         </td><td>1140          </td></tr>\n",
       "\t<tr><td>Bicol Region  </td><td>167.99        </td><td>169.95        </td><td>119.52        </td><td> 82988        </td><td>36579         </td><td>7348          </td><td>30381         </td><td> 8680         </td><td>72626         </td><td>2331          </td></tr>\n",
       "\t<tr><td>Cagayan Valley</td><td>228.77        </td><td>232.64        </td><td>199.98        </td><td>145809        </td><td>87353         </td><td>6909          </td><td>39894         </td><td>11653         </td><td>94212         </td><td>1482          </td></tr>\n",
       "\t<tr><td>Calabarzon    </td><td>230.92        </td><td>231.45        </td><td>172.04        </td><td>143075        </td><td>64498         </td><td>8444          </td><td>48590         </td><td>21543         </td><td>   NA         </td><td>5687          </td></tr>\n",
       "\t<tr><td>Car           </td><td>206.68        </td><td>211.04        </td><td>195.62        </td><td>127067        </td><td>71142         </td><td>9731          </td><td>37178         </td><td> 9016         </td><td>95635         </td><td> 765          </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       " regDesc & agr\\_wage\\_farm\\_workers\\_allgender\\_2015 & agr\\_wage\\_farm\\_workers\\_male\\_2015 & agr\\_wage\\_farm\\_workers\\_female\\_2015 & avg\\_annual\\_total\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_farm\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_off\\_farm\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_non\\_farm\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_other\\_sources\\_incm\\_farm\\_households\\_02\\_03 & avg\\_rural\\_income\\_2000 & total\\_emply\\_2016\\\\\n",
       "\\hline\n",
       "\t Armm           & 162.89         & 163.65         & 103.81         &  73356         & 34026          & 5737           & 24936          &  8657          & 73673          & 1140          \\\\\n",
       "\t Bicol Region   & 167.99         & 169.95         & 119.52         &  82988         & 36579          & 7348           & 30381          &  8680          & 72626          & 2331          \\\\\n",
       "\t Cagayan Valley & 228.77         & 232.64         & 199.98         & 145809         & 87353          & 6909           & 39894          & 11653          & 94212          & 1482          \\\\\n",
       "\t Calabarzon     & 230.92         & 231.45         & 172.04         & 143075         & 64498          & 8444           & 48590          & 21543          &    NA          & 5687          \\\\\n",
       "\t Car            & 206.68         & 211.04         & 195.62         & 127067         & 71142          & 9731           & 37178          &  9016          & 95635          &  765          \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "regDesc | agr_wage_farm_workers_allgender_2015 | agr_wage_farm_workers_male_2015 | agr_wage_farm_workers_female_2015 | avg_annual_total_incm_farm_households_02_03 | avg_annual_farm_incm_farm_households_02_03 | avg_annual_off_farm_incm_farm_households_02_03 | avg_annual_non_farm_incm_farm_households_02_03 | avg_annual_other_sources_incm_farm_households_02_03 | avg_rural_income_2000 | total_emply_2016 | \n",
       "|---|---|---|---|---|\n",
       "| Armm           | 162.89         | 163.65         | 103.81         |  73356         | 34026          | 5737           | 24936          |  8657          | 73673          | 1140           | \n",
       "| Bicol Region   | 167.99         | 169.95         | 119.52         |  82988         | 36579          | 7348           | 30381          |  8680          | 72626          | 2331           | \n",
       "| Cagayan Valley | 228.77         | 232.64         | 199.98         | 145809         | 87353          | 6909           | 39894          | 11653          | 94212          | 1482           | \n",
       "| Calabarzon     | 230.92         | 231.45         | 172.04         | 143075         | 64498          | 8444           | 48590          | 21543          |    NA          | 5687           | \n",
       "| Car            | 206.68         | 211.04         | 195.62         | 127067         | 71142          | 9731           | 37178          |  9016          | 95635          |  765           | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  regDesc        agr_wage_farm_workers_allgender_2015\n",
       "1 Armm           162.89                              \n",
       "2 Bicol Region   167.99                              \n",
       "3 Cagayan Valley 228.77                              \n",
       "4 Calabarzon     230.92                              \n",
       "5 Car            206.68                              \n",
       "  agr_wage_farm_workers_male_2015 agr_wage_farm_workers_female_2015\n",
       "1 163.65                          103.81                           \n",
       "2 169.95                          119.52                           \n",
       "3 232.64                          199.98                           \n",
       "4 231.45                          172.04                           \n",
       "5 211.04                          195.62                           \n",
       "  avg_annual_total_incm_farm_households_02_03\n",
       "1  73356                                     \n",
       "2  82988                                     \n",
       "3 145809                                     \n",
       "4 143075                                     \n",
       "5 127067                                     \n",
       "  avg_annual_farm_incm_farm_households_02_03\n",
       "1 34026                                     \n",
       "2 36579                                     \n",
       "3 87353                                     \n",
       "4 64498                                     \n",
       "5 71142                                     \n",
       "  avg_annual_off_farm_incm_farm_households_02_03\n",
       "1 5737                                          \n",
       "2 7348                                          \n",
       "3 6909                                          \n",
       "4 8444                                          \n",
       "5 9731                                          \n",
       "  avg_annual_non_farm_incm_farm_households_02_03\n",
       "1 24936                                         \n",
       "2 30381                                         \n",
       "3 39894                                         \n",
       "4 48590                                         \n",
       "5 37178                                         \n",
       "  avg_annual_other_sources_incm_farm_households_02_03 avg_rural_income_2000\n",
       "1  8657                                               73673                \n",
       "2  8680                                               72626                \n",
       "3 11653                                               94212                \n",
       "4 21543                                                  NA                \n",
       "5  9016                                               95635                \n",
       "  total_emply_2016\n",
       "1 1140            \n",
       "2 2331            \n",
       "3 1482            \n",
       "4 5687            \n",
       "5  765            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing poverty indicators per region data\n",
    "philp_poverty_indicators <- read.csv(\"consolidated_philippines_poverty_data.csv\", header=T)\n",
    "head(philp_poverty_indicators,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A glimpse of economic indicators data. Key is the first column, regDesc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id</th><th scope=col>funded_amount</th><th scope=col>loan_amount</th><th scope=col>activity</th><th scope=col>sector</th><th scope=col>use</th><th scope=col>country_code</th><th scope=col>country</th><th scope=col>region</th><th scope=col>currency</th><th scope=col>...</th><th scope=col>agr_wage_farm_workers_allgender_2015</th><th scope=col>agr_wage_farm_workers_male_2015</th><th scope=col>agr_wage_farm_workers_female_2015</th><th scope=col>avg_annual_total_incm_farm_households_02_03</th><th scope=col>avg_annual_farm_incm_farm_households_02_03</th><th scope=col>avg_annual_off_farm_incm_farm_households_02_03</th><th scope=col>avg_annual_non_farm_incm_farm_households_02_03</th><th scope=col>avg_annual_other_sources_incm_farm_households_02_03</th><th scope=col>avg_rural_income_2000</th><th scope=col>total_emply_2016</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>653092                                    </td><td>175                                       </td><td>175                                       </td><td>Pigs                                      </td><td>Agriculture                               </td><td>to buy piglets and feed                   </td><td>PH                                        </td><td>Philippines                               </td><td>Liloy-Dela Paz                            </td><td>PHP                                       </td><td>...                                       </td><td>    NA                                    </td><td>    NA                                    </td><td>    NA                                    </td><td>   NA                                     </td><td>   NA                                     </td><td>  NA                                      </td><td>   NA                                     </td><td>  NA                                      </td><td>   NA                                     </td><td>  NA                                      </td></tr>\n",
       "\t<tr><td>653149                                    </td><td>175                                       </td><td>175                                       </td><td>Pigs                                      </td><td>Agriculture                               </td><td>to purchase feed and vitamins for her pigs</td><td>PH                                        </td><td>Philippines                               </td><td>Tanjay, Negros Oriental                   </td><td>PHP                                       </td><td>...                                       </td><td>156.17                                    </td><td>160.65                                    </td><td>150.67                                    </td><td>72177                                     </td><td>28074                                     </td><td>5574                                      </td><td>31055                                     </td><td>7474                                      </td><td>68447                                     </td><td>3234                                      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllll}\n",
       " id & funded\\_amount & loan\\_amount & activity & sector & use & country\\_code & country & region & currency & ... & agr\\_wage\\_farm\\_workers\\_allgender\\_2015 & agr\\_wage\\_farm\\_workers\\_male\\_2015 & agr\\_wage\\_farm\\_workers\\_female\\_2015 & avg\\_annual\\_total\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_farm\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_off\\_farm\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_non\\_farm\\_incm\\_farm\\_households\\_02\\_03 & avg\\_annual\\_other\\_sources\\_incm\\_farm\\_households\\_02\\_03 & avg\\_rural\\_income\\_2000 & total\\_emply\\_2016\\\\\n",
       "\\hline\n",
       "\t 653092                                     & 175                                        & 175                                        & Pigs                                       & Agriculture                                & to buy piglets and feed                    & PH                                         & Philippines                                & Liloy-Dela Paz                             & PHP                                        & ...                                        &     NA                                     &     NA                                     &     NA                                     &    NA                                      &    NA                                      &   NA                                       &    NA                                      &   NA                                       &    NA                                      &   NA                                      \\\\\n",
       "\t 653149                                     & 175                                        & 175                                        & Pigs                                       & Agriculture                                & to purchase feed and vitamins for her pigs & PH                                         & Philippines                                & Tanjay, Negros Oriental                    & PHP                                        & ...                                        & 156.17                                     & 160.65                                     & 150.67                                     & 72177                                      & 28074                                      & 5574                                       & 31055                                      & 7474                                       & 68447                                      & 3234                                      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "id | funded_amount | loan_amount | activity | sector | use | country_code | country | region | currency | ... | agr_wage_farm_workers_allgender_2015 | agr_wage_farm_workers_male_2015 | agr_wage_farm_workers_female_2015 | avg_annual_total_incm_farm_households_02_03 | avg_annual_farm_incm_farm_households_02_03 | avg_annual_off_farm_incm_farm_households_02_03 | avg_annual_non_farm_incm_farm_households_02_03 | avg_annual_other_sources_incm_farm_households_02_03 | avg_rural_income_2000 | total_emply_2016 | \n",
       "|---|---|\n",
       "| 653092                                     | 175                                        | 175                                        | Pigs                                       | Agriculture                                | to buy piglets and feed                    | PH                                         | Philippines                                | Liloy-Dela Paz                             | PHP                                        | ...                                        |     NA                                     |     NA                                     |     NA                                     |    NA                                      |    NA                                      |   NA                                       |    NA                                      |   NA                                       |    NA                                      |   NA                                       | \n",
       "| 653149                                     | 175                                        | 175                                        | Pigs                                       | Agriculture                                | to purchase feed and vitamins for her pigs | PH                                         | Philippines                                | Tanjay, Negros Oriental                    | PHP                                        | ...                                        | 156.17                                     | 160.65                                     | 150.67                                     | 72177                                      | 28074                                      | 5574                                       | 31055                                      | 7474                                       | 68447                                      | 3234                                       | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  id     funded_amount loan_amount activity sector     \n",
       "1 653092 175           175         Pigs     Agriculture\n",
       "2 653149 175           175         Pigs     Agriculture\n",
       "  use                                        country_code country    \n",
       "1 to buy piglets and feed                    PH           Philippines\n",
       "2 to purchase feed and vitamins for her pigs PH           Philippines\n",
       "  region                  currency ... agr_wage_farm_workers_allgender_2015\n",
       "1 Liloy-Dela Paz          PHP      ...     NA                              \n",
       "2 Tanjay, Negros Oriental PHP      ... 156.17                              \n",
       "  agr_wage_farm_workers_male_2015 agr_wage_farm_workers_female_2015\n",
       "1     NA                              NA                           \n",
       "2 160.65                          150.67                           \n",
       "  avg_annual_total_incm_farm_households_02_03\n",
       "1    NA                                      \n",
       "2 72177                                      \n",
       "  avg_annual_farm_incm_farm_households_02_03\n",
       "1    NA                                     \n",
       "2 28074                                     \n",
       "  avg_annual_off_farm_incm_farm_households_02_03\n",
       "1   NA                                          \n",
       "2 5574                                          \n",
       "  avg_annual_non_farm_incm_farm_households_02_03\n",
       "1    NA                                         \n",
       "2 31055                                         \n",
       "  avg_annual_other_sources_incm_farm_households_02_03 avg_rural_income_2000\n",
       "1   NA                                                   NA                \n",
       "2 7474                                                68447                \n",
       "  total_emply_2016\n",
       "1   NA            \n",
       "2 3234            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm(philp_loans)\n",
    "philp_loans <- left_join(philp_loans_new, philp_poverty_indicators, by = \"regDesc\")\n",
    "\n",
    "\n",
    "rm(philp_loans_new)\n",
    "philp_loans_new <- philp_loans %>% select(-id.x, -id.y, -psgcCode.y, -psgcCode.x)\n",
    "\n",
    "head(philp_loans_new,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is the final table containing all the data we need in the same table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **We can see that we are missing a lot of data. We are missing the data for the rows which did not find a region match with the external data.**\n",
    "\n",
    "\n",
    "* **Lets see how many % of the rows we have compromised** *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of REGION join matches missing due to a bit of unclean data are"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "    FALSE      TRUE \n",
       "0.8237171 0.1762829 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat(\"The proportion of REGION join matches missing due to a bit of unclean data are\")\n",
    "prop.table(table(is.na(philp_loans_new$regDesc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We are missing new region values for **17.6%** Philippines loans.\n",
    "\n",
    "\n",
    "\n",
    "* Lets delete these rows for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "completeFun <- function(data, desiredCols) {\n",
    "  completeVec <- complete.cases(data[, desiredCols])\n",
    "  return(data[completeVec, ])\n",
    "}\n",
    "\n",
    "philp_data <- completeFun(philp_loans_new, \"regDesc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Now let us dig deep into the FEMALE-AGRICULTURE segment in Philippines\n",
    "\n",
    "\n",
    "\n",
    "* Correlations of different poverty metrics and loan features using 'corrplot' package\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pMkCSEMBoMTrsnOZleeezqEAABwWL1e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\nz720uQu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gu9REdTffCgBHtDZd83ISkpKT09nSflFhYW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\nKEEIAAAQShACAACEEoQAAAChvgAnWNRmLW+5VgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "philp_data$gender <- if_else((philp_data$borrower_genders == \"male\"), \"male\",\"female\")\n",
    "\n",
    "# Segment: Female-Agriculture\n",
    "\n",
    "\n",
    "female_agr_summary <- philp_data %>% filter(gender == \"female\" & sector == \"Agriculture\") %>%\n",
    "         group_by(regDesc) %>% summarise(num_loans = n_distinct(id),\n",
    "                                            med_loan_amt = median(loan_amount),\n",
    "                                            med_loan_term = median(term_in_months),\n",
    "                                              agr_wage_farm_workers_female_2015 = mean(agr_wage_farm_workers_female_2015),\n",
    "                                              avg_annual_total_incm_farm_households_02_03 = mean(avg_annual_total_incm_farm_households_02_03),\n",
    "                                              avg_annual_farm_incm_farm_households_02_03 = mean(avg_annual_farm_incm_farm_households_02_03),\n",
    "                                              avg_annual_off_farm_incm_farm_households_02_03 = mean(avg_annual_off_farm_incm_farm_households_02_03),\n",
    "                                              avg_rural_income_2000 = mean(avg_rural_income_2000),\n",
    "                                              total_emply_2016 = mean(total_emply_2016))\n",
    "\n",
    "corr_1 = cor(female_agr_summary[,c(2:8,10)])\n",
    "\n",
    "col <- colorRampPalette(c(\"#BB4444\", \"#EE9988\", \"#FFFFFF\", \"#77AADD\", \"#4477AA\"))\n",
    "\n",
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "\n",
    "corrplot(corr_1, method=\"color\", col=col(200),  \n",
    "         type=\"upper\", order=\"hclust\", \n",
    "         addCoef.col = \"black\", # Add coefficient of correlation\n",
    "         tl.col=\"black\", tl.srt=90, #Text label color and rotation\n",
    "         # Combine with significance\n",
    "          sig.level = 0.01, insig = \"blank\", \n",
    "         # hide correlation coefficient on the principal diagonal\n",
    "         diag=FALSE \n",
    "         )\n",
    "\n",
    "options(repr.plot.width=7, repr.plot.height=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Following are the variables**\n",
    "\n",
    "1) agr_wage_farm_workers_allgender_2015 - Agriculture Wage Rates of Farm Workers (All Gender) in 2015 - UNIT: pesos\n",
    "\n",
    "2) agr_wage_farm_workers_male_2015 - Similar to above for males - UNIT: pesos\n",
    "\n",
    "3) agr_wage_farm_workers_female_2015 - Similar to above for females - UNIT: pesos\n",
    "\n",
    "4) avg_annual_total_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos\n",
    "\n",
    "5) avg_annual_farm_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos\n",
    "\n",
    "6) avg_annual_off_farm_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos\n",
    "\n",
    "7) avg_annual_non_farm_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos\n",
    "\n",
    "8) avg_annual_other_sources_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos\n",
    "\n",
    "9) avg_rural_income_2000 - Average rural income - UNIT: pesos\n",
    "\n",
    "10) total_emply_2016 - Total employment 2016 - UNIT: thousand persons\n",
    "\n",
    "\n",
    "* We can see **Number of loans & Loan terms have negative correlation with all the income variables** implying as the number of loans and/or loan_terms in a region go up, the income indicators are coming down. This is intuitive in a sense that poor people are the ones who take more and longer loans.\n",
    "\n",
    "\n",
    "* **Median loan amount on the other hand as positive correlation with most of the income indicators** implying that as the median loan amount goes up in a region, the income indicators are seen to be on an increase as well. This is also intuitive.\n",
    "\n",
    "\n",
    "\n",
    "* **The negative correlation of number of loans with farm income is more in magnitude than total income or off farm income in farm households**. So KIVA can also prioritize loans based on different incomes of a particular person. If lets say a female is a farmer in a poor area with a very low farm income, then the loan to that female can be prioritized over a loan request of a female in a rich area having more farm income.\n",
    "\n",
    "\n",
    "#### Q) Which region is the poorest w.r.t the income indicators we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TableGrob (2 x 1) \"arrange\": 2 grobs\n",
       "  z     cells    name           grob\n",
       "1 1 (1-1,1-1) arrange gtable[layout]\n",
       "2 2 (2-2,1-1) arrange gtable[layout]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W+qF8YCpEmaHaAX1lUyqXybFhtkkIJ5HTuLA0gRaXaAXlhXyaTypS423H/arMUG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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_5 <- ggplot(data = female_agr_summary,\n",
    "       aes(x = regDesc, y = avg_annual_farm_incm_farm_households_02_03)) +\n",
    "  geom_bar(aes(fill = regDesc), stat = \"identity\", width = 0.6) +\n",
    "  scale_x_discrete(\"New Region\") + scale_y_continuous(\"Average Annual Farm Income - Farm household - 2002/03\") +\n",
    "  #ggtitle(\"Top 10 activities for females in top 5 countries\") +\n",
    "    coord_flip() + theme_grey()\n",
    "\n",
    "plt_6 <- ggplot(data = female_agr_summary,\n",
    "       aes(x = regDesc, y = num_loans)) +\n",
    "  geom_bar(aes(fill = regDesc),stat = \"identity\", width = 0.6) +\n",
    "  scale_x_discrete(\"New Region\") + scale_y_continuous(\"Number of loans\") +\n",
    "  #ggtitle(\"Top 10 activities for females in top 5 countries\") +\n",
    " coord_flip() + theme_grey()\n",
    "\n",
    "grid_1 <- grid.arrange(plt_5, plt_6, nrow = 2, ncol=1)\n",
    "grid_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Among this segment (Female-Agriculture), **loans should be prioritized in CENTRAL & WESTERN VISAYAS regions because clearly they are not doing too good with respect to Average Annual Farm Income** metrics in pesos and are also taking **high number of loans** indicating a more needy section of people inhabiting in these regions.\n",
    "\n",
    "\n",
    "\n",
    "* Agriculture females in regions like **Soccsksargen, Cagayan Valley, Davao, CAR** take less loans or rather their might be less population from this segment their in the first place. These regions where the loans are less do well though with respect to Farm incomes. So Farming/Agriculture families staying in these places can be assumed to be richer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TableGrob (2 x 1) \"arrange\": 2 grobs\n",
       "  z     cells    name           grob\n",
       "1 1 (1-1,1-1) arrange gtable[layout]\n",
       "2 2 (2-2,1-1) arrange gtable[layout]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gNIrbQ/9vDbg2QckIxxiHA7kkEd3mpAAJBaaX/s4YW2v5XFwuSCvzjnm4AzfgCk\nkLc+XdNVMBkCSK20P/bwYiDRZRgpVkUxMSYEkr1LYbsf0+sucnqFAFIr7Y89/NYghVZoLEj2\n+o+MZ29OFAsgtdL+2MNLfbIhyElws8FuRTq7HWyQat0wCSC10v7Yw28Ckuecb21/mzhIrrc+\nGc86vUIAqZX2xx7+aJ+1q173AKmV9sceHiDVHnArBXL5b2g/HQokAU99gARV0aFAEhBAgqoI\nIB1UAKktAaTKcr3yM3o6R8vizhNAakuZIMH7m5fvlZ/R1zkCpCMJIFWV75Wf3dl2NwZIB1Em\nSKdTXZAsH0jbGcgyUdVBa/wFpMU4KNBzEEBqS5kg/ScuzEjubOJ71ekZh/m0Jt2I2SrxkHR6\nxk30966lSyuzUgASK3ch5vo6EqfiyOllCBck+yxmpMaEGammLJDiBvk6dNpQVOaTenGKBEgt\nCyDVlHYeU1KWPzp0euyjnb46cLYXQGpLAKmqll0717Kb0ONcBvmoBK6RyNlBAKktAaSqCnrl\nW0cCkre0szfu3GskgNSyAFJluV753tFYp4MgkZPaGpLEAUhtCSBtIR19UjwMQGpLAElYzpTj\nPskfaxRAaksiIKnZgCugktKND8iMlhRIfkZyPsVa+pFWt2cApPa/M3re4XO/IZsxI8VKtAik\nkq6NgCSk9bkHSMLtufSlgxStUIBURetzD5CE23PpywSJOOWr0T3IzI75rreQcSyDyGKOgOS0\nVv6AtonXk6+nA6RW2h97eDGQbEM7Yky3HI1zJK+7rnXLIdA6OmCK8x1AaqX9sYeXAklZD22q\nvBf51/vDYh/ut7ZecpsYgCRYKjXbH3t4aZACTvk+MHYj93U63IRUeNohHv3L60/WdgCplfbH\nHl4IJDoZPAUpsjKb/xgLJHr01m/+NZJ5wgpAaqX9sYeXBSkwyeQfycE8AwnXSNuWSs32xx5e\nBiRrQvJACsxA7tJOkSMd8Pk1kj0AHSOsw4IUUOhbsxh+m+Fzx08CSc17A+NOtjMj+dvfzqWO\nso7GKnd/04F2dAckY4QFkDD8HuOnbjas1Wb1DZAw/B7jbwDS8+uamgJIGH6P8beYkSrc9Sgj\n2GaRIGjRVku7zQSQoD0EkCCoggASBFUQvL8hCPIFkCCogs4DUvmNmZKGtuyMqg4+h6BHgeEl\n/g/cty2YhMZ1GpB8k5XKYwuFIHeU8t3Jag0v9X8QdQG9ngBS6tgyIXxv2cqVDpC2EUBKHFos\nhOyMZI8oM6cCJAOQkoZe7nt2VJAk/w8AUieAlD70gUESG94YCihAOr6kc3hokKZHAElMACl9\nfIAUGx8gAaSMoY8LkjSnAAkgJQ598M0GI/R/oO0gAOkEEv5kg1gILRtCdExYwBwAACAASURB\nVHjtfmICn2yAIGiFABIEVRBAgqAKAkgQVEEACYIqCCBBUAUBJAiqIIAEQRUEkCCogi4EUgUD\n25v6ffz8eYz08zj+qltKp5+7ijRUy+0WnivQKqv/8/e+ocHv+XSdv7s/jxL6s3KM136Ez8dI\nn/2IrymddLTUNwXp+XsHSCt0nb+7V/WSVviMPtX74+ebeldvj+N7X5JPFa/PrMoNgpTR//l7\nB0grdJ2/O6V+u0qZ1jQ39W1+X5V6/e3Pfeu7MV8vSumuzLrl2O1PX1hzm07f6t53/e2P98cQ\nz/uMU8ZP99rPEms4tbw98l7Mi3oxPzf10j2dx/dHtvqTdt34zijh9+68p364t27icv9i3rW6\nfVTIwYl1GZC6tUy/unnprxF+Op76RVfHlVL3x+k/wzrpUY2/el4yzW16DTd5uz3qcXzyvM/w\n4nBW/06xltFGkffy0i2/bo8fr3T82LuZ/veWdt349ijh9+6+p+61t36+cv5i3vqxQRKny4DU\nQfRnKM2uVLp/ePtFzltXIH0FPirss/uHW3ULn7v5vQ+Ppja9Xh7/kH/3SHbHl6Q+4z/0924a\neJtijWdm9uh7ee2uZN66H3T84Mhzf9pumJfoKOH37r6nR8u3fv3n/sV0+xNf6qLfj0jUZUBS\n8/10b8MXaMzwT7PpSmrYyXrMU3/e70P5/vQbXLRNr/dHdX08avazP74n9RkL/WecBqdYxgHB\nei9K/U4TzjR+cGSy17C0GxZr9iih9+6+pwc1w3aM+xej1evabZrT6yogjUufrlA+1Nfj39d3\nWodjsd3t5+NdgOm+2NfjX/TlX/Y/SX0WhA2JNZ+ZHlrjLD+s8UMjjwq8Dxo59N7d9zSv39y/\nmD+Ppd7tx0CMrgLS61gbr912Q7fq//Xr5fVxRf3nh4Xi97HA0X15dsffpD6rQLLHj4MUeh8O\nSN5790F61+PU5PwLY75vSn+tTMG5dRWQ+srpa6krup9+dXSb/+dJ8f2GlmmLbmrY/Xr8096P\nldDHW9rZZ6ZxrRfnH8v4sZHJw18WJO+9u+/p8fMz+BfT6UNdpVTKdJG/na9x7+r1sax7PFHd\nYdih+uxqayqkr3G7YLwMV7SNGQd4Ga/HX/shE/p4mw3zuyIPnfdCQJrGj408PaTtwiC57919\nT93Pe7dp4f7F6Mfg39hsYHURkN7UsDD5M27P9TvI44b191Qvb/OKZtnKXtoM+hyus/prro+0\nPsNkQbaa53dFHjrvZf6xjB8befoffHqN5L139z11P7+75+G/mPe1STi1LgLS7G3TP/gYf63f\n/ULy3hE2Flv/tH/c/XJ12Die2wz6VtNn1cZ6ft5nGJz88tPYZ0zgvSw/5vGjIxv7vcdB8t67\n8576n+/dZOX+xbxppcERq4uAVKSSxQwWQBcVQAqpv4Z6y/toXkkf6DQCSCGNVxxZvzop6QOd\nRgApqI/bePUg3Ac6iwASBFUQQIKgCgJIEFRBAAmCKgggQVAFASQIqiCABEEVBJAgqIIAEgRV\nEECCoAoCSBBUQQAJgioIIEFtafgQvX472Md/ARLUliYLo9V3PNhWAAlqS/M38/Xvs6YtCSBB\ne2h0519uCjDfgmDymHjt3VaWBrOR/8/LeNeCpgSQoD003kpgdutfPJhmT8redWxuMBv5Dy1f\nmLF30WFB+j9fodeeqqgTQqV04tKnxrs0TW79yy0ILB9Zauc/Gvl3thhf7blVNveGUiVfB9V7\nXS0Ul77B3GJx6198ai2Qlgazkf9wC8/mBJBKOiFUSicufZOTnm9raYG0NJiN/NubjHq1+a4S\nJF8H1XtdLRSXvucgfQ23lVnuGjAa+QOkupKvg+q9rhaKS9+Aw+LW7y/tXrorI/uuAR/Dag9L\nu5qSr4Pqva4WikvfZCo+ufUvtyBYfo9kNZiN/LuXvtublpp7Q6mSr4Pqva4WikvfQMLi1k+3\nv0d9WQ1mI/8f+z66rQgglXRCqJROXPrGKWW5KcB8C4KBotvbr9NgNvL/vrdoxHkmkP5DJVo8\nB67uDUPlJ/XAtyAASCXFc+Dq3jBUTjoPfwsCgFRSPAeu7g1D5aTz8LcgAEglxXPg6t4wVFY+\nj34LAoBUUjwHru4NQ+1dIpvqKUh6UORkQcT4gMxo/qlA4gBSW6EKiuO4SpuRYiVeBFJJV4B0\nwFAFxXFcJYEULXqAJN7pwKEKiuO4SgdpXI9p0x+tJ/NJp9HQTJPzxgLJaa39AbWZXrfGMADp\nAKHKq/KASgFJ0x9DfWv6xK5393Wt6Qj0EGgdHZCO8f86Bd6nBVLW3wIErVQCSNp6aFPlvci/\n3h/mzQaGFquP+7xX4F9AzEhthUqrwJMoA6Sh/J+AZDdyX6fDTUiFp52xtT0QXdsFEgeQ2gqV\nWYrH1nOQ6GTwFKTIymz+YyyQ6NFdvxn/GsnqHEgcQGorFFdV/47rn09LskUlgxSYZPKP5GCe\ngRS/RuoVSBxAaisUV1XXA8makDyQAjOQu7TT5EgHfH6NZA9AxzAA6QChuLK6HEh63hsYd7Kd\nGcnf/nYudbR1NBSkwB437egOSMYwAOkAobi6uhxIFaSfNylQIHEAqa1QXPoAUpac65qaCiQO\nILUViksfQMpT/POuayVfB9V7XS0Ulz6A1Irk66B6r6uF4tIHkFqRfB1U73W1UFz6AFIrkq+D\n6r2uFopLXzJIk89qvpRz7B+J1TtAKumEUCmduPQlgjQ6GRdVCEBKknwdVO91tVBc+lJBsg6Z\nUm5fgBSQfB1U73W1UFz60kCi1Tmt8bqDmp/PsIynlVmWggtI88lQzzoCSCWdECqlE5e+bJCm\nNd5Awvx8wmE+rUg3Rf6Mff2elQSQSjohVEonLn35M9L43LpoUgSU8OllCBck++xqAaSSTgiV\n0olLXwFIZEWm7OcqdNpQVOaTs7k4QBolXwfVe10tFJe+gmskY085yx8VOj32UU5fFThbRQCp\npBNCpXTi0pcGEtm1U5HjiIP38tiPTGAAKahA4v4FVVBbINkTkPKPBCRvaWdv3LnXSACpF0AS\nUlMgmeWTDWTb2zoa63QQJHJSWUOuL0M30vEEkITUGEhBqeiTLAEkA5DE1DhIzpTjPslSWyDp\n0CGvr9H53/4DSEJqHCT3U6zlt2Wue0PnWiDp+Rg+z/Ut+hItQBJS6yA1qgpUjt8nj32tnIUk\nRt9zASQhAaQiCYBkG+rTn73r0GI9ZAhIgY7zKcepaBBAEhJAKlIlkMifoDXd9Hg2p9N+Z6ej\ntWS0fMBiJvp7l+A5tL4gUvRPRtu8g8oSBsk4IBlDfwQ6kkEd3pz1H2YkIW0zIwGkgLTFwnKz\nieUCyLXLD4K0+KgujemazvYjAkhC2gak06kOSHQZZjmpGm9icc3urF0KvVxo2afo6UEASUjb\ngPSPuK48I0VACq3QWJDs9R8Zz98nB0hCAkhFqvJLqSAnwc0GuxXp7HawQXLv+mcAkpgAUpHq\ng+TdT9ba/jZxkFzjfDKedXoQQBISQCpS05+108w5gCQkgFQkgATZAkhFahgk3n4/K6VFdVC9\n19VCcekDSK1Ivg6q97paKC59AKkVyddB9V5XC8WlDyC1Ivk6qN7raqG49CWD5Jroq+DD/dXU\nm8mRfB1U73W1UFz6EkHyTfQBUmXJ10H1XlcLxaUvFSTrEH+4v5p6MzmSr4Pqva4WiktfGkj2\nd8pnX2/XMJ88n31UQ676kgJIJZ0QKqUTl75skGyTx+HouKtat6FwG0kLIJV0QqiUTlz68mek\n8blyHrrPDX3dN7oTE0Aq6YRQKZ249BWARF3yo376y0vjD4D0RPJ1UL3X1UJx6Su4RjKLVaqx\nGaHPZ+dVYwBSiuTroHqvq4Xi0pcGEmEjdI0UOgKkTMnXQfVeVwvFpS8VJHvWWUBSzmaDdzNL\ngJQo+Tqo3utqobj0JYJkPBN9RR/at5WdL5fs7W8DkDjJ10H1XlcLxaUvGaSjCCCVdEKolE5c\n+gBSK5Kvg+q9rhaKSx9ASpJtr8C0Kw8hXwfVe10tFJc+gJQix97kScMyyddB9V5XC8WlDyAl\nSFuHhJYlkq+D6r2uFopLH0BKkOW1at9JwrXqGpt7L48WXtSGi97EwgCkA4TiagTe3wlarpBC\nZpEB80jv5ZAx5EJe7G4UELSbpHbtnBlnetU/xlyOo/CNkv8HtXqvq4Xi6uPvcWFGcuRCYKjj\nN3ULJ85bgZtW+Hey6CVfB9V7XS0UVxwAKV0cSNNKbb4Ls3UfCn9GMgDpeKG44gBICVoukBiQ\nvHVdcBUHkA4ciqsRgJQg+y5HmZsN1ipOB+nqJV8H1XtdLRRXIwApSfO+Xf729zIJedvfBiAd\nKhRXIQBpW+noGfk6qN7raqG4xAKkzeTMQK7k66B6r6uF4rILkLYT/8lX+Tqo3utqobjkAqRW\nJF8H1XtdLRSXPoDUiuTroHqvq4Xi0pcMkmui36oO8SZDkq+D6r2uFopLXyJIvol+qzrCewxK\nvg6q97paKC59qSBZh5Z1gLcYlnwdVO91tVBc+tJAsgwiiVN+g2ryTaVIvg6q97paKC592SDN\nPnWNVmyjb+u55Ougeq+rheLSlz8jjc9bLdhW39dTyddB9V5XC8WlrwAk2ym/NbX6vp5Kvg6q\n97paKC59BddIBjOShOTroHqvq4Xi0pcGUthEv0m1+r6eKpC4v1XS9iV3zlBc+lJBCpnoN6lW\n39dTBRIHkNoKxaUvEaSwiX6LavV9PVUgcQCprVBc+pJBOooAEkCSCsWlDyC1okDiAFJbobj0\nAaRR2jqETj3tS51NkvvOCiQOILUViksfQBo1G9M5Lwdei/YNNARIJwrFpQ8gjdLhr4IDpNqd\nDhyKSx9AGkVAoib3g/NPwBKfWuCP5kDj0o4298YjwzhfPQ8kDiC1FYqrH5jojwqY0k2zkQ6e\nIHOVAxJpHjLN933tYib6tUAq/CuBrqyKII3HqBtkDCSvWcJzzEgHCMXVz//EdbkZKQYCdX30\nLPHnxjra33fSTzTRB0htheLqByCNGsv66Yxk8kHST54PCiQOILUViqsfgDRKDqRnx1GBxAGk\ntkJx9QOQRg0VHTS5T7hGii0NtXN0XwdIRwrF1Q9AGkWZoCb3OrQvblJAotvfsdcXjgBS+6G4\n+gFIrSiQOIDUVigufQCpFQUSB5DaCsWlDyC1Ivk6qN7raqG49AGkViRfB9V7XS0Ulz6A1Irk\n66B6r6uF4tKXDBJM9IUlXwfVe10tFJe+RJBgoi8u+Tqo3utqobj0pYJkHVrWAd5iWPJ1UL3X\n1UJx6UsDKWqiPy34RmuhBhaAAKmkE0KldOLSlw2SZaJP3SJns7t9SxkglXRCqJROXPryZ6Tx\nufKeqEjjbQWQSjohVEonLn0FIFkm+uSJWp7vKIBU0gmhUjpx6Su4RjJkRqJPFPmznwBSSSeE\nSunEpS8NpJiJvvsE10jlCiTuL7W0ecmdMxSXvlSQwib69Ak97iiABJCkQnHpSwQpZqJPtr2t\n434CSABJKhSXvmSQnquNEm7jXRQokDiA1FYoLn11QGrg2mhSG++iQIHEAaS2QnHpqzQj7b7r\nPct9G29aDRKLSDxRNNuQVyBxAKmtUFz6Ki7t2pADzJtS4iBpeihWIHEAqa1QXPrODpJWH9IR\nAdIGvZoIxaXv7CBtsOIMWugHbPejZvqDAokDSG2F4qrg7Cb6L+pXOmLIQt8xU7VcvNyTcRP9\naiBJ/xVA55MD0o++/whHDFvo+5sQQYdV1iCyGkib/9t9zlBcFfw1rlPMSGqDzYagYfFiMBkH\nyVrbBRIHkNoKxVUBQFotDiRvFedaIQOkA4XiquDsIG0gBqSgW3hwiQeQDhCKqwKAtFoR5++w\nX76z2YCl3ZFCcVVwepB+325K3d4E9+4YkCz//ND2N2akI4XiquDsIP2MnxDS0nt36Yr84jaQ\nOIDUViguq2cH6VV1298/d/W6z9sJCCAdNRSX1bODNO3WtfKZWpMD0nFL7pyhuKwCpFYkXwfV\ne10tFJe+ZJCSfxWjyM/eRzK/ptao/aVdRPJ1UL3X1UJx6UsEKe797b3mgrSx2t9siEi+Dqr3\nulooLn2pIFkH45+JNN1+QbX99nclyddB9V5XC8WlLw2kiPf3ZP5t+YC7IKmlj+UtqdyX66j5\na6GY5Ougeq+rheLSlw3SbGFHvCE9H/CpoeMfOR4nkETc8ABSSSeESunEpa8cJHJU9GwMJKeJ\n8ntUkT15bvGh1UqSr4Pqva4WiktfPkjKtvumqzlqCm45GM9lvDSha7qaVQ6QSjohVEonLn1l\nM5IJgeROP8vazdBtP2WfoqdrqHlgYpKvg+q9rhaKS18aSGQHIQqSe41kg2RfDCmnm6kHAEAq\n6YRQKZ249KWCZM8goaXdtEazLpgCbW2Qqt+ezBlnWdrp17Z/lRRI3H8JSrbkqvdqIhSXvkSQ\nyBY13eXud+/mbThqCm4MAcm7Q6bdQ3L7W1E1TVIgcQCprVBc+pJBqiu5BZgz8p/pI0Kf5q3t\njwkFEgeQ2grFpe/sIN1GOy51q7s5WF+BxAGktkJx6dsFJMmK9pZ2yxEgAaRVnbj07TQjycmB\n5T4t7e7mq5uVkqVH5UVf7OqyDYwDiQNIbYXi0nd2kMinv1WWDXgcBA4R3/cxWYHEAaS2QnHp\nOztI5vd9+vS3essZpwykyeYkJ9KoQOIAUluhuPSdHqRSERa0ZXpPfw7u+LMtkCEgab/jfCrV\nRB8gtRWKq5ezm+gXS9uPHGNH26OOWqYG/FXH4wSSbxAZM9GXBKnSXxN0VnkgfbwoZe7fueM4\nmw3aRyXg9G18kIw9QGC8UYF/ASVBkv23u3qvJkJx9fLfcZ1iRvq9DV89VF+Z49Ayn9ZxywXQ\nBBkL0uKjujTW/nijAokDSG2F4url7CC9qrfu90ef6p45jrYfTl6pxptYXBNvTZ9rTQcgp+jp\nQYHEAaS2QnH1cnaQpl/EZv8y1r9Gmp7kgGSv//TS198nDyQOILUViqsXgBSRDRJdxvHXSM7G\n3bKBZyhI9rKwVyBxAKmtUFy9nB2kcWmX/4FVstkwmd2Tp8v2t4mDRPe57fWeNd6oQOIAUluh\nuHo5O0i/TfnaaeZcIHEAqa1QXGrPDpIx7w352gGkQ4fiUnt+kNoR/wlY+Tqo3utqobj0XQak\n903fRYHk66B6r6uF4tKXBlKeoxVjgCU/X1gRvm7q1v8m9vvW8Ew1SL4Oqve6WigufYkgWYdn\nqm0MlCUa9Kvn+fsxHams7yLtIvk6qN7raqG49GWBlIhGMyC9qLd+4/uuVPMrO4DUfiguffkg\nPTfC91tbFkO2C39t0SGV+jW/St3VLfszq9tLvg6q97paKC592SCNbloTSK7NXbg1tbBbfLyM\nEZi0bJD6H3nf6NtL8nVQvdfVQnHpK1vaUWNI44O0bDYE7CRD6FVUAKQ/1YNISL4Oqve6Wigu\nfQUgBY3wvRlpcZNUe4NUPYaI5Ougeq+rheLSV3CNNP9R5CV/aafcjgCJlXwdVO91tVBc+kqv\nkSaQ4tdIAClP8nVQvdfVQnHpywJpnowWCMgugrvZ4HLmbjaQphVlg0RUPVJlyddB9V5XC8Wl\nLxEkUovPjfCthSCz/W0AEpF8HVTvdbVQXPrSQKqjTWq5eWBikq+D6r2uFopL30YgCU0/sUhH\nlHwdVO91tVBc+raakTZbXAGkkk4IldKJS9+WS7tNJANSnqM+Y8AfH0K+Dqr3ulooLucAKUna\nOiS2Tm0+SL4Oqve6WigufWcHqdLHg/LQAEjnDMWl7+ze30q91mDJ8lF9aonvt56tg6gbkXnm\nInTckjtnKK5Czg7SS7fL8fK51vvEt3ucQPId7oLmkMTMbn5p9vOKmehD0G7yrpG+Oxd9df9c\nNWpgrtH+0xkk4onnnNb+S6Pk/0Gt3utqobgKYQybTjEjDXpbu/luWxSHLPG9GWk2g9QaIJ0j\nFFchFwDp600rdVv3XXPrqmf+o8lL/tJOux0B0sFDcRVydpD+dBRJXCNRJ2+AdI1QXIWcHaTu\ni+YVXFYJIdO2AUUqvNngcuZuNpCmBiAdIBRXIWcH6WuYkdY6f9OPKjy3xE/d/jYA6VChuAo5\nO0hmukZqwQBFcyfl66B6r6uF4tJ3AZCM+X3f/ftIzvQTkHwdVO91tVBc+s4P0vf7XVX6fMMa\nPf3Mq3wdVO91tVBc+s4O0qtugaIUyddB9V5XC8WlLw2ksq9rL18533BdJfNZuw0kXwfVe10t\nFJe+RJCi/TlEXA+HTSTz6e8NJF8H1XtdLRSXPkmQqC/XZvKidR+1M/f2zb/l66B6r6uF4tKX\nDZJtmk9/dks4Zag3/gyS8jvOp3wT/lVyBvm99QMr9VVjcEnJ10H1XlcLxaUvFyTXlc4yr5s9\n7yxDLr+D4U34V8ndbBjuav6p7hXGFpV8HVTvdbVQXPqKNhuUj4oNQwwkYw8QGG+1vM2G5U/b\nkq+D6r2uFopLX/410rSOWy6AXKf8IEhLKQdN+KtVOkAq6YRQKZ249OVfI81PlbKehkAyjikr\nHcA24Te1aj28tOvu29e45Ougeq+rheLSV3qNND3JAcle/9km/E6YcrmbDXqYL/Xaj62KS74O\nqve6WigufSUgOTc8Yq6RnI27ZQPPUJBcE/5V8sZ4v3UfWa3wVQphyddB9V5XC8WlL3uzwTHN\nt7e/TRwk105/WdB5Jvyr1Py1UEyBxP0V2k8iIIlIpuQBElRFAIk8PvhtXfaupUvrKCBJVTZA\ngqroKCBJKQjMu9LrfO02EEBqSwDJ089N3Ut3v5PvQUGcg8xs1ZUjgNSWAJKrD6U+CgfT0W+I\ne6+5IGULILWlTJDO7v1tfu7l05Hr5e2fiTQt4AggNaZMkE4nB6QV05HNg30LCT24a80PfZAc\nKy5NT/n3rzAAqTVlgvSXuE4wIz2mo9uKzwZp56E28y0kZptIy+7RWKbGjhPkBJJvEBm7G8Xe\ntXRpZdbKqUH61GqV5XcIJHLU9GwMJKeJ6328CDNSW8KMRB6v/D0SBcm/q8S8miMLNwsks1iv\nhu5fYez9QIDUlgASeVwRJHq0QHKnH8fPmDQx9v0rjI0SQGpLAKmilh2EKEjuNZINkn0xpJ1u\n5AiQWhNAqih7QgleI01rNOuCKdDWBsm9f4UBSK0JIFUVvQeFBZSet+GW20yMXeafzp0ojN0D\n299NCyA1LM2cA0htCSA1rEyQjvtd0nOG4lILkLYT//FX+Tqo3utqobj0pYHkmpc0rNbfX1Ty\ndVC919VCcelLBKmmYZasDvAWw5Kvg+q9rhaKSx9AakXydVC919VCcelLXdrNBlr9f5N3UH+O\nOOjTJ/TkhgJIJZ0QKqUTl74ikIg33eJXZzncOSc3FEAq6YRQKZ249JXNSCZsuRqwijQAKVHy\ndVC919VCcelLBYlS5IAUcNB3T24ogFTSCaFSOnHpWw+St4pT/skNBZBKOiFUSicufatBCkxC\nkSXeNgJIJZ0QKqUTl75kkMjM4zHjL+2UfXJDAaSSTgiV0olL32qQJhf86TdN3vY3ZqQ0yddB\n9V5XC8WlLw2kXO1YzQCppBNCpXTi0geQWpF8HVTvdbVQXPoAUiuSr4Pqva4WikufDEg7CiCV\ndEKolE5c+gCShJKt94nk66B6r6uF4tIHkOorbr3PSb4Oqve6Wigufac30d9B2jqkSr4Oqve6\nWqj6pdKw9geJAkSs931ToacuQsctuXOG4rL+t7gwI5Up5BjuWuPZvnYxE33oOAJI1eUu6VzT\nu3Sn1eP+233OUFzWAVJ12TehoA77tg+/s7aTr4Pqva4Wiss6QKou7TyeQaJP6E0rBsnXQfVe\nVwvFZR0g1ZdjvW9PQqEXe8nXQfVeVwvFJR0g1ZdzLxdrEtL+cZR8HVTvdbVQXNIBkoSo9T5Z\n2i3b3tZxkHwdVO91tVBcygHSPvJ/WytfB9V7XS0Ul1CAtLkiHyCSr4Pqva4WissqQNpe4Y+0\nytdB9V5XC8UlNQ0kZR0sPS1c1/sup2+BDgBSWPJ1UL3X1UJx6UsEaXRkcDonmW0xvuEAiUi+\nDqr3ulooLn2pM1LQWAsgVZR8HVTvdbVQXPqyQaI+QYNHUMBMf35x7usY8C/+QjHb/VKLVoBU\n0gmhUjpx6Uu+RnIMVWfP/LCZPpmrQgb8vh2eN4wxZVAApJJOCJXSiUtfMUjjkXdcHQ72jOQ2\nS3ieI4BU0gmhUjpx6UsGKQbCMqUEzPTnxira3/fgd55nCiCVdEKolE5c+tJBMooHyW5qUkFS\nT57nCiCVdEKolE5c+vYG6dkxWwCppBNCpXTi0pcBkr05ML2ccI0UWxoq5+i+DpDq1kH1XlcL\nxaUvB6SxyslNLlVoX9ykgES3v2OvFzFxJpD+XrcOqve6WigufWkgHUgAqaTTgat7w1Bc+gBS\nKwokDiC1FYpLH0BqRYHEAaS2QnHpA0itKJA4gNRWKC59AGlLceb6gcQBpLZCcakFSNuJN9cP\nJA4gtRWKSy5M9LcTb64fSBxAaivUdqXSgNoFKWquPyiQOIDUViguu/8bF2akuoqZ68dN9P++\nxduCqgggbSbWXB8z0gFCcdkFSJspbq7fK5A4gNRWKC67AGkzRc31BwUSB5DaCsVlFyBtp4i5\n/qhA4gBSW6G45AKk7RQx1x8VSBxAaisUl1yAtKXC5vqDAokDSG2F4lILkFpRIHEAqa1QXPoA\nUisKJA4gtRWKS18qSKlfWN29jnd/A6WSr4Pqva4WiktfIkjJFgq71/Hub6BU8nVQvdfVQnHp\nSwNJWQdOu9fx7m+gVPJ1UL3X1UJx6csCqX8Y8eh2vE+8l5XyjE2oQ3g1AaSSTgiV0olLXyJI\nc8Wn+NGFXp69hEIv1hRAKumEUCmduPSlbjYY48w406v+0cPDdbvzLfAqCiCVdEKolE5c+tJB\nMj4EhtTtfC8yRZ8a39rbBGzCKwoglXRCqJROXPqqgTSt1CaSRpScIIXBHwAACP1JREFUpZ7f\nFyCNkq+D6r2uFopLX9ZmAw+St64LruIAUkTydVC919VCcelL3Wzof5qizQZrFRf0D68pgFTS\nCaFSOnHpS13azft2+dvfyyTkbX8bgDRLvg6q97paKC59WddINSRd6GcC6R+jKtVB9V5XC8Wl\nb0uQRGagYJAjKpA4gNRWKC59m85IpbdqyYohHUBKgcQBpLZCcenbfGknLYCUWTcrel0tFJc+\ngNSKAokDSG2F4tIHkIrlOuK7BpCZCiQOILUViksfQCqU74gPkM4eiksfTPQL5TviA6Szh1pV\nMEfTRiAFHfEtc3xt7Of9Yx1qNCiQOIDUViiuIP4VF2YkRiFHfMcc330+tXUbxU30J5CE/h+g\nigJIZWIc8QlAgVOOzeoyTuBfQMxIbYXiCgIglSnmiE8fOs8NQDp4KK4gAFKZIo74zpLOMcvX\ny0+AdMBQXEEApEIFHfFdRhyzfIB07FBcPQCkQgUd8eeHdJLSFkEA6bihuHoASMUKOeLPD5ft\n74UZb/vbAKRDheKqASC1okDiAFJbobj0AaRWFEgcQGorFJe+JJBU6JCoxelkmxI/E0jHLblz\nhuLSlwHSbFnnDcIVb9BuSFAAqaQTQqV04tKXtrQbvyQe+644W7wx+oQEkEo6IVRKJy59ZSDZ\n1kH0p1GDR+TypfIZpEDH+ZRjSbRCAKmkE0KldOLSlw4S+RMxsxuZMNMPv7PT0VoyVjK6A0gl\nnRAqpROXvvUgGQckYxwi3I5kUIe3GhAApJJOCJXSiUtfGkiOh+rkgr8455uAM34ApJC3Pl3T\nVTAZAkglnRAqpROXvmSQ6DKMFKuimBgTAsnepbDdj+l1Fzm9QgCppBNCpXTi0rcWpNAKjQXJ\nXv+R8ezNiWIBpJJOCJXSiUtfIkhhToKbDXYr0tntYINU64ZJAKmkE0KldOLSVwSS55xvbX+b\nOEiutz4Zzzq9QgCppBNCpXTi0pcKkpSq1z1AKumEUCmduPQBpFYUSNy/E1SneA5c3RuG4tK3\nL0gCnvoAqaR4DlzdG4bi0rf3jFRdAKmkeA5c3RuG4tIHkFpRIHEAqa1QXPoA0jO5XvkZPZ2j\nZXHnKZA4gNRWKC7Z8P7m5XvlZ/R1jgDp4KG4ZAMkXr5XfnZn290YIB03VFERHFV1QbJ8IG1n\nIMtEVQet8ReQFuOgQM9BgcQBpLZCcZXyn7gwI7mzie9Vp2cc5tOadCNmq8RD0ukZN9FPAanq\n/y5ULIDEyl2Iub6OxKk4cnoZwgXJPosZqf1QXKUAJFYWSHGDfB06bSgq80m9OEUCpIOF4ioF\nILHSzmNKyvJHh06PfbTTVwfO9gokDiC1FYqrFIDEa9m1cy27CT3OZZCPSuAaiZwdFEgcQGor\nFFcoAIlX0CvfOhKQvKWdvXHnXiMBpIOF4goFID2T65XvHY11OggSOamtIUmcQOIAUluhuDIB\nSEXS0SfFwwQSB5DaCsWlEiDlyply3Cf5Y40KJA4gtRWKS2UaSGo24AqopHTjAzKjJQWSn5Gc\nT7GWfqTV7SlfB9V7XS0Ul8yMGSlWokUglXRtBCQhyddB9V5XC8WlLx2kaIUCpCqSr4Pqva4W\niktfJkjEKV+N7kFmdsx3vYWMYxlEFnMEJKe18ge0TbyefD0dIJV0QqiUTlz6kkGyDe2IMd1y\nNM6RvO661i2HQOvogCnOdwCppBNCpXTi0pcKkrIe2lR5L/Kv94fFPtxvbb3kNjEAqbQOqve6\nWigufbkgBZzyfWDsRu7rdLgJqfC0Qzz6l9efrO0AUkknhErpxKUvESQ6GTwFKbIym/8YCyR6\n9NZv/jWSecIKQCrphFApnbj05YEUmGTyj+RgnoGEa6SKdVC919VCcelLA8makDyQAjOQu7RT\n5EgHfH6NZA9AxwjrsCAFFPrWLEK1GSoJJDXvDYw72c6M5G9/O5c6yjoaq9z9TQfa0R2QjBEW\nQEKoPUKlbjas1Wb1DZAQao9QG4D0/LqmpgASQu0RaosZqcJdjzKCbRYJghZttbTbTAAJ2kMA\nCYIqCCBBUAXB+xuCIF8ACYIq6Dwgld+YKWv42BGhrq3TgOSbrIgMH/W9RKhrCyDlDX+u6gZI\n1QSQ8mJsVd0GIB1LACkvxibVPd9mDSAdRgApOcBm1a3DIYRA0gCphgBSXoyTTROOAztAKhZA\nyotxMpA2DnViAaS84c8FEq6Rqgkg5Q1/ruoGSNV0GpDwyYbGQ51c5wEJgnYUQIKgCgJIEFRB\nAAmCKgggQVAFASQIqiCABEEVBJAgqIIAEgRVEECqJKU+pwdco8TRfu5K3fK6QLsKaaokpfTP\n+IBrlDiann2rAdIxhDRV0qPw7+MDrlHyaNldoF2FNFWSUu/qY3gwVf/46EW9mJ+bevkdn937\nmev3VanX377Vt76Pg/x0L/6Md9iaxiWvG/P18pj63vrXf16GR+Zdq9vHlv+vkC+AVEmPgr+p\nH+OD9Kh89Xl7/HidnumOn37xdutb3btTnX77Fx+nHZDm182f4RZ2b/1Scnz01r8EkvYVQKqk\nR8H/9Is7F6RX89nV++fw7P5r7l31vw8IfHSvvU1jvHUD3AdMyLjk9Vu3pfE9D/WhdD83mS+F\nbz3sK4BUSV1xfwxg2CD1K7Vf8uynm4huQ4uX8bVBt/m0DdLy+uPw5/0+D9Wf1er1z8b/s5An\ngFRJfenfH9XtXSORH/T15T7D9hjGftHu9ohgdet+/nms8W4zjNA+AkiVNGwKPGYNUZBe1e3j\nz48Dl/m+Kf21zf8mFBFAqqShpD/U+1zlPyGQ7KXd0rHX86Vd//qvC1IXF4ncV/j7r6Sxku9q\nuGz5NL/3EEj37vX3bv/gzZjPbhOBIPB8s0GpLzrweI30Zb6x2bCzAFIljaX/o4fKV4rMTQSk\naft72NFW3xZIyza3DdLy+pvyr5GG1963/d+FHAGkSppKv9/mNm/6UdnBa6QX9dJvDHS/Y71/\nGQuk5Revzicbll/I9r2cpd0jmAZHOwsgQVAFASQIqiCABEEVBJAgqIIAEgRVEECCoAoCSBBU\nQQAJgioIIEFQBQEkCKoggARBFQSQIKiC/j9j5nZaRy2QUQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_7 <- ggplot(data = female_agr_summary,\n",
    "       aes(x = regDesc, y = agr_wage_farm_workers_female_2015)) +\n",
    "  geom_bar(aes(fill = regDesc), stat = \"identity\", width = 0.6) +\n",
    "  scale_x_discrete(\"New Region\") + scale_y_continuous(\"Average Wage for Female Farm Workers\") +\n",
    "  #ggtitle(\"Top 10 activities for females in top 5 countries\") +\n",
    "    coord_flip() + theme_grey()\n",
    "\n",
    "plt_8 <- ggplot(data = female_agr_summary,\n",
    "       aes(x = regDesc, y = num_loans)) +\n",
    "  geom_bar(aes(fill = regDesc),stat = \"identity\", width = 0.6) +\n",
    "  scale_x_discrete(\"New Region\") + scale_y_continuous(\"Number of loans\") +\n",
    "  #ggtitle(\"Top 10 activities for females in top 5 countries\") +\n",
    " coord_flip() + theme_grey()\n",
    "\n",
    "grid_1 <- grid.arrange(plt_7, plt_8, nrow = 2, ncol=1)\n",
    "grid_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kgNkuoqHiQc1nE3PUsZli9rGAkrsVJWWJgL2gBh1UCqj4ywOhGW/Q7pKjdTw3eo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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(rgdal)\n",
    "library(maptools)\n",
    "library(rgeos)\n",
    "library(dplyr)\n",
    "\n",
    "ph.adm1.spdf <- readRDS(\"PHL_adm2.rds\")\n",
    "ph.adm1.df <- fortify(ph.adm1.spdf, region = \"NAME_1\")\n",
    "\n",
    "phadmunique = unique(ph.adm1.df$id)\n",
    "subregunique = unique(philp_loans$sub_region)\n",
    "common_regions = intersect(phadmunique, subregunique)\n",
    "\n",
    "gb_loan_amt_ph <-  philp_loans %>% \n",
    "      filter(sub_region %in% common_regions) %>%\n",
    "        group_by(sub_region) %>% summarise(loan_amt_median = median(loan_amount))\n",
    "\n",
    "gb_loan_amt_ph <- data.frame(gb_loan_amt_ph)\n",
    "names(gb_loan_amt_ph)[1] = \"id\"\n",
    "\n",
    "ph.adm1.df <- merge(ph.adm1.df, gb_loan_amt_ph, by.y = 'id', all.x = TRUE)\n",
    "ph.adm1.df$loan_amt_median[is.na(ph.adm1.df$loan_amt_median)] <- 0\n",
    "\n",
    "ph.adm1.centroids.df <- data.frame(long = coordinates(ph.adm1.spdf)[, 1], \n",
    "                                   lat = coordinates(ph.adm1.spdf)[, 2]) \n",
    "\n",
    "# Get names and id numbers corresponding to administrative areas\n",
    "ph.adm1.centroids.df[, 'ID_1'] <- ph.adm1.spdf@data[,'ID_1']\n",
    "ph.adm1.centroids.df[, 'NAME_1'] <- ph.adm1.spdf@data[,'NAME_1']\n",
    "\n",
    "\n",
    "options(repr.plot.width=10, repr.plot.height=10)\n",
    "p <- ggplot(ph.adm1.df, aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = cut(loan_amt_median,5))) +\n",
    " # geom_text(data = ph.adm1.centroids.df, aes(label = NAME_1, x = long, y = lat, group = NAME_1), size = 1) + \n",
    "  labs(x=\" \", y=\" \") + \n",
    "  theme_bw() + scale_fill_brewer('Loan Amount Distribution', palette  = 'OrRd') + \n",
    "  coord_map() + \n",
    "  theme(panel.grid.minor=element_blank(), panel.grid.major=element_blank()) + \n",
    "  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank()) + \n",
    "  theme(panel.border = element_blank())\n",
    "\n",
    "print(p)"
   ]
  }
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