GettingStarted_R.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3"
      ],
      "text/latex": [
       "3"
      ],
      "text/markdown": [
       "3"
      ],
      "text/plain": [
       "[1] 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assignment in R \n",
    "x<-3\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "3->x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [1] -0.94357048  0.66987346  2.04047576  0.83045573 -0.75687843 -0.12003547\n",
      " [7]  0.40147034  0.66494347  0.51723504 -1.55554775 -0.13795607  0.86585394\n",
      "[13]  0.47842626 -0.28823473  0.55417453  0.07078715 -0.09495921 -1.62265853\n",
      "[19] -0.41821704  0.61522008 -0.44962202  0.14057782 -0.28948102  0.26667843\n",
      "[25]  1.36000819  1.05724317 -1.28798115 -0.76247432  0.01048562  0.18363545\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>y</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>3</td></tr>\n",
       "\t<tr><td>5</td></tr>\n",
       "\t<tr><td>2</td></tr>\n",
       "\t<tr><td>4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 1\n",
       "\\begin{tabular}{l}\n",
       " y\\\\\n",
       " <dbl>\\\\\n",
       "\\hline\n",
       "\t 3\\\\\n",
       "\t 5\\\\\n",
       "\t 2\\\\\n",
       "\t 4\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 1\n",
       "\n",
       "| y &lt;dbl&gt; |\n",
       "|---|\n",
       "| 3 |\n",
       "| 5 |\n",
       "| 2 |\n",
       "| 4 |\n",
       "\n"
      ],
      "text/plain": [
       "  y\n",
       "1 3\n",
       "2 5\n",
       "3 2\n",
       "4 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random number generation\n",
    "x<-c(rnorm(30))\n",
    "print(x)\n",
    "# Creating a vector\n",
    "y<-c(3,5,2,4)\n",
    "# Converting the vectcor to a dataframe\n",
    "data.frame(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 10 × 10 of type dbl</caption>\n",
       "<tbody>\n",
       "\t<tr><td> 0.23967363</td><td> 0.78926556</td><td>-0.5749426</td><td> 0.1953827</td><td>-0.531577886</td><td> 0.03099485</td><td>-0.59410530</td><td>-0.43778578</td><td> 0.5039455</td><td> 1.1325274</td></tr>\n",
       "\t<tr><td> 0.24244617</td><td>-0.01057392</td><td>-0.6215322</td><td>-1.8799747</td><td> 0.188147387</td><td> 1.77490447</td><td> 1.91418932</td><td> 0.37029579</td><td>-1.0290394</td><td> 0.1917403</td></tr>\n",
       "\t<tr><td> 0.04431906</td><td>-0.39661094</td><td> 1.3587527</td><td>-0.6912579</td><td> 0.468513987</td><td>-1.89935798</td><td>-1.19549460</td><td>-0.17479382</td><td> 0.8013274</td><td> 1.4002384</td></tr>\n",
       "\t<tr><td> 1.39987461</td><td> 1.11477862</td><td>-0.9165962</td><td>-1.3684041</td><td>-1.651083967</td><td> 1.50265696</td><td> 2.35573527</td><td>-0.12125783</td><td>-1.0562328</td><td> 1.3576558</td></tr>\n",
       "\t<tr><td>-2.24445298</td><td>-0.33451112</td><td> 1.4854244</td><td>-1.0682867</td><td> 1.226876864</td><td> 0.57110827</td><td> 1.08313555</td><td>-0.58203604</td><td>-0.1892634</td><td>-2.2622850</td></tr>\n",
       "\t<tr><td> 0.83243345</td><td> 1.77410395</td><td>-0.5346737</td><td> 0.8448853</td><td>-1.011580447</td><td> 1.89795420</td><td>-1.89929201</td><td> 0.14893425</td><td>-1.0071622</td><td> 0.4725406</td></tr>\n",
       "\t<tr><td> 0.73880204</td><td> 0.91375421</td><td> 2.2291059</td><td>-0.6760469</td><td>-0.164168930</td><td> 1.34092847</td><td>-0.99644878</td><td> 0.93351831</td><td>-1.7888597</td><td>-2.8061236</td></tr>\n",
       "\t<tr><td> 0.36568907</td><td> 0.69166232</td><td>-0.5311620</td><td>-0.7206215</td><td> 0.004583995</td><td> 1.21986552</td><td>-0.07795401</td><td>-1.65265638</td><td>-1.1022655</td><td> 0.5579629</td></tr>\n",
       "\t<tr><td>-0.35112402</td><td> 0.17030455</td><td> 1.3771536</td><td> 1.4233101</td><td>-0.860648824</td><td>-1.47827855</td><td>-0.39261047</td><td> 0.80221210</td><td> 1.9481512</td><td> 0.2701822</td></tr>\n",
       "\t<tr><td> 2.13107532</td><td> 0.13718951</td><td> 0.1282606</td><td>-0.6292720</td><td>-0.060331155</td><td>-0.09295629</td><td> 1.01771608</td><td> 0.06682849</td><td> 0.6136679</td><td>-1.0118277</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 10 × 10 of type dbl\n",
       "\\begin{tabular}{llllllllll}\n",
       "\t  0.23967363 &  0.78926556 & -0.5749426 &  0.1953827 & -0.531577886 &  0.03099485 & -0.59410530 & -0.43778578 &  0.5039455 &  1.1325274\\\\\n",
       "\t  0.24244617 & -0.01057392 & -0.6215322 & -1.8799747 &  0.188147387 &  1.77490447 &  1.91418932 &  0.37029579 & -1.0290394 &  0.1917403\\\\\n",
       "\t  0.04431906 & -0.39661094 &  1.3587527 & -0.6912579 &  0.468513987 & -1.89935798 & -1.19549460 & -0.17479382 &  0.8013274 &  1.4002384\\\\\n",
       "\t  1.39987461 &  1.11477862 & -0.9165962 & -1.3684041 & -1.651083967 &  1.50265696 &  2.35573527 & -0.12125783 & -1.0562328 &  1.3576558\\\\\n",
       "\t -2.24445298 & -0.33451112 &  1.4854244 & -1.0682867 &  1.226876864 &  0.57110827 &  1.08313555 & -0.58203604 & -0.1892634 & -2.2622850\\\\\n",
       "\t  0.83243345 &  1.77410395 & -0.5346737 &  0.8448853 & -1.011580447 &  1.89795420 & -1.89929201 &  0.14893425 & -1.0071622 &  0.4725406\\\\\n",
       "\t  0.73880204 &  0.91375421 &  2.2291059 & -0.6760469 & -0.164168930 &  1.34092847 & -0.99644878 &  0.93351831 & -1.7888597 & -2.8061236\\\\\n",
       "\t  0.36568907 &  0.69166232 & -0.5311620 & -0.7206215 &  0.004583995 &  1.21986552 & -0.07795401 & -1.65265638 & -1.1022655 &  0.5579629\\\\\n",
       "\t -0.35112402 &  0.17030455 &  1.3771536 &  1.4233101 & -0.860648824 & -1.47827855 & -0.39261047 &  0.80221210 &  1.9481512 &  0.2701822\\\\\n",
       "\t  2.13107532 &  0.13718951 &  0.1282606 & -0.6292720 & -0.060331155 & -0.09295629 &  1.01771608 &  0.06682849 &  0.6136679 & -1.0118277\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 10 × 10 of type dbl\n",
       "\n",
       "|  0.23967363 |  0.78926556 | -0.5749426 |  0.1953827 | -0.531577886 |  0.03099485 | -0.59410530 | -0.43778578 |  0.5039455 |  1.1325274 |\n",
       "|  0.24244617 | -0.01057392 | -0.6215322 | -1.8799747 |  0.188147387 |  1.77490447 |  1.91418932 |  0.37029579 | -1.0290394 |  0.1917403 |\n",
       "|  0.04431906 | -0.39661094 |  1.3587527 | -0.6912579 |  0.468513987 | -1.89935798 | -1.19549460 | -0.17479382 |  0.8013274 |  1.4002384 |\n",
       "|  1.39987461 |  1.11477862 | -0.9165962 | -1.3684041 | -1.651083967 |  1.50265696 |  2.35573527 | -0.12125783 | -1.0562328 |  1.3576558 |\n",
       "| -2.24445298 | -0.33451112 |  1.4854244 | -1.0682867 |  1.226876864 |  0.57110827 |  1.08313555 | -0.58203604 | -0.1892634 | -2.2622850 |\n",
       "|  0.83243345 |  1.77410395 | -0.5346737 |  0.8448853 | -1.011580447 |  1.89795420 | -1.89929201 |  0.14893425 | -1.0071622 |  0.4725406 |\n",
       "|  0.73880204 |  0.91375421 |  2.2291059 | -0.6760469 | -0.164168930 |  1.34092847 | -0.99644878 |  0.93351831 | -1.7888597 | -2.8061236 |\n",
       "|  0.36568907 |  0.69166232 | -0.5311620 | -0.7206215 |  0.004583995 |  1.21986552 | -0.07795401 | -1.65265638 | -1.1022655 |  0.5579629 |\n",
       "| -0.35112402 |  0.17030455 |  1.3771536 |  1.4233101 | -0.860648824 | -1.47827855 | -0.39261047 |  0.80221210 |  1.9481512 |  0.2701822 |\n",
       "|  2.13107532 |  0.13718951 |  0.1282606 | -0.6292720 | -0.060331155 | -0.09295629 |  1.01771608 |  0.06682849 |  0.6136679 | -1.0118277 |\n",
       "\n"
      ],
      "text/plain": [
       "      [,1]        [,2]        [,3]       [,4]       [,5]         [,6]       \n",
       " [1,]  0.23967363  0.78926556 -0.5749426  0.1953827 -0.531577886  0.03099485\n",
       " [2,]  0.24244617 -0.01057392 -0.6215322 -1.8799747  0.188147387  1.77490447\n",
       " [3,]  0.04431906 -0.39661094  1.3587527 -0.6912579  0.468513987 -1.89935798\n",
       " [4,]  1.39987461  1.11477862 -0.9165962 -1.3684041 -1.651083967  1.50265696\n",
       " [5,] -2.24445298 -0.33451112  1.4854244 -1.0682867  1.226876864  0.57110827\n",
       " [6,]  0.83243345  1.77410395 -0.5346737  0.8448853 -1.011580447  1.89795420\n",
       " [7,]  0.73880204  0.91375421  2.2291059 -0.6760469 -0.164168930  1.34092847\n",
       " [8,]  0.36568907  0.69166232 -0.5311620 -0.7206215  0.004583995  1.21986552\n",
       " [9,] -0.35112402  0.17030455  1.3771536  1.4233101 -0.860648824 -1.47827855\n",
       "[10,]  2.13107532  0.13718951  0.1282606 -0.6292720 -0.060331155 -0.09295629\n",
       "      [,7]        [,8]        [,9]       [,10]     \n",
       " [1,] -0.59410530 -0.43778578  0.5039455  1.1325274\n",
       " [2,]  1.91418932  0.37029579 -1.0290394  0.1917403\n",
       " [3,] -1.19549460 -0.17479382  0.8013274  1.4002384\n",
       " [4,]  2.35573527 -0.12125783 -1.0562328  1.3576558\n",
       " [5,]  1.08313555 -0.58203604 -0.1892634 -2.2622850\n",
       " [6,] -1.89929201  0.14893425 -1.0071622  0.4725406\n",
       " [7,] -0.99644878  0.93351831 -1.7888597 -2.8061236\n",
       " [8,] -0.07795401 -1.65265638 -1.1022655  0.5579629\n",
       " [9,] -0.39261047  0.80221210  1.9481512  0.2701822\n",
       "[10,]  1.01771608  0.06682849  0.6136679 -1.0118277"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Constructing a matrix\n",
    "x_mat<-matrix(rnorm(100), ncol=10, nrow=10, byrow=TRUE)\n",
    "x_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>-0.943570477940623</li>\n",
       "\t<li>2.04047575748303</li>\n",
       "\t<li>-0.756878427619172</li>\n",
       "\t<li>0.401470338983433</li>\n",
       "\t<li>0.517235039718053</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item -0.943570477940623\n",
       "\\item 2.04047575748303\n",
       "\\item -0.756878427619172\n",
       "\\item 0.401470338983433\n",
       "\\item 0.517235039718053\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. -0.943570477940623\n",
       "2. 2.04047575748303\n",
       "3. -0.756878427619172\n",
       "4. 0.401470338983433\n",
       "5. 0.517235039718053\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] -0.9435705  2.0404758 -0.7568784  0.4014703  0.5172350"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>-0.943570477940623</li>\n",
       "\t<li>0.669873459713092</li>\n",
       "\t<li>2.04047575748303</li>\n",
       "\t<li>0.830455727647029</li>\n",
       "\t<li>-0.756878427619172</li>\n",
       "\t<li>-0.120035465003325</li>\n",
       "\t<li>0.401470338983433</li>\n",
       "\t<li>0.664943465188501</li>\n",
       "\t<li>0.517235039718053</li>\n",
       "\t<li>-1.55554774982928</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item -0.943570477940623\n",
       "\\item 0.669873459713092\n",
       "\\item 2.04047575748303\n",
       "\\item 0.830455727647029\n",
       "\\item -0.756878427619172\n",
       "\\item -0.120035465003325\n",
       "\\item 0.401470338983433\n",
       "\\item 0.664943465188501\n",
       "\\item 0.517235039718053\n",
       "\\item -1.55554774982928\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. -0.943570477940623\n",
       "2. 0.669873459713092\n",
       "3. 2.04047575748303\n",
       "4. 0.830455727647029\n",
       "5. -0.756878427619172\n",
       "6. -0.120035465003325\n",
       "7. 0.401470338983433\n",
       "8. 0.664943465188501\n",
       "9. 0.517235039718053\n",
       "10. -1.55554774982928\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] -0.9435705  0.6698735  2.0404758  0.8304557 -0.7568784 -0.1200355\n",
       " [7]  0.4014703  0.6649435  0.5172350 -1.5555477"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 2 × 2 of type dbl</caption>\n",
       "<tbody>\n",
       "\t<tr><td>0.2396736</td><td> 0.78926556</td></tr>\n",
       "\t<tr><td>0.2424462</td><td>-0.01057392</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type dbl\n",
       "\\begin{tabular}{ll}\n",
       "\t 0.2396736 &  0.78926556\\\\\n",
       "\t 0.2424462 & -0.01057392\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type dbl\n",
       "\n",
       "| 0.2396736 |  0.78926556 |\n",
       "| 0.2424462 | -0.01057392 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1]      [,2]       \n",
       "[1,] 0.2396736  0.78926556\n",
       "[2,] 0.2424462 -0.01057392"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 2 × 2 of type chr</caption>\n",
       "<tbody>\n",
       "\t<tr><td>rich </td><td>rich3</td></tr>\n",
       "\t<tr><td>rich2</td><td>rich4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 2 of type chr\n",
       "\\begin{tabular}{ll}\n",
       "\t rich  & rich3\\\\\n",
       "\t rich2 & rich4\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 2 of type chr\n",
       "\n",
       "| rich  | rich3 |\n",
       "| rich2 | rich4 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1]  [,2] \n",
       "[1,] rich  rich3\n",
       "[2,] rich2 rich4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slicing a vector using indices \n",
    "x[seq(1, 10, by=2)]\n",
    "x[1:10]\n",
    "\n",
    "# Creating a matrix whose elements are \"string\"\n",
    "x_chr_mat<-matrix(c(\"rich\", \"rich2\", \"rich3\", \"rich4\"), ncol=2)\n",
    "# Subseting a matrix using sequence \n",
    "x_mat[1:2, 1:2]\n",
    "x_chr_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 10 × 10 of type dbl</caption>\n",
       "<tbody>\n",
       "\t<tr><td> 0.23967363</td><td> 0.78926556</td><td>-0.5749426</td><td> 0.1953827</td><td>-0.531577886</td><td> 0.03099485</td><td>-0.59410530</td><td>-0.43778578</td><td> 0.5039455</td><td> 1.1325274</td></tr>\n",
       "\t<tr><td> 0.24244617</td><td>-0.01057392</td><td>-0.6215322</td><td>-1.8799747</td><td> 0.188147387</td><td> 1.77490447</td><td> 1.91418932</td><td> 0.37029579</td><td>-1.0290394</td><td> 0.1917403</td></tr>\n",
       "\t<tr><td> 0.04431906</td><td>-0.39661094</td><td> 1.3587527</td><td>-0.6912579</td><td> 0.468513987</td><td>-1.89935798</td><td>-1.19549460</td><td>-0.17479382</td><td> 0.8013274</td><td> 1.4002384</td></tr>\n",
       "\t<tr><td> 1.39987461</td><td> 1.11477862</td><td>-0.9165962</td><td>-1.3684041</td><td>-1.651083967</td><td> 1.50265696</td><td> 2.35573527</td><td>-0.12125783</td><td>-1.0562328</td><td> 1.3576558</td></tr>\n",
       "\t<tr><td>-2.24445298</td><td>-0.33451112</td><td> 1.4854244</td><td>-1.0682867</td><td> 1.226876864</td><td> 0.57110827</td><td> 1.08313555</td><td>-0.58203604</td><td>-0.1892634</td><td>-2.2622850</td></tr>\n",
       "\t<tr><td> 0.83243345</td><td> 1.77410395</td><td>-0.5346737</td><td> 0.8448853</td><td>-1.011580447</td><td> 1.89795420</td><td>-1.89929201</td><td> 0.14893425</td><td>-1.0071622</td><td> 0.4725406</td></tr>\n",
       "\t<tr><td> 0.73880204</td><td> 0.91375421</td><td> 2.2291059</td><td>-0.6760469</td><td>-0.164168930</td><td> 1.34092847</td><td>-0.99644878</td><td> 0.93351831</td><td>-1.7888597</td><td>-2.8061236</td></tr>\n",
       "\t<tr><td> 0.36568907</td><td> 0.69166232</td><td>-0.5311620</td><td>-0.7206215</td><td> 0.004583995</td><td> 1.21986552</td><td>-0.07795401</td><td>-1.65265638</td><td>-1.1022655</td><td> 0.5579629</td></tr>\n",
       "\t<tr><td>-0.35112402</td><td> 0.17030455</td><td> 1.3771536</td><td> 1.4233101</td><td>-0.860648824</td><td>-1.47827855</td><td>-0.39261047</td><td> 0.80221210</td><td> 1.9481512</td><td> 0.2701822</td></tr>\n",
       "\t<tr><td> 2.13107532</td><td> 0.13718951</td><td> 0.1282606</td><td>-0.6292720</td><td>-0.060331155</td><td>-0.09295629</td><td> 1.01771608</td><td> 0.06682849</td><td> 0.6136679</td><td>-1.0118277</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 10 × 10 of type dbl\n",
       "\\begin{tabular}{llllllllll}\n",
       "\t  0.23967363 &  0.78926556 & -0.5749426 &  0.1953827 & -0.531577886 &  0.03099485 & -0.59410530 & -0.43778578 &  0.5039455 &  1.1325274\\\\\n",
       "\t  0.24244617 & -0.01057392 & -0.6215322 & -1.8799747 &  0.188147387 &  1.77490447 &  1.91418932 &  0.37029579 & -1.0290394 &  0.1917403\\\\\n",
       "\t  0.04431906 & -0.39661094 &  1.3587527 & -0.6912579 &  0.468513987 & -1.89935798 & -1.19549460 & -0.17479382 &  0.8013274 &  1.4002384\\\\\n",
       "\t  1.39987461 &  1.11477862 & -0.9165962 & -1.3684041 & -1.651083967 &  1.50265696 &  2.35573527 & -0.12125783 & -1.0562328 &  1.3576558\\\\\n",
       "\t -2.24445298 & -0.33451112 &  1.4854244 & -1.0682867 &  1.226876864 &  0.57110827 &  1.08313555 & -0.58203604 & -0.1892634 & -2.2622850\\\\\n",
       "\t  0.83243345 &  1.77410395 & -0.5346737 &  0.8448853 & -1.011580447 &  1.89795420 & -1.89929201 &  0.14893425 & -1.0071622 &  0.4725406\\\\\n",
       "\t  0.73880204 &  0.91375421 &  2.2291059 & -0.6760469 & -0.164168930 &  1.34092847 & -0.99644878 &  0.93351831 & -1.7888597 & -2.8061236\\\\\n",
       "\t  0.36568907 &  0.69166232 & -0.5311620 & -0.7206215 &  0.004583995 &  1.21986552 & -0.07795401 & -1.65265638 & -1.1022655 &  0.5579629\\\\\n",
       "\t -0.35112402 &  0.17030455 &  1.3771536 &  1.4233101 & -0.860648824 & -1.47827855 & -0.39261047 &  0.80221210 &  1.9481512 &  0.2701822\\\\\n",
       "\t  2.13107532 &  0.13718951 &  0.1282606 & -0.6292720 & -0.060331155 & -0.09295629 &  1.01771608 &  0.06682849 &  0.6136679 & -1.0118277\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 10 × 10 of type dbl\n",
       "\n",
       "|  0.23967363 |  0.78926556 | -0.5749426 |  0.1953827 | -0.531577886 |  0.03099485 | -0.59410530 | -0.43778578 |  0.5039455 |  1.1325274 |\n",
       "|  0.24244617 | -0.01057392 | -0.6215322 | -1.8799747 |  0.188147387 |  1.77490447 |  1.91418932 |  0.37029579 | -1.0290394 |  0.1917403 |\n",
       "|  0.04431906 | -0.39661094 |  1.3587527 | -0.6912579 |  0.468513987 | -1.89935798 | -1.19549460 | -0.17479382 |  0.8013274 |  1.4002384 |\n",
       "|  1.39987461 |  1.11477862 | -0.9165962 | -1.3684041 | -1.651083967 |  1.50265696 |  2.35573527 | -0.12125783 | -1.0562328 |  1.3576558 |\n",
       "| -2.24445298 | -0.33451112 |  1.4854244 | -1.0682867 |  1.226876864 |  0.57110827 |  1.08313555 | -0.58203604 | -0.1892634 | -2.2622850 |\n",
       "|  0.83243345 |  1.77410395 | -0.5346737 |  0.8448853 | -1.011580447 |  1.89795420 | -1.89929201 |  0.14893425 | -1.0071622 |  0.4725406 |\n",
       "|  0.73880204 |  0.91375421 |  2.2291059 | -0.6760469 | -0.164168930 |  1.34092847 | -0.99644878 |  0.93351831 | -1.7888597 | -2.8061236 |\n",
       "|  0.36568907 |  0.69166232 | -0.5311620 | -0.7206215 |  0.004583995 |  1.21986552 | -0.07795401 | -1.65265638 | -1.1022655 |  0.5579629 |\n",
       "| -0.35112402 |  0.17030455 |  1.3771536 |  1.4233101 | -0.860648824 | -1.47827855 | -0.39261047 |  0.80221210 |  1.9481512 |  0.2701822 |\n",
       "|  2.13107532 |  0.13718951 |  0.1282606 | -0.6292720 | -0.060331155 | -0.09295629 |  1.01771608 |  0.06682849 |  0.6136679 | -1.0118277 |\n",
       "\n"
      ],
      "text/plain": [
       "      [,1]        [,2]        [,3]       [,4]       [,5]         [,6]       \n",
       " [1,]  0.23967363  0.78926556 -0.5749426  0.1953827 -0.531577886  0.03099485\n",
       " [2,]  0.24244617 -0.01057392 -0.6215322 -1.8799747  0.188147387  1.77490447\n",
       " [3,]  0.04431906 -0.39661094  1.3587527 -0.6912579  0.468513987 -1.89935798\n",
       " [4,]  1.39987461  1.11477862 -0.9165962 -1.3684041 -1.651083967  1.50265696\n",
       " [5,] -2.24445298 -0.33451112  1.4854244 -1.0682867  1.226876864  0.57110827\n",
       " [6,]  0.83243345  1.77410395 -0.5346737  0.8448853 -1.011580447  1.89795420\n",
       " [7,]  0.73880204  0.91375421  2.2291059 -0.6760469 -0.164168930  1.34092847\n",
       " [8,]  0.36568907  0.69166232 -0.5311620 -0.7206215  0.004583995  1.21986552\n",
       " [9,] -0.35112402  0.17030455  1.3771536  1.4233101 -0.860648824 -1.47827855\n",
       "[10,]  2.13107532  0.13718951  0.1282606 -0.6292720 -0.060331155 -0.09295629\n",
       "      [,7]        [,8]        [,9]       [,10]     \n",
       " [1,] -0.59410530 -0.43778578  0.5039455  1.1325274\n",
       " [2,]  1.91418932  0.37029579 -1.0290394  0.1917403\n",
       " [3,] -1.19549460 -0.17479382  0.8013274  1.4002384\n",
       " [4,]  2.35573527 -0.12125783 -1.0562328  1.3576558\n",
       " [5,]  1.08313555 -0.58203604 -0.1892634 -2.2622850\n",
       " [6,] -1.89929201  0.14893425 -1.0071622  0.4725406\n",
       " [7,] -0.99644878  0.93351831 -1.7888597 -2.8061236\n",
       " [8,] -0.07795401 -1.65265638 -1.1022655  0.5579629\n",
       " [9,] -0.39261047  0.80221210  1.9481512  0.2701822\n",
       "[10,]  1.01771608  0.06682849  0.6136679 -1.0118277"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying our 10 by 10 matrix;  This matrix will be used in the subsequent Bootstrap demonstraing\n",
    "x_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>X1</th><th scope=col>X2</th><th scope=col>X3</th><th scope=col>X4</th><th scope=col>X5</th><th scope=col>X6</th><th scope=col>X7</th><th scope=col>X8</th><th scope=col>X9</th><th scope=col>X10</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;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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 0.23967363</td><td> 0.78926556</td><td>-0.5749426</td><td> 0.1953827</td><td>-0.531577886</td><td> 0.03099485</td><td>-0.59410530</td><td>-0.43778578</td><td> 0.5039455</td><td> 1.1325274</td></tr>\n",
       "\t<tr><td> 0.24244617</td><td>-0.01057392</td><td>-0.6215322</td><td>-1.8799747</td><td> 0.188147387</td><td> 1.77490447</td><td> 1.91418932</td><td> 0.37029579</td><td>-1.0290394</td><td> 0.1917403</td></tr>\n",
       "\t<tr><td> 0.04431906</td><td>-0.39661094</td><td> 1.3587527</td><td>-0.6912579</td><td> 0.468513987</td><td>-1.89935798</td><td>-1.19549460</td><td>-0.17479382</td><td> 0.8013274</td><td> 1.4002384</td></tr>\n",
       "\t<tr><td> 1.39987461</td><td> 1.11477862</td><td>-0.9165962</td><td>-1.3684041</td><td>-1.651083967</td><td> 1.50265696</td><td> 2.35573527</td><td>-0.12125783</td><td>-1.0562328</td><td> 1.3576558</td></tr>\n",
       "\t<tr><td>-2.24445298</td><td>-0.33451112</td><td> 1.4854244</td><td>-1.0682867</td><td> 1.226876864</td><td> 0.57110827</td><td> 1.08313555</td><td>-0.58203604</td><td>-0.1892634</td><td>-2.2622850</td></tr>\n",
       "\t<tr><td> 0.83243345</td><td> 1.77410395</td><td>-0.5346737</td><td> 0.8448853</td><td>-1.011580447</td><td> 1.89795420</td><td>-1.89929201</td><td> 0.14893425</td><td>-1.0071622</td><td> 0.4725406</td></tr>\n",
       "\t<tr><td> 0.73880204</td><td> 0.91375421</td><td> 2.2291059</td><td>-0.6760469</td><td>-0.164168930</td><td> 1.34092847</td><td>-0.99644878</td><td> 0.93351831</td><td>-1.7888597</td><td>-2.8061236</td></tr>\n",
       "\t<tr><td> 0.36568907</td><td> 0.69166232</td><td>-0.5311620</td><td>-0.7206215</td><td> 0.004583995</td><td> 1.21986552</td><td>-0.07795401</td><td>-1.65265638</td><td>-1.1022655</td><td> 0.5579629</td></tr>\n",
       "\t<tr><td>-0.35112402</td><td> 0.17030455</td><td> 1.3771536</td><td> 1.4233101</td><td>-0.860648824</td><td>-1.47827855</td><td>-0.39261047</td><td> 0.80221210</td><td> 1.9481512</td><td> 0.2701822</td></tr>\n",
       "\t<tr><td> 2.13107532</td><td> 0.13718951</td><td> 0.1282606</td><td>-0.6292720</td><td>-0.060331155</td><td>-0.09295629</td><td> 1.01771608</td><td> 0.06682849</td><td> 0.6136679</td><td>-1.0118277</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " X1 & X2 & X3 & X4 & X5 & X6 & X7 & X8 & X9 & X10\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t  0.23967363 &  0.78926556 & -0.5749426 &  0.1953827 & -0.531577886 &  0.03099485 & -0.59410530 & -0.43778578 &  0.5039455 &  1.1325274\\\\\n",
       "\t  0.24244617 & -0.01057392 & -0.6215322 & -1.8799747 &  0.188147387 &  1.77490447 &  1.91418932 &  0.37029579 & -1.0290394 &  0.1917403\\\\\n",
       "\t  0.04431906 & -0.39661094 &  1.3587527 & -0.6912579 &  0.468513987 & -1.89935798 & -1.19549460 & -0.17479382 &  0.8013274 &  1.4002384\\\\\n",
       "\t  1.39987461 &  1.11477862 & -0.9165962 & -1.3684041 & -1.651083967 &  1.50265696 &  2.35573527 & -0.12125783 & -1.0562328 &  1.3576558\\\\\n",
       "\t -2.24445298 & -0.33451112 &  1.4854244 & -1.0682867 &  1.226876864 &  0.57110827 &  1.08313555 & -0.58203604 & -0.1892634 & -2.2622850\\\\\n",
       "\t  0.83243345 &  1.77410395 & -0.5346737 &  0.8448853 & -1.011580447 &  1.89795420 & -1.89929201 &  0.14893425 & -1.0071622 &  0.4725406\\\\\n",
       "\t  0.73880204 &  0.91375421 &  2.2291059 & -0.6760469 & -0.164168930 &  1.34092847 & -0.99644878 &  0.93351831 & -1.7888597 & -2.8061236\\\\\n",
       "\t  0.36568907 &  0.69166232 & -0.5311620 & -0.7206215 &  0.004583995 &  1.21986552 & -0.07795401 & -1.65265638 & -1.1022655 &  0.5579629\\\\\n",
       "\t -0.35112402 &  0.17030455 &  1.3771536 &  1.4233101 & -0.860648824 & -1.47827855 & -0.39261047 &  0.80221210 &  1.9481512 &  0.2701822\\\\\n",
       "\t  2.13107532 &  0.13718951 &  0.1282606 & -0.6292720 & -0.060331155 & -0.09295629 &  1.01771608 &  0.06682849 &  0.6136679 & -1.0118277\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| X1 &lt;dbl&gt; | X2 &lt;dbl&gt; | X3 &lt;dbl&gt; | X4 &lt;dbl&gt; | X5 &lt;dbl&gt; | X6 &lt;dbl&gt; | X7 &lt;dbl&gt; | X8 &lt;dbl&gt; | X9 &lt;dbl&gt; | X10 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "|  0.23967363 |  0.78926556 | -0.5749426 |  0.1953827 | -0.531577886 |  0.03099485 | -0.59410530 | -0.43778578 |  0.5039455 |  1.1325274 |\n",
       "|  0.24244617 | -0.01057392 | -0.6215322 | -1.8799747 |  0.188147387 |  1.77490447 |  1.91418932 |  0.37029579 | -1.0290394 |  0.1917403 |\n",
       "|  0.04431906 | -0.39661094 |  1.3587527 | -0.6912579 |  0.468513987 | -1.89935798 | -1.19549460 | -0.17479382 |  0.8013274 |  1.4002384 |\n",
       "|  1.39987461 |  1.11477862 | -0.9165962 | -1.3684041 | -1.651083967 |  1.50265696 |  2.35573527 | -0.12125783 | -1.0562328 |  1.3576558 |\n",
       "| -2.24445298 | -0.33451112 |  1.4854244 | -1.0682867 |  1.226876864 |  0.57110827 |  1.08313555 | -0.58203604 | -0.1892634 | -2.2622850 |\n",
       "|  0.83243345 |  1.77410395 | -0.5346737 |  0.8448853 | -1.011580447 |  1.89795420 | -1.89929201 |  0.14893425 | -1.0071622 |  0.4725406 |\n",
       "|  0.73880204 |  0.91375421 |  2.2291059 | -0.6760469 | -0.164168930 |  1.34092847 | -0.99644878 |  0.93351831 | -1.7888597 | -2.8061236 |\n",
       "|  0.36568907 |  0.69166232 | -0.5311620 | -0.7206215 |  0.004583995 |  1.21986552 | -0.07795401 | -1.65265638 | -1.1022655 |  0.5579629 |\n",
       "| -0.35112402 |  0.17030455 |  1.3771536 |  1.4233101 | -0.860648824 | -1.47827855 | -0.39261047 |  0.80221210 |  1.9481512 |  0.2701822 |\n",
       "|  2.13107532 |  0.13718951 |  0.1282606 | -0.6292720 | -0.060331155 | -0.09295629 |  1.01771608 |  0.06682849 |  0.6136679 | -1.0118277 |\n",
       "\n"
      ],
      "text/plain": [
       "   X1          X2          X3         X4         X5           X6         \n",
       "1   0.23967363  0.78926556 -0.5749426  0.1953827 -0.531577886  0.03099485\n",
       "2   0.24244617 -0.01057392 -0.6215322 -1.8799747  0.188147387  1.77490447\n",
       "3   0.04431906 -0.39661094  1.3587527 -0.6912579  0.468513987 -1.89935798\n",
       "4   1.39987461  1.11477862 -0.9165962 -1.3684041 -1.651083967  1.50265696\n",
       "5  -2.24445298 -0.33451112  1.4854244 -1.0682867  1.226876864  0.57110827\n",
       "6   0.83243345  1.77410395 -0.5346737  0.8448853 -1.011580447  1.89795420\n",
       "7   0.73880204  0.91375421  2.2291059 -0.6760469 -0.164168930  1.34092847\n",
       "8   0.36568907  0.69166232 -0.5311620 -0.7206215  0.004583995  1.21986552\n",
       "9  -0.35112402  0.17030455  1.3771536  1.4233101 -0.860648824 -1.47827855\n",
       "10  2.13107532  0.13718951  0.1282606 -0.6292720 -0.060331155 -0.09295629\n",
       "   X7          X8          X9         X10       \n",
       "1  -0.59410530 -0.43778578  0.5039455  1.1325274\n",
       "2   1.91418932  0.37029579 -1.0290394  0.1917403\n",
       "3  -1.19549460 -0.17479382  0.8013274  1.4002384\n",
       "4   2.35573527 -0.12125783 -1.0562328  1.3576558\n",
       "5   1.08313555 -0.58203604 -0.1892634 -2.2622850\n",
       "6  -1.89929201  0.14893425 -1.0071622  0.4725406\n",
       "7  -0.99644878  0.93351831 -1.7888597 -2.8061236\n",
       "8  -0.07795401 -1.65265638 -1.1022655  0.5579629\n",
       "9  -0.39261047  0.80221210  1.9481512  0.2701822\n",
       "10  1.01771608  0.06682849  0.6136679 -1.0118277"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 2 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X1</th><th scope=col>X2</th><th scope=col>X3</th><th scope=col>X4</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></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0.2396736</td><td> 0.78926556</td><td>-0.5749426</td><td> 0.1953827</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0.2424462</td><td>-0.01057392</td><td>-0.6215322</td><td>-1.8799747</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & X1 & X2 & X3 & X4\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 0.2396736 &  0.78926556 & -0.5749426 &  0.1953827\\\\\n",
       "\t2 & 0.2424462 & -0.01057392 & -0.6215322 & -1.8799747\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 4\n",
       "\n",
       "| <!--/--> | X1 &lt;dbl&gt; | X2 &lt;dbl&gt; | X3 &lt;dbl&gt; | X4 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 0.2396736 |  0.78926556 | -0.5749426 |  0.1953827 |\n",
       "| 2 | 0.2424462 | -0.01057392 | -0.6215322 | -1.8799747 |\n",
       "\n"
      ],
      "text/plain": [
       "  X1        X2          X3         X4        \n",
       "1 0.2396736  0.78926556 -0.5749426  0.1953827\n",
       "2 0.2424462 -0.01057392 -0.6215322 -1.8799747"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert a matrix to a dataframe\n",
    "x_df<-data.frame(x_mat)\n",
    "x_df\n",
    "x_df[1:2, 1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.239673633518077</li>\n",
       "\t<li>0.242446171767508</li>\n",
       "\t<li>0.0443190600913792</li>\n",
       "\t<li>1.39987460761377</li>\n",
       "\t<li>-2.24445297835443</li>\n",
       "\t<li>0.832433452275432</li>\n",
       "\t<li>0.738802039893802</li>\n",
       "\t<li>0.365689068131471</li>\n",
       "\t<li>-0.351124017441015</li>\n",
       "\t<li>2.13107532097924</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.239673633518077\n",
       "\\item 0.242446171767508\n",
       "\\item 0.0443190600913792\n",
       "\\item 1.39987460761377\n",
       "\\item -2.24445297835443\n",
       "\\item 0.832433452275432\n",
       "\\item 0.738802039893802\n",
       "\\item 0.365689068131471\n",
       "\\item -0.351124017441015\n",
       "\\item 2.13107532097924\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.239673633518077\n",
       "2. 0.242446171767508\n",
       "3. 0.0443190600913792\n",
       "4. 1.39987460761377\n",
       "5. -2.24445297835443\n",
       "6. 0.832433452275432\n",
       "7. 0.738802039893802\n",
       "8. 0.365689068131471\n",
       "9. -0.351124017441015\n",
       "10. 2.13107532097924\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  0.23967363  0.24244617  0.04431906  1.39987461 -2.24445298  0.83243345\n",
       " [7]  0.73880204  0.36568907 -0.35112402  2.13107532"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.789265556777641</li>\n",
       "\t<li>-0.0105739197662762</li>\n",
       "\t<li>-0.396610944253193</li>\n",
       "\t<li>1.11477862393831</li>\n",
       "\t<li>-0.334511116325842</li>\n",
       "\t<li>1.77410395233319</li>\n",
       "\t<li>0.913754209672284</li>\n",
       "\t<li>0.691662318813852</li>\n",
       "\t<li>0.170304547803654</li>\n",
       "\t<li>0.137189509755674</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.789265556777641\n",
       "\\item -0.0105739197662762\n",
       "\\item -0.396610944253193\n",
       "\\item 1.11477862393831\n",
       "\\item -0.334511116325842\n",
       "\\item 1.77410395233319\n",
       "\\item 0.913754209672284\n",
       "\\item 0.691662318813852\n",
       "\\item 0.170304547803654\n",
       "\\item 0.137189509755674\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.789265556777641\n",
       "2. -0.0105739197662762\n",
       "3. -0.396610944253193\n",
       "4. 1.11477862393831\n",
       "5. -0.334511116325842\n",
       "6. 1.77410395233319\n",
       "7. 0.913754209672284\n",
       "8. 0.691662318813852\n",
       "9. 0.170304547803654\n",
       "10. 0.137189509755674\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  0.78926556 -0.01057392 -0.39661094  1.11477862 -0.33451112  1.77410395\n",
       " [7]  0.91375421  0.69166232  0.17030455  0.13718951"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.239673633518077</li>\n",
       "\t<li>0.242446171767508</li>\n",
       "\t<li>0.0443190600913792</li>\n",
       "\t<li>1.39987460761377</li>\n",
       "\t<li>-2.24445297835443</li>\n",
       "\t<li>0.832433452275432</li>\n",
       "\t<li>0.738802039893802</li>\n",
       "\t<li>0.365689068131471</li>\n",
       "\t<li>-0.351124017441015</li>\n",
       "\t<li>2.13107532097924</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.239673633518077\n",
       "\\item 0.242446171767508\n",
       "\\item 0.0443190600913792\n",
       "\\item 1.39987460761377\n",
       "\\item -2.24445297835443\n",
       "\\item 0.832433452275432\n",
       "\\item 0.738802039893802\n",
       "\\item 0.365689068131471\n",
       "\\item -0.351124017441015\n",
       "\\item 2.13107532097924\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.239673633518077\n",
       "2. 0.242446171767508\n",
       "3. 0.0443190600913792\n",
       "4. 1.39987460761377\n",
       "5. -2.24445297835443\n",
       "6. 0.832433452275432\n",
       "7. 0.738802039893802\n",
       "8. 0.365689068131471\n",
       "9. -0.351124017441015\n",
       "10. 2.13107532097924\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  0.23967363  0.24244617  0.04431906  1.39987461 -2.24445298  0.83243345\n",
       " [7]  0.73880204  0.36568907 -0.35112402  2.13107532"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.789265556777641</li>\n",
       "\t<li>-0.0105739197662762</li>\n",
       "\t<li>-0.396610944253193</li>\n",
       "\t<li>1.11477862393831</li>\n",
       "\t<li>-0.334511116325842</li>\n",
       "\t<li>1.77410395233319</li>\n",
       "\t<li>0.913754209672284</li>\n",
       "\t<li>0.691662318813852</li>\n",
       "\t<li>0.170304547803654</li>\n",
       "\t<li>0.137189509755674</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.789265556777641\n",
       "\\item -0.0105739197662762\n",
       "\\item -0.396610944253193\n",
       "\\item 1.11477862393831\n",
       "\\item -0.334511116325842\n",
       "\\item 1.77410395233319\n",
       "\\item 0.913754209672284\n",
       "\\item 0.691662318813852\n",
       "\\item 0.170304547803654\n",
       "\\item 0.137189509755674\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.789265556777641\n",
       "2. -0.0105739197662762\n",
       "3. -0.396610944253193\n",
       "4. 1.11477862393831\n",
       "5. -0.334511116325842\n",
       "6. 1.77410395233319\n",
       "7. 0.913754209672284\n",
       "8. 0.691662318813852\n",
       "9. 0.170304547803654\n",
       "10. 0.137189509755674\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  0.78926556 -0.01057392 -0.39661094  1.11477862 -0.33451112  1.77410395\n",
       " [7]  0.91375421  0.69166232  0.17030455  0.13718951"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'a'</li>\n",
       "\t<li>'b'</li>\n",
       "\t<li>'c'</li>\n",
       "\t<li>'d'</li>\n",
       "\t<li>'e'</li>\n",
       "\t<li>'f'</li>\n",
       "\t<li>'g'</li>\n",
       "\t<li>'h'</li>\n",
       "\t<li>'i'</li>\n",
       "\t<li>'j'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'a'\n",
       "\\item 'b'\n",
       "\\item 'c'\n",
       "\\item 'd'\n",
       "\\item 'e'\n",
       "\\item 'f'\n",
       "\\item 'g'\n",
       "\\item 'h'\n",
       "\\item 'i'\n",
       "\\item 'j'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'a'\n",
       "2. 'b'\n",
       "3. 'c'\n",
       "4. 'd'\n",
       "5. 'e'\n",
       "6. 'f'\n",
       "7. 'g'\n",
       "8. 'h'\n",
       "9. 'i'\n",
       "10. 'j'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"a\" \"b\" \"c\" \"d\" \"e\" \"f\" \"g\" \"h\" \"i\" \"j\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'var1'</li>\n",
       "\t<li>'var2'</li>\n",
       "\t<li>'var3'</li>\n",
       "\t<li>'var4'</li>\n",
       "\t<li>'var5'</li>\n",
       "\t<li>'var6'</li>\n",
       "\t<li>'var7'</li>\n",
       "\t<li>'var8'</li>\n",
       "\t<li>'var9'</li>\n",
       "\t<li>'var10'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'var1'\n",
       "\\item 'var2'\n",
       "\\item 'var3'\n",
       "\\item 'var4'\n",
       "\\item 'var5'\n",
       "\\item 'var6'\n",
       "\\item 'var7'\n",
       "\\item 'var8'\n",
       "\\item 'var9'\n",
       "\\item 'var10'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'var1'\n",
       "2. 'var2'\n",
       "3. 'var3'\n",
       "4. 'var4'\n",
       "5. 'var5'\n",
       "6. 'var6'\n",
       "7. 'var7'\n",
       "8. 'var8'\n",
       "9. 'var9'\n",
       "10. 'var10'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"var1\"  \"var2\"  \"var3\"  \"var4\"  \"var5\"  \"var6\"  \"var7\"  \"var8\"  \"var9\" \n",
       "[10] \"var10\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slicing columns of a dataframe\n",
    "x_df[, 1]\n",
    "x_df[, 2]\n",
    "# Using the \"$\" operator to extract a vectr (a column from the dataframe)\n",
    "x_df$X1\n",
    "x_df$X2\n",
    "# Using rownames and colnames atributes of a dataframe to reassign the row names\n",
    "# and column names of the dataframe\n",
    "rownames(x_df)<-c(\"a\", \"b\", \"c\", \"d\", \"e\", \"f\",\"g\", \"h\",\"i\",\"j\")\n",
    "rownames(x_df)\n",
    "colnames(x_df)<-c(\"var1\", \"var2\", \"var3\", \"var4\", \"var5\", \"var6\", \"var7\", \"var8\", \"var9\", \"var10\")\n",
    "colnames(x_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>TRUE</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item FALSE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item FALSE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. FALSE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. FALSE\n",
       "10. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_df$var1>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 8 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>var1</th><th scope=col>var2</th><th scope=col>var3</th><th scope=col>var4</th><th scope=col>var5</th><th scope=col>var6</th><th scope=col>var7</th><th scope=col>var8</th><th scope=col>var9</th><th scope=col>var10</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;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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>0.23967363</td><td> 0.78926556</td><td>-0.5749426</td><td> 0.1953827</td><td>-0.531577886</td><td> 0.03099485</td><td>-0.59410530</td><td>-0.43778578</td><td> 0.5039455</td><td> 1.1325274</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>0.24244617</td><td>-0.01057392</td><td>-0.6215322</td><td>-1.8799747</td><td> 0.188147387</td><td> 1.77490447</td><td> 1.91418932</td><td> 0.37029579</td><td>-1.0290394</td><td> 0.1917403</td></tr>\n",
       "\t<tr><th scope=row>c</th><td>0.04431906</td><td>-0.39661094</td><td> 1.3587527</td><td>-0.6912579</td><td> 0.468513987</td><td>-1.89935798</td><td>-1.19549460</td><td>-0.17479382</td><td> 0.8013274</td><td> 1.4002384</td></tr>\n",
       "\t<tr><th scope=row>d</th><td>1.39987461</td><td> 1.11477862</td><td>-0.9165962</td><td>-1.3684041</td><td>-1.651083967</td><td> 1.50265696</td><td> 2.35573527</td><td>-0.12125783</td><td>-1.0562328</td><td> 1.3576558</td></tr>\n",
       "\t<tr><th scope=row>f</th><td>0.83243345</td><td> 1.77410395</td><td>-0.5346737</td><td> 0.8448853</td><td>-1.011580447</td><td> 1.89795420</td><td>-1.89929201</td><td> 0.14893425</td><td>-1.0071622</td><td> 0.4725406</td></tr>\n",
       "\t<tr><th scope=row>g</th><td>0.73880204</td><td> 0.91375421</td><td> 2.2291059</td><td>-0.6760469</td><td>-0.164168930</td><td> 1.34092847</td><td>-0.99644878</td><td> 0.93351831</td><td>-1.7888597</td><td>-2.8061236</td></tr>\n",
       "\t<tr><th scope=row>h</th><td>0.36568907</td><td> 0.69166232</td><td>-0.5311620</td><td>-0.7206215</td><td> 0.004583995</td><td> 1.21986552</td><td>-0.07795401</td><td>-1.65265638</td><td>-1.1022655</td><td> 0.5579629</td></tr>\n",
       "\t<tr><th scope=row>j</th><td>2.13107532</td><td> 0.13718951</td><td> 0.1282606</td><td>-0.6292720</td><td>-0.060331155</td><td>-0.09295629</td><td> 1.01771608</td><td> 0.06682849</td><td> 0.6136679</td><td>-1.0118277</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 8 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & var1 & var2 & var3 & var4 & var5 & var6 & var7 & var8 & var9 & var10\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\ta & 0.23967363 &  0.78926556 & -0.5749426 &  0.1953827 & -0.531577886 &  0.03099485 & -0.59410530 & -0.43778578 &  0.5039455 &  1.1325274\\\\\n",
       "\tb & 0.24244617 & -0.01057392 & -0.6215322 & -1.8799747 &  0.188147387 &  1.77490447 &  1.91418932 &  0.37029579 & -1.0290394 &  0.1917403\\\\\n",
       "\tc & 0.04431906 & -0.39661094 &  1.3587527 & -0.6912579 &  0.468513987 & -1.89935798 & -1.19549460 & -0.17479382 &  0.8013274 &  1.4002384\\\\\n",
       "\td & 1.39987461 &  1.11477862 & -0.9165962 & -1.3684041 & -1.651083967 &  1.50265696 &  2.35573527 & -0.12125783 & -1.0562328 &  1.3576558\\\\\n",
       "\tf & 0.83243345 &  1.77410395 & -0.5346737 &  0.8448853 & -1.011580447 &  1.89795420 & -1.89929201 &  0.14893425 & -1.0071622 &  0.4725406\\\\\n",
       "\tg & 0.73880204 &  0.91375421 &  2.2291059 & -0.6760469 & -0.164168930 &  1.34092847 & -0.99644878 &  0.93351831 & -1.7888597 & -2.8061236\\\\\n",
       "\th & 0.36568907 &  0.69166232 & -0.5311620 & -0.7206215 &  0.004583995 &  1.21986552 & -0.07795401 & -1.65265638 & -1.1022655 &  0.5579629\\\\\n",
       "\tj & 2.13107532 &  0.13718951 &  0.1282606 & -0.6292720 & -0.060331155 & -0.09295629 &  1.01771608 &  0.06682849 &  0.6136679 & -1.0118277\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 8 × 10\n",
       "\n",
       "| <!--/--> | var1 &lt;dbl&gt; | var2 &lt;dbl&gt; | var3 &lt;dbl&gt; | var4 &lt;dbl&gt; | var5 &lt;dbl&gt; | var6 &lt;dbl&gt; | var7 &lt;dbl&gt; | var8 &lt;dbl&gt; | var9 &lt;dbl&gt; | var10 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| a | 0.23967363 |  0.78926556 | -0.5749426 |  0.1953827 | -0.531577886 |  0.03099485 | -0.59410530 | -0.43778578 |  0.5039455 |  1.1325274 |\n",
       "| b | 0.24244617 | -0.01057392 | -0.6215322 | -1.8799747 |  0.188147387 |  1.77490447 |  1.91418932 |  0.37029579 | -1.0290394 |  0.1917403 |\n",
       "| c | 0.04431906 | -0.39661094 |  1.3587527 | -0.6912579 |  0.468513987 | -1.89935798 | -1.19549460 | -0.17479382 |  0.8013274 |  1.4002384 |\n",
       "| d | 1.39987461 |  1.11477862 | -0.9165962 | -1.3684041 | -1.651083967 |  1.50265696 |  2.35573527 | -0.12125783 | -1.0562328 |  1.3576558 |\n",
       "| f | 0.83243345 |  1.77410395 | -0.5346737 |  0.8448853 | -1.011580447 |  1.89795420 | -1.89929201 |  0.14893425 | -1.0071622 |  0.4725406 |\n",
       "| g | 0.73880204 |  0.91375421 |  2.2291059 | -0.6760469 | -0.164168930 |  1.34092847 | -0.99644878 |  0.93351831 | -1.7888597 | -2.8061236 |\n",
       "| h | 0.36568907 |  0.69166232 | -0.5311620 | -0.7206215 |  0.004583995 |  1.21986552 | -0.07795401 | -1.65265638 | -1.1022655 |  0.5579629 |\n",
       "| j | 2.13107532 |  0.13718951 |  0.1282606 | -0.6292720 | -0.060331155 | -0.09295629 |  1.01771608 |  0.06682849 |  0.6136679 | -1.0118277 |\n",
       "\n"
      ],
      "text/plain": [
       "  var1       var2        var3       var4       var5         var6       \n",
       "a 0.23967363  0.78926556 -0.5749426  0.1953827 -0.531577886  0.03099485\n",
       "b 0.24244617 -0.01057392 -0.6215322 -1.8799747  0.188147387  1.77490447\n",
       "c 0.04431906 -0.39661094  1.3587527 -0.6912579  0.468513987 -1.89935798\n",
       "d 1.39987461  1.11477862 -0.9165962 -1.3684041 -1.651083967  1.50265696\n",
       "f 0.83243345  1.77410395 -0.5346737  0.8448853 -1.011580447  1.89795420\n",
       "g 0.73880204  0.91375421  2.2291059 -0.6760469 -0.164168930  1.34092847\n",
       "h 0.36568907  0.69166232 -0.5311620 -0.7206215  0.004583995  1.21986552\n",
       "j 2.13107532  0.13718951  0.1282606 -0.6292720 -0.060331155 -0.09295629\n",
       "  var7        var8        var9       var10     \n",
       "a -0.59410530 -0.43778578  0.5039455  1.1325274\n",
       "b  1.91418932  0.37029579 -1.0290394  0.1917403\n",
       "c -1.19549460 -0.17479382  0.8013274  1.4002384\n",
       "d  2.35573527 -0.12125783 -1.0562328  1.3576558\n",
       "f -1.89929201  0.14893425 -1.0071622  0.4725406\n",
       "g -0.99644878  0.93351831 -1.7888597 -2.8061236\n",
       "h -0.07795401 -1.65265638 -1.1022655  0.5579629\n",
       "j  1.01771608  0.06682849  0.6136679 -1.0118277"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 2 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>var1</th><th scope=col>var2</th><th scope=col>var3</th><th scope=col>var4</th><th scope=col>var5</th><th scope=col>var6</th><th scope=col>var7</th><th scope=col>var8</th><th scope=col>var9</th><th scope=col>var10</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;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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>e</th><td>-2.244453</td><td>-0.3345111</td><td>1.485424</td><td>-1.068287</td><td> 1.2268769</td><td> 0.5711083</td><td> 1.0831356</td><td>-0.5820360</td><td>-0.1892634</td><td>-2.2622850</td></tr>\n",
       "\t<tr><th scope=row>i</th><td>-0.351124</td><td> 0.1703045</td><td>1.377154</td><td> 1.423310</td><td>-0.8606488</td><td>-1.4782786</td><td>-0.3926105</td><td> 0.8022121</td><td> 1.9481512</td><td> 0.2701822</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & var1 & var2 & var3 & var4 & var5 & var6 & var7 & var8 & var9 & var10\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\te & -2.244453 & -0.3345111 & 1.485424 & -1.068287 &  1.2268769 &  0.5711083 &  1.0831356 & -0.5820360 & -0.1892634 & -2.2622850\\\\\n",
       "\ti & -0.351124 &  0.1703045 & 1.377154 &  1.423310 & -0.8606488 & -1.4782786 & -0.3926105 &  0.8022121 &  1.9481512 &  0.2701822\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 10\n",
       "\n",
       "| <!--/--> | var1 &lt;dbl&gt; | var2 &lt;dbl&gt; | var3 &lt;dbl&gt; | var4 &lt;dbl&gt; | var5 &lt;dbl&gt; | var6 &lt;dbl&gt; | var7 &lt;dbl&gt; | var8 &lt;dbl&gt; | var9 &lt;dbl&gt; | var10 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| e | -2.244453 | -0.3345111 | 1.485424 | -1.068287 |  1.2268769 |  0.5711083 |  1.0831356 | -0.5820360 | -0.1892634 | -2.2622850 |\n",
       "| i | -0.351124 |  0.1703045 | 1.377154 |  1.423310 | -0.8606488 | -1.4782786 | -0.3926105 |  0.8022121 |  1.9481512 |  0.2701822 |\n",
       "\n"
      ],
      "text/plain": [
       "  var1      var2       var3     var4      var5       var6       var7      \n",
       "e -2.244453 -0.3345111 1.485424 -1.068287  1.2268769  0.5711083  1.0831356\n",
       "i -0.351124  0.1703045 1.377154  1.423310 -0.8606488 -1.4782786 -0.3926105\n",
       "  var8       var9       var10     \n",
       "e -0.5820360 -0.1892634 -2.2622850\n",
       "i  0.8022121  1.9481512  0.2701822"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using boolean filters to extract rows from a dataframe\n",
    "#  <, >, ==, ! (negate boolean), && (ampersand is AND), || (two-pipes is OR)  \n",
    "\n",
    "# rows with postive values on \"var1\"\n",
    "x_df[x_df$var1>0, ]\n",
    "# rows with negative values on \"var1\" - demonstating the \"negation\" operator\n",
    "x_df[!x_df$var1>0, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>10</li>\n",
       "\t<li>10</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 10\n",
       "\\item 10\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 10\n",
       "2. 10\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 10 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the dimensions of a dataframe\n",
    "dim(x_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>var1</th><th scope=col>var2</th><th scope=col>var3</th><th scope=col>var4</th><th scope=col>var5</th><th scope=col>var6</th><th scope=col>var7</th><th scope=col>var8</th><th scope=col>var9</th><th scope=col>var10</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;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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>i</th><td>-0.3511240</td><td> 0.17030455</td><td> 1.3771536</td><td> 1.4233101</td><td>-0.860648824</td><td>-1.47827855</td><td>-0.39261047</td><td> 0.80221210</td><td> 1.9481512</td><td> 0.2701822</td></tr>\n",
       "\t<tr><th scope=row>e</th><td>-2.2444530</td><td>-0.33451112</td><td> 1.4854244</td><td>-1.0682867</td><td> 1.226876864</td><td> 0.57110827</td><td> 1.08313555</td><td>-0.58203604</td><td>-0.1892634</td><td>-2.2622850</td></tr>\n",
       "\t<tr><th scope=row>j</th><td> 2.1310753</td><td> 0.13718951</td><td> 0.1282606</td><td>-0.6292720</td><td>-0.060331155</td><td>-0.09295629</td><td> 1.01771608</td><td> 0.06682849</td><td> 0.6136679</td><td>-1.0118277</td></tr>\n",
       "\t<tr><th scope=row>h</th><td> 0.3656891</td><td> 0.69166232</td><td>-0.5311620</td><td>-0.7206215</td><td> 0.004583995</td><td> 1.21986552</td><td>-0.07795401</td><td>-1.65265638</td><td>-1.1022655</td><td> 0.5579629</td></tr>\n",
       "\t<tr><th scope=row>f</th><td> 0.8324335</td><td> 1.77410395</td><td>-0.5346737</td><td> 0.8448853</td><td>-1.011580447</td><td> 1.89795420</td><td>-1.89929201</td><td> 0.14893425</td><td>-1.0071622</td><td> 0.4725406</td></tr>\n",
       "\t<tr><th scope=row>b</th><td> 0.2424462</td><td>-0.01057392</td><td>-0.6215322</td><td>-1.8799747</td><td> 0.188147387</td><td> 1.77490447</td><td> 1.91418932</td><td> 0.37029579</td><td>-1.0290394</td><td> 0.1917403</td></tr>\n",
       "\t<tr><th scope=row>f.1</th><td> 0.8324335</td><td> 1.77410395</td><td>-0.5346737</td><td> 0.8448853</td><td>-1.011580447</td><td> 1.89795420</td><td>-1.89929201</td><td> 0.14893425</td><td>-1.0071622</td><td> 0.4725406</td></tr>\n",
       "\t<tr><th scope=row>d</th><td> 1.3998746</td><td> 1.11477862</td><td>-0.9165962</td><td>-1.3684041</td><td>-1.651083967</td><td> 1.50265696</td><td> 2.35573527</td><td>-0.12125783</td><td>-1.0562328</td><td> 1.3576558</td></tr>\n",
       "\t<tr><th scope=row>e.1</th><td>-2.2444530</td><td>-0.33451112</td><td> 1.4854244</td><td>-1.0682867</td><td> 1.226876864</td><td> 0.57110827</td><td> 1.08313555</td><td>-0.58203604</td><td>-0.1892634</td><td>-2.2622850</td></tr>\n",
       "\t<tr><th scope=row>d.1</th><td> 1.3998746</td><td> 1.11477862</td><td>-0.9165962</td><td>-1.3684041</td><td>-1.651083967</td><td> 1.50265696</td><td> 2.35573527</td><td>-0.12125783</td><td>-1.0562328</td><td> 1.3576558</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       "  & var1 & var2 & var3 & var4 & var5 & var6 & var7 & var8 & var9 & var10\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\ti & -0.3511240 &  0.17030455 &  1.3771536 &  1.4233101 & -0.860648824 & -1.47827855 & -0.39261047 &  0.80221210 &  1.9481512 &  0.2701822\\\\\n",
       "\te & -2.2444530 & -0.33451112 &  1.4854244 & -1.0682867 &  1.226876864 &  0.57110827 &  1.08313555 & -0.58203604 & -0.1892634 & -2.2622850\\\\\n",
       "\tj &  2.1310753 &  0.13718951 &  0.1282606 & -0.6292720 & -0.060331155 & -0.09295629 &  1.01771608 &  0.06682849 &  0.6136679 & -1.0118277\\\\\n",
       "\th &  0.3656891 &  0.69166232 & -0.5311620 & -0.7206215 &  0.004583995 &  1.21986552 & -0.07795401 & -1.65265638 & -1.1022655 &  0.5579629\\\\\n",
       "\tf &  0.8324335 &  1.77410395 & -0.5346737 &  0.8448853 & -1.011580447 &  1.89795420 & -1.89929201 &  0.14893425 & -1.0071622 &  0.4725406\\\\\n",
       "\tb &  0.2424462 & -0.01057392 & -0.6215322 & -1.8799747 &  0.188147387 &  1.77490447 &  1.91418932 &  0.37029579 & -1.0290394 &  0.1917403\\\\\n",
       "\tf.1 &  0.8324335 &  1.77410395 & -0.5346737 &  0.8448853 & -1.011580447 &  1.89795420 & -1.89929201 &  0.14893425 & -1.0071622 &  0.4725406\\\\\n",
       "\td &  1.3998746 &  1.11477862 & -0.9165962 & -1.3684041 & -1.651083967 &  1.50265696 &  2.35573527 & -0.12125783 & -1.0562328 &  1.3576558\\\\\n",
       "\te.1 & -2.2444530 & -0.33451112 &  1.4854244 & -1.0682867 &  1.226876864 &  0.57110827 &  1.08313555 & -0.58203604 & -0.1892634 & -2.2622850\\\\\n",
       "\td.1 &  1.3998746 &  1.11477862 & -0.9165962 & -1.3684041 & -1.651083967 &  1.50265696 &  2.35573527 & -0.12125783 & -1.0562328 &  1.3576558\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 10\n",
       "\n",
       "| <!--/--> | var1 &lt;dbl&gt; | var2 &lt;dbl&gt; | var3 &lt;dbl&gt; | var4 &lt;dbl&gt; | var5 &lt;dbl&gt; | var6 &lt;dbl&gt; | var7 &lt;dbl&gt; | var8 &lt;dbl&gt; | var9 &lt;dbl&gt; | var10 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| i | -0.3511240 |  0.17030455 |  1.3771536 |  1.4233101 | -0.860648824 | -1.47827855 | -0.39261047 |  0.80221210 |  1.9481512 |  0.2701822 |\n",
       "| e | -2.2444530 | -0.33451112 |  1.4854244 | -1.0682867 |  1.226876864 |  0.57110827 |  1.08313555 | -0.58203604 | -0.1892634 | -2.2622850 |\n",
       "| j |  2.1310753 |  0.13718951 |  0.1282606 | -0.6292720 | -0.060331155 | -0.09295629 |  1.01771608 |  0.06682849 |  0.6136679 | -1.0118277 |\n",
       "| h |  0.3656891 |  0.69166232 | -0.5311620 | -0.7206215 |  0.004583995 |  1.21986552 | -0.07795401 | -1.65265638 | -1.1022655 |  0.5579629 |\n",
       "| f |  0.8324335 |  1.77410395 | -0.5346737 |  0.8448853 | -1.011580447 |  1.89795420 | -1.89929201 |  0.14893425 | -1.0071622 |  0.4725406 |\n",
       "| b |  0.2424462 | -0.01057392 | -0.6215322 | -1.8799747 |  0.188147387 |  1.77490447 |  1.91418932 |  0.37029579 | -1.0290394 |  0.1917403 |\n",
       "| f.1 |  0.8324335 |  1.77410395 | -0.5346737 |  0.8448853 | -1.011580447 |  1.89795420 | -1.89929201 |  0.14893425 | -1.0071622 |  0.4725406 |\n",
       "| d |  1.3998746 |  1.11477862 | -0.9165962 | -1.3684041 | -1.651083967 |  1.50265696 |  2.35573527 | -0.12125783 | -1.0562328 |  1.3576558 |\n",
       "| e.1 | -2.2444530 | -0.33451112 |  1.4854244 | -1.0682867 |  1.226876864 |  0.57110827 |  1.08313555 | -0.58203604 | -0.1892634 | -2.2622850 |\n",
       "| d.1 |  1.3998746 |  1.11477862 | -0.9165962 | -1.3684041 | -1.651083967 |  1.50265696 |  2.35573527 | -0.12125783 | -1.0562328 |  1.3576558 |\n",
       "\n"
      ],
      "text/plain": [
       "    var1       var2        var3       var4       var5         var6       \n",
       "i   -0.3511240  0.17030455  1.3771536  1.4233101 -0.860648824 -1.47827855\n",
       "e   -2.2444530 -0.33451112  1.4854244 -1.0682867  1.226876864  0.57110827\n",
       "j    2.1310753  0.13718951  0.1282606 -0.6292720 -0.060331155 -0.09295629\n",
       "h    0.3656891  0.69166232 -0.5311620 -0.7206215  0.004583995  1.21986552\n",
       "f    0.8324335  1.77410395 -0.5346737  0.8448853 -1.011580447  1.89795420\n",
       "b    0.2424462 -0.01057392 -0.6215322 -1.8799747  0.188147387  1.77490447\n",
       "f.1  0.8324335  1.77410395 -0.5346737  0.8448853 -1.011580447  1.89795420\n",
       "d    1.3998746  1.11477862 -0.9165962 -1.3684041 -1.651083967  1.50265696\n",
       "e.1 -2.2444530 -0.33451112  1.4854244 -1.0682867  1.226876864  0.57110827\n",
       "d.1  1.3998746  1.11477862 -0.9165962 -1.3684041 -1.651083967  1.50265696\n",
       "    var7        var8        var9       var10     \n",
       "i   -0.39261047  0.80221210  1.9481512  0.2701822\n",
       "e    1.08313555 -0.58203604 -0.1892634 -2.2622850\n",
       "j    1.01771608  0.06682849  0.6136679 -1.0118277\n",
       "h   -0.07795401 -1.65265638 -1.1022655  0.5579629\n",
       "f   -1.89929201  0.14893425 -1.0071622  0.4725406\n",
       "b    1.91418932  0.37029579 -1.0290394  0.1917403\n",
       "f.1 -1.89929201  0.14893425 -1.0071622  0.4725406\n",
       "d    2.35573527 -0.12125783 -1.0562328  1.3576558\n",
       "e.1  1.08313555 -0.58203604 -0.1892634 -2.2622850\n",
       "d.1  2.35573527 -0.12125783 -1.0562328  1.3576558"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bootstrap sampling (sampling with replacement): We produce a bootstrap sample by sampling \n",
    "# the indices that are then used to extract the rows corresponding to those indices.\n",
    "# Notice that the rownames keep track of which rows were duplicated in the bootstrap sample\n",
    "sub_row_index<-sample(1:nrow(x_df), 10, replace=TRUE)\n",
    "x_df[sub_row_index, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = var1 ~ ., data = x_df)\n",
       "\n",
       "Residuals:\n",
       "ALL 10 residuals are 0: no residual degrees of freedom!\n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)\n",
       "(Intercept)  15.6013         NA      NA       NA\n",
       "var2         15.5569         NA      NA       NA\n",
       "var3        -11.1288         NA      NA       NA\n",
       "var4         19.4063         NA      NA       NA\n",
       "var5         20.5719         NA      NA       NA\n",
       "var6        -25.8181         NA      NA       NA\n",
       "var7         12.6919         NA      NA       NA\n",
       "var8          9.6271         NA      NA       NA\n",
       "var9        -27.7238         NA      NA       NA\n",
       "var10        -0.3252         NA      NA       NA\n",
       "\n",
       "Residual standard error: NaN on 0 degrees of freedom\n",
       "Multiple R-squared:      1,\tAdjusted R-squared:    NaN \n",
       "F-statistic:   NaN on 9 and 0 DF,  p-value: NA\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the regression of our n by p matrix (10x10);  here we have n=p;\n",
    "# Notice that we can't calculate standard errors, t-values or p-values.  We can only \n",
    "# get coefficients, and then only because all the rows are unique.   Bootstrapping will\n",
    "# produce samples with NON-unique rows, and those non-unique rows will not allow ALL 10 \n",
    "# beta coefficients to be estimated. \n",
    "lm_fit<-lm(var1 ~ ., x_df)\n",
    "summary(lm_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'coefficients'</li>\n",
       "\t<li>'residuals'</li>\n",
       "\t<li>'effects'</li>\n",
       "\t<li>'rank'</li>\n",
       "\t<li>'fitted.values'</li>\n",
       "\t<li>'assign'</li>\n",
       "\t<li>'qr'</li>\n",
       "\t<li>'df.residual'</li>\n",
       "\t<li>'xlevels'</li>\n",
       "\t<li>'call'</li>\n",
       "\t<li>'terms'</li>\n",
       "\t<li>'model'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'coefficients'\n",
       "\\item 'residuals'\n",
       "\\item 'effects'\n",
       "\\item 'rank'\n",
       "\\item 'fitted.values'\n",
       "\\item 'assign'\n",
       "\\item 'qr'\n",
       "\\item 'df.residual'\n",
       "\\item 'xlevels'\n",
       "\\item 'call'\n",
       "\\item 'terms'\n",
       "\\item 'model'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'coefficients'\n",
       "2. 'residuals'\n",
       "3. 'effects'\n",
       "4. 'rank'\n",
       "5. 'fitted.values'\n",
       "6. 'assign'\n",
       "7. 'qr'\n",
       "8. 'df.residual'\n",
       "9. 'xlevels'\n",
       "10. 'call'\n",
       "11. 'terms'\n",
       "12. 'model'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"coefficients\"  \"residuals\"     \"effects\"       \"rank\"         \n",
       " [5] \"fitted.values\" \"assign\"        \"qr\"            \"df.residual\"  \n",
       " [9] \"xlevels\"       \"call\"          \"terms\"         \"model\"        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>(Intercept)</dt>\n",
       "\t\t<dd>15.6013287295936</dd>\n",
       "\t<dt>var2</dt>\n",
       "\t\t<dd>15.556910555399</dd>\n",
       "\t<dt>var3</dt>\n",
       "\t\t<dd>-11.1288402298668</dd>\n",
       "\t<dt>var4</dt>\n",
       "\t\t<dd>19.4062608963897</dd>\n",
       "\t<dt>var5</dt>\n",
       "\t\t<dd>20.5719177599967</dd>\n",
       "\t<dt>var6</dt>\n",
       "\t\t<dd>-25.8181316583076</dd>\n",
       "\t<dt>var7</dt>\n",
       "\t\t<dd>12.6918908610936</dd>\n",
       "\t<dt>var8</dt>\n",
       "\t\t<dd>9.62709885922313</dd>\n",
       "\t<dt>var9</dt>\n",
       "\t\t<dd>-27.7238327061071</dd>\n",
       "\t<dt>var10</dt>\n",
       "\t\t<dd>-0.325153730590828</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[(Intercept)] 15.6013287295936\n",
       "\\item[var2] 15.556910555399\n",
       "\\item[var3] -11.1288402298668\n",
       "\\item[var4] 19.4062608963897\n",
       "\\item[var5] 20.5719177599967\n",
       "\\item[var6] -25.8181316583076\n",
       "\\item[var7] 12.6918908610936\n",
       "\\item[var8] 9.62709885922313\n",
       "\\item[var9] -27.7238327061071\n",
       "\\item[var10] -0.325153730590828\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "(Intercept)\n",
       ":   15.6013287295936var2\n",
       ":   15.556910555399var3\n",
       ":   -11.1288402298668var4\n",
       ":   19.4062608963897var5\n",
       ":   20.5719177599967var6\n",
       ":   -25.8181316583076var7\n",
       ":   12.6918908610936var8\n",
       ":   9.62709885922313var9\n",
       ":   -27.7238327061071var10\n",
       ":   -0.325153730590828\n",
       "\n"
      ],
      "text/plain": [
       "(Intercept)        var2        var3        var4        var5        var6 \n",
       " 15.6013287  15.5569106 -11.1288402  19.4062609  20.5719178 -25.8181317 \n",
       "       var7        var8        var9       var10 \n",
       " 12.6918909   9.6270989 -27.7238327  -0.3251537 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>a</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>b</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>c</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>d</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>e</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>f</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>g</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>h</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>i</dt>\n",
       "\t\t<dd>0</dd>\n",
       "\t<dt>j</dt>\n",
       "\t\t<dd>0</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[a] 0\n",
       "\\item[b] 0\n",
       "\\item[c] 0\n",
       "\\item[d] 0\n",
       "\\item[e] 0\n",
       "\\item[f] 0\n",
       "\\item[g] 0\n",
       "\\item[h] 0\n",
       "\\item[i] 0\n",
       "\\item[j] 0\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "a\n",
       ":   0b\n",
       ":   0c\n",
       ":   0d\n",
       ":   0e\n",
       ":   0f\n",
       ":   0g\n",
       ":   0h\n",
       ":   0i\n",
       ":   0j\n",
       ":   0\n",
       "\n"
      ],
      "text/plain": [
       "a b c d e f g h i j \n",
       "0 0 0 0 0 0 0 0 0 0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Objects of the fitted model that can be extracted;  We extract the coefficients and the residuals;\n",
    "# Notice the residuals are all 0\n",
    "names(lm_fit)\n",
    "lm_fit$coefficients\n",
    "lm_fit$residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method I - Calculate the bootstrap ourselves using a \"for-loop\" and resampling the indices.  Additionally, we will vary (or permute) the order of the predictors on each model estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will estimate bootstrap confidence intervals using two different approaches.  Both methods will \n",
    "need a function to be defined that calculates the regression coefficients and returns them for accumulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'var8+var5+var10+var2+var7+var9+var6+var3+var4'"
      ],
      "text/latex": [
       "'var8+var5+var10+var2+var7+var9+var6+var3+var4'"
      ],
      "text/markdown": [
       "'var8+var5+var10+var2+var7+var9+var6+var3+var4'"
      ],
      "text/plain": [
       "[1] \"var8+var5+var10+var2+var7+var9+var6+var3+var4\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The code segment that we'll need to construct the formula from one model estimation to the \n",
    "# next model estimation;  Notice how we are constructing a permuted variable ordering for the \n",
    "# model to estimate\n",
    "paste(sample(names(x_df)[-1], ncol(x_df)-1, replace=FALSE), collapse=\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function that will be called many times; \"boot_samps\" will determine how \n",
    "# how many bootstrap samples to take;  This will be 1000 for our purposes.\n",
    "# We create a calling argument \"permute_vars\", that will allow us to use this function\n",
    "# for Method I and Method II when constructing the bootstrap CI\n",
    "\n",
    "lm_function<-function(data, i, col_index, permute_vars=FALSE){\n",
    "   col_index=1\n",
    "   if(permute_vars==TRUE){\n",
    "       lm_fit_coefs<-lm(as.formula(paste(names(data)[col_index],\"~\",\n",
    "                                   paste(sample(names(data)[-1], \n",
    "                                                ncol(x_df)-1, replace=FALSE), collapse=\"+\"))), \n",
    "                        data)$coefficients\n",
    "    }\n",
    "    if(permute_vars==FALSE){\n",
    "       data<-data[i, ] \n",
    "       lm_fit_coefs<-lm(var1 ~ ., data)$coefficients     \n",
    "    }\n",
    "   return(lm_fit_coefs)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Begin the main looping for Method I construction of bootstrap CI\n",
    "# Initialize variables\n",
    "col_index<-1\n",
    "boot_samps<-1000\n",
    "coef_mat_accum<-matrix(NA, ncol=ncol(x_df), nrow=boot_samps)\n",
    "\n",
    "# Get the coefficients and the coefficient names\n",
    "# Note that the returning coefficients are of type \"vector\"\n",
    "coef_out<-lm(var1 ~. , data=x_df)$coefficients\n",
    "is.vector(coef_out)\n",
    "names(coef_out)\n",
    "\n",
    "# Order the coefficient names and reassign the names attribute \n",
    "# back to the matrix object;  Note that the vector has a \"names\"\n",
    "# attribute for the row names;  but a \"matrix\" has a \"colnames\" attribute\n",
    "# for it's columns; this is why the right side uses \"names\", but the \n",
    "# column names assignment ot our matrix uses \"colnames\"\n",
    "colnames(coef_mat_accum)<-names(coef_out)[order(names(coef_out))]\n",
    "\n",
    "# Display names\n",
    "colnames(coef_mat_accum)\n",
    "\n",
    "# Bootstrap sampling - using for-loop and sampling with replacement (SWR) \n",
    "for(i in 1:boot_samps){\n",
    "    print(i)\n",
    "    boot_index<-sample(1:nrow(x_df), 10, replace=TRUE)\n",
    "    x_df_rep<-x_df[boot_index, ]\n",
    "    coef_out<-lm_function(data=x_df_rep, col_index=col_index, permute_vars=TRUE)\n",
    "    coef_out<-coef_out[order(names(coef_out))]\n",
    "    coef_mat_accum[i, ] <- coef_out \n",
    "    print(coef_mat_accum[i,1])\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the quantiles of each column;  These are the CI:  [LL_beta_.025,  UL_beta_.975]\n",
    "# These betas define an uncertainty interval that capture 95% of the variation in the sample's betas\n",
    "apply(coef_mat_accum, 2, quantile, probs=c(.025, .975), na.rm=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original coefficients listed for comparison:\n",
    "#\n",
    "#             Estimate Std. Error t value Pr(>|t|)\n",
    "# (Intercept)  0.05456         NA      NA       NA\n",
    "# var2        -0.14774         NA      NA       NA\n",
    "# var3         0.07723         NA      NA       NA\n",
    "# var4        -0.18304         NA      NA       NA\n",
    "# var5         0.52600         NA      NA       NA\n",
    "# var6        -0.32386         NA      NA       NA\n",
    "# var7         0.49406         NA      NA       NA\n",
    "# var8        -0.56801         NA      NA       NA\n",
    "# var9         0.37399         NA      NA       NA\n",
    "# var10        0.30558         NA      NA       NA\n",
    "library(np)\n",
    "\n",
    "# Calculate \"smoothed\" quantiles \n",
    "apply(coef_mat_accum, 2, npquantile, tau=c(.025, .975), na.rm=TRUE)\n",
    "# apply(coef_mat_accum, 2, mean, na.rm=TRUE)\n",
    "\n",
    "# Calculate how far on average the median of the 1000 bootstrap\n",
    "# coefficients are from the known values in the population:  0  \n",
    "sum(abs(apply(coef_mat_accum, 2, median, na.rm=TRUE)))/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the standard error of the beta coefficients\n",
    "apply(coef_mat_accum, 2, sd, na.rm=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original coefficients listed for comparison\n",
    "#\n",
    "#             Estimate Std. Error t value Pr(>|t|)\n",
    "# (Intercept)  0.05456         NA      NA       NA\n",
    "# var2        -0.14774         NA      NA       NA\n",
    "# var3         0.07723         NA      NA       NA\n",
    "# var4        -0.18304         NA      NA       NA\n",
    "# var5         0.52600         NA      NA       NA\n",
    "# var6        -0.32386         NA      NA       NA\n",
    "# var7         0.49406         NA      NA       NA\n",
    "# var8        -0.56801         NA      NA       NA\n",
    "# var9         0.37399         NA      NA       NA\n",
    "# var10        0.30558         NA      NA       NA\n",
    "\n",
    "\n",
    "# Calculate how far on average the \"trimmed means\" of the 1000 bootstrap\n",
    "# coefficients are from the known values in the population:  0\n",
    "# The lowest and highest 10% of the coefficients are REMOVED (trimmed)\n",
    "sum(abs(apply(coef_mat_accum, 2, mean, na.rm=TRUE, tr=.10)))/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate how far on average the \"ordinary means\" of the 1000 bootstrap\n",
    "# coefficients are from the known values in the population:  0\n",
    "sum(abs(apply(coef_mat_accum, 2, mean, na.rm=TRUE)))/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(reshape2)\n",
    "\n",
    "# Histogram with density plot\n",
    "# ggplot needs a dataframe to plot\n",
    "# The data are then stacked using the \"melt\" function from \n",
    "# package \"reshape2\"\n",
    "#\n",
    "# Convert to a dataframe\n",
    "coef_mat_accum_df<-melt(data.frame(coef_mat_accum))\n",
    "# List first 6 and last six rows of the dataframe\n",
    "head(coef_mat_accum_df)\n",
    "tail(coef_mat_accum_df)\n",
    "# List the column names of the dataframe\n",
    "names(coef_mat_accum_df)\n",
    "head(coef_mat_accum_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 3493 rows containing non-finite values (stat_boxplot).”\n",
      "Warning message:\n",
      "“Removed 4397 rows containing non-finite values (stat_density).”\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nCjsAAAAABYHCDgAAAEBB/A9PeY0Pdhy77gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n",
      "\n",
      "Warning message:\n",
      "“Removed 4397 rows containing non-finite values (stat_bin).”\n",
      "Warning message:\n",
      "“Removed 10 rows containing missing values (geom_bar).”\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\nAAAAACbx3zmAdYA3Bo2cAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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\n4v8Dtx9qZwS8XwUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create boxplots and stacked density plots to describe the sampling\n",
    "# distributions of the betas; Notice that there are some outlying values \n",
    "# for every predictor\n",
    "ggplot(coef_mat_accum_df,aes(x=variable, y=value, fill=variable)) + geom_boxplot()\n",
    "ggplot(coef_mat_accum_df,aes(x=value, fill=variable)) + geom_density(alpha=0.25) + xlim(-3.5,3.5)\n",
    "ggplot(coef_mat_accum_df,aes(x=value, fill=variable)) + geom_histogram(alpha=0.25) + xlim(-3.5, 3.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method II - Calculate bootstrap confidence intervals using the R package \"boot\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bootstrap analysis using package \"boot\" -  We DON'T want to permute the predictors\n",
    "# when using package \"boot\";  The bootstrap averaging of coefficients occurs across \n",
    "# models where the ordering of the predictors is the same for each bootstrap sample.\n",
    "# Notice var10 will not be able to be estimated;  n must be LARGER than p to get estimates\n",
    "# for all 10 variables \n",
    "\n",
    "library(boot)\n",
    "boot.fit<-boot(data=x_df, lm_function, R=1000, col_index=1, permute_vars=FALSE)\n",
    "boot.fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'(Intercept)'</li>\n",
       "\t<li>'var2'</li>\n",
       "\t<li>'var3'</li>\n",
       "\t<li>'var4'</li>\n",
       "\t<li>'var5'</li>\n",
       "\t<li>'var6'</li>\n",
       "\t<li>'var7'</li>\n",
       "\t<li>'var8'</li>\n",
       "\t<li>'var9'</li>\n",
       "\t<li>'var10'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '(Intercept)'\n",
       "\\item 'var2'\n",
       "\\item 'var3'\n",
       "\\item 'var4'\n",
       "\\item 'var5'\n",
       "\\item 'var6'\n",
       "\\item 'var7'\n",
       "\\item 'var8'\n",
       "\\item 'var9'\n",
       "\\item 'var10'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '(Intercept)'\n",
       "2. 'var2'\n",
       "3. 'var3'\n",
       "4. 'var4'\n",
       "5. 'var5'\n",
       "6. 'var6'\n",
       "7. 'var7'\n",
       "8. 'var8'\n",
       "9. 'var9'\n",
       "10. 'var10'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"(Intercept)\" \"var2\"        \"var3\"        \"var4\"        \"var5\"       \n",
       " [6] \"var6\"        \"var7\"        \"var8\"        \"var9\"        \"var10\"      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "[1] \"(Intercept)\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 1000 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-3.5145, 14.7669 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "\n",
      "\n",
      "[1] \"var2\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 1000 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-2.9765,  7.3495 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "\n",
      "\n",
      "[1] \"var3\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 1000 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-27.2476,  11.5902 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "\n",
      "\n",
      "[1] \"var4\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 997 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-30.9033,  10.9258 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "\n",
      "\n",
      "[1] \"var5\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 982 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-13.3417,   3.9653 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "\n",
      "\n",
      "[1] \"var6\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 841 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-17.5442,   4.7608 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "\n",
      "\n",
      "[1] \"var7\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 484 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-5.5221,  6.3436 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "\n",
      "\n",
      "[1] \"var8\"\n",
      "\n",
      "\n",
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 128 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-2.8884,  0.7995 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "Some percentile intervals may be unstable\n",
      "\n",
      "\n",
      "[1] \"var9\"\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in norm.inter(t, alpha):\n",
      "“extreme order statistics used as endpoints”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\n",
      "Based on 16 bootstrap replicates\n",
      "\n",
      "CALL : \n",
      "boot.ci(boot.out = boot.fit, type = \"perc\", t0 = boot.fit$t0[j], \n",
      "    t = boot.fit$t[, j])\n",
      "\n",
      "Intervals : \n",
      "Level     Percentile     \n",
      "95%   (-0.5201,  0.9884 )  \n",
      "Calculations and Intervals on Original Scale\n",
      "Warning : Percentile Intervals used Extreme Quantiles\n",
      "Some percentile intervals may be unstable\n",
      "\n",
      "\n",
      "[1] \"var10\"\n",
      "\n",
      "\n"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in if (const(t, min(1e-08, mean(t, na.rm = TRUE)/1e+06))) {: missing value where TRUE/FALSE needed\n",
     "output_type": "error",
     "traceback": [
      "Error in if (const(t, min(1e-08, mean(t, na.rm = TRUE)/1e+06))) {: missing value where TRUE/FALSE needed\nTraceback:\n",
      "1. print(boot.ci(boot.fit, t0 = boot.fit$t0[j], t = boot.fit$t[, \n .     j], type = \"perc\"))",
      "2. boot.ci(boot.fit, t0 = boot.fit$t0[j], t = boot.fit$t[, j], type = \"perc\")"
     ]
    }
   ],
   "source": [
    "# Using the function \"boot.ci\" from package \"boot\", calculate \n",
    "# bootstrap CI for each predictor (including the intercept).\n",
    "# Note that var10 will be missing for every model;  this variable is the \n",
    "# Last predictor in the model\n",
    "\n",
    "# List the coefficient names \n",
    "names(boot.fit$t0)\n",
    "\n",
    "# Print the bootstrap CI\n",
    "for(j in 1:10){\n",
    "    cat(\"\\n\")\n",
    "    cat(\"\\n\")\n",
    "    print(names(boot.fit$t0[j]))\n",
    "    cat(\"\\n\")\n",
    "    cat(\"\\n\")\n",
    "    print(boot.ci(boot.fit,  t0 = boot.fit$t0[j], t = boot.fit$t[,j], type=\"perc\"))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's \n",
      "     NA      NA      NA     NaN      NA      NA    1000 \n"
     ]
    }
   ],
   "source": [
    "# Notice that the last predictor in the sorted vector of predictors is \"var9\"\n",
    "# and it consists of all NA's;  The point here is that Method II which uses the SAME\n",
    "# model for each bootstrap sample, CANNOT estimate ALL of the cofficients and their standard\n",
    "# errors;  But Method I, which uses permutations of the predictors, or each model estimation, \n",
    "# DOES ALLOW every predictor to be estimated a number of times - enough times to get a rough \n",
    "# estimate of the average coefficient value and its standard devaition (the standard error)\n",
    "print(summary(boot.fit$t[,10]))"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}