2024-01-12.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 150 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.6</td><td>3.4</td><td>1.4</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0</td><td>3.4</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.4</td><td>2.9</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.9</td><td>3.1</td><td>1.5</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4</td><td>3.7</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.8</td><td>3.4</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.8</td><td>3.0</td><td>1.4</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.8</td><td>4.0</td><td>1.2</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.7</td><td>4.4</td><td>1.5</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4</td><td>3.9</td><td>1.3</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1</td><td>3.5</td><td>1.4</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.7</td><td>3.8</td><td>1.7</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1</td><td>3.8</td><td>1.5</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4</td><td>3.4</td><td>1.7</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1</td><td>3.7</td><td>1.5</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.6</td><td>3.6</td><td>1.0</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.8</td><td>3.4</td><td>1.9</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0</td><td>3.0</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0</td><td>3.4</td><td>1.6</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.2</td><td>3.5</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>5.2</td><td>3.4</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.7</td><td>3.2</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>6.9</td><td>3.2</td><td>5.7</td><td>2.3</td><td>virginica</td></tr>\n",
       "\t<tr><td>5.6</td><td>2.8</td><td>4.9</td><td>2.0</td><td>virginica</td></tr>\n",
       "\t<tr><td>7.7</td><td>2.8</td><td>6.7</td><td>2.0</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3</td><td>2.7</td><td>4.9</td><td>1.8</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7</td><td>3.3</td><td>5.7</td><td>2.1</td><td>virginica</td></tr>\n",
       "\t<tr><td>7.2</td><td>3.2</td><td>6.0</td><td>1.8</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.2</td><td>2.8</td><td>4.8</td><td>1.8</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.1</td><td>3.0</td><td>4.9</td><td>1.8</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.4</td><td>2.8</td><td>5.6</td><td>2.1</td><td>virginica</td></tr>\n",
       "\t<tr><td>7.2</td><td>3.0</td><td>5.8</td><td>1.6</td><td>virginica</td></tr>\n",
       "\t<tr><td>7.4</td><td>2.8</td><td>6.1</td><td>1.9</td><td>virginica</td></tr>\n",
       "\t<tr><td>7.9</td><td>3.8</td><td>6.4</td><td>2.0</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.4</td><td>2.8</td><td>5.6</td><td>2.2</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3</td><td>2.8</td><td>5.1</td><td>1.5</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.1</td><td>2.6</td><td>5.6</td><td>1.4</td><td>virginica</td></tr>\n",
       "\t<tr><td>7.7</td><td>3.0</td><td>6.1</td><td>2.3</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3</td><td>3.4</td><td>5.6</td><td>2.4</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.4</td><td>3.1</td><td>5.5</td><td>1.8</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.0</td><td>3.0</td><td>4.8</td><td>1.8</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.9</td><td>3.1</td><td>5.4</td><td>2.1</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7</td><td>3.1</td><td>5.6</td><td>2.4</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.9</td><td>3.1</td><td>5.1</td><td>2.3</td><td>virginica</td></tr>\n",
       "\t<tr><td>5.8</td><td>2.7</td><td>5.1</td><td>1.9</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.8</td><td>3.2</td><td>5.9</td><td>2.3</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7</td><td>3.3</td><td>5.7</td><td>2.5</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7</td><td>3.0</td><td>5.2</td><td>2.3</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3</td><td>2.5</td><td>5.0</td><td>1.9</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.5</td><td>3.0</td><td>5.2</td><td>2.0</td><td>virginica</td></tr>\n",
       "\t<tr><td>6.2</td><td>3.4</td><td>5.4</td><td>2.3</td><td>virginica</td></tr>\n",
       "\t<tr><td>5.9</td><td>3.0</td><td>5.1</td><td>1.8</td><td>virginica</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 150 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n",
       "\t 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n",
       "\t 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n",
       "\t 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n",
       "\t 5.0 & 3.4 & 1.5 & 0.2 & setosa\\\\\n",
       "\t 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 4.9 & 3.1 & 1.5 & 0.1 & setosa\\\\\n",
       "\t 5.4 & 3.7 & 1.5 & 0.2 & setosa\\\\\n",
       "\t 4.8 & 3.4 & 1.6 & 0.2 & setosa\\\\\n",
       "\t 4.8 & 3.0 & 1.4 & 0.1 & setosa\\\\\n",
       "\t 4.3 & 3.0 & 1.1 & 0.1 & setosa\\\\\n",
       "\t 5.8 & 4.0 & 1.2 & 0.2 & setosa\\\\\n",
       "\t 5.7 & 4.4 & 1.5 & 0.4 & setosa\\\\\n",
       "\t 5.4 & 3.9 & 1.3 & 0.4 & setosa\\\\\n",
       "\t 5.1 & 3.5 & 1.4 & 0.3 & setosa\\\\\n",
       "\t 5.7 & 3.8 & 1.7 & 0.3 & setosa\\\\\n",
       "\t 5.1 & 3.8 & 1.5 & 0.3 & setosa\\\\\n",
       "\t 5.4 & 3.4 & 1.7 & 0.2 & setosa\\\\\n",
       "\t 5.1 & 3.7 & 1.5 & 0.4 & setosa\\\\\n",
       "\t 4.6 & 3.6 & 1.0 & 0.2 & setosa\\\\\n",
       "\t 5.1 & 3.3 & 1.7 & 0.5 & setosa\\\\\n",
       "\t 4.8 & 3.4 & 1.9 & 0.2 & setosa\\\\\n",
       "\t 5.0 & 3.0 & 1.6 & 0.2 & setosa\\\\\n",
       "\t 5.0 & 3.4 & 1.6 & 0.4 & setosa\\\\\n",
       "\t 5.2 & 3.5 & 1.5 & 0.2 & setosa\\\\\n",
       "\t 5.2 & 3.4 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 4.7 & 3.2 & 1.6 & 0.2 & setosa\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 6.9 & 3.2 & 5.7 & 2.3 & virginica\\\\\n",
       "\t 5.6 & 2.8 & 4.9 & 2.0 & virginica\\\\\n",
       "\t 7.7 & 2.8 & 6.7 & 2.0 & virginica\\\\\n",
       "\t 6.3 & 2.7 & 4.9 & 1.8 & virginica\\\\\n",
       "\t 6.7 & 3.3 & 5.7 & 2.1 & virginica\\\\\n",
       "\t 7.2 & 3.2 & 6.0 & 1.8 & virginica\\\\\n",
       "\t 6.2 & 2.8 & 4.8 & 1.8 & virginica\\\\\n",
       "\t 6.1 & 3.0 & 4.9 & 1.8 & virginica\\\\\n",
       "\t 6.4 & 2.8 & 5.6 & 2.1 & virginica\\\\\n",
       "\t 7.2 & 3.0 & 5.8 & 1.6 & virginica\\\\\n",
       "\t 7.4 & 2.8 & 6.1 & 1.9 & virginica\\\\\n",
       "\t 7.9 & 3.8 & 6.4 & 2.0 & virginica\\\\\n",
       "\t 6.4 & 2.8 & 5.6 & 2.2 & virginica\\\\\n",
       "\t 6.3 & 2.8 & 5.1 & 1.5 & virginica\\\\\n",
       "\t 6.1 & 2.6 & 5.6 & 1.4 & virginica\\\\\n",
       "\t 7.7 & 3.0 & 6.1 & 2.3 & virginica\\\\\n",
       "\t 6.3 & 3.4 & 5.6 & 2.4 & virginica\\\\\n",
       "\t 6.4 & 3.1 & 5.5 & 1.8 & virginica\\\\\n",
       "\t 6.0 & 3.0 & 4.8 & 1.8 & virginica\\\\\n",
       "\t 6.9 & 3.1 & 5.4 & 2.1 & virginica\\\\\n",
       "\t 6.7 & 3.1 & 5.6 & 2.4 & virginica\\\\\n",
       "\t 6.9 & 3.1 & 5.1 & 2.3 & virginica\\\\\n",
       "\t 5.8 & 2.7 & 5.1 & 1.9 & virginica\\\\\n",
       "\t 6.8 & 3.2 & 5.9 & 2.3 & virginica\\\\\n",
       "\t 6.7 & 3.3 & 5.7 & 2.5 & virginica\\\\\n",
       "\t 6.7 & 3.0 & 5.2 & 2.3 & virginica\\\\\n",
       "\t 6.3 & 2.5 & 5.0 & 1.9 & virginica\\\\\n",
       "\t 6.5 & 3.0 & 5.2 & 2.0 & virginica\\\\\n",
       "\t 6.2 & 3.4 & 5.4 & 2.3 & virginica\\\\\n",
       "\t 5.9 & 3.0 & 5.1 & 1.8 & virginica\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 150 × 5\n",
       "\n",
       "| Sepal.Length &lt;dbl&gt; | Sepal.Width &lt;dbl&gt; | Petal.Length &lt;dbl&gt; | Petal.Width &lt;dbl&gt; | Species &lt;fct&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n",
       "| 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n",
       "| 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n",
       "| 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n",
       "| 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n",
       "| 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n",
       "| 4.6 | 3.4 | 1.4 | 0.3 | setosa |\n",
       "| 5.0 | 3.4 | 1.5 | 0.2 | setosa |\n",
       "| 4.4 | 2.9 | 1.4 | 0.2 | setosa |\n",
       "| 4.9 | 3.1 | 1.5 | 0.1 | setosa |\n",
       "| 5.4 | 3.7 | 1.5 | 0.2 | setosa |\n",
       "| 4.8 | 3.4 | 1.6 | 0.2 | setosa |\n",
       "| 4.8 | 3.0 | 1.4 | 0.1 | setosa |\n",
       "| 4.3 | 3.0 | 1.1 | 0.1 | setosa |\n",
       "| 5.8 | 4.0 | 1.2 | 0.2 | setosa |\n",
       "| 5.7 | 4.4 | 1.5 | 0.4 | setosa |\n",
       "| 5.4 | 3.9 | 1.3 | 0.4 | setosa |\n",
       "| 5.1 | 3.5 | 1.4 | 0.3 | setosa |\n",
       "| 5.7 | 3.8 | 1.7 | 0.3 | setosa |\n",
       "| 5.1 | 3.8 | 1.5 | 0.3 | setosa |\n",
       "| 5.4 | 3.4 | 1.7 | 0.2 | setosa |\n",
       "| 5.1 | 3.7 | 1.5 | 0.4 | setosa |\n",
       "| 4.6 | 3.6 | 1.0 | 0.2 | setosa |\n",
       "| 5.1 | 3.3 | 1.7 | 0.5 | setosa |\n",
       "| 4.8 | 3.4 | 1.9 | 0.2 | setosa |\n",
       "| 5.0 | 3.0 | 1.6 | 0.2 | setosa |\n",
       "| 5.0 | 3.4 | 1.6 | 0.4 | setosa |\n",
       "| 5.2 | 3.5 | 1.5 | 0.2 | setosa |\n",
       "| 5.2 | 3.4 | 1.4 | 0.2 | setosa |\n",
       "| 4.7 | 3.2 | 1.6 | 0.2 | setosa |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 6.9 | 3.2 | 5.7 | 2.3 | virginica |\n",
       "| 5.6 | 2.8 | 4.9 | 2.0 | virginica |\n",
       "| 7.7 | 2.8 | 6.7 | 2.0 | virginica |\n",
       "| 6.3 | 2.7 | 4.9 | 1.8 | virginica |\n",
       "| 6.7 | 3.3 | 5.7 | 2.1 | virginica |\n",
       "| 7.2 | 3.2 | 6.0 | 1.8 | virginica |\n",
       "| 6.2 | 2.8 | 4.8 | 1.8 | virginica |\n",
       "| 6.1 | 3.0 | 4.9 | 1.8 | virginica |\n",
       "| 6.4 | 2.8 | 5.6 | 2.1 | virginica |\n",
       "| 7.2 | 3.0 | 5.8 | 1.6 | virginica |\n",
       "| 7.4 | 2.8 | 6.1 | 1.9 | virginica |\n",
       "| 7.9 | 3.8 | 6.4 | 2.0 | virginica |\n",
       "| 6.4 | 2.8 | 5.6 | 2.2 | virginica |\n",
       "| 6.3 | 2.8 | 5.1 | 1.5 | virginica |\n",
       "| 6.1 | 2.6 | 5.6 | 1.4 | virginica |\n",
       "| 7.7 | 3.0 | 6.1 | 2.3 | virginica |\n",
       "| 6.3 | 3.4 | 5.6 | 2.4 | virginica |\n",
       "| 6.4 | 3.1 | 5.5 | 1.8 | virginica |\n",
       "| 6.0 | 3.0 | 4.8 | 1.8 | virginica |\n",
       "| 6.9 | 3.1 | 5.4 | 2.1 | virginica |\n",
       "| 6.7 | 3.1 | 5.6 | 2.4 | virginica |\n",
       "| 6.9 | 3.1 | 5.1 | 2.3 | virginica |\n",
       "| 5.8 | 2.7 | 5.1 | 1.9 | virginica |\n",
       "| 6.8 | 3.2 | 5.9 | 2.3 | virginica |\n",
       "| 6.7 | 3.3 | 5.7 | 2.5 | virginica |\n",
       "| 6.7 | 3.0 | 5.2 | 2.3 | virginica |\n",
       "| 6.3 | 2.5 | 5.0 | 1.9 | virginica |\n",
       "| 6.5 | 3.0 | 5.2 | 2.0 | virginica |\n",
       "| 6.2 | 3.4 | 5.4 | 2.3 | virginica |\n",
       "| 5.9 | 3.0 | 5.1 | 1.8 | virginica |\n",
       "\n"
      ],
      "text/plain": [
       "    Sepal.Length Sepal.Width Petal.Length Petal.Width Species  \n",
       "1   5.1          3.5         1.4          0.2         setosa   \n",
       "2   4.9          3.0         1.4          0.2         setosa   \n",
       "3   4.7          3.2         1.3          0.2         setosa   \n",
       "4   4.6          3.1         1.5          0.2         setosa   \n",
       "5   5.0          3.6         1.4          0.2         setosa   \n",
       "6   5.4          3.9         1.7          0.4         setosa   \n",
       "7   4.6          3.4         1.4          0.3         setosa   \n",
       "8   5.0          3.4         1.5          0.2         setosa   \n",
       "9   4.4          2.9         1.4          0.2         setosa   \n",
       "10  4.9          3.1         1.5          0.1         setosa   \n",
       "11  5.4          3.7         1.5          0.2         setosa   \n",
       "12  4.8          3.4         1.6          0.2         setosa   \n",
       "13  4.8          3.0         1.4          0.1         setosa   \n",
       "14  4.3          3.0         1.1          0.1         setosa   \n",
       "15  5.8          4.0         1.2          0.2         setosa   \n",
       "16  5.7          4.4         1.5          0.4         setosa   \n",
       "17  5.4          3.9         1.3          0.4         setosa   \n",
       "18  5.1          3.5         1.4          0.3         setosa   \n",
       "19  5.7          3.8         1.7          0.3         setosa   \n",
       "20  5.1          3.8         1.5          0.3         setosa   \n",
       "21  5.4          3.4         1.7          0.2         setosa   \n",
       "22  5.1          3.7         1.5          0.4         setosa   \n",
       "23  4.6          3.6         1.0          0.2         setosa   \n",
       "24  5.1          3.3         1.7          0.5         setosa   \n",
       "25  4.8          3.4         1.9          0.2         setosa   \n",
       "26  5.0          3.0         1.6          0.2         setosa   \n",
       "27  5.0          3.4         1.6          0.4         setosa   \n",
       "28  5.2          3.5         1.5          0.2         setosa   \n",
       "29  5.2          3.4         1.4          0.2         setosa   \n",
       "30  4.7          3.2         1.6          0.2         setosa   \n",
       "⋮   ⋮            ⋮           ⋮            ⋮           ⋮        \n",
       "121 6.9          3.2         5.7          2.3         virginica\n",
       "122 5.6          2.8         4.9          2.0         virginica\n",
       "123 7.7          2.8         6.7          2.0         virginica\n",
       "124 6.3          2.7         4.9          1.8         virginica\n",
       "125 6.7          3.3         5.7          2.1         virginica\n",
       "126 7.2          3.2         6.0          1.8         virginica\n",
       "127 6.2          2.8         4.8          1.8         virginica\n",
       "128 6.1          3.0         4.9          1.8         virginica\n",
       "129 6.4          2.8         5.6          2.1         virginica\n",
       "130 7.2          3.0         5.8          1.6         virginica\n",
       "131 7.4          2.8         6.1          1.9         virginica\n",
       "132 7.9          3.8         6.4          2.0         virginica\n",
       "133 6.4          2.8         5.6          2.2         virginica\n",
       "134 6.3          2.8         5.1          1.5         virginica\n",
       "135 6.1          2.6         5.6          1.4         virginica\n",
       "136 7.7          3.0         6.1          2.3         virginica\n",
       "137 6.3          3.4         5.6          2.4         virginica\n",
       "138 6.4          3.1         5.5          1.8         virginica\n",
       "139 6.0          3.0         4.8          1.8         virginica\n",
       "140 6.9          3.1         5.4          2.1         virginica\n",
       "141 6.7          3.1         5.6          2.4         virginica\n",
       "142 6.9          3.1         5.1          2.3         virginica\n",
       "143 5.8          2.7         5.1          1.9         virginica\n",
       "144 6.8          3.2         5.9          2.3         virginica\n",
       "145 6.7          3.3         5.7          2.5         virginica\n",
       "146 6.7          3.0         5.2          2.3         virginica\n",
       "147 6.3          2.5         5.0          1.9         virginica\n",
       "148 6.5          3.0         5.2          2.0         virginica\n",
       "149 6.2          3.4         5.4          2.3         virginica\n",
       "150 5.9          3.0         5.1          1.8         virginica"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   \n",
       " Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  \n",
       " 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  \n",
       " Median :5.800   Median :3.000   Median :4.350   Median :1.300  \n",
       " Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  \n",
       " 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  \n",
       " Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  \n",
       "       Species  \n",
       " setosa    :50  \n",
       " versicolor:50  \n",
       " virginica :50  \n",
       "                \n",
       "                \n",
       "                "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract a particular variable from a dataset, we can use \\$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1.4</li><li>1.4</li><li>1.3</li><li>1.5</li><li>1.4</li><li>1.7</li><li>1.4</li><li>1.5</li><li>1.4</li><li>1.5</li><li>1.5</li><li>1.6</li><li>1.4</li><li>1.1</li><li>1.2</li><li>1.5</li><li>1.3</li><li>1.4</li><li>1.7</li><li>1.5</li><li>1.7</li><li>1.5</li><li>1</li><li>1.7</li><li>1.9</li><li>1.6</li><li>1.6</li><li>1.5</li><li>1.4</li><li>1.6</li><li>1.6</li><li>1.5</li><li>1.5</li><li>1.4</li><li>1.5</li><li>1.2</li><li>1.3</li><li>1.4</li><li>1.3</li><li>1.5</li><li>1.3</li><li>1.3</li><li>1.3</li><li>1.6</li><li>1.9</li><li>1.4</li><li>1.6</li><li>1.4</li><li>1.5</li><li>1.4</li><li>4.7</li><li>4.5</li><li>4.9</li><li>4</li><li>4.6</li><li>4.5</li><li>4.7</li><li>3.3</li><li>4.6</li><li>3.9</li><li>3.5</li><li>4.2</li><li>4</li><li>4.7</li><li>3.6</li><li>4.4</li><li>4.5</li><li>4.1</li><li>4.5</li><li>3.9</li><li>4.8</li><li>4</li><li>4.9</li><li>4.7</li><li>4.3</li><li>4.4</li><li>4.8</li><li>5</li><li>4.5</li><li>3.5</li><li>3.8</li><li>3.7</li><li>3.9</li><li>5.1</li><li>4.5</li><li>4.5</li><li>4.7</li><li>4.4</li><li>4.1</li><li>4</li><li>4.4</li><li>4.6</li><li>4</li><li>3.3</li><li>4.2</li><li>4.2</li><li>4.2</li><li>4.3</li><li>3</li><li>4.1</li><li>6</li><li>5.1</li><li>5.9</li><li>5.6</li><li>5.8</li><li>6.6</li><li>4.5</li><li>6.3</li><li>5.8</li><li>6.1</li><li>5.1</li><li>5.3</li><li>5.5</li><li>5</li><li>5.1</li><li>5.3</li><li>5.5</li><li>6.7</li><li>6.9</li><li>5</li><li>5.7</li><li>4.9</li><li>6.7</li><li>4.9</li><li>5.7</li><li>6</li><li>4.8</li><li>4.9</li><li>5.6</li><li>5.8</li><li>6.1</li><li>6.4</li><li>5.6</li><li>5.1</li><li>5.6</li><li>6.1</li><li>5.6</li><li>5.5</li><li>4.8</li><li>5.4</li><li>5.6</li><li>5.1</li><li>5.1</li><li>5.9</li><li>5.7</li><li>5.2</li><li>5</li><li>5.2</li><li>5.4</li><li>5.1</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.4\n",
       "\\item 1.4\n",
       "\\item 1.3\n",
       "\\item 1.5\n",
       "\\item 1.4\n",
       "\\item 1.7\n",
       "\\item 1.4\n",
       "\\item 1.5\n",
       "\\item 1.4\n",
       "\\item 1.5\n",
       "\\item 1.5\n",
       "\\item 1.6\n",
       "\\item 1.4\n",
       "\\item 1.1\n",
       "\\item 1.2\n",
       "\\item 1.5\n",
       "\\item 1.3\n",
       "\\item 1.4\n",
       "\\item 1.7\n",
       "\\item 1.5\n",
       "\\item 1.7\n",
       "\\item 1.5\n",
       "\\item 1\n",
       "\\item 1.7\n",
       "\\item 1.9\n",
       "\\item 1.6\n",
       "\\item 1.6\n",
       "\\item 1.5\n",
       "\\item 1.4\n",
       "\\item 1.6\n",
       "\\item 1.6\n",
       "\\item 1.5\n",
       "\\item 1.5\n",
       "\\item 1.4\n",
       "\\item 1.5\n",
       "\\item 1.2\n",
       "\\item 1.3\n",
       "\\item 1.4\n",
       "\\item 1.3\n",
       "\\item 1.5\n",
       "\\item 1.3\n",
       "\\item 1.3\n",
       "\\item 1.3\n",
       "\\item 1.6\n",
       "\\item 1.9\n",
       "\\item 1.4\n",
       "\\item 1.6\n",
       "\\item 1.4\n",
       "\\item 1.5\n",
       "\\item 1.4\n",
       "\\item 4.7\n",
       "\\item 4.5\n",
       "\\item 4.9\n",
       "\\item 4\n",
       "\\item 4.6\n",
       "\\item 4.5\n",
       "\\item 4.7\n",
       "\\item 3.3\n",
       "\\item 4.6\n",
       "\\item 3.9\n",
       "\\item 3.5\n",
       "\\item 4.2\n",
       "\\item 4\n",
       "\\item 4.7\n",
       "\\item 3.6\n",
       "\\item 4.4\n",
       "\\item 4.5\n",
       "\\item 4.1\n",
       "\\item 4.5\n",
       "\\item 3.9\n",
       "\\item 4.8\n",
       "\\item 4\n",
       "\\item 4.9\n",
       "\\item 4.7\n",
       "\\item 4.3\n",
       "\\item 4.4\n",
       "\\item 4.8\n",
       "\\item 5\n",
       "\\item 4.5\n",
       "\\item 3.5\n",
       "\\item 3.8\n",
       "\\item 3.7\n",
       "\\item 3.9\n",
       "\\item 5.1\n",
       "\\item 4.5\n",
       "\\item 4.5\n",
       "\\item 4.7\n",
       "\\item 4.4\n",
       "\\item 4.1\n",
       "\\item 4\n",
       "\\item 4.4\n",
       "\\item 4.6\n",
       "\\item 4\n",
       "\\item 3.3\n",
       "\\item 4.2\n",
       "\\item 4.2\n",
       "\\item 4.2\n",
       "\\item 4.3\n",
       "\\item 3\n",
       "\\item 4.1\n",
       "\\item 6\n",
       "\\item 5.1\n",
       "\\item 5.9\n",
       "\\item 5.6\n",
       "\\item 5.8\n",
       "\\item 6.6\n",
       "\\item 4.5\n",
       "\\item 6.3\n",
       "\\item 5.8\n",
       "\\item 6.1\n",
       "\\item 5.1\n",
       "\\item 5.3\n",
       "\\item 5.5\n",
       "\\item 5\n",
       "\\item 5.1\n",
       "\\item 5.3\n",
       "\\item 5.5\n",
       "\\item 6.7\n",
       "\\item 6.9\n",
       "\\item 5\n",
       "\\item 5.7\n",
       "\\item 4.9\n",
       "\\item 6.7\n",
       "\\item 4.9\n",
       "\\item 5.7\n",
       "\\item 6\n",
       "\\item 4.8\n",
       "\\item 4.9\n",
       "\\item 5.6\n",
       "\\item 5.8\n",
       "\\item 6.1\n",
       "\\item 6.4\n",
       "\\item 5.6\n",
       "\\item 5.1\n",
       "\\item 5.6\n",
       "\\item 6.1\n",
       "\\item 5.6\n",
       "\\item 5.5\n",
       "\\item 4.8\n",
       "\\item 5.4\n",
       "\\item 5.6\n",
       "\\item 5.1\n",
       "\\item 5.1\n",
       "\\item 5.9\n",
       "\\item 5.7\n",
       "\\item 5.2\n",
       "\\item 5\n",
       "\\item 5.2\n",
       "\\item 5.4\n",
       "\\item 5.1\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.4\n",
       "2. 1.4\n",
       "3. 1.3\n",
       "4. 1.5\n",
       "5. 1.4\n",
       "6. 1.7\n",
       "7. 1.4\n",
       "8. 1.5\n",
       "9. 1.4\n",
       "10. 1.5\n",
       "11. 1.5\n",
       "12. 1.6\n",
       "13. 1.4\n",
       "14. 1.1\n",
       "15. 1.2\n",
       "16. 1.5\n",
       "17. 1.3\n",
       "18. 1.4\n",
       "19. 1.7\n",
       "20. 1.5\n",
       "21. 1.7\n",
       "22. 1.5\n",
       "23. 1\n",
       "24. 1.7\n",
       "25. 1.9\n",
       "26. 1.6\n",
       "27. 1.6\n",
       "28. 1.5\n",
       "29. 1.4\n",
       "30. 1.6\n",
       "31. 1.6\n",
       "32. 1.5\n",
       "33. 1.5\n",
       "34. 1.4\n",
       "35. 1.5\n",
       "36. 1.2\n",
       "37. 1.3\n",
       "38. 1.4\n",
       "39. 1.3\n",
       "40. 1.5\n",
       "41. 1.3\n",
       "42. 1.3\n",
       "43. 1.3\n",
       "44. 1.6\n",
       "45. 1.9\n",
       "46. 1.4\n",
       "47. 1.6\n",
       "48. 1.4\n",
       "49. 1.5\n",
       "50. 1.4\n",
       "51. 4.7\n",
       "52. 4.5\n",
       "53. 4.9\n",
       "54. 4\n",
       "55. 4.6\n",
       "56. 4.5\n",
       "57. 4.7\n",
       "58. 3.3\n",
       "59. 4.6\n",
       "60. 3.9\n",
       "61. 3.5\n",
       "62. 4.2\n",
       "63. 4\n",
       "64. 4.7\n",
       "65. 3.6\n",
       "66. 4.4\n",
       "67. 4.5\n",
       "68. 4.1\n",
       "69. 4.5\n",
       "70. 3.9\n",
       "71. 4.8\n",
       "72. 4\n",
       "73. 4.9\n",
       "74. 4.7\n",
       "75. 4.3\n",
       "76. 4.4\n",
       "77. 4.8\n",
       "78. 5\n",
       "79. 4.5\n",
       "80. 3.5\n",
       "81. 3.8\n",
       "82. 3.7\n",
       "83. 3.9\n",
       "84. 5.1\n",
       "85. 4.5\n",
       "86. 4.5\n",
       "87. 4.7\n",
       "88. 4.4\n",
       "89. 4.1\n",
       "90. 4\n",
       "91. 4.4\n",
       "92. 4.6\n",
       "93. 4\n",
       "94. 3.3\n",
       "95. 4.2\n",
       "96. 4.2\n",
       "97. 4.2\n",
       "98. 4.3\n",
       "99. 3\n",
       "100. 4.1\n",
       "101. 6\n",
       "102. 5.1\n",
       "103. 5.9\n",
       "104. 5.6\n",
       "105. 5.8\n",
       "106. 6.6\n",
       "107. 4.5\n",
       "108. 6.3\n",
       "109. 5.8\n",
       "110. 6.1\n",
       "111. 5.1\n",
       "112. 5.3\n",
       "113. 5.5\n",
       "114. 5\n",
       "115. 5.1\n",
       "116. 5.3\n",
       "117. 5.5\n",
       "118. 6.7\n",
       "119. 6.9\n",
       "120. 5\n",
       "121. 5.7\n",
       "122. 4.9\n",
       "123. 6.7\n",
       "124. 4.9\n",
       "125. 5.7\n",
       "126. 6\n",
       "127. 4.8\n",
       "128. 4.9\n",
       "129. 5.6\n",
       "130. 5.8\n",
       "131. 6.1\n",
       "132. 6.4\n",
       "133. 5.6\n",
       "134. 5.1\n",
       "135. 5.6\n",
       "136. 6.1\n",
       "137. 5.6\n",
       "138. 5.5\n",
       "139. 4.8\n",
       "140. 5.4\n",
       "141. 5.6\n",
       "142. 5.1\n",
       "143. 5.1\n",
       "144. 5.9\n",
       "145. 5.7\n",
       "146. 5.2\n",
       "147. 5\n",
       "148. 5.2\n",
       "149. 5.4\n",
       "150. 5.1\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5 1.6 1.4 1.1 1.2 1.5 1.3 1.4\n",
       " [19] 1.7 1.5 1.7 1.5 1.0 1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5 1.4 1.5 1.2\n",
       " [37] 1.3 1.4 1.3 1.5 1.3 1.3 1.3 1.6 1.9 1.4 1.6 1.4 1.5 1.4 4.7 4.5 4.9 4.0\n",
       " [55] 4.6 4.5 4.7 3.3 4.6 3.9 3.5 4.2 4.0 4.7 3.6 4.4 4.5 4.1 4.5 3.9 4.8 4.0\n",
       " [73] 4.9 4.7 4.3 4.4 4.8 5.0 4.5 3.5 3.8 3.7 3.9 5.1 4.5 4.5 4.7 4.4 4.1 4.0\n",
       " [91] 4.4 4.6 4.0 3.3 4.2 4.2 4.2 4.3 3.0 4.1 6.0 5.1 5.9 5.6 5.8 6.6 4.5 6.3\n",
       "[109] 5.8 6.1 5.1 5.3 5.5 5.0 5.1 5.3 5.5 6.7 6.9 5.0 5.7 4.9 6.7 4.9 5.7 6.0\n",
       "[127] 4.8 4.9 5.6 5.8 6.1 6.4 5.6 5.1 5.6 6.1 5.6 5.5 4.8 5.4 5.6 5.1 5.1 5.9\n",
       "[145] 5.7 5.2 5.0 5.2 5.4 5.1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris$Petal.Length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a subset of a datset, we can use the **subset** function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 50 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>4.6</td><td>3.4</td><td>1.4</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>5.0</td><td>3.4</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>4.4</td><td>2.9</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>4.9</td><td>3.1</td><td>1.5</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>5.4</td><td>3.7</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>4.8</td><td>3.4</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>4.8</td><td>3.0</td><td>1.4</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>5.8</td><td>4.0</td><td>1.2</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>5.7</td><td>4.4</td><td>1.5</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>5.4</td><td>3.9</td><td>1.3</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>18</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>19</th><td>5.7</td><td>3.8</td><td>1.7</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>5.1</td><td>3.8</td><td>1.5</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>21</th><td>5.4</td><td>3.4</td><td>1.7</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>22</th><td>5.1</td><td>3.7</td><td>1.5</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>23</th><td>4.6</td><td>3.6</td><td>1.0</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>24</th><td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>4.8</td><td>3.4</td><td>1.9</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>26</th><td>5.0</td><td>3.0</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>27</th><td>5.0</td><td>3.4</td><td>1.6</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>28</th><td>5.2</td><td>3.5</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>29</th><td>5.2</td><td>3.4</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>30</th><td>4.7</td><td>3.2</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>31</th><td>4.8</td><td>3.1</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>32</th><td>5.4</td><td>3.4</td><td>1.5</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>33</th><td>5.2</td><td>4.1</td><td>1.5</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>34</th><td>5.5</td><td>4.2</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>35</th><td>4.9</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>36</th><td>5.0</td><td>3.2</td><td>1.2</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>37</th><td>5.5</td><td>3.5</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>38</th><td>4.9</td><td>3.6</td><td>1.4</td><td>0.1</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>39</th><td>4.4</td><td>3.0</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>40</th><td>5.1</td><td>3.4</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>41</th><td>5.0</td><td>3.5</td><td>1.3</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>42</th><td>4.5</td><td>2.3</td><td>1.3</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>43</th><td>4.4</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>44</th><td>5.0</td><td>3.5</td><td>1.6</td><td>0.6</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>45</th><td>5.1</td><td>3.8</td><td>1.9</td><td>0.4</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>46</th><td>4.8</td><td>3.0</td><td>1.4</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>47</th><td>5.1</td><td>3.8</td><td>1.6</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>48</th><td>4.6</td><td>3.2</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>49</th><td>5.3</td><td>3.7</td><td>1.5</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>50</th><td>5.0</td><td>3.3</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 50 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n",
       "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n",
       "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n",
       "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n",
       "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n",
       "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n",
       "\t7 & 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n",
       "\t8 & 5.0 & 3.4 & 1.5 & 0.2 & setosa\\\\\n",
       "\t9 & 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n",
       "\t10 & 4.9 & 3.1 & 1.5 & 0.1 & setosa\\\\\n",
       "\t11 & 5.4 & 3.7 & 1.5 & 0.2 & setosa\\\\\n",
       "\t12 & 4.8 & 3.4 & 1.6 & 0.2 & setosa\\\\\n",
       "\t13 & 4.8 & 3.0 & 1.4 & 0.1 & setosa\\\\\n",
       "\t14 & 4.3 & 3.0 & 1.1 & 0.1 & setosa\\\\\n",
       "\t15 & 5.8 & 4.0 & 1.2 & 0.2 & setosa\\\\\n",
       "\t16 & 5.7 & 4.4 & 1.5 & 0.4 & setosa\\\\\n",
       "\t17 & 5.4 & 3.9 & 1.3 & 0.4 & setosa\\\\\n",
       "\t18 & 5.1 & 3.5 & 1.4 & 0.3 & setosa\\\\\n",
       "\t19 & 5.7 & 3.8 & 1.7 & 0.3 & setosa\\\\\n",
       "\t20 & 5.1 & 3.8 & 1.5 & 0.3 & setosa\\\\\n",
       "\t21 & 5.4 & 3.4 & 1.7 & 0.2 & setosa\\\\\n",
       "\t22 & 5.1 & 3.7 & 1.5 & 0.4 & setosa\\\\\n",
       "\t23 & 4.6 & 3.6 & 1.0 & 0.2 & setosa\\\\\n",
       "\t24 & 5.1 & 3.3 & 1.7 & 0.5 & setosa\\\\\n",
       "\t25 & 4.8 & 3.4 & 1.9 & 0.2 & setosa\\\\\n",
       "\t26 & 5.0 & 3.0 & 1.6 & 0.2 & setosa\\\\\n",
       "\t27 & 5.0 & 3.4 & 1.6 & 0.4 & setosa\\\\\n",
       "\t28 & 5.2 & 3.5 & 1.5 & 0.2 & setosa\\\\\n",
       "\t29 & 5.2 & 3.4 & 1.4 & 0.2 & setosa\\\\\n",
       "\t30 & 4.7 & 3.2 & 1.6 & 0.2 & setosa\\\\\n",
       "\t31 & 4.8 & 3.1 & 1.6 & 0.2 & setosa\\\\\n",
       "\t32 & 5.4 & 3.4 & 1.5 & 0.4 & setosa\\\\\n",
       "\t33 & 5.2 & 4.1 & 1.5 & 0.1 & setosa\\\\\n",
       "\t34 & 5.5 & 4.2 & 1.4 & 0.2 & setosa\\\\\n",
       "\t35 & 4.9 & 3.1 & 1.5 & 0.2 & setosa\\\\\n",
       "\t36 & 5.0 & 3.2 & 1.2 & 0.2 & setosa\\\\\n",
       "\t37 & 5.5 & 3.5 & 1.3 & 0.2 & setosa\\\\\n",
       "\t38 & 4.9 & 3.6 & 1.4 & 0.1 & setosa\\\\\n",
       "\t39 & 4.4 & 3.0 & 1.3 & 0.2 & setosa\\\\\n",
       "\t40 & 5.1 & 3.4 & 1.5 & 0.2 & setosa\\\\\n",
       "\t41 & 5.0 & 3.5 & 1.3 & 0.3 & setosa\\\\\n",
       "\t42 & 4.5 & 2.3 & 1.3 & 0.3 & setosa\\\\\n",
       "\t43 & 4.4 & 3.2 & 1.3 & 0.2 & setosa\\\\\n",
       "\t44 & 5.0 & 3.5 & 1.6 & 0.6 & setosa\\\\\n",
       "\t45 & 5.1 & 3.8 & 1.9 & 0.4 & setosa\\\\\n",
       "\t46 & 4.8 & 3.0 & 1.4 & 0.3 & setosa\\\\\n",
       "\t47 & 5.1 & 3.8 & 1.6 & 0.2 & setosa\\\\\n",
       "\t48 & 4.6 & 3.2 & 1.4 & 0.2 & setosa\\\\\n",
       "\t49 & 5.3 & 3.7 & 1.5 & 0.2 & setosa\\\\\n",
       "\t50 & 5.0 & 3.3 & 1.4 & 0.2 & setosa\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 50 × 5\n",
       "\n",
       "| <!--/--> | Sepal.Length &lt;dbl&gt; | Sepal.Width &lt;dbl&gt; | Petal.Length &lt;dbl&gt; | Petal.Width &lt;dbl&gt; | Species &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n",
       "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n",
       "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n",
       "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n",
       "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n",
       "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n",
       "| 7 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |\n",
       "| 8 | 5.0 | 3.4 | 1.5 | 0.2 | setosa |\n",
       "| 9 | 4.4 | 2.9 | 1.4 | 0.2 | setosa |\n",
       "| 10 | 4.9 | 3.1 | 1.5 | 0.1 | setosa |\n",
       "| 11 | 5.4 | 3.7 | 1.5 | 0.2 | setosa |\n",
       "| 12 | 4.8 | 3.4 | 1.6 | 0.2 | setosa |\n",
       "| 13 | 4.8 | 3.0 | 1.4 | 0.1 | setosa |\n",
       "| 14 | 4.3 | 3.0 | 1.1 | 0.1 | setosa |\n",
       "| 15 | 5.8 | 4.0 | 1.2 | 0.2 | setosa |\n",
       "| 16 | 5.7 | 4.4 | 1.5 | 0.4 | setosa |\n",
       "| 17 | 5.4 | 3.9 | 1.3 | 0.4 | setosa |\n",
       "| 18 | 5.1 | 3.5 | 1.4 | 0.3 | setosa |\n",
       "| 19 | 5.7 | 3.8 | 1.7 | 0.3 | setosa |\n",
       "| 20 | 5.1 | 3.8 | 1.5 | 0.3 | setosa |\n",
       "| 21 | 5.4 | 3.4 | 1.7 | 0.2 | setosa |\n",
       "| 22 | 5.1 | 3.7 | 1.5 | 0.4 | setosa |\n",
       "| 23 | 4.6 | 3.6 | 1.0 | 0.2 | setosa |\n",
       "| 24 | 5.1 | 3.3 | 1.7 | 0.5 | setosa |\n",
       "| 25 | 4.8 | 3.4 | 1.9 | 0.2 | setosa |\n",
       "| 26 | 5.0 | 3.0 | 1.6 | 0.2 | setosa |\n",
       "| 27 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |\n",
       "| 28 | 5.2 | 3.5 | 1.5 | 0.2 | setosa |\n",
       "| 29 | 5.2 | 3.4 | 1.4 | 0.2 | setosa |\n",
       "| 30 | 4.7 | 3.2 | 1.6 | 0.2 | setosa |\n",
       "| 31 | 4.8 | 3.1 | 1.6 | 0.2 | setosa |\n",
       "| 32 | 5.4 | 3.4 | 1.5 | 0.4 | setosa |\n",
       "| 33 | 5.2 | 4.1 | 1.5 | 0.1 | setosa |\n",
       "| 34 | 5.5 | 4.2 | 1.4 | 0.2 | setosa |\n",
       "| 35 | 4.9 | 3.1 | 1.5 | 0.2 | setosa |\n",
       "| 36 | 5.0 | 3.2 | 1.2 | 0.2 | setosa |\n",
       "| 37 | 5.5 | 3.5 | 1.3 | 0.2 | setosa |\n",
       "| 38 | 4.9 | 3.6 | 1.4 | 0.1 | setosa |\n",
       "| 39 | 4.4 | 3.0 | 1.3 | 0.2 | setosa |\n",
       "| 40 | 5.1 | 3.4 | 1.5 | 0.2 | setosa |\n",
       "| 41 | 5.0 | 3.5 | 1.3 | 0.3 | setosa |\n",
       "| 42 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |\n",
       "| 43 | 4.4 | 3.2 | 1.3 | 0.2 | setosa |\n",
       "| 44 | 5.0 | 3.5 | 1.6 | 0.6 | setosa |\n",
       "| 45 | 5.1 | 3.8 | 1.9 | 0.4 | setosa |\n",
       "| 46 | 4.8 | 3.0 | 1.4 | 0.3 | setosa |\n",
       "| 47 | 5.1 | 3.8 | 1.6 | 0.2 | setosa |\n",
       "| 48 | 4.6 | 3.2 | 1.4 | 0.2 | setosa |\n",
       "| 49 | 5.3 | 3.7 | 1.5 | 0.2 | setosa |\n",
       "| 50 | 5.0 | 3.3 | 1.4 | 0.2 | setosa |\n",
       "\n"
      ],
      "text/plain": [
       "   Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
       "1  5.1          3.5         1.4          0.2         setosa \n",
       "2  4.9          3.0         1.4          0.2         setosa \n",
       "3  4.7          3.2         1.3          0.2         setosa \n",
       "4  4.6          3.1         1.5          0.2         setosa \n",
       "5  5.0          3.6         1.4          0.2         setosa \n",
       "6  5.4          3.9         1.7          0.4         setosa \n",
       "7  4.6          3.4         1.4          0.3         setosa \n",
       "8  5.0          3.4         1.5          0.2         setosa \n",
       "9  4.4          2.9         1.4          0.2         setosa \n",
       "10 4.9          3.1         1.5          0.1         setosa \n",
       "11 5.4          3.7         1.5          0.2         setosa \n",
       "12 4.8          3.4         1.6          0.2         setosa \n",
       "13 4.8          3.0         1.4          0.1         setosa \n",
       "14 4.3          3.0         1.1          0.1         setosa \n",
       "15 5.8          4.0         1.2          0.2         setosa \n",
       "16 5.7          4.4         1.5          0.4         setosa \n",
       "17 5.4          3.9         1.3          0.4         setosa \n",
       "18 5.1          3.5         1.4          0.3         setosa \n",
       "19 5.7          3.8         1.7          0.3         setosa \n",
       "20 5.1          3.8         1.5          0.3         setosa \n",
       "21 5.4          3.4         1.7          0.2         setosa \n",
       "22 5.1          3.7         1.5          0.4         setosa \n",
       "23 4.6          3.6         1.0          0.2         setosa \n",
       "24 5.1          3.3         1.7          0.5         setosa \n",
       "25 4.8          3.4         1.9          0.2         setosa \n",
       "26 5.0          3.0         1.6          0.2         setosa \n",
       "27 5.0          3.4         1.6          0.4         setosa \n",
       "28 5.2          3.5         1.5          0.2         setosa \n",
       "29 5.2          3.4         1.4          0.2         setosa \n",
       "30 4.7          3.2         1.6          0.2         setosa \n",
       "31 4.8          3.1         1.6          0.2         setosa \n",
       "32 5.4          3.4         1.5          0.4         setosa \n",
       "33 5.2          4.1         1.5          0.1         setosa \n",
       "34 5.5          4.2         1.4          0.2         setosa \n",
       "35 4.9          3.1         1.5          0.2         setosa \n",
       "36 5.0          3.2         1.2          0.2         setosa \n",
       "37 5.5          3.5         1.3          0.2         setosa \n",
       "38 4.9          3.6         1.4          0.1         setosa \n",
       "39 4.4          3.0         1.3          0.2         setosa \n",
       "40 5.1          3.4         1.5          0.2         setosa \n",
       "41 5.0          3.5         1.3          0.3         setosa \n",
       "42 4.5          2.3         1.3          0.3         setosa \n",
       "43 4.4          3.2         1.3          0.2         setosa \n",
       "44 5.0          3.5         1.6          0.6         setosa \n",
       "45 5.1          3.8         1.9          0.4         setosa \n",
       "46 4.8          3.0         1.4          0.3         setosa \n",
       "47 5.1          3.8         1.6          0.2         setosa \n",
       "48 4.6          3.2         1.4          0.2         setosa \n",
       "49 5.3          3.7         1.5          0.2         setosa \n",
       "50 5.0          3.3         1.4          0.2         setosa "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setosa <- subset(iris, Species == \"setosa\")\n",
    "setosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Plot with title “Histogram of setosa$Petal.Length”"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist(setosa$Petal.Length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following gives an **empirical cumulative distribution plot**.  It gives the proportion of specimens with a petal length less than or equal to the given x\\-value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Plot with title “ecdf(x)”"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot.ecdf(setosa$Petal.Length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a data set has multiple levels like iris does, we can create multiple side\\-by\\-side box plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UKVOaddq6+ssFH47dbvgF5bmGA48+bZ8dt+7UPLz9StnYO8YfulPfZ+e88YOWjWOMCqSSUybEqKysbOjQoU888cT+++8fdxbyV5KK3SoeOPTi8srcRU99cNGAjf6z6LifnHn0dZ33OPvY45+d+af9Yk0HpJBTJsSovLy8+iOsToKL3XXvf9W86/n/a3UhhBDa7/bzS3pcdsmzl4cQqdhFmXc6e/bsEEJlZeX3CAukgVMmQJ5LcLH7Ynlls267f3t5n+7Nln/0dsSdRJ93OmvWrDUIB6SRUyZAnktwsTu6Q9N7P345hEGrLH9h+sKGzbaNuJMo807Hjh07ZcqUHj16fMegAPC9uSmbKBJW7Cq+LD32pMJevXr16tVrn9N2vfncyy6ceMIlA/73gJH3/3zeZR8v7Lb/ORF3GGXeaWlpaQghrkdDAkBwUzbRJKnY7bzNZtNnzrznthtWXnjlgXtf8vWMEELIrTh6v74PPjU107jz78atpw9SBOqVUybEyE3ZRJGkYvfKm++FEObP/XjG9OkzZsyYPmPGjBkzZn288L/rs/eV/b31Jj/47R//PLBtUYw5gbRyyoQYuSmbKJJU7Kq0ar/Rju032nH3fVZdUdDwpWmzdt26uwdPAPXEKRNi5KZsokjT+8YK+2p1QH3KZrMTJ07MZrNxB2F95KZsokhTsQOoX2VlZQMHDiwrK4s7CEDNFDuAqJwyAfKcYgcACeCmbKJI3s0TALAeclM2USh2AFE5ZUKM3JRNFC7FAkRVdcpk8ODBcQdhfeSmbKJQ7ACiqjplYjwssXBTNlEodgBROWVCjNyUTRSKHUBUTpkAeU6xA4jKKRMgzyl2AJAAbsomCuNOACABzLEjCsUOICqnTIiROXZE4VIsQFTm2BEjN2UThWIHEJU5dsTITdlEodgBROWUCTFyUzZRKHYAUTllAuQ5xQ4gKqdMgDyn2AFAArgpmyiMOwGABDDHjigUO4ConDIhRubYEYVLsQBRmWNHjNyUTRSKHUBU5tgRIzdlE4ViBxCVUybEyE3ZRKHYAUTllAmQ5xQ7gKicMgHynGIHAAngpmyiMO4EABLAHDuiUOwAonLKhBiZY0cULsUCRGWOHTFyUzZRKHYAUZljR4zclE0Uih1AVE6ZECM3ZROF99gBUc2bN2+zzTabN29e3EGITdu2bd9///22bdvGHQSomWIHRDV37tx58+b17NmzZ8+ecWeJzbx589bbWjNjxowZM2bMnTt3vf0bgPyn2AFrZsCAASeffHLcKYjB2LFjb7nllrhTrL/clE0Uih0AJIA5dkSh2AFAAphjRxTuigWABHBTNlEodgCQAObYEYViBwAJYI4dUSh2AAApEfXmiVx20R2/OvHGPz0/67PFNW6waNGitZcKAPJLNpstLS2tqKiIK8DLL79c/TEuRUVFQ4YM8VS9fBa12L1w1h4/ueGtBk067bTjjo0zBfWaCQDyzeTJk4cNGxZ3ijBmzJgxY8bEGOCZZ57p379/jAGoXdRi98tx7zdqtv0Ls17aeYOieg0EAHmopKRk/PjxMZ6xq6ysnDZtWu/evQsLY3sbVVFRUUlJSVyvThRRi907S5ZvdNhvtDoA1k+ZTGbo0KHxZjjssMPiDUD+i9r6d2/RqMAFWACAPBa12F39y74fPT7ynSXL6zUNAADfWW2XYj/66KPqz1v96HenTe6/y2YlF1x8xh7bbdGlXfNVNu7WrVu9BAQAIJrail337t2/tezjc0/8S40b53K5tZMIAIDvpLZid+qpp66zHAAAfE+1Fbsbb7yx+vOpU6e26NV7k5aNvr3Zko/e+WB+DcsBAFiXot480adPn59OmVPjqvdvP3Knnfdae5EAAPgu6phjN+7mGxesqKz6/J9P3P3b2W1W3SK34i9/mBVC4/oIBwBAdHUUu0t/cdbMihVVn3945yUjV7NZ9yG3rdVUAACssTqK3X2lT5dX5kII/fv33370/dfu3qGGXTRpu8su29VLOgAAIquj2O1Wsk/VJ4MGDdpu3/79+rav/0hAXrvllltuueWWuFMAUIOoz4otKyur1xwAAHxPUYvdFltssbpVmQYNm7Zst/Emm+81+OARw/s19EhZAIA4RC12Xbp0+eqdl6Z+8nUIIVPUYoPWTSoWfLHg6xUhhDYdO2bmzP7bX577w7ixo68f8foLt3VoGHWKCgAAa0vUBvbnO46bM29p131OKv3bhxVfL/h0zifzlyyd9drEkwd0a9ht0NQ5X5XPm/nAlcd98be7hlz8Wr0mBgCgRlHP2N2y/+lfNdt37sRbWmSqL7UWdt9+35tK3/5n+w33OmK/mY/88PBz78o+PfGnt18bLv9jPcUFYvezn/3s5JNPjjsFMRg7dqz7ZiDPRT1jN+aD+Rvu/POVWt1/FGSajdyjwz8nnF315c6Hd6/46pm1GRAAgGiiFruWDQqXfPxujas+mLWo+vMlHy8pyDRZC7kAAFhDUYvdZft0+vIfZ537h1XfP/fmIxecNm1ep32uDCEsWzDtVze/16K7azQAADGI+h67A37/pz17/eDqH+/4h1/3H7j79u1bFVXMn/vGSxOf+fvsJh32+fMfD17y6e+6bHTygmyDyyaeWK+JAQCoUdRi17DZjs9+MPXys86+6d6y26Y+W7WwoLCo5Ihzbrn10s2aNVy8oHyTvQ484oyrzujTrt7SAgCwWlGLXQihYYvNL77jiQtvWfDuG+98Mm9ho+ZtNtt2hw7N/rOHZp1H/u2ZkfUTEgCAuq1BsatS2LDl1jvttnV9ZAEA4HvwiAgAgJSIWuxy2cW3nX3oVt3bFa9GvaYEAKBOUS/FvviLPU/6zRuZxhvusHPflo0z9ZoJAIDvIGqxO+eufzRqtt1fZr7cp11RvQYCAOC7iXQpNldZ/uqiZd0OuEGrAwDIW9GKXXZJLoRcZWV9pwEA4DuLVOwKG25wyc4bfjz+9LcXL6/vQAAAfDdR32P3y+cmf9C/365b7HPh6NN332aLDq1XvQ22Z8+eazsbAABrIPIjxZpuGUII4ZNzRrxY4wa5XG4tRQIA4LuIWuxOPfXUes0BAMD3FLXY3XjjjfWaAwCA72nNnhVbueLLl56Z/NYHsxcsLv/V+aOWzP6ouHs3TyUDAMgHa9DKPpk8dteuXfcccsgpI39x3qgLQghvjB7YpsdON0z8uN7iAQAQVdRit/hff9x+0OlTv2h0+MhRl59VdSNF6Dzkh20+e/PM/XrfPWthvSUEACCSqMXuocNGfp4tuuetWQ+MufSoAZ2rFnYffvmbbz/SIiw+7/CH6i0hAACRRC12V78+r81Wvz1yi1arLG/eY9hNW28w763r1nYwAADWTNRiN3d5tmmX7jWu6rhRk+yyOWstEQAA30nUYjeoddEXU++paQZx5bhXPm/ccq+1GQoAgDUXtdidd9b2S+be1/+cu5ZUrtTucssfvXjwfXOXbHr8+fWSbk0cddRRZ1wxLe4UAACxiVrsep894dRd2z93zYgNu2x+5Og3QggnHndE303bHTx6YstNhj95WZ/6DBnJ/fff/6dnXBEGANZfUQcUF2Ra3vDi9D5XnnvdbQ/838vzQwh3jHuwqG33w8+68NorR3ZqtC6mFM984Df3TV9QywaLZj8wevRfqz6/6KKL1kEkAID8sQZPnijINDtm1E3HjLrpyzkfzf1yceMWbbpv1HFdPnbi4z/fePGfZ9aywcLZ91188X8+V+wAgPXNmj1SrEqbTt3adPrfl6V7bHbM+19+/vnnay3Uavzg93+56uTDzr3z/4rabHfZjaN6Nf1G+AMPPLDt1hfdedn2a7TPbDZbWlpaUVFRyzazZ88OIVRWVq55ZEihiRMnzpgxI+4UsZk3b17btm3jThGP9fnnDknxXYrdKpZ99eUXX3zx/fdTp8JGHc654/khQ67+4TEXjDrjiusffPhn+2688gZFG/Q94ICBa7TPyZMnDxs2LMqWs2bNWqM9Q/p06NChY8eOM2bM8A/8eqtTp04dOnSIOwWwWmuh2K1jvQ8+Z9peA0YedvApAzcdf+r1919/atsG3/2CcElJyfjx42s/Yzd27NgpU6b06NHjO78KpEObNm3mzFmvb1F6+OGHDz300Iceemj48OFxZwGoQfKKXQihcdvtb3l2xpDrTznynDN7lk244+EHDtlug++2q0wmM3To0Nq3KS0tDSEUFq7L9xMCAKyx5JaVwqFn3fLRG4/ulvnbYX26HXv5H+POAwAQs+QWuxBCaLXVsAlvz/jNyT+474Ifx50FSL/i4uLqjwB5KJGXYldW0KDNaTeUDRl675PvftWsyxZxxwHSbPDgwU8//XS/fv3iDgJQs9qK3a9+9asou/jgs6/XUpjvrue+R5+xb9whgLTLZDIDBgyIOwXAatVW7K666qp1lgMg/2Wz2UmTJvXr1y+TycSdBaAGtRW7cePGrasYAAlQVlY2dOjQJ554Yv/99487C0ANait2xxxzzDrLAZD/ysvLqz8C5KFk3xULAEA1xQ4AICW+b7FbtvAvHTt27Nix41pJA5DPzLED8tz3nWOXyy379NNP10oUgDxnjh2Q575vsWvUrM9f//rXtRIFIM+ZYwfkue97KbYg03yXXXbZZZdd1koagHyWzWYnTpyYzWbjDgJQs9rO2P373/+OvqPOnTt/7zAAec0cOyDP1VbsunTpEn1HuVzue4cByGvm2AF5rrZid8IJJ6yzHAAAfE+1Fbvbb799neUAAOB7MqAYICpz7IA8txaK3Xu37tOm/U7ffz8Aea5qjt3gwYPjDgJQszWYY/fRM+NuenTy7M+//ubiynee/svCpa3WbiyAPGSOHZDnoha7OZPP3WzQNUsra7j1tWGzDgeefe9aTQWQj7LZ7KRJk/r165fJZOLOAlCDqJdibxtx6/JM63tfmf71os/P7922c8kfKioqFn0++7qjtyzesOR3F3vADpB+ZWVlAwcOLCsrizsIQM2iFru7P1nSZrPrjtq5Z3GzDY49Z6t5b45r3Lhxsw26nXnXX3f68tGhV0+r15QA+cAcOyDPRS12ny/PNu3WterztjtvtnT+5CWVuRBCQab5Rft3feM3o+srIAAA0UQtdts1bbTw/beqPi9q3T9XufT+uf+5i6K4Y/HSr56tl3QAAEQWtdj9fLf282eec959k75cXlnUZr+OjTI3XP5CCCHkVvzh0Y8bFG9SjxkB8oM5dkCei1rshtw7tluj3JVH9z/ipU8LCpuOGdz1H2OH7Drw4AG7dr9u+vxuB15arykB8oE5dkCeizrupLjdfu/MeOHqa+8qalccQvjh78uOGLDf/RMfLShstMMhv3rsjoH1GRIgL5hjB+S5qMVu6tSpLXrtMHrMrv/5tuLN73thxs2f/2tFs46NP3vvg/dmdt3W1Vgg5cyxA/Jc1Euxffr0+emUOassbNGuS5vizPu3H7nTznut7WAAecccOyDP1XHGbtzNNy5YUVn1+T+fuPu3s9usukVuxV/+MCuExvURDiCvmGMH5Lk6it2lvzhrZsWKqs8/vPOSkavZrPuQ29ZqKgAA1lgdxe6+0qfLK3MhhP79+28/+v5rd+9Qwy6atN1ll+3qJR0AAJHVUex2K9mn6pNBgwZtt2//fn3b138kgDxljh2Q56LeFVv1ZuHKFV++9Mzktz6YvWBx+a/OH7Vk9kfF3btFvf8CIOGq5tj169cv7iAANVuDVvbJ5LG7du2655BDThn5i/NGXRBCeGP0wDY9drph4sf1Fg8gj1TNsTPrBMhbUYvd4n/9cftBp0/9otHhI0ddftaWVQs7D/lhm8/ePHO/3nfPWlhvCQHyRTabnThxYjabjTsIQM2iFruHDhv5ebbonrdmPTDm0qMGdK5a2H345W++/UiLsPi8wx+qt4QA+cIcOyDPRS12V78+r81Wvz1yi1arLG/eY9hNW28w763r1nYwgLxjjh2Q56IWu7nLs027dK9xVceNmmSXrfpQCgAA1rGoxW5Q6/KdXv8AACAASURBVKIvpt6Tq2FN5bhXPm/c0iPFAABiFrXYnXfW9kvm3tf/nLuWVK7U7nLLH7148H1zl2x6/Pn1kg4gn5hjB+S5qHPsep894dTHN7vpmhEb3nd1n+5fhRBOPO6It1+c8NfpC1puMvzJy/rUZ0iAvGCOHZDnop6xK8i0vOHF6eMuPaVng8/+7+XPQwh3jHvwja9aH37Wde++/YcujUx1AtLPHDsgz0U9YxdCKMg0O2bUTceMuunLOR/N/XJx4xZtum/U0WMngPVHNpudNGlSv379dDsgP9VZ7Cpfm/Dgo5NemfNVecuOPQccdsKgbdu16dStTad1EQ4gr5SVlQ0dOvSJJ57Yf//9484CUIPail2ucskv99vm10/NrF7ym6suPPr6/xs3sm/9BwPIO+bYAXmutkupb1836NdPzWxQtNFpo664/bYbfn58v8KQvffne93+z0XrLB8AABHVdsbu2uteLyhsePu7047t0SKEEE487ZDuffte+NfrLn7rxDt3X0cBAQCIprYzdhO+LG/e+Rf/aXUhhBB2POu2EMK8Vz6p91wA+cccOyDP1XbG7svllW2bbr3ykoZNtw4h5LIr6jcUQF4yxw7Ic3XeFbvKKb2C+goCkPeq5tjFnQJgtcyhA4gqm81OnDgxm83GHQSgZoodQFRlZWUDBw4sKyuLOwhAzeq4FLvw44v33PPmKAtfeOGFtZkLIP+YYwfkuTqK3fKv33/xxfejLAQAIF61Fbvp06evsxwAAHxPtRW7nj17rrMcAPnPHDsgz9U57gSA/zDHDshzih1AVObYAXnOuBOAqMyxA/KcYgcQlTl2QJ5T7ACiMscOyHOKHQBASrh5AkiMbDZbWlpaUVERV4CXX365+mMsioqKhgwZkslk4goA5DnFDkiMyZMnDxs2LO4UYcyYMWPGjInr1Z955pn+/fvH9epAnlPsgMQoKSkZP358jGfsKisrp02b1rt378LCeN7HUlRUVFJSEstLA4mg2AGJkclkhg4dGm+Gww47LN4AALVw8wQAQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASDeIOAJAAixYtmjhx4nvvvRdC2HzzzQcMGNC8efO4QwGsKhXFLrds6osvzP5kfstOvXbfbZviwoK4AwGpctNNN11wwQXz58+vXtKqVavLLrvslFNOiTEVwLcl7FLsopmTTj1sYM+NOnbqvtlBJ1/66bLKpV+9su8mG/b5Qf9DDjtk3z23a9d155um/DvumEB6jB49+rTTTmvbtu3YsWNff/311157bezYsW3btj311FMvueSSuNMBfEOSztiVf/7UNlsNnV2xoqCwuFWTLx+75cJX329+3tJrJs1cvNfwY/tu3v7T91578E/PnjFgmw4f//OQDk3izgsk3rRp0y699NKddtpp0qRJ1ddet99++yOPPLJfv36XXHLJwQcfvPXWW8cbEqBaks7YjT/iJ7MrVhx7/fiFS5d8uaj85ftO+/dzZ57+0qcH3fPGlIfuvvKSq+5+aOLsF36byX511jGlcYcF0uDuu+/OZrNjx45d5R11zZs3Hzt2bDabveuuu+LKBvBtSTpjd83Lc1v1vOjuM4eGEEIo2PXIGw49684/L2z7+yP/97/LHXc77cqNLzr/r78O4ZAo+8xms6WlpRUVFbVsM3v27BBCZWXl98gOJNLUqVPbtWvXp0+fb6/q06dPu3btXnvttXWfCmB1klTs/lG+fMMt9lx5yYFti5+o3KHRN2+W2Gbj5stnvRNxn5MnTx42bFiULWfNmhVxn0BqLF68uGXLlqtb27Jly0WLFq3LPAC1S1Kx27y44cx3/i+EftVLdj7ngou/6rrKZu/OXtSguFfEfZaUlIwfP772M3Zjx46dMmVKjx491jQwkHQdO3Z87733KioqioqKVllVUVHx73//e7PNNoslGECNklTszum74Y+fvfSUuwbcePzuVe8N7HnsGb/85jZfTL393Onz2+x8fcR9ZjKZoUOH1r5NaWlpCKGwMEnvRwTWin333XfChAn333//CSecsMqq+++/v7y8fN99940lGECNklRWhj14R6/iBmNH7NGyy+YDfjRhlbXv3nbNST8e2GOXny4raHrFfQfFkhBImREjRnTq1GnkyJETJnzjd86ECRNGjhzZuXPnESNGxJUN4NuSdMaueIOBb3ww6cJfXfmnshemvv7ZKmvfGnPNbe/Na92r782/+8NxvVb7nhiA6Jo1a/bYY48NGjRo//3332mnnXbbbbcQwksvvfTqq6+2adPmsccea9asWdwZAf4nScUuhNCk856/vnfPX4dQuXzVe1R3/819f2m/ad/tenruBLAW7bTTTm+++ebo0aMfeeSRV199NYTQunXrE0444aKLLurSpUvc6QC+IWHFrlphw1UvIncdOHjV2ygA1oYuXbrcfvvtt95666efflpQUNC+fftMJhN3KIAaJLXYAaxjmUymc+fOcacAqE2Sbp4AAKAWih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkRIO4A6yxZQs+/utLf3vrg8879tpqyOA9iwsLVtngnccffmPxsiOOOCKWeAAAcUlYsfvrbacfeNrYucuyVV8267bLLY+XHrltm5W3eXzkiefPXqDYAQDrmyQVu8/+dvHuP70pZFodNfLkXTfv8PHfn7757tJjd96y0fTph3ZtFnc6AICYJanY3Xn0DaGw6T1vzjhyy9YhhHDSqacf+dtN+5114g9OGjrj/m9fkwUAWK8kqdjdMntR261/959WF0IIodNeZ0wa/fCuox784R0XlP5k8++wz2w2W1paWlFRUcs2s2fPDiFUVlZ+h/0DAKwzSSp2i7OVzdp1XWXhzudOGPSbjs+OHPbuke9u2WSN/ziTJ08eNmxYlC1nzZq1pjsHAFiXklTs9mlVNGHqNYuz/Ztl/nfVtSDT8p4nz+vY98JBh9z4UemZa3o5tqSkZPz48bWfsRs7duyUKVN69OjxnVIDAKwjSSp2556w+Z+uenbHH1/8p9/8cutOTauXb7jLqEdO+P3Bt5+1xxlNSq//yRrtM5PJDB06tPZtSktLQwiFhWb+AQB5LUllZYdLyn68TZsPHr5kmy4tO/XY9NF55dWrDhj7wnn793zphp926NDrjk+XxBgSACAuSSp2hQ03vH/q+3dcctoe22+67KtPFqzI/W9VgzaXj3/33ktP6p75dFbFihhDAgDEJUnFLoRQ2GCDERfc8H9T3/1i/qJj2zf5xrqCRkeNuvUfny781wdvTp5YGlNAAIDYJOk9dtFkOm+yTedNtok7BgDAupawM3YAAKyOYgcAkBLpuxRLPYryoI56VVlZOW3atN69e8c4faaoqGjIkCGZTCauAACwOoodayD6gzrS7Zlnnunfv3/cKQBgVYodayDKgzrq1csvvzxmzJgzzzyzb9++cWUoKioqKSmJ69UBoBaKHWsgyoM66tuYMWP69u07fPjweGMAQB5y8wQAQEoodiRJcXFx9UcAYBUuxZIkgwcPfvrpp/v16xd3EADIR4odSZLJZAYMGBB3CgDIUy7FkiTZbHbixInZbDbuIACQjxQ7kqSsrGzgwIFlZWVxBwGAfKTYkSTl5eXVHwGAVSh2AAApodgBAKSEYkeSmGMHALUw7oQkMccOAGqh2JEk5tgBQC1ciiVJzLEDgFoodiSJOXYAUAvFjiQxxw4AaqHYAQCkhGIHAJASih1JYo4dANTCuBOSxBw7AKiFYkeSmGMHALVwKZYkMccOAGqh2JEk5tgBQC0UO5LEHDsAqIViBwCQEoodAEBKuCs2YT799NP77ruvsrIy7iDx+Mc//hFCmDBhwsyZM+POEpu+ffv+4Ac/iDsFAPlIsUuYO+6444ILLog7RczuueeeuCPEadttt33jjTfiTgFAPlLsEmbFihUhhOuuu65Lly5xZyEGP//5z6uOAQD4NsUukXr27NmzZ8+4UxCDoqKiuCMAkL/cPAEAkBKKHQBASih2AAApodgBAKSEYgcAkBLuik2kAw88MO4IxKNNmzYdO3aMOwUAecoZOwCAlFDsAABSQrEDAEgJxQ4AICXcPJFIjz32mEeKrZ8OOuiguCMAkL+csQMASAnFDgAgJRQ7AICUUOwAAFLCzROJtGTJkoULF8adghhks9m4IwCQvxS7hCksLAwhHHHEEXEHITZbb7113BEAyFOKXcIccsghc+bMyeVycQeJx8cff/zUU08NGjRoo402ijtLbPbcc8+4IwCQpxS7hNlyyy1vvfXWuFPE5uGHH37qqaeOP/744cOHx50FAPKOmycAAFJCsQMASAnFjiQpLi6u/ggArMJ77EiSwYMHP/300/369Ys7CADkI8WOJMlkMgMGDIg7BQDkKZdiSZJsNjtx4kRDegGgRoodSVJWVjZw4MCysrK4gwBAPlLsSJLy8vLqjwDAKhQ7AICUUOwAAFJCsSNJzLEDgFoYd0KSmGMHALVQ7EgSc+wAoBYuxZIk5tgBQC0UO5LEHDsAqIViR5KYYwcAtVDsAABSQrEDAEgJxY4kMccOAGph3AlJYo4dANRCsSNJzLEDgFq4FEuSmGMHALVQ7EgSc+wAoBaKHUlijh0A1EKxAwBICTdPsAay2WxpaWlFRUVcAV5++eXqj3EpKioaMmRIJpOJMQMA1EixYw1Mnjx52LBhcacIY8aMGTNmTIwBnnnmmf79+8cYAABqpNixBkpKSsaPHx/jGbvKyspp06b17t27sDC2dxEUFRWVlJTE9eoAUAvFjjWQyWSGDh0ab4bDDjss3gAAkLfcPAEAkBLO2JEkCxcu/Pzzz5s0adKxY8e4swBA3nHGjmQoLS3dfffdW7du3atXr06dOnXr1u2KK65YunRp3LkAII84Y0cCXHzxxaNHj27SpMnw4cM33njj+fPnl5aWnn/++U888cTEiRObN28ed0AAyAuKHfnuySefHD169C677PLoo49WX4FdsWLFqFGjrr766lNPPfWee+6JNyEA5IkkFbv5n36yJFsZcePOnTvXaxjWmSuuuKJ58+aPP/54+/btqxc2aNDgqquuevvtt++7775LL710o402ijEhAOSJJBW7s7ff9I5PF0fcOJfLRdksyqMUZs+eHUKorIzaKVmLFi1a9Morrxx66KErt7pqxx9//IQJE5577rljjz12nUcDgLyTpGJ32bNlm427+cIxfyzP5lr33nv3bs2+/z6jP0rhX//61/d/OdbUZ599VllZ2aNHjxrXVi3/5JNP1m0oAMhTSSp27bfa4xfX7lHSZmaf8/62xSm3PHHS5t9/n1EepTBhwoR77rnn8MMP//4vx5qqujFi/vz5Na6tWt6iRYt1mgkA8lWSil2V3qdcF87bc23tLcqjFObMmXPPPfc0bNhwbb0o0bVr165bt25PP/30ihUrGjRY9XCdMGFCCKFPnz5xRAOAvJO8OXaNWuyxQ5cOLYsycQdhXSgoKPjJT34yc+bM888/f5X3Tb744os333zz9ttvv/POO8cVDwDySvLO2IUQpv7Te6rWI2edddaECROuueaaqVOnnnjiib169Zo/f/7jjz/+u9/9rnHjxnfddVdBQUHcGQEgLySy2LFeKSoqeuqpp84666xx48ZNmjSpenmfPn3uvPPObbbZJsZsAJBXFDsSoHnz5rfffvvFF1/83HPPzZkzp1mzZrvssou31gHAKhQ7EqNz585HHXVU3CkAIH8l7+YJAABqpNgBAKSEYgcAkBKKHQBASih2AAApodgBAKSEYgcAkBKKHQBASih2AAApodgBAKSEYgcAkBKKHQBASih2AAApodgBAKREg7gDJMb7779fVFQUd4r13fLly8eNG9etW7fCQv9PQgwqKyunT5/eq1cvRyDrXmVl5UcffXTsscc2bNgw7izru/fffz/uCKul2NWt6j+hESNGxB0EgPXd7373u7gj8B/52bAVu7odccQRK1asKC8vjzsI4a233nrwwQf32GOPbt26xZ2F9dFHH3304osvOgKJRdXhd/jhh2+zzTZxZyEUFxcfccQRcaeoQUEul4s7A0T18MMPH3rooQ899NDw4cPjzsL6yBFIjBx+ROFtIgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYAQCkhGIHAJASih0AQEoodgAAKaHYkSTFxcXVH2HdcwQSI4cfUXikGEmSzWYnTZrUr1+/TCYTdxbWR45AYuTwIwrFDgAgJVyKBQBICcUOACAlFDsAgJRQ7AAAUkKxAwBICcUOACAlFDsAgJRQ7AAAUkKxAwBICcUOACAlFDsAgJRQ7AAAUkKxAwBICcUOACAlFDsAgJRQ7AAAUkKxA6jDk9u3LygomL00u1b29uzgbgUFBS8vWrZW9kaeWysHz5ruZO0esSSLYkf+WvjRqNatWw/5/Yy4gwBAMjSIOwCsVq6yYv78+YuXVcYdhPVdyWMvvVexokujTNxBSJ61cvCs6U4cseszxQ5Yf309b1mTto3q3Kxpt56brYM00UTMTJ6o/eD5nkdg5dJloXGjb196y6sjlnXMpVjq1wv3XTF4161bNy9uVNys17Z7/uqmCbmV1uayCx648vTdtuzWorjxhl177Xvkzye+t6Bq1S2btGm18XUhhBeO3bSgoODmT5ZULV+++P2rTzt8624dihs2btuhx5Ajzpwyc1H0V1z80ZRfHDV0s87tiho2bNZywx32OvC3j75dr38DrEWPDe5WUFAw8p15Ky+s+Kq0sLCwdc/zqr6s5aAKIUw+aOPCTJMQwiOXHN91g6Y7/OLVquW1HzZlfTut/I6lZQveveSk4Zt2bte4UdMuvXY8adRtny//xnnlOo/SNdp4dZnJB3Uek6scPDX+NJfNn/arY/br0q5FUYsNdhp89JR/LxnTs3XTdsOrd7jyTv7vx5sUFBSsKP/gzKE7N2lS1CBT1HWT3kf98paF2VyN24cIR6xfjKmSg3rzyuUDQwjFG2512FEjTjjqsM3aNA4h9L/ytaq1ldnFp+7RIYTQZou+Pzp2xAH77ta4sCDTqP2vp3ySy+Xe+f3dYy7rH0Lodcwlt95669tLludyueVL3tq7Y9MQQpdtdvvxccfsu9s2mYKCBkUb3TN9QZRX/Pqz8d2LGhQUNOwz6OARJ/3kRwf1a92gsKCg8NyXPo3jr4c19sW0M0MIPQ4sXXnhW9fuFEIY+uisXF0HVS6Xe+7AHgWFxX+9ct9GzXsccuzPrv7jrFxdh00ulyvdtWMIYVbFilwut3ThK3u0Ky4oKNy6777HjTiqZJsNQwjtdvppReV/Nq7zKH1m0EYhhJcWLo2y8eoykyfqPCZXPnhyNf00ly95p6R9k4KCgu1+MOS4o3647UbNGjbddO9WRU02OKR6hyvv5Pkf9Qoh/HznDRs222T4caeefdoJW7VuHELYckRpjdvXecT6xZgyih31p3LjogaNmvep/o22dOHf2zQsLGrdv+rLN6/aI4Sw45n3Lv3v75dPX7m/U+NMo2bbz1temcvl5s/8eQhhz3EfVO/xkQO7hxAGXP5U9ZIPx48qLCho0e3EKK/4yhlbhxB+9MD71d/+xRu/DiF03ut/OySfVWa/3qJJw4ZNe1cfM7lc7rgOTTMN231csSIX4aB67sAeBQWZDToMeXvRsuq91n7Y5L75z+Rt+3QOIZz+x3f+u3LFbT/uGUL48TP/rPq6rqP0G8Wuzo1Xk5l8UecxWUOx++ZPc/yPeoYQTrj91aovs0v/feqO7UIItRe74rb9XvmsvGptxfy/tG+Uadi0d43b13nE+sWYMood9aUyu7hBQUFxm/0WrPjfL7zpb7w2deqbVZ/v1apx4xa7L1xpbS6Xe/GULUMIZ0//KvetYle5Yn7rBoVFbQYt/8Z35G7cYcMQwu8/+7rOV/zXk38cN27cF8uz1WtXVHwUQthgy0fX4h+celX2w41DCBdOn1/15ddf/CmE0HXfh6q+rPOgeu7AHiGEIU/Mrl5b52GTW+mfyeVL3mlcWNCq53kr77/8iz/vuuuuwy9+IxfhKM2tVOyibFxjZvJK7cfkt4vdyj/NyhXz2zXMNOtwwso7XPjxDXUWu/0e/8bxMGqjFoUNWn97+zqP2JxfjKnj5gnqS0Fh06tKOv3iuQldN9vz2MMP2Gv33Xbtu3PPbbevWrt88dTn5y9t1nGLh8bdtfJ3zW9aGEL429/nhZ6tVtnh158/9NWKym59f96g4BvLB5y2aTjuswemL/hRuw61vGIIofN+hx4TQi779ax/fDBz9uzZM2e88MTY+vizU392vfLw8KfLfj/69dH37h1CeH/s5SGEI2/oH9bkoBq+U7vqtbUfqKtYPOfmpZW5LY88ZOWFRW0Pevnlg6o+j3CUFlcvXKONV85MXqnlmFyd6p/m13Pv+3x5ttfeR628tnmXn7VpOLKi1hc9bNdvHA9tGtT8jvk6j9jgF2PqKHbUo7OefqvN1Rffes9DN1z6yxtCKChs1Hvvg8675sbDdmy3ovyDEMLiT+444YQ7vv2N5XPKv70wu/SjEELzTVqssrzFFi1CCIv/+XXoW9srhhBWfP3exT87fewfnvtqWbagsGGHbr2222nvEGau7T839ahVrwt3bH7N2+NHVYYXC0O45qb3Grfc65LNWocQoh9UXRt/YwxE7YfNypZ+9VH47yFXoyhH6XfbeJXM5I9ajsnVqf5pLi9/L4TQdOOm31hd0KB74wbv1fqibRtGuvexziM2+MWYOu6KpR4VNGhz3Pk3vPLBp/P/+Y8nf3/7yKMHzHj+4SN22/qFhcsyjTqHEDrsPL7GM8mvnLn1t/eWadwthLDow1XvLlw8fXEIoUmn4tpfMYRwft89Lr/3mZKRv37xzemLly6dM/PdCQ9eX99/CaxlBQ1/fVD3pQv+8tt/Lf76swd+/9nXm5/866ozXtEPqsJvniGr/bBZWcMWbUIIX3/89erSRTlKv9vGq2Qmj6z+mFyd6p9mplHHEMKS2Uu+ub7yX8vWzkMj6jxig1+M6VOf13lZr5V/8di555573SPfeCPI1Mt2DCEMeemTXC67ZZOGTTY4OPvN7/rw3stGjhz54oKluW+/x275V60aFBa32W/FN7/l1l3ahxDumbuk9ldcvuTtEEKrnteuvHZF+YfBW0mSZsHsq0IIW5328t/P27agoPCJeeX/XVP3QVX1Dqfn5ldUr63rQM3lVnrH0tL5zxcUFLTd6pqVN1668KXCgoJ229yfi3CU5lZ+j12EjWvMTL5Z/TFZ83vsqn+aKyo+LiosaNbxpyvvbfGc20Jd77Gb8GX5yt9y/catanyPXZ1HrF+M6aPYUV/Kv3g0hNC045ErvSe38pYf9gghnDNjfi6Xe/7UrUMI+178ePXqhTOf6FXcoHGLvouz/7srdvc73qve50NDu4UQ9rvmueolMyZclCkoaLHRCXW+4vKv3yssKGjW8SfVb1TPLvvsN8f3DiFssOUj9ff3QD3I7tWycXHb/Q9oW9xq41+tvKLOg6qmYlfHgZr75j+rF/duW1BQcN746dUbPzBiixDC/g/NqPq69qM09827YuvcuMbM5J/VHpO1F7tcLnffkI1CCCff8/p/drRs7ll926+tYper64j1izF9FDvq0RUlnUIITTtv98PDjz/5xGNLtm4fQmi/21lVv0GyS//9w81ahRDabbrj8ON+etTwQa0aFBZmml06eU7Vty/85zUhhFabHnzx6Iv+smBpLpdbtviNH7RvEkLo3mfvY046cb+9d8wUFDQo6n7/jAVRXvHKPTqEEHrsccg5oy46/cQjd2jfpMPOP+rauEHDpltd8ZvfxfJXxHfz0ilbVl1zqBoVVq3Og6rGklT7YZP75j+TX88t3ap5o4KCzI5773fiz0YM2qVLCKHN1sct+u9NtXUepSsXuzo3Xl1m8s3qjsk6i93yJe8M2rhFQUFm534H/OT4H+3Yo3nbbX/Wu2nDph2Oq3Ena1rs6jxi/WJMGcWOepRd9vnNvxqx/aZdmjTKNChqunHvvqddeve8lUY7rFj6zxvPOW77jTsWN2y44Uablhxwwp+mfrbS95efP7xvqyYNGzVpXX1ZatnCdy8/5bAtu7YratCwVbtug348csrMRRFfcUXFR5eedECPDVs0Km69za79Tr/mkaWVuUnnH9yquGHzDjusi78R1pJF/745hPD/7d17UJVlHsDx33vuwAG5xE0E4xIl4AVIA9RRNJPSHW3SzBKFlR27uE3aaos6jW2Jtm25YzU1ZmVrbGnTmnmtTRFrV6gw1kOqxAAACUpJREFUFM3VUDIMyqAEuR0u590/UPYIKdoobI/fz3+873Oe8/DOGfie95z3nI6PCnN18QfVz0ZStw/UTv+b6ysKFqZPDA/0MZus/v3j0hesrHSc9/LvxR+lrmHX7eALrRn/by70mOw27HRdb2365vGMyTGhPm7eAeNnLTnR1OprNniFLfnZSS437PTuHrH8YVSMpuuuX5wDAAB6zhcFex0Gv6Rh0R1bWhsOmj0G9kvdVr7r9l5cGH6luCoWAIBekzstbfjwW4rrWjq27HtproiMXjqk9xaFXzHO2AEA0Gsq85f0H5NjDU15MHNCSB9zadGOl3N39Yl/4NvPX7TwGTe4fIQdAAC9qeyjVxbmrPn00JGKmtag62PSpmQ++ficIAsvqeGXIOwAAAAUwRMCAAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwA6COr/Pfun/q+BtC/N0tZm//vrGJYxcsX13Z7OzJNWyJD9Q07WtHW0/eKQC003Rd7+01AMAVkLf8rlsXb9Q125CU4dHh/YxN1SUFu0vKz9jDUt8v2pZ6na1nlrElPvA3xafKmlqvtxp75h4BoANhB0AFDd+v8w6eZfQeseWzrWMjPc9u1Vu3rpw58dG3/OL+WFWyvGdWUn/i2Mmm1sjoG01az9whAPwPYQdABftzbh6yuGj0hmN5UyM67Zof6rXy5Jk9NY6RXhbX7Y6WNquZk2oAlMJ77ACooL6sXkRaalu67pq7YtlTTz3lZdREJNHT6h/73lebnokP97FZTFa7b9zISS9s/bLTTfS2mtzlD6fE9PdyswaERo2b8eiH/6lxHdBc8+Wf5kyNDvG3Wjz6RSXOWbL6h5az7+Tbnty303vsup3t43U5tyfF+Xi6WdzsUYNHZr+wlSfcAH4hHQB+/b7NmyEiJrfIp3N3nm5xXmhYgt3i5nuHu9Fg9QkfN3l62sgED6NB0wyZqw91jHG21c0dESQivgOS78mYPWlcitWgGS2Bf9ld2T7AUVs4wt9N0wxxyeMyZ6enDgoQEf+h9zc5dV3XtyUFi0hZU+slzla4bLyIuAXETkufnZU+7UZfq4jcunzfVTpQANRG2AFQgrMlZ+qg9uerRqtf8m13Zec8/8G/S+rbzou8BLtFRPwGZR6ua27fUl2yvr/NZDT7l9S3tG/Zv2KEiCTO+5vj3E2/K3yzr9VoscdXtzh1XV89JkREHl7f0YKtq6dHisj0f5brXcKuu9mcETaTxfPmjvGO2s99zQabz61X5zABUBxhB0AdB3euz35oZlJsmEE7e+WCyT1owm8XH/ixqX1Ae9it/bbO9Vb7n7lFRMa8Xdr+4yhvq9VreG3reUX4yUMxIrKg9KeW+kNWg+Yduch1b2PVP5KSkqYuLda7hN3FZ3O21Zk0zc13Qo3LgNLifUVF+6/MEQFwjTH11Eu+AHDVxY65O2fM3SLSWFW2Z3d+3kc7Nvx949bXlu3ctLPgxJ7BHmYRsdgTZvX1cL1VVPocWVD41avHZVpkS11R/mmHPXjAhrWvuY457WEQkU8/r65LfNHh1GNmTHHda/O7c+/eO7uup9vZtMjIFal9/7Bra+iNIzPunTRqeEpS8rDIwfFX5nAAuPYQdgBU4HA4NM1ksZy9ytXtuvDxU8LHT8lY9teTi9OGPZ1fkP548YFnh4qI2T2m023NHoNFpOFktYi0Nh4VkbrKNVlZa7reS2NFoyPihIh4DfC6lFV1O5uIzP/ggO/TS19+Y8OqJxeuEtEMloGj71z05+enJfpf6i8PAOdwVSwABTi93d18QzO67jDa+i1amyEiFduOtG9paeh8DWz7Fquft4gYLSEiEjTs/Z99jaNwXpzZy1dEGr5puJRldTubiGgm38zFqwqPfne6/PCWt155ZOZtx/LfuS8l7uPa5l92LABcywg7AAow3Bfg3lD1zuZTjV331ZWViYhPQlj7j811+9787rwsK1v/sohEzAoXEUufETHu5trjazt9DVnpumXz5s37V22zPShL07Tjb+xw3dt8Zq/RYAgYnNvprrudral6U3Z29nPvnhCRPv1umnBP1nOvb97zRHxb86kVh3687MMAAFfzDXwA0ENKc6eJiGdY2o6SH1y3/3R4x0gfm2awrCk/o5+7eOK6Ib8rbTh7Dez3hW9E2EwGk3dhraN9S/7cOBEZt3RT27lJao9vjnIzWb2S69qcuq4vHeinadqi90vP7Xfmzh4gIhM3HNO7XDxx8dkaqzaKiEfwjKqWjv3Ol+4KF5HHjp2+4kcJgPL45gkAinh7/tjpK3dpmrF/7M0DwoNthtZT5UcLv/iqTYz3PZe/7pEUEUn0tB7UBiXYDhVL2NjUWwxVR/LyP6t36tNXfZE7d3D7PM7mirsHxb575LR/dOLo4UNtdV9v3vhhre7+xEdHl4wOFpHGU9uHRk3+sq4tYVRawoCg8n0f7Cg86RuXeaL4VbtR257c946Cyo7viu12tuVjQhblVXiEDEkblRDo4Ty8d3vewe8DU+af/ORZvpQMwGXr7bIEgCvm2O51D9w74YbQILvNZLbZQ6IGTc549L3PKjsGJNgt9qAsR82BByeP9O/jbnbzuin5jpUbO3+2SKuj/PnHMuMjgt3M5oCw6NRJWe8WnXIdUF9RsDB9Ynigj9lk9e8fl75gZaXj7Cm3Tmfsup2trfmHF7Nnx0f3c7cYTTaPiIHJv3/y9eoLf8YyAFwEZ+wAXEMSPa1H7TPPVL7S2wsBgKuCiycAAAAUQdgBAAAogrADAABQBO+xAwAAUARn7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIr4LzHfzdy5wlx4AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxplot(Petal.Length ~ Species, data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've used the tilde (`~`) operator a few times.  What it does is create formulas.  In general, we use `y ~ x`.  The dependent variable (or in statistics terms, the **response** variable) goes on the left and the independent variable (or **explanatory** variable) goes on the right.\n",
    "\n",
    "Using this, we can create **scatter plots**.  Each point in a scatter plot is of the form $(x_i, y_i)$, where $x_i$ is the value of the explanatory variable for the $i$th element of the data set and $y_i$ is the value of the response variable for the $i$th element of the data set.\n",
    "\n",
    "So for example, suppose we want to visualize the relationship between petal length and sepal length of iris setosa flowers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lJkmO7aaB9fU08YnsRjrZpvH/fGXmp4EkndJ3qPE7uX8EFCigx5yameu5pXJYbH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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(Sepal.Length ~ Petal.Length, data=setosa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the longer the petal, the longer the sepal, which makes intuitive sense!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.2"
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 },
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