Legendary Pokémon Analysis.ipynb

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 "cells": [
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   "source": [
    "# What Makes a Pokémon Legendary?"
   ]
  },
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    "## 1. Introduction\n",
    "<p>In the world of Pokémon academia, one name towers above any other – Professor Samuel Oak. While his colleague Professor Elm specializes in Pokémon evolution, Oak has dedicated his career to understanding the relationship between Pokémon and their human trainers. A former trainer himself, the professor has first-hand experience of how obstinate Pokémon can be – particularly when they hold legendary status.</p>\n",
    "<p>For his latest research project, Professor Oak has decided to investigate the defining characteristics of legendary Pokémon to improve our understanding of their temperament. Hearing of our expertise in classification problems, he has enlisted us as the lead researchers.</p>\n",
    "<p>Our journey begins at the professor's research lab in Pallet Town, Kanto. The first step is to open up the Pokédex, an encyclopaedic guide to 801 Pokémon from all seven generations.</p>\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_712/img/legendary_pokemon.jpg\" alt=\"Legendary Pokémon\"></p>\n",
    "<p><em>Source: <a href=\"https://www.flickr.com/photos/bagogames/\">bagogames</a> on Flickr</em></p>"
   ]
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     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>pokedex_number</th><th scope=col>name</th><th scope=col>attack</th><th scope=col>defense</th><th scope=col>height_m</th><th scope=col>hp</th><th scope=col>percentage_male</th><th scope=col>sp_attack</th><th scope=col>sp_defense</th><th scope=col>speed</th><th scope=col>type</th><th scope=col>weight_kg</th><th scope=col>generation</th><th scope=col>is_legendary</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1         </td><td>Bulbasaur </td><td> 49       </td><td> 49       </td><td>0.7       </td><td>45        </td><td>88.1      </td><td> 65       </td><td> 65       </td><td> 45       </td><td>grass     </td><td>  6.9     </td><td>1         </td><td>0         </td></tr>\n",
       "\t<tr><td>2         </td><td>Ivysaur   </td><td> 62       </td><td> 63       </td><td>1.0       </td><td>60        </td><td>88.1      </td><td> 80       </td><td> 80       </td><td> 60       </td><td>grass     </td><td> 13.0     </td><td>1         </td><td>0         </td></tr>\n",
       "\t<tr><td>3         </td><td>Venusaur  </td><td>100       </td><td>123       </td><td>2.0       </td><td>80        </td><td>88.1      </td><td>122       </td><td>120       </td><td> 80       </td><td>grass     </td><td>100.0     </td><td>1         </td><td>0         </td></tr>\n",
       "\t<tr><td>4         </td><td>Charmander</td><td> 52       </td><td> 43       </td><td>0.6       </td><td>39        </td><td>88.1      </td><td> 60       </td><td> 50       </td><td> 65       </td><td>fire      </td><td>  8.5     </td><td>1         </td><td>0         </td></tr>\n",
       "\t<tr><td>5         </td><td>Charmeleon</td><td> 64       </td><td> 58       </td><td>1.1       </td><td>58        </td><td>88.1      </td><td> 80       </td><td> 65       </td><td> 80       </td><td>fire      </td><td> 19.0     </td><td>1         </td><td>0         </td></tr>\n",
       "\t<tr><td>6         </td><td>Charizard </td><td>104       </td><td> 78       </td><td>1.7       </td><td>78        </td><td>88.1      </td><td>159       </td><td>115       </td><td>100       </td><td>fire      </td><td> 90.5     </td><td>1         </td><td>0         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       " pokedex\\_number & name & attack & defense & height\\_m & hp & percentage\\_male & sp\\_attack & sp\\_defense & speed & type & weight\\_kg & generation & is\\_legendary\\\\\n",
       "\\hline\n",
       "\t 1          & Bulbasaur  &  49        &  49        & 0.7        & 45         & 88.1       &  65        &  65        &  45        & grass      &   6.9      & 1          & 0         \\\\\n",
       "\t 2          & Ivysaur    &  62        &  63        & 1.0        & 60         & 88.1       &  80        &  80        &  60        & grass      &  13.0      & 1          & 0         \\\\\n",
       "\t 3          & Venusaur   & 100        & 123        & 2.0        & 80         & 88.1       & 122        & 120        &  80        & grass      & 100.0      & 1          & 0         \\\\\n",
       "\t 4          & Charmander &  52        &  43        & 0.6        & 39         & 88.1       &  60        &  50        &  65        & fire       &   8.5      & 1          & 0         \\\\\n",
       "\t 5          & Charmeleon &  64        &  58        & 1.1        & 58         & 88.1       &  80        &  65        &  80        & fire       &  19.0      & 1          & 0         \\\\\n",
       "\t 6          & Charizard  & 104        &  78        & 1.7        & 78         & 88.1       & 159        & 115        & 100        & fire       &  90.5      & 1          & 0         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "pokedex_number | name | attack | defense | height_m | hp | percentage_male | sp_attack | sp_defense | speed | type | weight_kg | generation | is_legendary | \n",
       "|---|---|---|---|---|---|\n",
       "| 1          | Bulbasaur  |  49        |  49        | 0.7        | 45         | 88.1       |  65        |  65        |  45        | grass      |   6.9      | 1          | 0          | \n",
       "| 2          | Ivysaur    |  62        |  63        | 1.0        | 60         | 88.1       |  80        |  80        |  60        | grass      |  13.0      | 1          | 0          | \n",
       "| 3          | Venusaur   | 100        | 123        | 2.0        | 80         | 88.1       | 122        | 120        |  80        | grass      | 100.0      | 1          | 0          | \n",
       "| 4          | Charmander |  52        |  43        | 0.6        | 39         | 88.1       |  60        |  50        |  65        | fire       |   8.5      | 1          | 0          | \n",
       "| 5          | Charmeleon |  64        |  58        | 1.1        | 58         | 88.1       |  80        |  65        |  80        | fire       |  19.0      | 1          | 0          | \n",
       "| 6          | Charizard  | 104        |  78        | 1.7        | 78         | 88.1       | 159        | 115        | 100        | fire       |  90.5      | 1          | 0          | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  pokedex_number name       attack defense height_m hp percentage_male\n",
       "1 1              Bulbasaur   49     49     0.7      45 88.1           \n",
       "2 2              Ivysaur     62     63     1.0      60 88.1           \n",
       "3 3              Venusaur   100    123     2.0      80 88.1           \n",
       "4 4              Charmander  52     43     0.6      39 88.1           \n",
       "5 5              Charmeleon  64     58     1.1      58 88.1           \n",
       "6 6              Charizard  104     78     1.7      78 88.1           \n",
       "  sp_attack sp_defense speed type  weight_kg generation is_legendary\n",
       "1  65        65         45   grass   6.9     1          0           \n",
       "2  80        80         60   grass  13.0     1          0           \n",
       "3 122       120         80   grass 100.0     1          0           \n",
       "4  60        50         65   fire    8.5     1          0           \n",
       "5  80        65         80   fire   19.0     1          0           \n",
       "6 159       115        100   fire   90.5     1          0           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spec_tbl_df [801 x 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)\n",
      " $ pokedex_number : num [1:801] 1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ name           : Factor w/ 801 levels \"Bulbasaur\",\"Ivysaur\",..: 1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ attack         : num [1:801] 49 62 100 52 64 104 48 63 103 30 ...\n",
      " $ defense        : num [1:801] 49 63 123 43 58 78 65 80 120 35 ...\n",
      " $ height_m       : num [1:801] 0.7 1 2 0.6 1.1 1.7 0.5 1 1.6 0.3 ...\n",
      " $ hp             : num [1:801] 45 60 80 39 58 78 44 59 79 45 ...\n",
      " $ percentage_male: num [1:801] 88.1 88.1 88.1 88.1 88.1 88.1 88.1 88.1 88.1 50 ...\n",
      " $ sp_attack      : num [1:801] 65 80 122 60 80 159 50 65 135 20 ...\n",
      " $ sp_defense     : num [1:801] 65 80 120 50 65 115 64 80 115 20 ...\n",
      " $ speed          : num [1:801] 45 60 80 65 80 100 43 58 78 45 ...\n",
      " $ type           : Factor w/ 18 levels \"grass\",\"fire\",..: 1 1 1 2 2 2 3 3 3 4 ...\n",
      " $ weight_kg      : num [1:801] 6.9 13 100 8.5 19 90.5 9 22.5 85.5 2.9 ...\n",
      " $ generation     : num [1:801] 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ is_legendary   : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n",
      " - attr(*, \"spec\")=\n",
      "  .. cols(\n",
      "  ..   pokedex_number = col_double(),\n",
      "  ..   name = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),\n",
      "  ..   attack = col_double(),\n",
      "  ..   defense = col_double(),\n",
      "  ..   height_m = col_double(),\n",
      "  ..   hp = col_double(),\n",
      "  ..   percentage_male = col_double(),\n",
      "  ..   sp_attack = col_double(),\n",
      "  ..   sp_defense = col_double(),\n",
      "  ..   speed = col_double(),\n",
      "  ..   type = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),\n",
      "  ..   weight_kg = col_double(),\n",
      "  ..   generation = col_double(),\n",
      "  ..   is_legendary = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE)\n",
      "  .. )\n",
      " - attr(*, \"problems\")=<externalptr> \n"
     ]
    }
   ],
   "source": [
    "# Load the tidyverse\n",
    "library(tidyverse)\n",
    "\n",
    "# Import the dataset and convert variables\n",
    "pokedex <- read_csv(\"datasets/pokedex.csv\", \n",
    "                    col_types = cols(name = col_factor(), \n",
    "                                     type = col_factor(),\n",
    "                                     is_legendary = col_factor()))\n",
    "\n",
    "# Look at the first six rows\n",
    "head(pokedex)\n",
    "\n",
    "# Examine the structure\n",
    "str(pokedex)"
   ]
  },
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   "cell_type": "markdown",
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     "key": "12"
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   "source": [
    "## 2. How many Pokémon are legendary?\n",
    "<p>After browsing the Pokédex, we can see several variables that could feasibly explain what makes a Pokémon legendary. We have a series of numerical fighter stats – <code>attack</code>, <code>defense</code>, <code>speed</code> and so on – as well as a categorization of Pokemon <code>type</code> (bug, dark, dragon, etc.). <code>is_legendary</code> is the binary classification variable we will eventually be predicting, tagged <code>1</code> if a Pokémon is legendary and <code>0</code> if it is not.</p>\n",
    "<p>Before we explore these variables in any depth, let's find out how many Pokémon are legendary out of the 801 total, using the handy <code>count()</code> function from the <code>dplyr</code> package.</p>"
   ]
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   "cell_type": "code",
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    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>is_legendary</th><th scope=col>n</th><th scope=col>prop</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0         </td><td>731       </td><td>0.91260924</td></tr>\n",
       "\t<tr><td>1         </td><td> 70       </td><td>0.08739076</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " is\\_legendary & n & prop\\\\\n",
       "\\hline\n",
       "\t 0          & 731        & 0.91260924\\\\\n",
       "\t 1          &  70        & 0.08739076\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "is_legendary | n | prop | \n",
       "|---|---|\n",
       "| 0          | 731        | 0.91260924 | \n",
       "| 1          |  70        | 0.08739076 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  is_legendary n   prop      \n",
       "1 0            731 0.91260924\n",
       "2 1             70 0.08739076"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Prepare the data\n",
    "legendary_pokemon <- pokedex %>% \n",
    "  count(is_legendary) %>% \n",
    "  mutate(prop = n / nrow(pokedex))\n",
    "\n",
    "# Print the data frame\n",
    "legendary_pokemon\n",
    "\n",
    "\n"
   ]
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   "source": [
    "## 3. Legendary Pokémon by height and weight\n",
    "<p>We now know that there are 70 legendary Pokémon – a sizable minority at 9% of the population! Let's start to explore some of their distinguishing characteristics.</p>\n",
    "<p>First of all, we'll plot the relationship between <code>height_m</code> and <code>weight_kg</code> for all 801 Pokémon, highlighting those that are classified as legendary. We'll also add conditional labels to the plot, which will only print a Pokémon's name if it is taller than 7.5m or heavier than 600kg.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "Warning message:\n",
      "\"Removed 20 rows containing non-finite values (stat_smooth).\"Warning message:\n",
      "\"Removed 20 rows containing missing values (geom_point).\"Warning message:\n",
      "\"Removed 20 rows containing missing values (geom_text).\""
     ]
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    {
     "data": {
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70AurH5yxM9evRIS0tj/q08e/asiYlJly5d6r60ISxzsO8h0RoBn7Li8b52aNVw\njQK0KO5Ck+Wt5JXLF9ra9DCregA5AADQxKN/PLSBlJaWXr58OSsr6/fffy8uLrawsMjKypLL\n5XSne2dn55MnT169elUikVy5cmX//v2jR4+mhxWo49IqKZXKuoztKTYxmerkwC8vL62oUFJU\ne6HwHZlkm6O9i9A4h4Lj8Xh8Pr/KkaWNEj3wbAu5VUcIEQqF5eXlTXC0215mojfNREVqSqmm\npDzuW+Zmq1vbTrKsU794+uGqsrKypnbruYFwOByhUFhaWsp2II3E1NSUx+MVFxezHYheGnSw\nVQDS0A9PPH36dObMmVqFW7ZsYe6ZZmZm7tixg/5ZsCFDhgQHB2v+LFhdllaWn59fVlamYwXd\nRCKRWCxWKBQtJNcRCASmpqYFBQVsB9JIrKysCCEt5xfSJBJJSUlJC0l0hEKhRCIpKipqIYk7\nl8uVSqV1GQuzeZHJZAKBICenqkeqmx4bGxu2QwAj10hPxTYFSOwMgsTOuCGxM2JI7JoyJHbQ\n0NjsYwcAAAAA9QiJHQAAAICRQGIHAAAAYCSQ2AEAAAAYCSR2AAAAAEYCiR0AAACAkUBiBwAA\nAGAkkNgBAAAAGAkkdgAAAABGAokdAAAAgJFAYgcAAABgJJDYAQAAABgJJHYAAAAARgKJHYC2\nvn37+vn5sR0FAACAwfhsBwBAlEplYmJicnLyrVu3FAqFmZlZ+/bt+/fvHxoa6ujoyHZ0AAAA\nzQYSO2BZVlZWSEjI3bt33d3dJ06caGtrW1xcfOvWrW3btm3evPn333+3s7NjO0YAAIDmAYkd\nsEmpVIaEhNy/fz8qKmrSpEmaixQKRUxMTEVFRZUbVlRUqFQqkUjUKGECAAA0D+hjB2xKTEy8\ne/futGnTtLI6QohUKl2yZIm9vT09e+DAAblcfuLEiRUrVnTr1s3BweHgwYOEkNzc3IiICG9v\nb3t7ey8vr/Dw8FevXjGVREdHy+Xyx48fa9bs4+MzbNgwZvbJkyfTpk2zsrKysLCYMGHCvXv3\ntCLR3QQd2KlTp9atW9e9e3d7e/vevXvv3bu3PnYPAACAYXDFDtiUnJxMCJk8ebKe6y9atKhj\nx45r166VyWQWFhYKhcLf3//x48chISHe3t5paWm7d+/++eefT506JZPJ9KkwPz8/ICDg2bNn\ns2bN6ty5c2pq6qhRo/h8PnP/V88mIiIiunbtunHjRqFQGBMTM3fu3Hbt2vXq1Q87dNkAACAA\nSURBVMvA/QEAAFAnSOyATXfu3BGJRO3bt2dKVCrV8+fPmVmZTCaVSplZa2vrvXv3cjgcevbz\nzz9/9OjRunXrpkyZQpd4enpGRERERUWtWLFCnwA2b96clZW1Y8eOqVOnEkLGjh0bGRkZFRXF\nJHYxMTH6NOHs7Lxz50562sPDw9PTMzY2FokdAAA0MtyKBTYVFBSYm5trlty/f7+7hri4OM2l\nwcHBTFZHCElJSbG2tg4LC2NKwsLC5HL5sWPH9AwgJSXFyclp1KhRTMmcOXO4XK7mCvo0MX78\neGba3Ny8Q4cOjx490jMGAACA+oLEDtgkkUgKCws1S5ydnXfv3r179+5PP/208vpt2rTRnM3M\nzGzfvj2f//eFZz6f36FDh6ysLIqi9AkgIyOjQ4cOmsmiVCpt1aqVoU0wfQGZ15Wbm6tPAAAA\nAPUIt2KhsWWpyvbkvU4rUZZSlEk715xfLz948IC5GyuVSocPH04IMTU1rbytiYmJQW1pZmyM\n8vJyw6OuTUMAAACNDFfsoFH9XFj09oOMjS9zUwuLLhYVP3nLlxCyYNv22tXm4uLy4MEDzUSt\noqIiPT3dycmJzrQsLS0JIXl5ecwKZWVlf/31l2YN6enpmtfeFArFs2fP9G8CAACg6UBiB40n\nr7xietbTQrX67yL/EaSNy6U9e1Z9u1tr5epGsNPk7+//6tWrhIQEpiQ+Pv7ly5cjRoygZ11d\nXQkhqampzApbt24tKyvTrCErK+vo0aNMyZYtW9QaEdbYBAAAQNOBW7HQeI4oCl5rpWsmJmTt\nF2Txwk3hn56K29W/f3+5XF5YWPjw4cMTJ06YmJjQmVl15s6de/To0YiIiLS0NE9Pz5s3byYk\nJLi4uMyfP59ewcfHp3Pnzhs2bMjJyWnbtu2VK1euXbum2YVuzpw533333axZs27evOnu7p6a\nmnr69GnN37qosQkAAICmA4kdNJ57pcoqSlu1Jtt3tv7pR6tLF/bu3Uv/Vqyrq+sHH3wQFhbm\n5OSko0KpVJqSkrJu3boTJ04kJSXZ2NiEhoYuWrTIwsKCXoHD4ezevXvRokWJiYl8Pr9fv35H\njx4dN24cU4OFhUVycvKyZct27txJUVTPnj0PHz48bdo0/ZsAAABoOjh6PjxoBPLz8zXvwRlK\nJBKJxWKFQqFSqeoxqiZLIBCYmpoWFBTUY53/9/TFtld5VS7yEpmedm1T5aLGYWVlRQhpOY+y\nSiSSkpKShniOpAkSCoUSiaSoqKikpITtWBoDl8uVSqWvX79mO5BGIpPJBAJBTk4O24HoxcbG\nhu0QwMihjx00nq6iKh50pXWrfhEAAADoCYkdNJ4AmcTFRFC5XMjhzLC2bPx4AAAAjAwSO2g8\nQg7n320ctXI7CY+7w6m1m9CwAeoAAACgMjw8AY3KTWhyvkPbY4qCGyXKIrW6s6lwpExizeOx\nHRcAAIAxQGIHjU3I4YyRScfI2I4DAADA6OBWLLCvb9++fn5+jbDtyZMn5XJ5SkpK7doCAABo\n4pDYgS6XL1+Wy+VyufzTTz/VWpSTk2Nvby+Xy/EbDAAAAE0EEjuomVAoPHToUGlpqWbhgQMH\nKIri83E3HwAAoKlAYgc1GzFihEKhOHbsmGZhUlKSn5+fiQmeZgUAAGgqkNhBzTp27Ni9e/ek\npCSm5Pr163fu3AkODtZaMzo6Wi6XP378WLPQx8dn2LBhzOyTJ0+mTZvm6uratm3bCRMm3Lt3\nT3PlsrKyqKioPn36ODs7t2vXztfXd/HixXpum5eXt2rVKj8/Pzc3NwcHBx8fn6ioKN0/rqBS\nqTZu3Ojr62tmZmZlZRUSEnLjxg29dwwAAEDTgvtooJfg4OCIiIjs7GxHR0dCSGJiorW19eDB\ngw2tJz8/PyAg4NmzZ5MnT+7YsePly5dHjRrF5/Pt7OzoFZYvXx4bGxsSEjJr1iy1Wp2RkXH6\n9Gk9t83MzNy3b9/IkSODgoIoikpNTY2MjMzIyIiOjq4ymLKysgkTJly6dGn8+PEfffRRfn7+\n9u3bhw8fnpyc3LVr19rsJgAAAFYhsQO9jB49eunSpXv37v3000+VSuX3338fHBwsEFTxMxK6\nbd68OSsra8eOHaNHjyaEhIWFRUZGRkVFMclZSkrK0KFDN27cyGyybNkyPbd1d3e/fv060+1v\n+vTpCxYsSExMjIiIsLe3rxzMzp07L1y4EBcXFxAQQP9W7IQJE3x9fVeuXHno0CFDXxoAAADr\ncCsW9CKTyQICApKSkiiKSklJyc/PDwkJqUU9KSkpTk5Oo0aNYkrmzJnD5f79PpRKpbdu3bp9\n+3YtthUKhUxWp1KplEqlv7+/Wq2+du1alcEcOHDA2dl58ODBSqWytLS0tLTU1NR0yJAhly5d\nUqlUtXh1AAAA7MIVO9BXcHDwd999d/HixaSkpK5du7q7u9eikoyMDF9fXw6Hw5RIpdJWrVox\nsytXrpw+fXq/fv2cnZ19fX0HDx7s7+/P4/H02ZYQEh8fHx8ff/v2baVSyRTm5+dXGcz9+/dL\nS0vpm8ta8vLymAuBAAAAzQUSO6iEonh//cl/8ZyoK/iZmUxx3759HR0dv/rqq/Pnz69Zs6bK\nTTWzLobuxxe0DBgw4OrVq6dOnTp//vy5c+cSExO7det25MgRkUhU47bR0dGrV68ODAycPXu2\nra2tQCC4efNmeHi4Wq2ucn2Kojp37rxhwwZCiFQqJYQoFAp6kaWlpf4xAwAANBFI7OAfOCUl\nosP7eNl/0rMm2U8IIdxXOYQQLpcbFBT05ZdfCoXCsWPHVrk5nQ/l5eW1bduWLikrK/vrr7+s\nra3pWRcXl/T0dIqimBRQoVA8e/ZMLpczlchksrFjx9JNbNq0adWqVYcOHQoJCalx26SkJHd3\n99jYWKaq9PR0HS+2Xbt22dnZXl5eJiYmdB+73NxcA3YWAABAE4M+dqCBokRH9jNZHYN/9xbv\nxTNCSGhoaHh4+JdffimTVf1Tr66uroSQ1NRUpmTr1q1lZWXMrL+/f1ZW1tGjR5mSLVu2MFfU\nKIrSum3ao0cPQsjr169r3JYQwuFw1Go1RVH0rFKp3Lp1q46XO3HiRIVCsXbtWq3y58+f69gK\nAACgycIVO/gb768sXlZmFQsoteDyhYqRY+3t7RcuXKijBh8fH/rmZk5OTtu2ba9cuXLt2jXN\nbnBz5sz57rvvZs2a9dtvv7m5uV2+fPn06dNMbzaVSuXh4eHv7+/p6WlnZ/f06dO4uDixWBwQ\nEFDjtoSQgICAjRs3hoaG+vv75+bmJiUlicViHdHOmDHjl19+iYmJuXr16jvvvCOTye7evXv2\n7FmpVHrw4EFD9hwAAECTgMQO/sZ7kl3dIn71izRxOJzdu3cvWrQoMTGRz+f369fv6NGj48aN\nY1awsLBITk5etmxZQkICRVG9evU6fPjwtGnT/tsKnz9z5swLFy6cPXu2sLDQ1ta2T58+8+fP\nd3Z2rnFbQkh4eDiHwzl48OCZM2dat24dHBzs6+tLJ4VVEggEiYmJcXFx+/bt+9e//kUIsbOz\n69Gjx8SJE/V5sQAAAE0Nh7lvZfTy8/M17wkaSiQSicVihUJhxANhCP9z1uT8z1UuoszEhbM/\nadxwGlVL62MnkUhKSkoMeq6l+RIKhRKJpKioqKSkhO1YGgOXy5VKpXQHhpZAJpMJBIKcnBy2\nA9GLjY0N2yGAkUMfO/ib2qraTxy1lXVjRgIAAAC1gMQO/lberj0lNq9ykcqzWyMHAwAAAIZC\nYgd/owQmJQGjqUo/FFbWxbvcw4uVkAAAAEB/eHgC/qHCuW3x+x+YXL3Me/aEq1SWW1uXd+pS\n1rEz23EBAABAzZDYgTa1hWXpoGECgcDU1LSkoIDtcAAAAEBfuBULAAAAYCSQ2AEAQFNRgLsE\nAHWDxA6gGTt58qRcLk9JSan3mvv27evn51fv1QLolpCQMHXqVIVCwXYgAM0VEjuAulIqlXFx\ncWPGjOnYsWPr1q1dXV2HDh26Zs2a7Gy9fq4DABizZs165513Ro0adfPmTbZjAWiW8PAEQJ1k\nZWWFhITcvXvX3d194sSJtra2xcXFt27d2rZt2+bNm3///XfNX7MFgBoFBgZ6e3sb8W/8ADQo\nJHYAtadUKkNCQu7fvx8VFTVp0iTNRQqFIiYmpqKigq3Y9FFRUaFSqUQiEduBAPyDi4sL2yEA\nNFe4FQtQe4mJiXfv3p02bZpWVkcIkUqlS5Yssbe3p2ejo6Plcvnjx4811/Hx8Rk2bBg9La/K\n5MmTdS+qLDc3NyIiwtvb297e3svLKzw8/NWrV8zSAwcOyOXyEydOLF682NPT08HB4eDBg4SQ\nJ0+eTJs2zdXVtW3bthMmTLh371497SEAAGhUuGIHUHvJycmEkOpyLIPs27dPc/bYsWN79uxx\ncnLSvUiLQqHw9/d//PhxSEiIt7d3Wlra7t27f/7551OnTslkMma1RYsWeXh4fPHFF+bm5hYW\nFvn5+QEBAc+ePZs8eXLHjh0vX748atQoPp+Pm8gAAM0OEjuA2rtz545IJGrfvj1TolKpnj9/\nzszKZDKpVKpPVQMHDmSm//jjj/379/fs2XPp0qW6F2mJiYl59OjRunXrpkyZQpd4enpGRERE\nRUWtWLGCWc3a2vrIkSOlpaXl5eWEkM8//zwrK2vHjh2jR48mhISFhUVGRkZFRSGxAwBodnAr\nFqD2CgoKzM3NNUvu37/fXUNcXJyhdT59+nTSpElyuXzPnj0mJiZ6LqKlpKRYW1uHhYUxJWFh\nYXK5/NixY5qrBQcHczgcza2cnJxGjRrFlMyZM4fLxYcDAEDzgyt2ALUnkUgKCws1S5ydnXfv\n3k0IuXHjxpdffmlohSUlJaGhoQUFBcePH7exsdFzESMzM7Nr1658/t/nNZ/P79Chw+XLlymK\nYpK5Nm3aaG6VkZHh6+urmepJpdJWrVoZGjwAALAOiR2AYVIUBfF5+XeVKh4hpG27kiu/Pnjw\ngLkbK5VKhw8fTggxNTXV3EozbWLQd0IZFEV9+OGHN27c2LNnj7u7u56LaqHKq30AAGAEcLcF\nwAD/ev7yvT+fnCooylaVZarKcvr0JYR8un1HjRtaWloSQvLy8piSsrKyv/76S3OdNWvW/PDD\nD8uXLx86dKjW5joWaXJxcXnw4IFmvlhRUZGenu7k5FRlZslslZ6eTlEUU6JQKJ49e1bjiwIA\ngKYGiR2Avi4WFce8zP1Hkf8I0sblwp7dG/fEa62sNYKdq6srISQ1NZUp2bp1a1lZGTN78ODB\nqKiokJCQDz/8UKsqHYu0+Pv7v3r1KiEhgSmJj49/+fLliBEjdG+VlZV19OhRpmTLli1qtVp3\nWwC1plQq2Q4BwGjhViyAvva9rvT7lSYmZO0XZPHCzz/5+NDO2P79+8vl8sLCwocPH544ccLE\nxITO5wghPj4+nTt33rBhQ05OTtu2ba9cuXLt2jWmH9uff/45b948Ozu74cOHayZ/tra2Uqm0\nukVdunTRCmfu3LlHjx6NiIhIS0vz9PS8efNmQkKCi4vL/PnzdbyuOXPmfPfdd7Nmzfrtt9/c\n3NwuX758+vRpPBILDUStVk+aNMnW1nb9+vVisZjtcACMDRI7AH1lqMqqKG3Vmmzf6X3mlOT8\n2b179yoUCjMzM1dX1w8++CAsLIwZbY7D4ezevXvRokWJiYl8Pr9fv35Hjx4dN24cvfTFixf0\nOClaAx0HBATMnj27ukWVH7mVSqUpKSnr1q07ceJEUlKSjY1NaGjookWLLCwsdLwuCwuL5OTk\nZcuWJSQkUBTVq1evw4cPT5s2zcDdA6CXr7766ueffyaEXL9+fefOnZ06dWI7IgCjwtHsWGPc\n8vPzNe98GUokEonFYoVC0UJ+wVAgEJiamhYUFLAdSCOxsrIihOTm5upYJygj+3RhUZWL3rWU\nbXRoTo+RSiSSkpISrac3jJVQKJRIJEVFRSUlJWzH0hi4XK5UKn39+jXbgVTh4sWLY8aMoTsq\nCASCI0eO9OzZs451ymQygUCQk5NTHwE2uOoeaQeoL+hjB6CvN8VmtVgEALScnJyZM2cy3U+X\nLl1a96wOALQgsQPQ13uWMlt+Fb0X3IQmY2SSxo8HoBlRq9Uffvgh87S1n5/frFmz2A0JwCgh\nsQPQlyWfd7CtUwfhPwaB625mureNo0n1g4kAACFk06ZNZ86coacdHBy+/vprHUPwAECt4eEJ\nAAO4C03OtXf5T3HJrVKlgBBPkegNM1N8OwHo9ttvv61fv56e5vP527dvp3u1AkC9Q2IHYBge\nh+MrNvNFpzoA/bx+/XrGjBnMs2uLFy/u3bs3uyEBGDHcigUAgIZCUdRHH32UlZVFzw4cOHDO\nnDnshgRg3JDYAQBAQ/n666+PHz9OT8vl8piYGC4X3zsADagFjWNXVlZWlw8UDofD5XLVanUL\n2WMcDofD4bSc35Xi8Xik0u+AGTEul0tRVMt5M7eok5cQQr9etqMgV65c6devHz32J5fLPXHi\nxMCBA+u9FS6Xy+FwmsvJS3/UADScFtTHrri4uO4DFBcWFmKAYqNEd+XOy8tjO5BG0gIHKC4p\nKcEAxY3sjz/+YB59XbhwYbdu3RriFKMHKG4uJy8GKIaG1oISOwAAaExjx47t0qXLtGnTrK2t\ndf9gMQDUFyR2AADQUDp27HjixAmlUolbkACNA4kdAAA0ILFYLBaL2Y4CoKXA00kAAAAARgKJ\nHQAAAICRQGIHAAAAYCSQ2AEAAAAYCSR2AAAAAEYCiR0AAACAkUBiBwAAAGAkkNgBAEDtZWdn\nt5AfWgRoFpDYAQBALRUXF0+cOHH48OEZGRlsxwIAhCCxAwCAWgsPD79///4ff/zh5+d39uxZ\ntsMBACR2AABQK0lJSfv372dmXVxc2IsFAP4LiR0AABjs3r17ERER9DSHw9m0aZOzszO7IQEA\nQWIHAACGUiqVM2fOLCkpoWenT58+fPhwdkMCABoSOwAAMMzChQtv3bpFT3ft2nX58uXsxgMA\nDCR2AABggEOHDiUmJtLTUqk0NjbWxMSE3ZAAgIHEDgAA9PXo0aOPP/6Ymf3iiy/atGnDYjwA\noAWJHQAA6EWpVE6bNq2wsJCenTp16pgxY9gNCQC0ILEDAAC9fPbZZzdu3KCnO3fuvGLFClbD\nAYAqILEDAICaHTlyZM+ePfS0WCzeuXOnqakpuyEBQGVI7AAAoGatWrWyt7enp6Oiotq3b89u\nPABQJT7bAQAAQDPQu3fvM2fOzJkzp3Xr1qNHj2Y7HACoGhI7AADQi5WV1b///e+ysjK2AwGA\nauFWLAAA6IvD4WDUOoCmDIkdAAAAgJFAYgcAAABgJJDYAQAAABgJJHYAAAAARgKJHQAAAICR\nQGIHAAAAYCSQ2AEAAAAYCSR2AADwX1euXHn58iXbUQBA7SGxAwAAQgh5+vTppEmT3n777TNn\nzrAdCwDUEhI7AAAg5eXlM2bMyM3NzcnJCQ4OTk5OZjsiAKgNJHYAAEDWrVt36dIletrJyalf\nv37sxgMAtYPEDgCgpTt//nxMTAw9bWJiEhsbK5VK2Q0JAGoHiR0AQIv28uXLmTNnVlRU0LMr\nV6709vZmNyQAqDUkdgAALZdarZ41a9aLFy/o2aFDh06dOpXdkACgLpDYAQC0XF9++eUvv/xC\nTzs6OsbExHA4HHZDAoC6QGIHANBCXbx48auvvqKnBQLBjh07LC0t2Q0JAOoIiR0AQEuUk5Oj\n2bVu2bJlPXv2ZDckAKg7JHYAAC2OWq3+8MMPnz17Rs8OHjx45syZ7IYEAPUCiR0AQIuzZcsW\n5uclHB0dv/76a3StAzAOSOwAAFqcMWPG9O7dmxDC5/O3b9+OrnUARoPPdgAAANDY7O3tDx8+\nvHbtWgsLi169erEdDgDUGyR2AAAtEZ/PX7p0KdtRAEA9w61YAAAAACOBxA4AAADASCCxAwAA\nADASSOwAAAAAjAQSOwAAAAAjgcQOAAAAwEggsQMAAAAwEkjsAAAAAIwEEjsAAOO0f//+69ev\nsx0FADQqJHYAAEbo9u3bH3/8sb+//6ZNmyiKYjscAGgkSOwAAIyNQqF47733lEpleXn5qlWr\nDh48yHZEANBIkNgBABibOXPmZGRk0NNvvvnm6NGjWQ0HABoPEjsAAKOya9eupKQketrS0nLr\n1q18Pp/dkACg0SCxAwAwHnfu3Pm///s/eprD4WzatMnR0ZHdkACgMSGxAwAwEkVFRVOnTi0t\nLaVn586dO2zYMHZDAoBGhsQOAMBILFy4MD09nZ7u3r37okWL2I0HABofEjsAAGOQkJCwf/9+\netrS0jI2NlYgELAbEgA0PiR2AADN3t27dz/77DN6msPhfPPNN05OTuyGBACsQGIHANC8FRcX\nT5s2raSkhJ6dNWtWYGAguyEBAFuQ2AEANG+bNm26d+8ePd29e3fmqVgAaIGQ2AEANG8LFiyY\nPn06IUQmk33zzTcmJiZsRwQArEFiB0AIIceOHePxeCkpKfVe88mTJ+VyeUPUDEATCoWRkZE7\nduyIiYlxdnZmOxwAYBOGIwfjp1QqExMTk5OTb926pVAozMzM2rdv379//9DQ0Podu/XevXtH\njhwZO3asq6trPVYLoA/8bhgAECR2YPSysrJCQkLu3r3r7u4+ceJEW1vb4uLiW7dubdu2bfPm\nzb///rudnR0hxN/fv6ioqLCwsC5t3b9//4svvujatatmYufn55ednY2BJwAAoBEgsQNjplQq\nQ0JC7t+/HxUVNWnSJM1FCoUiJiamoqKCnuVyuaampsXFxVXWU1FRoVKpRCJRLWLgcrlCobAW\nGwIAABgKfezAmCUmJt69e3fatGlaWR0hRCqVLlmyxN7enp7V6mN34MABuVx+4sSJFStWdOvW\nzcHB4eDBg3l5eatWrfLz83Nzc3NwcPDx8YmKiiovL6c3iYyMnDJlCiHk3Xfflcvlcrl87ty5\npFIfO7rmU6dOrVu3rnv37vb29r179967dy8TmO5WAAAAdMAVOzBmycnJhJDJkyfXbvNFixZ1\n7Nhx7dq1MpnMwsIiMzNz3759I0eODAoKoigqNTU1MjIyIyMjOjqaEDJp0iSBQLB+/folS5b0\n7NmTEGJra1tdzREREV27dt24caNQKIyJiZk7d267du169epFCNHdCgAAgA5I7MCY3blzRyQS\ntW/fnilRqVTPnz9nZmUymVQqrW5za2vrvXv3cjgcelapVF6/fp3P/+9ZM3369AULFiQmJkZE\nRNjb2zs7O3fq1IkQ0rlz5z59+ugOzNnZeefOnfS0h4eHp6dnbGwsndi5u7vraMXQPQAAAC0K\nbsWCMSsoKDA3N9csuX//fncNcXFxOjYPDg5msjpCiFAoZPItlUqlVCr9/f3VavW1a9cMDWz8\n+PHMtLm5eYcOHR49elTvrQAAQEvD5hW7iIiIO3fuaBVGRkZ26dKFEHL69Gmte08zZ84cMWIE\nM/vnn39u37793r17YrF46NChQUFBXC7yVPgHiUSi9aCrs7Pz7t27CSE3btz48ssvdW/epk0b\nrZL4+Pj4+Pjbt28rlUqmMD8/39DAtK69SSSSx48f13srAADQ0rCZ2E2aNEmhUDCzP/zww+PH\nj93c3JgSHo/36aefMrOaQ0goFIqlS5e2atVq4cKFT548iY+PV6vVlTvIQwv0Z1nZv3PzbyuV\nFRQlcm2fc/nSgwcPmLuxUql0+PDhhBBTU9Maq9IawT86Onr16tWBgYGzZ8+2tbUVCAQ3b94M\nDw9Xq9WGBql5IVBLPbYCAAAtDZuJnaenJzOtVqu3bdvm4+Oj+VXK5XKr66t0/Pjx4uLiJUuW\nyGQyQkhhYeHhw4fHjBljZmbW0GFDU3ZCUTgz+2kxkwP59CGXLy3d8c3e9evqXnlSUpK7u3ts\nbCxTkp6errmCjnStHluBlmn16tUdOnSYOHEi24EAQJPWVO5dXr9+PT8//+2339YqpyhKpVJV\nXv/q1ateXl50VkcI6du3r0qlunnzZoMHCk3Yk7LyGdlPijWvbPmPIG1cTu/+duOeeK2VmRHs\n9MfhcNRqNUVR9KxSqdy6davmCmKxmBCSl5dnaM0GtQIt0MmTJzdt2jRnzpzZs2dXN9oiAABp\nOk/Fnj171sLCwtvbW7OwvLx84sSJpaWl1tbW77zzzqhRo5grItnZ2X5+fsyaDg4OHA4nOzub\nfq4QWqZ9rxUlauofRSYmZO0XZPHCzz/5+NDO2P79+8vl8sLCwocPH544ccLExMSg3/4KCAjY\nuHFjaGiov79/bm5uUlISnckxvLy8BALB119/rVKpzM3NnZ2de/ToYeirqLEVaGmysrJmz55N\n5/r79+8fMmRIYGAg20EBQBPVJBI7lUp16dKlgQMHaj79IJPJxo8f36FDh/Ly8vPnz8fFxZWU\nlISEhNBLi4qKNL/t+Hy+UCjU6iZ//vz5ZcuWMbNffPFF9+7d6xiqRCKpYw3NCIfD0epk1sQ9\nfPGqitJWrcn2nZ1+SbX7z4V9+/bl5+eLxWI3N7ePP/542rRpLi4u9Fr0/wwSicTa2poQQj9L\nK5VK6Vna2rVrxWJxQkLCmTNnHB0d33vvvQEDBrz99ttisZhezdra+ttvv/38888XLVqkUqne\ne++9IUOG0O8Z3TULBAIul0uX1NhKvWh2B7fuzMzMmmNXjbKysjlz5rx+/ZqeDQoKosfB1o3D\n4dTju6WJo0/elvN6AXTjMHd8WHThwoV169atX7+eHgasSl988cXly5cTExPpb6PAwMCQkBDN\n7ibjx48fOXJkWFgYU3Lp0qU1a9YwsytXrtTs1WcoDofD5XI175EZNw6HQ98TZDsQA4Tee5D0\nIqfKRUMtLY51qfbdRQjh8XikVvdnmykul0tRVMt5Mzffkzc8PDwqKoqe7tChw6+//lrjv5fN\n8eStCy6Xy+FwmsvJS3/UADScJnHF7uzZs3Z2djqyOkJInz59zp07l5WVqDdHaQAAIABJREFU\nRd87E4vFRUVFzNLy8nL65pfmJm+++eaRI0eY2fz8/Lp0fhKJRGKxuLCwsMo+f8ZHIBCYmpoW\nFBSwHYgBOnKrfXahM5+r++hbWVmROnePa0YkEklJSUkL+aUyoVBIv96SkhK2YzHMqVOnNm7c\nSE8LhcIdO3aUl5fX+C7lcrlSqZS5yGf0ZDKZQCBoLievjY0N2yGAkWP/4Yni4uKrV69WfmxC\nC/3fGNPHztHRMTs7m1manZ1NUZSjo2PDxQlNX7ClzJJfxX/DYi73PSuLxo8HoC6ePHnCdK0j\nhKxdu5Ye4xMAQAf2E7v//Oc/KpWqcmKndV39zJkzpqamTOrWo0ePtLQ0ZsjWs2fPmpiY4FOv\nhbPi8eKdHaz/eadDxuPFOTs4CARsRQVQC+Xl5TNmzMjNzaVnAwMDMU4nAOiD/Vuxv/zyS5s2\nbSoP8b948eL27ds7OTmp1eqLFy/euHHjvffeY7p7+/v7//DDD6tWrRo7duzTp08xiB3QepuJ\nLrm1PZxfcKNUqaYoD5HpaJnEGp1aoLlZs2bN5cuX6em2bdsy3ewAAHRjObF7/fp1Wlpalf+J\nent7X7x48dSpU2q12snJad68eYMGDWKWSqXS1atX79ixY8OGDWKxeOzYscHBwY0YODRdFjze\n+7jxCs1Zamrq5s2b6WkTE5PY2NgW9Tw+ANRFk3gqtnHk5+eXlZXVenP64QmFQoGHJ4wS/fAE\nc+fL6LXAhyeKioqaxcMTL168GDBgwIsXL+jZ9evXT5482aAaWubDEzk5VT8R39Tg4QloaOz3\nsQMAAEZCQgKT1b3zzjuGZnUA0MIhsQMAaEI+/vjjzZs3i0QiFxcXdK0DAEOx//AEAABomjhx\nYufOnQkhUqmU7VgAoJlBYgcA0OTU5WdyAKAlw61YAAAAACOBxA4AAADASCCxAwAAADASSOwA\nAAAAjAQSOwAAAAAjgcQOAAAAwEggsQMAAAAwEkjsAAAaW0VFBdshAIBxQmIHANCoKIqaMmXK\nZ599plKp2I4FAIwNfnkCAKBRbdmyJSUlhRBy5cqV2NjYNm3asB0RABgPXLEDAGg8v//+e2Rk\nJD2dlpaWmZnJbjwAYGSQ2AEANJL8/Pzp06czd2AXLlz49ttvsxsSABgZJHYAAI2Boqh58+b9\n+eef9GyfPn3mz5/PbkgAYHyQ2AEANIZvvvnm2LFj9LSNjc327dt5PB67IQGA8UFiBwDQ4K5f\nv75y5Up6msvlbt261c7Ojt2QAMAoIbEDAGhYhYWFH3zwAdO1bsGCBf3792c1IgAwWkjsAAAa\n1ieffPLw4UN62sfHJzw8nN14AMCIIbEDAGhAu3bt+v777+lpa2trdK0DgAaFxA4AoKHcuXNn\n+fLl9DSHw4mOjm7dujW7IQGAcUNiBwDQUH799deysjJ6+qOPPho6dCi78QCA0UNiB9CATp48\nKZfL6d+Paoj1oYl77733Dh061Lp16x49ekRERLAdDgAYP/xWLIDBLl++HBAQQE9zuVyJRGJn\nZ+ft7R0YGDh48GAuF/8vwd98fHzOnDlTVlYmEAjYjgUAjB8SO4Ba8vX1HTZsGCGkqKjo0aNH\np0+fPnDgwJtvvvntt99aW1vT6/j5+WVnZ+MbvYVj3g8AAA0NiR1ALXl5ec2cOZOZValUK1as\n+Oabb95///3k5GS6kMvlCoVClgIEAIAWB/eMAOqHiYlJZGSkr6/vpUuXUlNT6UKtPnN5eXmr\nVq3y8/Nzc3NzcHDw8fGJiooqLy/XUW1WVtbkyZPbtWvXtm3b4ODgBw8e9O3b18/Pr9YVAgCA\nEcMVO4D69P77758/f/6nn34aOHBg5aWZmZn79u0bOXJkUFAQRVGpqamRkZEZGRnR0dFV1pab\nmxsQEJCTkzNlypQOHTr8+uuvgYGBXC6X+TUqQysEAADjhsQOoD516dKFEML8zIAWd3f369ev\n8/n/Pe+mT5++YMGCxMTEiIgIe3v7yuvHxMQ8efIkLi6OflYjLCxs7dq1GzZsYBI7QysEAADj\nhluxAPVJIpEQQgoKCqpcKhQKmSRMpVIplUp/f3+1Wn3t2rUq1//xxx/btGnDPIFLCJk9e7bm\nU7eGVggAAMYNV+wA6pNCoSD/S++qFB8fHx8ff/v2baVSyRTm5+dXuXJmZmbfvn01SyQSSatW\nrWpdIQAAGDckdgB6ya+o+Lmw+L5SZcXjmihV1a1248YNQoirq2uVS6Ojo1evXh0YGDh79mxb\nW1uBQHDz5s3w8HC1Wl27qOq9QgAAaNaQ2AHU7If8gnlPnikq/pct/fWMEKKiKK3VKIr69ttv\nCSFDhgypsp6kpCR3d/fY2FimJD09XUe7zs7ODx480CwpKCh49uyZXC6vXYXQEHJycqysrDAw\nNQA0BfgkAqjB1eKSmdlP/87q/udUQZHmrFKpXLx48cWLF996660BAwZUWRWHw1Gr1dT/MkKl\nUrl161YdTQ8dOjQzM1PzF8a2bdumeTXO0Aqh3imVygkTJowbN+758+dsxwIAgCt2ADXZlJNX\n+eIcISTjRtrSjdGtBfzi4uJHjx6lpqbm5ua+9dZbcXFx1VUVEBCwcePG0NBQf3//3NzcpKQk\nsViso+mPPvrou+++mz59+tSpU+nhTlJTU1u1asXhcGpXIdS7JUuW0PffBw0aFBcX17NnT7Yj\nAoAWDYkdQA2uFBdXveCP69v/uM7lcsVicatWrQYNGhQYGDhkyBAm66osPDycw+EcPHjwzJkz\nrVu3Dg4O9vX11XzoVYuVldWxY8eWLVsWHx9PUVSvXr0OHToUFBRkampauwqhfh09enT37t30\ndGFhoYWFBbvxAABwqKouRRil/Pz8srKyWm8uEonEYrFCoVCpqu04b0wEAoGpqWl1w3YYHysr\nK0JIbm5u5UXt7qQXVLoPS1tmJ58rt2rYyP6ptLTUxcVl9OjRdbzlKpFISkpKWshvVAiFQolE\nUlRUVFJSUo/VZmRkDBo0iH4OmhCyZcuW8ePH12P9tcblcqVS6evXr9kOpJHIZDKBQJCTk8N2\nIHqxsbFhOwQwcuhjB1ADVxOT6hZ1EFa7qL5oDmJCCNmyZUtFRUX//v0bul3QTaVSTZ06lcnq\nwsLCmkhWBwAtHG7FAtQg2EJ6vaS0crm9gN9f0uAd2saMGePu7u7l5aVWqy9cuHD48OGuXbuO\nGTOmodsF3ZYtW5aWlkZPu7u7r169mt14AABoSOwAavCelcX5ouJkRaFmoZjL3eZob1p9d7r6\n4ufnd+jQoe+++06pVLZu3fqDDz4IDw8XCAQN3S7o8OOPP+7atYueNjMzi42NFYlE7IYEAEBD\nYgdQAx6Hs9PZYV9e/lFFwb1SlRWf18NMNMvaso1JY2RXCxYsWLBgQSM0BHrKzs6eO3cu0zt5\n/fr1bm5u7IYEAMBAYgdQMw4hQZayIEsZ24EAy8rKymbMmJGXl0fPBgcHT5w4kd2QAAA04eEJ\nAAB9rVy58sqVK/S0q6trZGQku/EAAGhBYgcAoJeffvppx44d9LRQKNy5c6e5uTm7IQEAaEFi\nBwCgL2YI4vXr13t4eLAbDABAZehjBwCgl8GDB585c2bGjBn29vYhISFshwMAUAUkdgAA/8/e\nfcc1dbVxAD83mxHCluEAceIqjrpQa0UUFypOXKi4iqu+Ku5KrZNaUUHrwImtq9TROnChAu6K\nWvcuqCjKhkDm+8dtb1MgGCEDkt/3Dz/3nnPHk4DhyblnaMrV1fXw4cMVWcMGAECnkNgBAHwC\nDofD4eCTEwAqKfSxAwAAADASSOwAAAAAjAQSOwAAAAAjgcQOAAAAwEggsQMAAAAwEkjsAAAA\nAIwEEjsAAAAAI4HEDgDgX3/++Wdubq6howAAKCckdgAAf3v//v3QoUM7dep0/fp1Q8cCAFAe\nSOwAAAghRKFQTJo0KS0tLSUlxd/fPy4uztARAQB8MiR2AACEELJ27dr4+Hh628HBoWXLlgYN\nBwCgPJDYAQCQy5cvr1q1it7mcDibN2+2tbU1bEgAAOWAxA4ATF1mZuakSZNkMhm9O2/evM8/\n/9ywIQEAlA8SOwAwaUqlcurUqampqfRuly5dQkJCDBsSAEC5IbEDAJO2fv36EydO0NvOzs4b\nNmxgsfDBCABVFT6/AMB0Xbt2bcWKFfQ2utYBgBFAYgcAJiorK2vChAlSqZTeDQ0NbdOmjWFD\nAgCoICR2AGCKlErllClTUlJS6N0vvvhi6tSphg0JAKDikNgBgCnasmUL07WuWrVq6FoHAMYB\nH2QAYIq+/PJLT09PQgiLxYqKinJwcDB0RAAAWsAxdAAAAAZQp06duLi4sLAwa2vrTp06GToc\nAADtQGIHACaKz+cvW7ZMqVQaOhAAAK3Bo1gAMGkURRk6BAAArUFiBwAAAGAkkNgBAAAAGAkk\ndgAAAABGwoQGT1AUVZHONPS5FbxIlWNSL5aY0uul/mHoQPTHdF4v82Fl6ED0xNReL0DZKNMZ\nESaRSCoyASmLxWKxWHK53ETeMYqi6Ndr6ED0hMPhEEJkMpmhA9ETNputUChM55eZfr0KhcLQ\nsegJm802nf+8bDaboqiq8p+X/qgB0B0T+g0Ti8XMopDlYGZmZmFhkZ+fL5FItBhVpcXlcgUC\nQW5urqED0RN66fesrCxDB6InQqFQLBZXlb+FFcTn8+nXKxaLDR2LPrBYLCsrK9P5ZRaJRFwu\nt6q8Xnt7e0OHAEYOfewAAAAAjAQSOwAwWseOHXvy5ImhowAA0B8kdgBgnB48eDBx4kQfH5/9\n+/cbOhYAAD0xoT52AGA6xGJxcHAw3akuODjYwsKid+/ehg4KAEDn0GIHAEZo9uzZDx8+pLe9\nvLz69+9v2HgAAPQDiR0AGJtff/1179699LZIJNq5cyePxzNsSAAA+oHEDgCMyrNnz2bMmMHs\nrlq1ys3NzXDhAADoFRI7ADAeRUVFY8eOzcvLo3eDg4PxEBYATAoSOwAwHnPmzPnzzz/pbU9P\nz2+++caw8QAA6BkSOwAwErGxsTExMfS2UCjcsWOHQCAwbEgAAHqGxA4AjMHz589nzpzJ7K5a\ntcrd3d2A8QAAGAQSOwCo8uiudczSxqNGjRowYIBhQwIAMAgkdgBQ5f3www937tyhtxs1arR0\n6VLDxgMAYChI7ACgyps8eXLfvn0JIebm5ps3b+bz+YaOCADAMLCkGABUeUKhcMuWLa1bt7ax\nsalXr56hwwEAMBgkdgBgJIKDgw0dAgCAgeFRLAAAAICRQGIHAAAAYCSQ2AEAAAAYCSR2AAAA\nAEYCiR0AAACAkUBiBwAAAGAkkNgBAAAAGAkkdgAAAABGAokdAFQNERERp0+fNnQUAACVGhI7\nAKgCzp49u3z58sDAwHnz5kkkEkOHAwBQSSGxA4DKLi0t7auvvlIoFEqlcsuWLcePHzd0RAAA\nlRQSOwCo1BQKRUhIyIcPH+jd3r17+/v7GzYkAIBKC4kdAFRqK1euvHDhAr1dq1atiIgIw8YD\nAFCZIbEDgMorMTFx7dq19DaXy/3xxx+trKwMGxIAQGWGxA4AKqn3799PmDBBLpfTu2FhYS1b\ntjRsSAAAlRwSOwCojBQKxaRJk96+fUvv+vr6BgcHGzYkAIDKD4kdAFRGP/zwQ3x8PL3t6uq6\nfv16iqIMGhEAQBWAxA4AKp2kpKTvv/+e3uZyuVu3brW1tTVsSAAAVQJHk4OUSuXdu3fv3r37\n7t07iqIcHBwaN27cqFEjXQcHACbow4cPEydOZLrWLViwAF3rAAA09JHE7tatWz/++OPBgwff\nv39frMrBwWHgwIETJkxo2rSpzsIDAJOzadOmN2/e0Ntdu3adNGmSYeMBAKhC1CZ2jx49mjVr\n1pEjR4RCYceOHdu2bevh4WFnZ6dUKjMyMp48eXL58uVdu3Zt2LDB398/PDy8bt26+owbAIzV\n3Llz7e3tv/nmG0dHx8jISHStAwDQnNrErnHjxs2aNYuJienXr5+5uXmpx+Tn5//6668RERGN\nGjXC6o0AoBUURY0fP75Ro0Y8Hg9d6wAAPonaxO7gwYN9+vQp+2QLC4vhw4cPHz788OHD2g4M\nAExa+/btDR0CAEDVo3ZU7EezOlVYuhEAAADA4DDdCQAAAICR0Gi6E3VzDVAUZWZmVrNmTV9f\n36FDh3K5XK3GBgAAAACfQKPErrCwMDMz8/Xr1ywWy8XFhRDy+vVrhULh4uLC5XKTkpL27Nmz\nfv36M2fOYH1uAAAAAEPR6FHsgQMHFArF4sWLMzMzU1JSUlJSMjMzFy5cqFAoTp48mZOTExYW\ndv369bCwMF2HCwAAAADqUEql8qMHde/e3dnZefv27cXKg4KC0tPTf//9d0LIoEGDbty48fTp\nU52EqQ3Z2dlSqbTcp5uZmVlYWOTk5FSiiV2USu7jB+yXz6mcbKXIWlbLXVanPtHSpF9cLlcg\nEOTm5mrlapUfPa1GRkaGoQPRE6FQKBaLZTKZoQPRBz6fLxQK8/PzxWKxoWPRBxaLZWVllZWV\nZehA9EQkEnG53JKz6FdO9vb2hg6hFDExMSNGjDh16pSPj4+hY4GK0qjFLjExsV27diXL27Vr\nd/HiRXr7yy+/TE1N1WZoUCZKJjU/uEdw+AA3+Trn2WPuzWtmh/abxf5MyU3iTzUAADASEhIo\nFZaWli1atFi7di2zNJ+Ju3fv3uLFix89eqSHswxOo8SOoqgHDx6ULH/w4IHqpPACgUBrccHH\n8M/GsV88K1bIefaEd/6MQeIBAADD6ty585o1a9asWRMSEpKenj59+vSQkBBDB1Up3Lt3Lyws\nrByJXTnOMjiNEjsfH5+oqKhDhw6pFsbGxkZFRXXt2pXevXHjRq1atbQfIJSGkko4fyaXWsW9\n/QdlGs/XoEoLCQlZtWqVQqEwdCAAxqN58+bTp0+fPn36ypUrk5OTq1WrtmXLlqrykBq0RaPE\nbvXq1XZ2dv369fPw8OjRo0ePHj08PDwCAgIcHR2///57QkhhYeG9e/cGDx6s42jhb6yMD5Sa\nBnZKKmVlmUpHMaiitm/fvn///vDw8KFDh3748MHQ4QAYIVtbW29vb4VC8ezZM0LI+/fvQ0JC\natSowePxqlevPmnSpPT09DJOX7FiBYvF+uqrrxQKRVFR0bJlyxo1aiQQCKytrXv16nXz5k3m\nyJiYGIqi4uLiVq5c6e7ubmZm9vnnnyclJRFCLl682KFDBwsLC2dn5yVLlhS7Rdkh0Zc9duzY\nN9984+bmxuPx6tWrt3PnzjJilkqlS5cu9fT0tLCwEIlEjRo1mjJlCiFkwYIFAwcOJIT07t2b\nflQdFBRECMnIyJgzZ07Lli3t7OwEAkGDBg2WLl3K9DxWd9aKFSsoinry5InqrRs0aNCmTZuy\nw9AbjaY7cXd3v3Xr1sqVK48ePXr27Fm6ZNasWaGhoXZ2doQQgUCQmJio20gBwCj8+eefCxcu\npLfj4+Nv377duXNnw4YEYHyUSiWdfNjZ2WVnZ7dr1+7Jkydjxoxp0aLFH3/8sWnTplOnTl2/\nft3a2rrYiQqFYtq0aZGRkUuWLFmwYIFUKu3evfvFixeHDx8+efLk7OzsLVu2tG/f/sKFC6pz\n3C5atCg/P3/cuHESiWTt2rXdunXbvXv36NGjg4KC+vfvf/DgwUWLFrm7uw8fPpw+XsOQJk+e\n3LJly+joaD6fv2rVqqCgoDp16qhbb3DmzJnr168fM2bMjBkzFArF06dPT5w4QQgZO3Ysl8td\nvHjxsmXL2rZtSwhxcnIihDx79mzXrl0DBgwICgqiZ/lYsGDBs2fPoqOjyzjro9SFoTcaJXYK\nhcLe3j48PDw8PLxYVWZmpo2NjQ4Cg7IobO2VHE6pj1yVXJ7CBuumQyWVl5cXHBxcVFRE706e\nPBlZHYC25OTkvHjxghCSlpb2448/3rp1q0WLFh4eHvPnz3/8+HFUVNRXX31FH+nl5RUSErJ0\n6dJif9aLioqGDRt2+PDhbdu2jR49mhASGRkZHx9/8ODBgIAA+piJEyc2atRo9uzZdEMPLT8/\n/9q1a3RX+2bNmvXv3z8gIODixYv0yMtJkybVqlUrKiqKSexWrVqlSUhubm779++nt5s1a+bq\n6hoZGakusfv111979+69detWpmTlypWEEHd390aNGhFCmjRp8sUXXzC1jRs3/uuvvzicvxOh\nqVOnjhs3btu2bWFhYdWrV1d31kepC0NvNHoUO3Xq1FLLs7OzmT52oE9KLlfWtHmpVdLPWijZ\nGuXrAPo3e/ZsZlKkFi1azJkzx7DxABiTLVu2uLu7u7u7t23bdufOnZ06dYqNjSWE/Prrrw4O\nDuPHj2eOHD9+fLVq1X799VfV0zMzM7t27XrixInDhw/TWR0hJCYmxt3dvWfPnoX/EAgEvXr1\nunjxIvMNjRDy1VdfMQMoO3XqRAhp2bIlM5+GQCBo3br148ePmeM1DIlJBAkhQqGwQYMGqhcp\nRiQS3bp16/bt2xq+XQKBgMnqJBJJYWGhv7+/QqG4du2ahlfQShhap1EGEBUV5ezsPH/+fNXC\n3Nzcbt263b17VzeBwUcUfeFDZWVynv3nV1xWt0FRhy8NFRJA2Xbv3n3gwAF629raesuWLViH\nEECL/P39g4KC6NU+GzRoULNmTbr8+fPnLVu2ZJIYQgiHw2nQoEFCQoJSqWRmtxg7dmxubm58\nfDydmdHu378vFovNzMxK3i4jI8PZ2Znednd3Z8rpaUFVS+hC1Q61GoZUo0YN1YtYWVmVMV3u\n999/P2TIkGbNmrm7u3fu3Llnz57+/v5sNlvd8YSQLVu2bNmy5c6dO4WFhUxhZmZmGad8VDnC\n0C6NErvly5fPnTvX1dWV7jlICMnLy/Pz80tOTj58+LAOowP1lGyOuP8QzrPH7L9esLKzFNY2\n8lruMjcPbU1QDKBd9+/fZ74cUhS1bt26Yh/ZAFBBderU6du3b7lPHzRo0I4dO5YsWdKqVStz\nc3O6UKlUNm3adNOmTSWPpzvZ01RTNHUl5UCV+ItWxqoK3bp1e/78+bFjx86dO3f27Nlt27a1\natUqPj6eeS3FrFixYu7cuYMGDZo5c6aTkxOPx0tOTp40aVLZo/VLhkQIUZ3s/VPD0DqN3vc5\nc+akpqaOGzeuWrVqfn5+BQUFPXv2vHbtWmxsbLdu3XQdIqhFUTKPejKPeoaOA+AjCgoKgoOD\nmYUfJk2a5OfnZ9iQAExH7dq1Hz58KJPJmGRLLpc/ePDAzc1NNU0ZMmRIt27dAgMDe/To8dtv\nv1laWhJC6tat+/Lly+bNm/N4PP2H9Kmsra0DAwMDAwMJIStXrpwzZ87evXvHjBlT6jW3b9/e\npEmTffv2MSX3799XPaDUs0ouUySVSv/66y/VNUXUhVHu1/VJNOpjRwhZt25dnz59Bg4ceP78\n+d69eyclJe3bt69nz546DQ4AjMPs2bOZST69vLyK9esAAJ3q27dvenq6anf+LVu2vH37tl+/\nfsWOHDhw4P79+5OSkrp3704vKTly5Mjs7GxmJDvjzZs3+glJQ0qlstgyeq1btyb/PFcVCoWk\nxKKRFEXJ5XKmCbCwsHDNmjWqB5R6Vr169QghqgNdf/jhB2bB0rLD0A9NW0pZLNaePXt8fX2/\n+OILNpu9d+/eirT3AoDp2Lt3L/Od2NraOjo6Wrtf/QGgbLNnzz5w4EBISMgff/zh5eWVnJy8\ndetWDw+PefPmlTy4X79+sbGxAwYM8PX1PXHixLRp006dOrVq1aorV6706NFDJBK9ePHizJkz\nIpHo1KlT+glJE0VFRc7Ozv7+/l5eXs7OzqmpqRs3brS0tOzfvz8hxMvLi8vlhoeHFxUVCYVC\nd3f31q1b9+/ff/ny5f7+/v7+/h8+fNi+fTvdSMko9ayOHTs2bdp0yZIl7969q1OnzqVLl65d\nu+bi4qJJGPqhNrE7ffp0ycIpU6YkJycPHjzY2tqaOQBrBgOAOk+fPp07dy69TVFUREQEutYB\n6JlIJEpMTPzmm2+OHDmyfft2R0fHcePGffvtt+pmK+vVq9fhw4f79u3r4+MTFxf3+++/b9iw\nYdeuXWFhYYQQFxeX1q1bjxo1Sp8hfRSXy502bVp8fPzp06dzc3OdnJy++OKLefPm0WM4HBwc\ndu7c+d13302ePFkikYwaNap169aLFy9msVgxMTEnT56sXr16UFBQ586dO3TowFyz1LMoioqN\njZ0yZcr27ds5HI6Pj8/58+eZGULKDkM/KHX9EDV/yF1GT8ZKJTs7m2ksLQczMzMLC4ucnByJ\nRKLFqCotLpcrEAjopnhTULLbhHETCoVisVim+9XnoqKiwsLC6E+JcePGLVu2TNd3LInP5wuF\nwvz8fKaTn3FjsVhWVlbFngcZMZFIxOVyq8rCWao9sQB0QW2L3e7du/UZBwAYpZCQEA8Pj6lT\np9aqVWvx4sWGDgcAwMipTexUZwUEACi37t27nzlzRqlUomsdAICuYYkCANA59KsDANAPtdOd\nxMbGath5TqlU0ouWAAAAAIABqU3shg4d2qJFi507d+bl5ak7Jjc3d+fOnc2bNx86dKhuwgMA\nAAAATal9FHvv3r1Zs2YFBQWFhIR06NDh888/9/DwsLW1VSqVmZmZT548uXr16sWLFwsKCgIC\nAn755Rd9Bg0AAAAAJalN7Dw8PGJjY+/cubNhw4aDBw+qTrJMc3JyGjt27MSJEz09PXUcJAAA\nAAB83EcGTzRp0mTjxo0bNmy4d+/evXv30tPTKYpycHBo3LhxgwYN9BMiAAAAAGhCo1GxFEU1\natSoUaNGuo4GAAAAAMpN7eAJAAAAAKhaMI8dAFRUXl5escWzAUBvdLT2o1Ao1MVlQdfQYgcA\nFSKVSgcNGjR69Ojs7GxDxwIAYOrQYgcAFfLtt99eu3aNEHLv3r1t27ahMy4AgAGhxQ4Ayu/U\nqVObNm2it1+9emXYYAAAAIkdAJTTq1evJk+ezKw9uHLlSjTXAQAInXh6AAAgAElEQVQYlkaJ\nXVBQ0IMHD0qWX7lyJSgoSMsRgQ5QRYXs1JecJw9ZGR+IZksAA5RNJpONHz8+IyOD3u3bt++w\nYcMMGxIAAGjUx27nzp1BQUElZyR+/vz5zp07d+zYof24QFsUCn5CPPf6JUoupwvkDo5FPfrK\nHZ0MGxdUdcuWLbt69Sq97e7u/sMPPxg2HgAAIBV8FJubm8vj8bQVCugC/9xJ3pUEJqsjhLDT\n35nt28XKzjJgVFDVnTlzJioqit7m8/nR0dGYGQEAoDIoq8Xu0aNHjx49orcvXbqUl5enWpuR\nkbFu3bp69eqV+95nzpxZu3atasmECRN69uzJ7P7111+bNm16+PChhYVFt27dhgwZwmKxNKwF\nQggrO4v3x7WS5VRhIT/pvNjPX/8hgRF48+bNV199pVAo6N2lS5c2adLEsCEBgJ7NmTNn5cqV\nPXr0+P3335nCvn37ZmVlxcfHa/dGkZGRxTKQyiA5OdnLy+v+/fuVbYXVshK7n376KSwsjN6e\nN29eyQO4XO7u3bsrcns2mz1z5kxm18PDg9nOyclZsGCBk5PT7NmzX79+vXv3boVCMXz4cE1q\ngcZ++Vxt1Ytn+owEjIZcLg8JCWG61vXp02fUqFGGDQkAPi4vl/UujYjFSgdHpUM1QlFaueqx\nY8du3rzp5eWllauBVpSV2A0aNKhx48aEkIEDBy5evFh1vBtFUUKhsHnz5vb29hW5PYvFat++\nfalVx48fLygomD9/vkgkIoTk5eUdOnSof//+5ubmH60FGlVUqLauUKzHQMB4rFy58uLFi/S2\nm5tbRESEYeMBgI+QSTlxx1jJ18k/rexKRyeZ/wBlNecKXtjR0dHe3n7p0qUHDx6scJSmQiaT\nEUI4HB3OIlzWs0tPT88BAwYMGDBg/vz5I0eOHKAiICDA19e3glkdTalUSiSSkuU3btxo2rQp\nnbcRQjp06CCRSP78809NaoGmFFqprbIS6TMSMA4JCQnr1q2jt3k83tatW9G1DqCSYx+NZf1x\nlcnqCCHUuzROTDSVU9GlYiiKmjdvXmxs7P3790s9YNeuXZ6ennw+v1atWkuXLmWmRho+fHj3\n7t2jo6M9PDwsLS27dOny/Lna50tliIuLa9u2rZmZmYODQ0hIiFj8b4NFeHi4q6urpaVl//7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s9quUUqs4j+5Rsl5KDnrNAwAYoTSpdOjT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\nru4PAABAr4fALgAo598fo6oaVn7SYjFZ6yG+p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FABrDiB0QvO69997ExMTBgwe7\nXK4ff/zxq6++Sk5Ozs/PNxgM/m4aAKA1CHZA8Fq0aNGaNWtKSkpqa2vj4uIee+yxN954w2q1\n+rtdAIBWItgBAABoBHPsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbAD\nAADQiP8H5uRgh6nH9vQAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare the plot\n",
    "legend_by_heightweight_plot <- pokedex %>% \n",
    "  ggplot(aes(x = height_m, y = weight_kg)) +\n",
    "  geom_point(aes(color = is_legendary), size = 2) +\n",
    "  geom_text(aes(label = ifelse(height_m > 7.5 | weight_kg > 600, as.character(name), '')), \n",
    "            vjust = 0, hjust = 0) +\n",
    "  geom_smooth(method = \"lm\", se = FALSE, col = \"black\", linetype = \"dashed\") +\n",
    "  expand_limits(x = 16) +\n",
    "  labs(title = \"Legendary Pokemon by height and weight\",\n",
    "       x = \"Height (m)\",\n",
    "       y = \"Weight (kg)\") +\n",
    "  guides(color = guide_legend(title = \"Pokemon status\")) +\n",
    "  scale_color_manual(labels = c(\"Non-Legendary\", \"Legendary\"),\n",
    "                     values = c(\"#F8766D\", \"#00BFC4\"))\n",
    "\n",
    "# Print the plot\n",
    "legend_by_heightweight_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "26"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 4. Legendary Pokémon by type\n",
    "<p>It seems that legendary Pokémon are generally heavier and taller, but with many exceptions. For example, Onix (Gen 1), Steelix (Gen 2) and Wailord (Gen 3) are all extremely tall, but none of them have legendary status. There must be other factors at play.</p>\n",
    "<p>We will now look at the effect of a Pokémon's <code>type</code> on its legendary/non-legendary classification. There are 18 possible types, ranging from the common (Grass / Normal / Water) to the rare (Fairy / Flying / Ice). We will calculate the proportion of legendary Pokémon within each category, and then plot these proportions using a simple bar chart.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "dc": {
     "key": "26"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KgoIcSECRNeeOGF0tJSk8nkffm+fftefvll7+RLL73UtWvXpl0VvqRDiQAASHw/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\nfD61C4kAJpPJ5/O53W61C4kADQ0NjQxtqhnsAof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tslgsGzduFEI8+eST77333n333ac87mTbtm1/+XLlTg8AAAJjSURBVMtfOnTo4B9d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\nANCI/w9lha25xp6TvQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare the data\n",
    "legend_by_type <- pokedex %>% \n",
    "    group_by(type) %>% \n",
    "    mutate(is_legendary = as.numeric(is_legendary) - 1) %>% \n",
    "    summarise(prop_legendary = mean(is_legendary)) %>% \n",
    "    ungroup() %>% \n",
    "    mutate(type = fct_reorder(type, prop_legendary))\n",
    "\n",
    "# Prepare the plot\n",
    "legend_by_type_plot <- legend_by_type %>% \n",
    "    ggplot(aes(x = type, y = prop_legendary, fill = prop_legendary)) + \n",
    "    geom_col() +\n",
    "    labs(title = \"Legendary Pokemon by type\") +\n",
    "    coord_flip() +\n",
    "    guides(fill = \"none\")\n",
    "\n",
    "# Print the plot\n",
    "legend_by_type_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "33"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 5. Legendary Pokémon by fighter stats\n",
    "<p>There are clear differences between Pokémon types in their relation to legendary status. While more than 30% of flying and psychic Pokémon are legendary, there is no such thing as a legendary poison or fighting Pokémon!</p>\n",
    "<p>Before fitting the model, we will consider the influence of a Pokémon's fighter stats (<code>attack</code>, <code>defense</code>, etc.) on its status. Rather than considering each stat in isolation, we will produce a boxplot for all of them simultaneously using the <code>facet_wrap()</code> function.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "dc": {
     "key": "33"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dq16/Tp03q9Pj4+/t577+3Vq5f9DBkZGStXrjx58mRoaOiAAQPGjBmjVCrdnAoA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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare the data\n",
    "legend_by_stats <- pokedex  %>% \n",
    "  select(is_legendary, attack, sp_attack, defense, sp_defense, hp, speed)  %>% \n",
    "  gather(key = fght_stats, value = value, -is_legendary) \n",
    "\n",
    "# Prepare the plot\n",
    "legend_by_stats_plot <- legend_by_stats %>% \n",
    " ggplot(aes(x = is_legendary, y = value, fill = is_legendary)) +\n",
    " geom_boxplot(varwidth = TRUE) +\n",
    " facet_wrap(~fght_stats) +\n",
    " labs(title = \"Pokemon fight statistics\",\n",
    "        x = \"Legendary status\") +\n",
    " guides(fill = \"none\")\n",
    "\n",
    "# Print the plot\n",
    "legend_by_stats_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "40"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 6. Create a training/test split\n",
    "<p>As we might expect, legendary Pokémon outshine their ordinary counterparts in all fighter stats. Although we haven't formally tested a difference in means, the boxplots suggest a significant difference with respect to all six variables. Nonetheless, there are a number of outliers in each case, meaning that some legendary Pokémon are anomalously weak.</p>\n",
    "<p>We have now explored all of the predictor variables we will use to explain what makes a Pokémon legendary. Before fitting our model, we will split the <code>pokedex</code> into a training set (<code>pokedex_train</code>) and a test set (<code>pokedex_test</code>). This will allow us to test the model on unseen data.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true,
    "dc": {
     "key": "40"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# Set seed for reproducibility\n",
    "set.seed(1234)\n",
    "\n",
    "# Save number of rows in dataset\n",
    "n <- nrow(pokedex)\n",
    "\n",
    "# Generate 60% sample of rows\n",
    "sample_rows <- sample(n, 0.6*n)\n",
    "\n",
    "# Create training set\n",
    "pokedex_train <- pokedex  %>% \n",
    "  filter(row_number() %in% sample_rows)\n",
    "\n",
    "# Create test set\n",
    "pokedex_test <- pokedex  %>% \n",
    "  filter(!row_number() %in% sample_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "47"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 7. Fit a decision tree\n",
    "<p>Now we have our training and test sets, we can go about building our classifier. But before we fit a random forest, we will fit a simple <strong>classification decision tree</strong>. This will give us a baseline fit against which to compare the results of the random forest, as well as an informative graphical representation of the model.</p>\n",
    "<p>Here, and also in the random forest, we will omit incomplete observations by setting the <code>na.action</code> argument to <code>na.omit</code>. This will remove a few Pokémon with missing values for <code>height_m</code> and <code>weight_kg</code> from the training set. Remember the warning messages when we made our height/weight plot in Task 3? These are the Pokémon to blame!</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "dc": {
     "key": "47"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RMfH6/slpaWll1VpUqVhBBLly79YDFj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NFWWl+ibmg35Z2N63H29palxMblbaqIUr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\nCnzhrO4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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load packages and set seed\n",
    "library(rpart)\n",
    "library(rpart.plot)\n",
    "set.seed(1234)\n",
    "\n",
    "# Fit decision tree\n",
    "model_tree <- rpart(is_legendary ~ attack + defense + height_m + \n",
    "                    hp + sp_attack + sp_defense + speed + type + weight_kg,\n",
    "                       data = pokedex_train,\n",
    "                       method = \"class\",\n",
    "                       na.action = na.omit)\n",
    "\n",
    "# Plot decision tree\n",
    "rpart.plot(model_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "54"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 8. Fit a random forest\n",
    "<p>Each node of the tree shows the predicted class, the probability of being legendary, and the percentage of Pokémon in that node. The bottom-left node, for example – for those with <code>sp_attack &lt; 118</code> and <code>weight_kg &lt; 169</code> – represents 84% of Pokémon in the training set, predicting that each only has a 3% chance of being legendary.</p>\n",
    "<p>Decision trees place the most important variables at the top and exclude any they don't find to be useful. In this case, <code>sp_attack</code> occupies node 1 while <code>attack</code>, <code>defense</code>, <code>sp_defense</code> and <code>height_m</code> are all excluded.</p>\n",
    "<p>However, decision trees are unstable and sensitive to small variations in the data. It therefore makes sense to fit a <strong>random forest</strong> – an ensemble method that averages over several decision trees all at once. This should give us a more robust model that classifies Pokémon with greater accuracy.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "dc": {
     "key": "54"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      " randomForest(formula = is_legendary ~ attack + defense + height_m +      hp + sp_attack + sp_defense + speed + type + weight_kg, data = pokedex_train,      importance = TRUE, na.action = na.omit) \n",
      "               Type of random forest: classification\n",
      "                     Number of trees: 500\n",
      "No. of variables tried at each split: 3\n",
      "\n",
      "        OOB estimate of  error rate: 7.05%\n",
      "Confusion matrix:\n",
      "    0  1 class.error\n",
      "0 411  9  0.02142857\n",
      "1  24 24  0.50000000\n"
     ]
    }
   ],
   "source": [
    "# Load package and set seed\n",
    "library(randomForest)\n",
    "set.seed(1234)\n",
    "\n",
    "# Fit random forest\n",
    "model_forest <- randomForest(is_legendary ~ attack + defense + height_m + \n",
    "                         hp + sp_attack + sp_defense + speed + type + weight_kg,\n",
    "                         data = pokedex_train,\n",
    "                         importance = TRUE,\n",
    "                         na.action = na.omit)\n",
    "\n",
    "# Print model output\n",
    "print(model_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "61"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 9. Assess model fit\n",
    "<p>Looking at the model output, we can see that the random forest has an out-of-bag (OOB) error of 7.48%, which isn't bad by most accounts. However, since there are <strong>24 true positives</strong> and <strong>24 false negatives</strong>, the model only has a recall of 50%, which means that it struggles to successfully retrieve every legendary Pokémon in the dataset.</p>\n",
    "<p>In order to allow direct comparison with the decision tree, we will plot the <strong>ROC curves</strong> for both models using the <code>ROCR</code> package, which will visualize their true positive rate (TPR) and false positive rate (FPR) respectively. The closer the curve is to the top left of the plot, the higher the area under the curve (AUC) and the better the model.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "dc": {
     "key": "61"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5FfI0boySd1331GxwJgDIodANih48eVmCiTSZs2yd9fw4bp44/Vtq3RsQAYjGIH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\nEwQ7AAAAO0GwAwAAsBMEOwAAADtBsAMAALATBDs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devXq1T179vTt27dv374VHRry8TUaTaUWs39BDSoIH+wAAAO0SURBVKwEQNPjHjsA\nDXXu3DkRGTx4sHHjvn37TNzdw8Nj5MiR69atmzdv3s2bN5UdlaOZ91iL9PT0wsJC4xblTrLg\n4GDlpfLD/v37jfucOXPm4sWLnTt39vT0rLO8qpR72srKykyp0NXVdcqUKRcuXNi7d++2bdtK\nS0uNT9dJwz5+Vab8gmqq37KVAGhsBDsADdWlSxcRqZhIISLbtm2rM9gdOHDAYDAYt+Tl5YmI\ncn9YXFyco6PjmjVrKh2nsLBwx44dtR85Pz8/Pj6+4mV6evq2bdu8vb2joqKUlqeeekpElixZ\nolxnFJHS0tJ58+YZDIZZs2aZUl5Vytol58+fr722CjNnzhSRxMTExMRER0fH6Oho460N+fhV\nmfILqql+y1YCoLFxKRZAQ8XFxW3btm369OlTp04NDAxMT0/fs2fP5MmTa1+jeNKkSa6urqGh\noYGBgVqt9siRI6mpqUFBQWPHjhWRXr16rV+/PjY2NjIy8uGHHw4ODi4rKzt9+vS+ffs6deqk\nzIGoSXh4eEJCwpEjR8LCwpR17MrLy//2t79VZLIHH3zwpZdeWr16dVBQ0GOPPebm5pacnHzq\n1KmIiIiKZepqL68qDw+PQYMGpaWlTZ8+/d5773VwcJgwYUKvXr1qKjIsLOyee+7ZuXNnSUlJ\nVFSUj4+P8daGfPyqTPkF1VS/ZSsB0OhUXWwFgG1Q1rHT6XQ1dUhNTVWe4uDh4TF8+PCUlJQP\nP/xQRN555x2lQ9Xl3NatWzdhwoQuXbq4ubnpdLo+ffrEx8dfv37d+LDp6elPPPFEhw4dnJ2d\nvby8goKC5syZk5qaWlMZykJuMTExP/30U1RUlKenp6ura3h4eEpKStXOW7duDQ0NdXd3d3Fx\nCQoKio+PLy4uNrG8qh/HYDCcPXt27NixXl5eyl1uH374YU09FUuWLFH+Hf7kk09q+jjmffyq\nm+r8BdVUv3mVAFCLxvDbaw0AYLuOHz8eHBwcExPDM20BNE/cYwcAAGAnCHYAAAB2gmAHAABg\nJ7jHDgAAwE5wxg4AAMBOEOwAAADsBMEOAADAThDsAAAA7ATBDgAAwE4Q7AAAAOwEwQ4AAMBO\nEOwAAADsBMEOAADAThDsAAAA7ATBDgAAwE4Q7AAAAOwEwQ4AAMBOEOwAAADsBMEOAADAThDs\nAAAA7ATBDgAAwE4Q7AAAAOwEwQ4AAMBOEOwAAADsBMEOAADAThDsAAAA7ATBDgAAwE4Q7AAA\nAOwEwQ4AAMBOEOwAAADsxP8HAlVOSL3RMe8AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"ROC curves\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the ROCR package\n",
    "library(ROCR)\n",
    "\n",
    "# Create prediction and performance objects for the decision tree\n",
    "probs_tree <- predict(model_tree, pokedex_test, type = \"prob\")\n",
    "pred_tree <- prediction(probs_tree[,2], pokedex_test$is_legendary)\n",
    "perf_tree <- performance(pred_tree, \"tpr\", \"fpr\")\n",
    "\n",
    "# Create prediction and performance objects for the random forest\n",
    "probs_forest <- predict(model_forest, pokedex_test, type = \"prob\")\n",
    "pred_forest <- prediction(probs_forest[,2], pokedex_test$is_legendary)\n",
    "perf_forest <- performance(pred_forest, \"tpr\", \"fpr\")\n",
    "\n",
    "# Plot the ROC curves: first for the decision tree, then for the random forest\n",
    "plot(perf_tree, col = \"red\", main = \"ROC curves\")\n",
    "plot(perf_forest, add = TRUE, col = \"blue\")\n",
    "legend(x = \"bottomright\",  legend = c(\"Decision Tree\", \"Random Forest\"), fill = c(\"red\", \"blue\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "68"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 10. Analyze variable importance\n",
    "<p>It's clear from the ROC curves that the random forest is a substantially better model, boasting an AUC (not calculated above) of 91% versus the decision tree's 78%. When calculating variable importance, it makes sense to do so with the best model available, so we'll use the random forest for the final part of our analysis.</p>\n",
    "<p>Note that a random forest returns two measures of variable importance:</p>\n",
    "<ul>\n",
    "<li><code>MeanDecreaseAccuracy</code> – how much the model accuracy suffers if you leave out a particular variable</li>\n",
    "<li><code>MeanDecreaseGini</code> – the degree to which a variable improves the probability of an observation being classified one way or another (i.e. 'node purity').</li>\n",
    "</ul>\n",
    "<p>Together, these two measures will allow us to answer our original research question – what makes a Pokémon legendary?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "dc": {
     "key": "68"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>0</th><th scope=col>1</th><th scope=col>MeanDecreaseAccuracy</th><th scope=col>MeanDecreaseGini</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>attack</th><td> 5.5880374</td><td> 8.5275682</td><td> 9.145018 </td><td> 6.203285 </td></tr>\n",
       "\t<tr><th scope=row>defense</th><td> 0.6724663</td><td>12.2083778</td><td> 8.506461 </td><td> 6.793236 </td></tr>\n",
       "\t<tr><th scope=row>height_m</th><td> 4.1403952</td><td> 6.7509476</td><td> 7.533695 </td><td> 6.579863 </td></tr>\n",
       "\t<tr><th scope=row>hp</th><td> 2.7180639</td><td>19.2406506</td><td>14.980599 </td><td> 9.499892 </td></tr>\n",
       "\t<tr><th scope=row>sp_attack</th><td> 6.3947972</td><td>22.5417527</td><td>18.567857 </td><td>17.415111 </td></tr>\n",
       "\t<tr><th scope=row>sp_defense</th><td>-0.8501697</td><td>15.1682650</td><td>10.919369 </td><td> 9.857089 </td></tr>\n",
       "\t<tr><th scope=row>speed</th><td> 2.2799561</td><td>21.0596369</td><td>15.838444 </td><td>11.308568 </td></tr>\n",
       "\t<tr><th scope=row>type</th><td> 3.3473633</td><td> 0.7726262</td><td> 3.347259 </td><td> 9.440971 </td></tr>\n",
       "\t<tr><th scope=row>weight_kg</th><td> 8.8857876</td><td> 6.6917782</td><td>11.343187 </td><td> 9.760121 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & 0 & 1 & MeanDecreaseAccuracy & MeanDecreaseGini\\\\\n",
       "\\hline\n",
       "\tattack &  5.5880374 &  8.5275682 &  9.145018  &  6.203285 \\\\\n",
       "\tdefense &  0.6724663 & 12.2083778 &  8.506461  &  6.793236 \\\\\n",
       "\theight\\_m &  4.1403952 &  6.7509476 &  7.533695  &  6.579863 \\\\\n",
       "\thp &  2.7180639 & 19.2406506 & 14.980599  &  9.499892 \\\\\n",
       "\tsp\\_attack &  6.3947972 & 22.5417527 & 18.567857  & 17.415111 \\\\\n",
       "\tsp\\_defense & -0.8501697 & 15.1682650 & 10.919369  &  9.857089 \\\\\n",
       "\tspeed &  2.2799561 & 21.0596369 & 15.838444  & 11.308568 \\\\\n",
       "\ttype &  3.3473633 &  0.7726262 &  3.347259  &  9.440971 \\\\\n",
       "\tweight\\_kg &  8.8857876 &  6.6917782 & 11.343187  &  9.760121 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 0 | 1 | MeanDecreaseAccuracy | MeanDecreaseGini | \n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| attack |  5.5880374 |  8.5275682 |  9.145018  |  6.203285  | \n",
       "| defense |  0.6724663 | 12.2083778 |  8.506461  |  6.793236  | \n",
       "| height_m |  4.1403952 |  6.7509476 |  7.533695  |  6.579863  | \n",
       "| hp |  2.7180639 | 19.2406506 | 14.980599  |  9.499892  | \n",
       "| sp_attack |  6.3947972 | 22.5417527 | 18.567857  | 17.415111  | \n",
       "| sp_defense | -0.8501697 | 15.1682650 | 10.919369  |  9.857089  | \n",
       "| speed |  2.2799561 | 21.0596369 | 15.838444  | 11.308568  | \n",
       "| type |  3.3473633 |  0.7726262 |  3.347259  |  9.440971  | \n",
       "| weight_kg |  8.8857876 |  6.6917782 | 11.343187  |  9.760121  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "           0          1          MeanDecreaseAccuracy MeanDecreaseGini\n",
       "attack      5.5880374  8.5275682  9.145018             6.203285       \n",
       "defense     0.6724663 12.2083778  8.506461             6.793236       \n",
       "height_m    4.1403952  6.7509476  7.533695             6.579863       \n",
       "hp          2.7180639 19.2406506 14.980599             9.499892       \n",
       "sp_attack   6.3947972 22.5417527 18.567857            17.415111       \n",
       "sp_defense -0.8501697 15.1682650 10.919369             9.857089       \n",
       "speed       2.2799561 21.0596369 15.838444            11.308568       \n",
       "type        3.3473633  0.7726262  3.347259             9.440971       \n",
       "weight_kg   8.8857876  6.6917782 11.343187             9.760121       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>MeanDecreaseAccuracy</th><th scope=col>MeanDecreaseGini</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>attack</th><td> 9.145018</td><td> 6.203285</td></tr>\n",
       "\t<tr><th scope=row>defense</th><td> 8.506461</td><td> 6.793236</td></tr>\n",
       "\t<tr><th scope=row>height_m</th><td> 7.533695</td><td> 6.579863</td></tr>\n",
       "\t<tr><th scope=row>hp</th><td>14.980599</td><td> 9.499892</td></tr>\n",
       "\t<tr><th scope=row>sp_attack</th><td>18.567857</td><td>17.415111</td></tr>\n",
       "\t<tr><th scope=row>sp_defense</th><td>10.919369</td><td> 9.857089</td></tr>\n",
       "\t<tr><th scope=row>speed</th><td>15.838444</td><td>11.308568</td></tr>\n",
       "\t<tr><th scope=row>type</th><td> 3.347259</td><td> 9.440971</td></tr>\n",
       "\t<tr><th scope=row>weight_kg</th><td>11.343187</td><td> 9.760121</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & MeanDecreaseAccuracy & MeanDecreaseGini\\\\\n",
       "\\hline\n",
       "\tattack &  9.145018 &  6.203285\\\\\n",
       "\tdefense &  8.506461 &  6.793236\\\\\n",
       "\theight\\_m &  7.533695 &  6.579863\\\\\n",
       "\thp & 14.980599 &  9.499892\\\\\n",
       "\tsp\\_attack & 18.567857 & 17.415111\\\\\n",
       "\tsp\\_defense & 10.919369 &  9.857089\\\\\n",
       "\tspeed & 15.838444 & 11.308568\\\\\n",
       "\ttype &  3.347259 &  9.440971\\\\\n",
       "\tweight\\_kg & 11.343187 &  9.760121\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | MeanDecreaseAccuracy | MeanDecreaseGini | \n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| attack |  9.145018 |  6.203285 | \n",
       "| defense |  8.506461 |  6.793236 | \n",
       "| height_m |  7.533695 |  6.579863 | \n",
       "| hp | 14.980599 |  9.499892 | \n",
       "| sp_attack | 18.567857 | 17.415111 | \n",
       "| sp_defense | 10.919369 |  9.857089 | \n",
       "| speed | 15.838444 | 11.308568 | \n",
       "| type |  3.347259 |  9.440971 | \n",
       "| weight_kg | 11.343187 |  9.760121 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "           MeanDecreaseAccuracy MeanDecreaseGini\n",
       "attack      9.145018             6.203285       \n",
       "defense     8.506461             6.793236       \n",
       "height_m    7.533695             6.579863       \n",
       "hp         14.980599             9.499892       \n",
       "sp_attack  18.567857            17.415111       \n",
       "sp_defense 10.919369             9.857089       \n",
       "speed      15.838444            11.308568       \n",
       "type        3.347259             9.440971       \n",
       "weight_kg  11.343187             9.760121       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3jdk0t/MYr76CHjdcnITsHicR8p57Jh/909cksu29W+VFkpbDinfd6xLxnTt5FF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\nPxBJABCJPxBJABCJPxBJABCJPxBJABCJPxBJABCJPz5Eal8avaT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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print variable importance measures\n",
    "importance_forest <- importance(model_forest)\n",
    "importance_forest\n",
    "\n",
    "# Create a dotchart of variable importance\n",
    "varImpPlot_forest <- varImpPlot(model_forest, main = \"Variable Importance in Random Forest\")\n",
    "varImpPlot_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "75"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 11. Conclusion\n",
    "<p>According to the variable importance plot, <code>sp_attack</code> is the most important factor in determining whether or not a Pokémon is legendary, followed by <code>speed</code>. The plot doesn't tell us whether the variables have a positive or a negative effect, but we know from our exploratory analysis that the relationship is generally positive. We therefore conclude that legendary Pokémon are characterized primarily by the power of their special attacks and secondarily by their speediness, while also exhibiting higher fighting abilities across the board.</p>\n",
    "<p>Congratulations on completing your research into legendary Pokémon – Professor Oak is excited to share the findings! To finish, we'll answer a few of his questions about the variable importance results.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true,
    "dc": {
     "key": "75"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# According to the MeanDecreaseAccuracy plot:\n",
    "\n",
    "# Q1. Is the `attack` or `defense` variable more important?\n",
    "answer1 <- \"attack\"\n",
    "\n",
    "# Q2. Is the `weight_kg` or `height_m` variable more important?\n",
    "answer2 <- \"weight_kg\"\n",
    "\n",
    "# According to the MeanDecreaseGini plot:\n",
    "\n",
    "# Q3. Is the `attack` or `defense` variable more important?\n",
    "answer3 <- \"defense\"\n",
    "\n",
    "# Q4. Is the `weight_kg` or `height_m` variable more important?\n",
    "answer4 <- \"weight_kg\""
   ]
  }
 ],
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   "language": "R",
   "name": "ir"
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   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
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 },
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}