{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practice TWO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with dataframes\n",
    "----\n",
    "\n",
    "Practice TWO is meant to help you get comfortable working with data frames, and the basic ways you can slice and dice datafrmaes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) How many rows and columns are there in the `mtcars` dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "32"
      ],
      "text/latex": [
       "32"
      ],
      "text/markdown": [
       "32"
      ],
      "text/plain": [
       "[1] 32"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "11"
      ],
      "text/latex": [
       "11"
      ],
      "text/markdown": [
       "11"
      ],
      "text/plain": [
       "[1] 11"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nrow(mtcars))\n",
    "(ncol(mtcars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>32</li>\n",
       "\t<li>11</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 32\n",
       "\\item 11\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 32\n",
       "2. 11\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 32 11"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim(mtcars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) Show the last 6 rows of `mtcars`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Porsche 914-2</th><td>26</td><td>4</td><td>120.3</td><td>91</td><td>4.43</td><td>2.14</td><td>16.7</td><td>0</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Lotus Europa</th><td>30.4</td><td>4</td><td>95.1</td><td>113</td><td>3.77</td><td>1.513</td><td>16.9</td><td>1</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Ford Pantera L</th><td>15.8</td><td>8</td><td>351</td><td>264</td><td>4.22</td><td>3.17</td><td>14.5</td><td>0</td><td>1</td><td>5</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Ferrari Dino</th><td>19.7</td><td>6</td><td>145</td><td>175</td><td>3.62</td><td>2.77</td><td>15.5</td><td>0</td><td>1</td><td>5</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15</td><td>8</td><td>301</td><td>335</td><td>3.54</td><td>3.57</td><td>14.6</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121</td><td>109</td><td>4.11</td><td>2.78</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "\\hline\n",
       "\tPorsche 914-2 & 26 & 4 & 120.3 & 91 & 4.43 & 2.14 & 16.7 & 0 & 1 & 5 & 2\\\\\n",
       "\tLotus Europa & 30.4 & 4 & 95.1 & 113 & 3.77 & 1.513 & 16.9 & 1 & 1 & 5 & 2\\\\\n",
       "\tFord Pantera L & 15.8 & 8 & 351 & 264 & 4.22 & 3.17 & 14.5 & 0 & 1 & 5 & 4\\\\\n",
       "\tFerrari Dino & 19.7 & 6 & 145 & 175 & 3.62 & 2.77 & 15.5 & 0 & 1 & 5 & 6\\\\\n",
       "\tMaserati Bora & 15 & 8 & 301 & 335 & 3.54 & 3.57 & 14.6 & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121 & 109 & 4.11 & 2.78 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                mpg cyl  disp  hp drat    wt qsec vs am gear carb\n",
       "Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2\n",
       "Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2\n",
       "Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4\n",
       "Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6\n",
       "Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8\n",
       "Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail(mtcars, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) Show 6 rows at random (no duplicates) from `mtcars`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Cadillac Fleetwood</th><td>10.4</td><td>8</td><td>472</td><td>205</td><td>2.93</td><td>5.25</td><td>17.98</td><td>0</td><td>0</td><td>3</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Dodge Challenger</th><td>15.5</td><td>8</td><td>318</td><td>150</td><td>2.76</td><td>3.52</td><td>16.87</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Valiant</th><td>18.1</td><td>6</td><td>225</td><td>105</td><td>2.76</td><td>3.46</td><td>20.22</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121</td><td>109</td><td>4.11</td><td>2.78</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Lotus Europa</th><td>30.4</td><td>4</td><td>95.1</td><td>113</td><td>3.77</td><td>1.513</td><td>16.9</td><td>1</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Merc 450SL</th><td>17.3</td><td>8</td><td>275.8</td><td>180</td><td>3.07</td><td>3.73</td><td>17.6</td><td>0</td><td>0</td><td>3</td><td>3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "\\hline\n",
       "\tCadillac Fleetwood & 10.4 & 8 & 472 & 205 & 2.93 & 5.25 & 17.98 & 0 & 0 & 3 & 4\\\\\n",
       "\tDodge Challenger & 15.5 & 8 & 318 & 150 & 2.76 & 3.52 & 16.87 & 0 & 0 & 3 & 2\\\\\n",
       "\tValiant & 18.1 & 6 & 225 & 105 & 2.76 & 3.46 & 20.22 & 1 & 0 & 3 & 1\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121 & 109 & 4.11 & 2.78 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\tLotus Europa & 30.4 & 4 & 95.1 & 113 & 3.77 & 1.513 & 16.9 & 1 & 1 & 5 & 2\\\\\n",
       "\tMerc 450SL & 17.3 & 8 & 275.8 & 180 & 3.07 & 3.73 & 17.6 & 0 & 0 & 3 & 3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb\n",
       "Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4\n",
       "Dodge Challenger   15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2\n",
       "Valiant            18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1\n",
       "Volvo 142E         21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2\n",
       "Lotus Europa       30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2\n",
       "Merc 450SL         17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridx <- sample(1:nrow(mtcars), 6, replace=FALSE)\n",
    "mtcars[ridx,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) Display information only for the subset of cars with automatic transmission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.62</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8</td><td>4</td><td>108</td><td>93</td><td>3.85</td><td>2.32</td><td>18.61</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Fiat 128</th><td>32.4</td><td>4</td><td>78.7</td><td>66</td><td>4.08</td><td>2.2</td><td>19.47</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Honda Civic</th><td>30.4</td><td>4</td><td>75.7</td><td>52</td><td>4.93</td><td>1.615</td><td>18.52</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Toyota Corolla</th><td>33.9</td><td>4</td><td>71.1</td><td>65</td><td>4.22</td><td>1.835</td><td>19.9</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Fiat X1-9</th><td>27.3</td><td>4</td><td>79</td><td>66</td><td>4.08</td><td>1.935</td><td>18.9</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Porsche 914-2</th><td>26</td><td>4</td><td>120.3</td><td>91</td><td>4.43</td><td>2.14</td><td>16.7</td><td>0</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Lotus Europa</th><td>30.4</td><td>4</td><td>95.1</td><td>113</td><td>3.77</td><td>1.513</td><td>16.9</td><td>1</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Ford Pantera L</th><td>15.8</td><td>8</td><td>351</td><td>264</td><td>4.22</td><td>3.17</td><td>14.5</td><td>0</td><td>1</td><td>5</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Ferrari Dino</th><td>19.7</td><td>6</td><td>145</td><td>175</td><td>3.62</td><td>2.77</td><td>15.5</td><td>0</td><td>1</td><td>5</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15</td><td>8</td><td>301</td><td>335</td><td>3.54</td><td>3.57</td><td>14.6</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121</td><td>109</td><td>4.11</td><td>2.78</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21 & 6 & 160 & 110 & 3.9 & 2.62 & 16.46 & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21 & 6 & 160 & 110 & 3.9 & 2.875 & 17.02 & 0 & 1 & 4 & 4\\\\\n",
       "\tDatsun 710 & 22.8 & 4 & 108 & 93 & 3.85 & 2.32 & 18.61 & 1 & 1 & 4 & 1\\\\\n",
       "\tFiat 128 & 32.4 & 4 & 78.7 & 66 & 4.08 & 2.2 & 19.47 & 1 & 1 & 4 & 1\\\\\n",
       "\tHonda Civic & 30.4 & 4 & 75.7 & 52 & 4.93 & 1.615 & 18.52 & 1 & 1 & 4 & 2\\\\\n",
       "\tToyota Corolla & 33.9 & 4 & 71.1 & 65 & 4.22 & 1.835 & 19.9 & 1 & 1 & 4 & 1\\\\\n",
       "\tFiat X1-9 & 27.3 & 4 & 79 & 66 & 4.08 & 1.935 & 18.9 & 1 & 1 & 4 & 1\\\\\n",
       "\tPorsche 914-2 & 26 & 4 & 120.3 & 91 & 4.43 & 2.14 & 16.7 & 0 & 1 & 5 & 2\\\\\n",
       "\tLotus Europa & 30.4 & 4 & 95.1 & 113 & 3.77 & 1.513 & 16.9 & 1 & 1 & 5 & 2\\\\\n",
       "\tFord Pantera L & 15.8 & 8 & 351 & 264 & 4.22 & 3.17 & 14.5 & 0 & 1 & 5 & 4\\\\\n",
       "\tFerrari Dino & 19.7 & 6 & 145 & 175 & 3.62 & 2.77 & 15.5 & 0 & 1 & 5 & 6\\\\\n",
       "\tMaserati Bora & 15 & 8 & 301 & 335 & 3.54 & 3.57 & 14.6 & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121 & 109 & 4.11 & 2.78 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                mpg cyl  disp  hp drat    wt  qsec vs am gear carb\n",
       "Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4\n",
       "Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4\n",
       "Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1\n",
       "Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1\n",
       "Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2\n",
       "Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1\n",
       "Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1\n",
       "Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2\n",
       "Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2\n",
       "Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4\n",
       "Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6\n",
       "Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8\n",
       "Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtcars[mtcars$am == 1,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(5) Display information only for the subset of cars with weight between 2 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.62</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8</td><td>4</td><td>108</td><td>93</td><td>3.85</td><td>2.32</td><td>18.61</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Fiat 128</th><td>32.4</td><td>4</td><td>78.7</td><td>66</td><td>4.08</td><td>2.2</td><td>19.47</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Toyota Corona</th><td>21.5</td><td>4</td><td>120.1</td><td>97</td><td>3.7</td><td>2.465</td><td>20.01</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Porsche 914-2</th><td>26</td><td>4</td><td>120.3</td><td>91</td><td>4.43</td><td>2.14</td><td>16.7</td><td>0</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Ferrari Dino</th><td>19.7</td><td>6</td><td>145</td><td>175</td><td>3.62</td><td>2.77</td><td>15.5</td><td>0</td><td>1</td><td>5</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121</td><td>109</td><td>4.11</td><td>2.78</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21 & 6 & 160 & 110 & 3.9 & 2.62 & 16.46 & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21 & 6 & 160 & 110 & 3.9 & 2.875 & 17.02 & 0 & 1 & 4 & 4\\\\\n",
       "\tDatsun 710 & 22.8 & 4 & 108 & 93 & 3.85 & 2.32 & 18.61 & 1 & 1 & 4 & 1\\\\\n",
       "\tFiat 128 & 32.4 & 4 & 78.7 & 66 & 4.08 & 2.2 & 19.47 & 1 & 1 & 4 & 1\\\\\n",
       "\tToyota Corona & 21.5 & 4 & 120.1 & 97 & 3.7 & 2.465 & 20.01 & 1 & 0 & 3 & 1\\\\\n",
       "\tPorsche 914-2 & 26 & 4 & 120.3 & 91 & 4.43 & 2.14 & 16.7 & 0 & 1 & 5 & 2\\\\\n",
       "\tFerrari Dino & 19.7 & 6 & 145 & 175 & 3.62 & 2.77 & 15.5 & 0 & 1 & 5 & 6\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121 & 109 & 4.11 & 2.78 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "               mpg cyl  disp  hp drat    wt  qsec vs am gear carb\n",
       "Mazda RX4     21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4\n",
       "Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4\n",
       "Datsun 710    22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1\n",
       "Fiat 128      32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1\n",
       "Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1\n",
       "Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2\n",
       "Ferrari Dino  19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6\n",
       "Volvo 142E    21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtcars[(2 < mtcars$wt) & (mtcars$wt < 3),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(6) What is the mean weight of all cars?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "3.21725"
      ],
      "text/latex": [
       "3.21725"
      ],
      "text/markdown": [
       "3.21725"
      ],
      "text/plain": [
       "[1] 3.21725"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(mtcars$wt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in parse(text = x, srcfile = src): <text>:1:5: unexpected symbol\n1: (7) What\n        ^\n",
     "output_type": "error",
     "traceback": [
      "Error in parse(text = x, srcfile = src): <text>:1:5: unexpected symbol\n1: (7) What\n        ^\n"
     ]
    }
   ],
   "source": [
    "(7) What is the mean weight of cars wtih `mpg` greater than 20?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "2.41807142857143"
      ],
      "text/latex": [
       "2.41807142857143"
      ],
      "text/markdown": [
       "2.41807142857143"
      ],
      "text/plain": [
       "[1] 2.418071"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(mtcars[mtcars$mpg > 20, \"wt\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(8) Add a column `kpl` showing the number of kilometers per liter (1 mile = 1.609344 kilometers, and 1 gallon = 3.78541178 liters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpg.to.kpl <- function(mpg) {\n",
    "    return(mpg * 1.609344 / 3.78541178)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th><th scope=col>kpl</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.62</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td><td>8.928018</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td><td>8.928018</td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8</td><td>4</td><td>108</td><td>93</td><td>3.85</td><td>2.32</td><td>18.61</td><td>1</td><td>1</td><td>4</td><td>1</td><td>9.693277</td></tr>\n",
       "\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4</td><td>6</td><td>258</td><td>110</td><td>3.08</td><td>3.215</td><td>19.44</td><td>1</td><td>0</td><td>3</td><td>1</td><td>9.098075</td></tr>\n",
       "\t<tr><th scope=row>Hornet Sportabout</th><td>18.7</td><td>8</td><td>360</td><td>175</td><td>3.15</td><td>3.44</td><td>17.02</td><td>0</td><td>0</td><td>3</td><td>2</td><td>7.950187</td></tr>\n",
       "\t<tr><th scope=row>Valiant</th><td>18.1</td><td>6</td><td>225</td><td>105</td><td>2.76</td><td>3.46</td><td>20.22</td><td>1</td><td>0</td><td>3</td><td>1</td><td>7.695101</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb & kpl\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21 & 6 & 160 & 110 & 3.9 & 2.62 & 16.46 & 0 & 1 & 4 & 4 & 8.928018\\\\\n",
       "\tMazda RX4 Wag & 21 & 6 & 160 & 110 & 3.9 & 2.875 & 17.02 & 0 & 1 & 4 & 4 & 8.928018\\\\\n",
       "\tDatsun 710 & 22.8 & 4 & 108 & 93 & 3.85 & 2.32 & 18.61 & 1 & 1 & 4 & 1 & 9.693277\\\\\n",
       "\tHornet 4 Drive & 21.4 & 6 & 258 & 110 & 3.08 & 3.215 & 19.44 & 1 & 0 & 3 & 1 & 9.098075\\\\\n",
       "\tHornet Sportabout & 18.7 & 8 & 360 & 175 & 3.15 & 3.44 & 17.02 & 0 & 0 & 3 & 2 & 7.950187\\\\\n",
       "\tValiant & 18.1 & 6 & 225 & 105 & 2.76 & 3.46 & 20.22 & 1 & 0 & 3 & 1 & 7.695101\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                   mpg cyl disp  hp drat    wt  qsec vs am gear carb      kpl\n",
       "Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 8.928018\n",
       "Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 8.928018\n",
       "Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 9.693277\n",
       "Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 9.098075\n",
       "Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 7.950187\n",
       "Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 7.695101"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtcars$kpl <- mpg.to.kpl(mtcars$mpg)\n",
    "head(mtcars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(9) Make a new dataframe `mtcars.1` with only the `mpg` and `kpl` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>kpl</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21</td><td>8.928018</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>8.928018</td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8</td><td>9.693277</td></tr>\n",
       "\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4</td><td>9.098075</td></tr>\n",
       "\t<tr><th scope=row>Hornet Sportabout</th><td>18.7</td><td>7.950187</td></tr>\n",
       "\t<tr><th scope=row>Valiant</th><td>18.1</td><td>7.695101</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & mpg & kpl\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21 & 8.928018\\\\\n",
       "\tMazda RX4 Wag & 21 & 8.928018\\\\\n",
       "\tDatsun 710 & 22.8 & 9.693277\\\\\n",
       "\tHornet 4 Drive & 21.4 & 9.098075\\\\\n",
       "\tHornet Sportabout & 18.7 & 7.950187\\\\\n",
       "\tValiant & 18.1 & 7.695101\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                   mpg      kpl\n",
       "Mazda RX4         21.0 8.928018\n",
       "Mazda RX4 Wag     21.0 8.928018\n",
       "Datsun 710        22.8 9.693277\n",
       "Hornet 4 Drive    21.4 9.098075\n",
       "Hornet Sportabout 18.7 7.950187\n",
       "Valiant           18.1 7.695101"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtcars.1 <- mtcars[, c(\"mpg\", \"kpl\")]\n",
    "head(mtcars.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(10) Perform a linear regression model of `mpg` against `wt`. Plot the model fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = mpg ~ wt, data = mtcars)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-4.5432 -2.3647 -0.1252  1.4096  6.8727 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  37.2851     1.8776  19.858  < 2e-16 ***\n",
       "wt           -5.3445     0.5591  -9.559 1.29e-10 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 3.046 on 30 degrees of freedom\n",
       "Multiple R-squared:  0.7528,\tAdjusted R-squared:  0.7446 \n",
       "F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10\n"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit <- lm(mpg ~ wt, data=mtcars)\n",
    "summary(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      ],
      "text/plain": [
       "Plot with title “Linear regression of MPG against wt”"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(mtcars$wt, mtcars$mpg, col=rgb(0,0,1,0.5), pch=16, cex=2.0, \n",
    "    xlab=\"Weigth\", ylab=\"Miles per gallon\", \n",
    "     main=\"Linear regression of MPG against wt\")\n",
    "abline(fit, col=\"red\", lwd=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(11) Print 10 rows at ranodm from the `iris` dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>103</th><td>7.1</td><td>3</td><td>5.9</td><td>2.1</td><td>virginica</td></tr>\n",
       "\t<tr><th scope=row>117</th><td>6.5</td><td>3</td><td>5.5</td><td>1.8</td><td>virginica</td></tr>\n",
       "\t<tr><th scope=row>60</th><td>5.2</td><td>2.7</td><td>3.9</td><td>1.4</td><td>versicolor</td></tr>\n",
       "\t<tr><th scope=row>131</th><td>7.4</td><td>2.8</td><td>6.1</td><td>1.9</td><td>virginica</td></tr>\n",
       "\t<tr><th scope=row>78</th><td>6.7</td><td>3</td><td>5</td><td>1.7</td><td>versicolor</td></tr>\n",
       "\t<tr><th scope=row>137</th><td>6.3</td><td>3.4</td><td>5.6</td><td>2.4</td><td>virginica</td></tr>\n",
       "\t<tr><th scope=row>66</th><td>6.7</td><td>3.1</td><td>4.4</td><td>1.4</td><td>versicolor</td></tr>\n",
       "\t<tr><th scope=row>115</th><td>5.8</td><td>2.8</td><td>5.1</td><td>2.4</td><td>virginica</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>4.6</td><td>3.4</td><td>1.4</td><td>0.3</td><td>setosa</td></tr>\n",
       "\t<tr><th scope=row>116</th><td>6.4</td><td>3.2</td><td>5.3</td><td>2.3</td><td>virginica</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       "\\hline\n",
       "\t103 & 7.1 & 3 & 5.9 & 2.1 & virginica\\\\\n",
       "\t117 & 6.5 & 3 & 5.5 & 1.8 & virginica\\\\\n",
       "\t60 & 5.2 & 2.7 & 3.9 & 1.4 & versicolor\\\\\n",
       "\t131 & 7.4 & 2.8 & 6.1 & 1.9 & virginica\\\\\n",
       "\t78 & 6.7 & 3 & 5 & 1.7 & versicolor\\\\\n",
       "\t137 & 6.3 & 3.4 & 5.6 & 2.4 & virginica\\\\\n",
       "\t66 & 6.7 & 3.1 & 4.4 & 1.4 & versicolor\\\\\n",
       "\t115 & 5.8 & 2.8 & 5.1 & 2.4 & virginica\\\\\n",
       "\t7 & 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n",
       "\t116 & 6.4 & 3.2 & 5.3 & 2.3 & virginica\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species\n",
       "103          7.1         3.0          5.9         2.1  virginica\n",
       "117          6.5         3.0          5.5         1.8  virginica\n",
       "60           5.2         2.7          3.9         1.4 versicolor\n",
       "131          7.4         2.8          6.1         1.9  virginica\n",
       "78           6.7         3.0          5.0         1.7 versicolor\n",
       "137          6.3         3.4          5.6         2.4  virginica\n",
       "66           6.7         3.1          4.4         1.4 versicolor\n",
       "115          5.8         2.8          5.1         2.4  virginica\n",
       "7            4.6         3.4          1.4         0.3     setosa\n",
       "116          6.4         3.2          5.3         2.3  virginica"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridx <- sample(1:nrow(iris), 10, replace = FALSE)\n",
    "iris[ridx,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(12) Find the mean Sepal.Length Sepal.Width Petal.Length Petal.Width for each iris species using the `aggregate` command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Group.1</th><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>setosa</td><td>5.006</td><td>3.428</td><td>1.462</td><td>0.246</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>versicolor</td><td>5.936</td><td>2.77</td><td>4.26</td><td>1.326</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>virginica</td><td>6.588</td><td>2.974</td><td>5.552</td><td>2.026</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & Group.1 & Sepal.Length & Sepal.Width & Petal.Length & Petal.Width\\\\\n",
       "\\hline\n",
       "\t1 & setosa & 5.006 & 3.428 & 1.462 & 0.246\\\\\n",
       "\t2 & versicolor & 5.936 & 2.77 & 4.26 & 1.326\\\\\n",
       "\t3 & virginica & 6.588 & 2.974 & 5.552 & 2.026\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "     Group.1 Sepal.Length Sepal.Width Petal.Length Petal.Width\n",
       "1     setosa        5.006       3.428        1.462       0.246\n",
       "2 versicolor        5.936       2.770        4.260       1.326\n",
       "3  virginica        6.588       2.974        5.552       2.026"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregate(iris[,1:4], by=list(iris$Species), FUN=mean)"
   ]
  }
 ],
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