14 Introduction to Data Frames.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Frame Basics\n",
    "\n",
    "We've learned about vectors and their two-dimensional counterparts, matrices. Now, we will learn about Data Frames, one of the biggest tools for data analysis in R. Matrix inputs were limited because all the data inside the matrix had to be the same data type (numeric, logical, etc.). Now with Data Frames, we can organize and mix data types to create a very powerful data structure.\n",
    "\n",
    "R has some build in Data Frames we can use for quick reference. \n",
    "\n",
    "Here are a couple build in Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 50 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Population</th><th scope=col>Income</th><th scope=col>Illiteracy</th><th scope=col>Life Exp</th><th scope=col>Murder</th><th scope=col>HS Grad</th><th scope=col>Frost</th><th scope=col>Area</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Alabama</th><td> 3615</td><td>3624</td><td>2.1</td><td>69.05</td><td>15.1</td><td>41.3</td><td> 20</td><td> 50708</td></tr>\n",
       "\t<tr><th scope=row>Alaska</th><td>  365</td><td>6315</td><td>1.5</td><td>69.31</td><td>11.3</td><td>66.7</td><td>152</td><td>566432</td></tr>\n",
       "\t<tr><th scope=row>Arizona</th><td> 2212</td><td>4530</td><td>1.8</td><td>70.55</td><td> 7.8</td><td>58.1</td><td> 15</td><td>113417</td></tr>\n",
       "\t<tr><th scope=row>Arkansas</th><td> 2110</td><td>3378</td><td>1.9</td><td>70.66</td><td>10.1</td><td>39.9</td><td> 65</td><td> 51945</td></tr>\n",
       "\t<tr><th scope=row>California</th><td>21198</td><td>5114</td><td>1.1</td><td>71.71</td><td>10.3</td><td>62.6</td><td> 20</td><td>156361</td></tr>\n",
       "\t<tr><th scope=row>Colorado</th><td> 2541</td><td>4884</td><td>0.7</td><td>72.06</td><td> 6.8</td><td>63.9</td><td>166</td><td>103766</td></tr>\n",
       "\t<tr><th scope=row>Connecticut</th><td> 3100</td><td>5348</td><td>1.1</td><td>72.48</td><td> 3.1</td><td>56.0</td><td>139</td><td>  4862</td></tr>\n",
       "\t<tr><th scope=row>Delaware</th><td>  579</td><td>4809</td><td>0.9</td><td>70.06</td><td> 6.2</td><td>54.6</td><td>103</td><td>  1982</td></tr>\n",
       "\t<tr><th scope=row>Florida</th><td> 8277</td><td>4815</td><td>1.3</td><td>70.66</td><td>10.7</td><td>52.6</td><td> 11</td><td> 54090</td></tr>\n",
       "\t<tr><th scope=row>Georgia</th><td> 4931</td><td>4091</td><td>2.0</td><td>68.54</td><td>13.9</td><td>40.6</td><td> 60</td><td> 58073</td></tr>\n",
       "\t<tr><th scope=row>Hawaii</th><td>  868</td><td>4963</td><td>1.9</td><td>73.60</td><td> 6.2</td><td>61.9</td><td>  0</td><td>  6425</td></tr>\n",
       "\t<tr><th scope=row>Idaho</th><td>  813</td><td>4119</td><td>0.6</td><td>71.87</td><td> 5.3</td><td>59.5</td><td>126</td><td> 82677</td></tr>\n",
       "\t<tr><th scope=row>Illinois</th><td>11197</td><td>5107</td><td>0.9</td><td>70.14</td><td>10.3</td><td>52.6</td><td>127</td><td> 55748</td></tr>\n",
       "\t<tr><th scope=row>Indiana</th><td> 5313</td><td>4458</td><td>0.7</td><td>70.88</td><td> 7.1</td><td>52.9</td><td>122</td><td> 36097</td></tr>\n",
       "\t<tr><th scope=row>Iowa</th><td> 2861</td><td>4628</td><td>0.5</td><td>72.56</td><td> 2.3</td><td>59.0</td><td>140</td><td> 55941</td></tr>\n",
       "\t<tr><th scope=row>Kansas</th><td> 2280</td><td>4669</td><td>0.6</td><td>72.58</td><td> 4.5</td><td>59.9</td><td>114</td><td> 81787</td></tr>\n",
       "\t<tr><th scope=row>Kentucky</th><td> 3387</td><td>3712</td><td>1.6</td><td>70.10</td><td>10.6</td><td>38.5</td><td> 95</td><td> 39650</td></tr>\n",
       "\t<tr><th scope=row>Louisiana</th><td> 3806</td><td>3545</td><td>2.8</td><td>68.76</td><td>13.2</td><td>42.2</td><td> 12</td><td> 44930</td></tr>\n",
       "\t<tr><th scope=row>Maine</th><td> 1058</td><td>3694</td><td>0.7</td><td>70.39</td><td> 2.7</td><td>54.7</td><td>161</td><td> 30920</td></tr>\n",
       "\t<tr><th scope=row>Maryland</th><td> 4122</td><td>5299</td><td>0.9</td><td>70.22</td><td> 8.5</td><td>52.3</td><td>101</td><td>  9891</td></tr>\n",
       "\t<tr><th scope=row>Massachusetts</th><td> 5814</td><td>4755</td><td>1.1</td><td>71.83</td><td> 3.3</td><td>58.5</td><td>103</td><td>  7826</td></tr>\n",
       "\t<tr><th scope=row>Michigan</th><td> 9111</td><td>4751</td><td>0.9</td><td>70.63</td><td>11.1</td><td>52.8</td><td>125</td><td> 56817</td></tr>\n",
       "\t<tr><th scope=row>Minnesota</th><td> 3921</td><td>4675</td><td>0.6</td><td>72.96</td><td> 2.3</td><td>57.6</td><td>160</td><td> 79289</td></tr>\n",
       "\t<tr><th scope=row>Mississippi</th><td> 2341</td><td>3098</td><td>2.4</td><td>68.09</td><td>12.5</td><td>41.0</td><td> 50</td><td> 47296</td></tr>\n",
       "\t<tr><th scope=row>Missouri</th><td> 4767</td><td>4254</td><td>0.8</td><td>70.69</td><td> 9.3</td><td>48.8</td><td>108</td><td> 68995</td></tr>\n",
       "\t<tr><th scope=row>Montana</th><td>  746</td><td>4347</td><td>0.6</td><td>70.56</td><td> 5.0</td><td>59.2</td><td>155</td><td>145587</td></tr>\n",
       "\t<tr><th scope=row>Nebraska</th><td> 1544</td><td>4508</td><td>0.6</td><td>72.60</td><td> 2.9</td><td>59.3</td><td>139</td><td> 76483</td></tr>\n",
       "\t<tr><th scope=row>Nevada</th><td>  590</td><td>5149</td><td>0.5</td><td>69.03</td><td>11.5</td><td>65.2</td><td>188</td><td>109889</td></tr>\n",
       "\t<tr><th scope=row>New Hampshire</th><td>  812</td><td>4281</td><td>0.7</td><td>71.23</td><td> 3.3</td><td>57.6</td><td>174</td><td>  9027</td></tr>\n",
       "\t<tr><th scope=row>New Jersey</th><td> 7333</td><td>5237</td><td>1.1</td><td>70.93</td><td> 5.2</td><td>52.5</td><td>115</td><td>  7521</td></tr>\n",
       "\t<tr><th scope=row>New Mexico</th><td> 1144</td><td>3601</td><td>2.2</td><td>70.32</td><td> 9.7</td><td>55.2</td><td>120</td><td>121412</td></tr>\n",
       "\t<tr><th scope=row>New York</th><td>18076</td><td>4903</td><td>1.4</td><td>70.55</td><td>10.9</td><td>52.7</td><td> 82</td><td> 47831</td></tr>\n",
       "\t<tr><th scope=row>North Carolina</th><td> 5441</td><td>3875</td><td>1.8</td><td>69.21</td><td>11.1</td><td>38.5</td><td> 80</td><td> 48798</td></tr>\n",
       "\t<tr><th scope=row>North Dakota</th><td>  637</td><td>5087</td><td>0.8</td><td>72.78</td><td> 1.4</td><td>50.3</td><td>186</td><td> 69273</td></tr>\n",
       "\t<tr><th scope=row>Ohio</th><td>10735</td><td>4561</td><td>0.8</td><td>70.82</td><td> 7.4</td><td>53.2</td><td>124</td><td> 40975</td></tr>\n",
       "\t<tr><th scope=row>Oklahoma</th><td> 2715</td><td>3983</td><td>1.1</td><td>71.42</td><td> 6.4</td><td>51.6</td><td> 82</td><td> 68782</td></tr>\n",
       "\t<tr><th scope=row>Oregon</th><td> 2284</td><td>4660</td><td>0.6</td><td>72.13</td><td> 4.2</td><td>60.0</td><td> 44</td><td> 96184</td></tr>\n",
       "\t<tr><th scope=row>Pennsylvania</th><td>11860</td><td>4449</td><td>1.0</td><td>70.43</td><td> 6.1</td><td>50.2</td><td>126</td><td> 44966</td></tr>\n",
       "\t<tr><th scope=row>Rhode Island</th><td>  931</td><td>4558</td><td>1.3</td><td>71.90</td><td> 2.4</td><td>46.4</td><td>127</td><td>  1049</td></tr>\n",
       "\t<tr><th scope=row>South Carolina</th><td> 2816</td><td>3635</td><td>2.3</td><td>67.96</td><td>11.6</td><td>37.8</td><td> 65</td><td> 30225</td></tr>\n",
       "\t<tr><th scope=row>South Dakota</th><td>  681</td><td>4167</td><td>0.5</td><td>72.08</td><td> 1.7</td><td>53.3</td><td>172</td><td> 75955</td></tr>\n",
       "\t<tr><th scope=row>Tennessee</th><td> 4173</td><td>3821</td><td>1.7</td><td>70.11</td><td>11.0</td><td>41.8</td><td> 70</td><td> 41328</td></tr>\n",
       "\t<tr><th scope=row>Texas</th><td>12237</td><td>4188</td><td>2.2</td><td>70.90</td><td>12.2</td><td>47.4</td><td> 35</td><td>262134</td></tr>\n",
       "\t<tr><th scope=row>Utah</th><td> 1203</td><td>4022</td><td>0.6</td><td>72.90</td><td> 4.5</td><td>67.3</td><td>137</td><td> 82096</td></tr>\n",
       "\t<tr><th scope=row>Vermont</th><td>  472</td><td>3907</td><td>0.6</td><td>71.64</td><td> 5.5</td><td>57.1</td><td>168</td><td>  9267</td></tr>\n",
       "\t<tr><th scope=row>Virginia</th><td> 4981</td><td>4701</td><td>1.4</td><td>70.08</td><td> 9.5</td><td>47.8</td><td> 85</td><td> 39780</td></tr>\n",
       "\t<tr><th scope=row>Washington</th><td> 3559</td><td>4864</td><td>0.6</td><td>71.72</td><td> 4.3</td><td>63.5</td><td> 32</td><td> 66570</td></tr>\n",
       "\t<tr><th scope=row>West Virginia</th><td> 1799</td><td>3617</td><td>1.4</td><td>69.48</td><td> 6.7</td><td>41.6</td><td>100</td><td> 24070</td></tr>\n",
       "\t<tr><th scope=row>Wisconsin</th><td> 4589</td><td>4468</td><td>0.7</td><td>72.48</td><td> 3.0</td><td>54.5</td><td>149</td><td> 54464</td></tr>\n",
       "\t<tr><th scope=row>Wyoming</th><td>  376</td><td>4566</td><td>0.6</td><td>70.29</td><td> 6.9</td><td>62.9</td><td>173</td><td> 97203</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 50 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Population & Income & Illiteracy & Life Exp & Murder & HS Grad & Frost & Area\\\\\n",
       "\\hline\n",
       "\tAlabama &  3615 & 3624 & 2.1 & 69.05 & 15.1 & 41.3 &  20 &  50708\\\\\n",
       "\tAlaska &   365 & 6315 & 1.5 & 69.31 & 11.3 & 66.7 & 152 & 566432\\\\\n",
       "\tArizona &  2212 & 4530 & 1.8 & 70.55 &  7.8 & 58.1 &  15 & 113417\\\\\n",
       "\tArkansas &  2110 & 3378 & 1.9 & 70.66 & 10.1 & 39.9 &  65 &  51945\\\\\n",
       "\tCalifornia & 21198 & 5114 & 1.1 & 71.71 & 10.3 & 62.6 &  20 & 156361\\\\\n",
       "\tColorado &  2541 & 4884 & 0.7 & 72.06 &  6.8 & 63.9 & 166 & 103766\\\\\n",
       "\tConnecticut &  3100 & 5348 & 1.1 & 72.48 &  3.1 & 56.0 & 139 &   4862\\\\\n",
       "\tDelaware &   579 & 4809 & 0.9 & 70.06 &  6.2 & 54.6 & 103 &   1982\\\\\n",
       "\tFlorida &  8277 & 4815 & 1.3 & 70.66 & 10.7 & 52.6 &  11 &  54090\\\\\n",
       "\tGeorgia &  4931 & 4091 & 2.0 & 68.54 & 13.9 & 40.6 &  60 &  58073\\\\\n",
       "\tHawaii &   868 & 4963 & 1.9 & 73.60 &  6.2 & 61.9 &   0 &   6425\\\\\n",
       "\tIdaho &   813 & 4119 & 0.6 & 71.87 &  5.3 & 59.5 & 126 &  82677\\\\\n",
       "\tIllinois & 11197 & 5107 & 0.9 & 70.14 & 10.3 & 52.6 & 127 &  55748\\\\\n",
       "\tIndiana &  5313 & 4458 & 0.7 & 70.88 &  7.1 & 52.9 & 122 &  36097\\\\\n",
       "\tIowa &  2861 & 4628 & 0.5 & 72.56 &  2.3 & 59.0 & 140 &  55941\\\\\n",
       "\tKansas &  2280 & 4669 & 0.6 & 72.58 &  4.5 & 59.9 & 114 &  81787\\\\\n",
       "\tKentucky &  3387 & 3712 & 1.6 & 70.10 & 10.6 & 38.5 &  95 &  39650\\\\\n",
       "\tLouisiana &  3806 & 3545 & 2.8 & 68.76 & 13.2 & 42.2 &  12 &  44930\\\\\n",
       "\tMaine &  1058 & 3694 & 0.7 & 70.39 &  2.7 & 54.7 & 161 &  30920\\\\\n",
       "\tMaryland &  4122 & 5299 & 0.9 & 70.22 &  8.5 & 52.3 & 101 &   9891\\\\\n",
       "\tMassachusetts &  5814 & 4755 & 1.1 & 71.83 &  3.3 & 58.5 & 103 &   7826\\\\\n",
       "\tMichigan &  9111 & 4751 & 0.9 & 70.63 & 11.1 & 52.8 & 125 &  56817\\\\\n",
       "\tMinnesota &  3921 & 4675 & 0.6 & 72.96 &  2.3 & 57.6 & 160 &  79289\\\\\n",
       "\tMississippi &  2341 & 3098 & 2.4 & 68.09 & 12.5 & 41.0 &  50 &  47296\\\\\n",
       "\tMissouri &  4767 & 4254 & 0.8 & 70.69 &  9.3 & 48.8 & 108 &  68995\\\\\n",
       "\tMontana &   746 & 4347 & 0.6 & 70.56 &  5.0 & 59.2 & 155 & 145587\\\\\n",
       "\tNebraska &  1544 & 4508 & 0.6 & 72.60 &  2.9 & 59.3 & 139 &  76483\\\\\n",
       "\tNevada &   590 & 5149 & 0.5 & 69.03 & 11.5 & 65.2 & 188 & 109889\\\\\n",
       "\tNew Hampshire &   812 & 4281 & 0.7 & 71.23 &  3.3 & 57.6 & 174 &   9027\\\\\n",
       "\tNew Jersey &  7333 & 5237 & 1.1 & 70.93 &  5.2 & 52.5 & 115 &   7521\\\\\n",
       "\tNew Mexico &  1144 & 3601 & 2.2 & 70.32 &  9.7 & 55.2 & 120 & 121412\\\\\n",
       "\tNew York & 18076 & 4903 & 1.4 & 70.55 & 10.9 & 52.7 &  82 &  47831\\\\\n",
       "\tNorth Carolina &  5441 & 3875 & 1.8 & 69.21 & 11.1 & 38.5 &  80 &  48798\\\\\n",
       "\tNorth Dakota &   637 & 5087 & 0.8 & 72.78 &  1.4 & 50.3 & 186 &  69273\\\\\n",
       "\tOhio & 10735 & 4561 & 0.8 & 70.82 &  7.4 & 53.2 & 124 &  40975\\\\\n",
       "\tOklahoma &  2715 & 3983 & 1.1 & 71.42 &  6.4 & 51.6 &  82 &  68782\\\\\n",
       "\tOregon &  2284 & 4660 & 0.6 & 72.13 &  4.2 & 60.0 &  44 &  96184\\\\\n",
       "\tPennsylvania & 11860 & 4449 & 1.0 & 70.43 &  6.1 & 50.2 & 126 &  44966\\\\\n",
       "\tRhode Island &   931 & 4558 & 1.3 & 71.90 &  2.4 & 46.4 & 127 &   1049\\\\\n",
       "\tSouth Carolina &  2816 & 3635 & 2.3 & 67.96 & 11.6 & 37.8 &  65 &  30225\\\\\n",
       "\tSouth Dakota &   681 & 4167 & 0.5 & 72.08 &  1.7 & 53.3 & 172 &  75955\\\\\n",
       "\tTennessee &  4173 & 3821 & 1.7 & 70.11 & 11.0 & 41.8 &  70 &  41328\\\\\n",
       "\tTexas & 12237 & 4188 & 2.2 & 70.90 & 12.2 & 47.4 &  35 & 262134\\\\\n",
       "\tUtah &  1203 & 4022 & 0.6 & 72.90 &  4.5 & 67.3 & 137 &  82096\\\\\n",
       "\tVermont &   472 & 3907 & 0.6 & 71.64 &  5.5 & 57.1 & 168 &   9267\\\\\n",
       "\tVirginia &  4981 & 4701 & 1.4 & 70.08 &  9.5 & 47.8 &  85 &  39780\\\\\n",
       "\tWashington &  3559 & 4864 & 0.6 & 71.72 &  4.3 & 63.5 &  32 &  66570\\\\\n",
       "\tWest Virginia &  1799 & 3617 & 1.4 & 69.48 &  6.7 & 41.6 & 100 &  24070\\\\\n",
       "\tWisconsin &  4589 & 4468 & 0.7 & 72.48 &  3.0 & 54.5 & 149 &  54464\\\\\n",
       "\tWyoming &   376 & 4566 & 0.6 & 70.29 &  6.9 & 62.9 & 173 &  97203\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 50 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Population | Income | Illiteracy | Life Exp | Murder | HS Grad | Frost | Area |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Alabama |  3615 | 3624 | 2.1 | 69.05 | 15.1 | 41.3 |  20 |  50708 |\n",
       "| Alaska |   365 | 6315 | 1.5 | 69.31 | 11.3 | 66.7 | 152 | 566432 |\n",
       "| Arizona |  2212 | 4530 | 1.8 | 70.55 |  7.8 | 58.1 |  15 | 113417 |\n",
       "| Arkansas |  2110 | 3378 | 1.9 | 70.66 | 10.1 | 39.9 |  65 |  51945 |\n",
       "| California | 21198 | 5114 | 1.1 | 71.71 | 10.3 | 62.6 |  20 | 156361 |\n",
       "| Colorado |  2541 | 4884 | 0.7 | 72.06 |  6.8 | 63.9 | 166 | 103766 |\n",
       "| Connecticut |  3100 | 5348 | 1.1 | 72.48 |  3.1 | 56.0 | 139 |   4862 |\n",
       "| Delaware |   579 | 4809 | 0.9 | 70.06 |  6.2 | 54.6 | 103 |   1982 |\n",
       "| Florida |  8277 | 4815 | 1.3 | 70.66 | 10.7 | 52.6 |  11 |  54090 |\n",
       "| Georgia |  4931 | 4091 | 2.0 | 68.54 | 13.9 | 40.6 |  60 |  58073 |\n",
       "| Hawaii |   868 | 4963 | 1.9 | 73.60 |  6.2 | 61.9 |   0 |   6425 |\n",
       "| Idaho |   813 | 4119 | 0.6 | 71.87 |  5.3 | 59.5 | 126 |  82677 |\n",
       "| Illinois | 11197 | 5107 | 0.9 | 70.14 | 10.3 | 52.6 | 127 |  55748 |\n",
       "| Indiana |  5313 | 4458 | 0.7 | 70.88 |  7.1 | 52.9 | 122 |  36097 |\n",
       "| Iowa |  2861 | 4628 | 0.5 | 72.56 |  2.3 | 59.0 | 140 |  55941 |\n",
       "| Kansas |  2280 | 4669 | 0.6 | 72.58 |  4.5 | 59.9 | 114 |  81787 |\n",
       "| Kentucky |  3387 | 3712 | 1.6 | 70.10 | 10.6 | 38.5 |  95 |  39650 |\n",
       "| Louisiana |  3806 | 3545 | 2.8 | 68.76 | 13.2 | 42.2 |  12 |  44930 |\n",
       "| Maine |  1058 | 3694 | 0.7 | 70.39 |  2.7 | 54.7 | 161 |  30920 |\n",
       "| Maryland |  4122 | 5299 | 0.9 | 70.22 |  8.5 | 52.3 | 101 |   9891 |\n",
       "| Massachusetts |  5814 | 4755 | 1.1 | 71.83 |  3.3 | 58.5 | 103 |   7826 |\n",
       "| Michigan |  9111 | 4751 | 0.9 | 70.63 | 11.1 | 52.8 | 125 |  56817 |\n",
       "| Minnesota |  3921 | 4675 | 0.6 | 72.96 |  2.3 | 57.6 | 160 |  79289 |\n",
       "| Mississippi |  2341 | 3098 | 2.4 | 68.09 | 12.5 | 41.0 |  50 |  47296 |\n",
       "| Missouri |  4767 | 4254 | 0.8 | 70.69 |  9.3 | 48.8 | 108 |  68995 |\n",
       "| Montana |   746 | 4347 | 0.6 | 70.56 |  5.0 | 59.2 | 155 | 145587 |\n",
       "| Nebraska |  1544 | 4508 | 0.6 | 72.60 |  2.9 | 59.3 | 139 |  76483 |\n",
       "| Nevada |   590 | 5149 | 0.5 | 69.03 | 11.5 | 65.2 | 188 | 109889 |\n",
       "| New Hampshire |   812 | 4281 | 0.7 | 71.23 |  3.3 | 57.6 | 174 |   9027 |\n",
       "| New Jersey |  7333 | 5237 | 1.1 | 70.93 |  5.2 | 52.5 | 115 |   7521 |\n",
       "| New Mexico |  1144 | 3601 | 2.2 | 70.32 |  9.7 | 55.2 | 120 | 121412 |\n",
       "| New York | 18076 | 4903 | 1.4 | 70.55 | 10.9 | 52.7 |  82 |  47831 |\n",
       "| North Carolina |  5441 | 3875 | 1.8 | 69.21 | 11.1 | 38.5 |  80 |  48798 |\n",
       "| North Dakota |   637 | 5087 | 0.8 | 72.78 |  1.4 | 50.3 | 186 |  69273 |\n",
       "| Ohio | 10735 | 4561 | 0.8 | 70.82 |  7.4 | 53.2 | 124 |  40975 |\n",
       "| Oklahoma |  2715 | 3983 | 1.1 | 71.42 |  6.4 | 51.6 |  82 |  68782 |\n",
       "| Oregon |  2284 | 4660 | 0.6 | 72.13 |  4.2 | 60.0 |  44 |  96184 |\n",
       "| Pennsylvania | 11860 | 4449 | 1.0 | 70.43 |  6.1 | 50.2 | 126 |  44966 |\n",
       "| Rhode Island |   931 | 4558 | 1.3 | 71.90 |  2.4 | 46.4 | 127 |   1049 |\n",
       "| South Carolina |  2816 | 3635 | 2.3 | 67.96 | 11.6 | 37.8 |  65 |  30225 |\n",
       "| South Dakota |   681 | 4167 | 0.5 | 72.08 |  1.7 | 53.3 | 172 |  75955 |\n",
       "| Tennessee |  4173 | 3821 | 1.7 | 70.11 | 11.0 | 41.8 |  70 |  41328 |\n",
       "| Texas | 12237 | 4188 | 2.2 | 70.90 | 12.2 | 47.4 |  35 | 262134 |\n",
       "| Utah |  1203 | 4022 | 0.6 | 72.90 |  4.5 | 67.3 | 137 |  82096 |\n",
       "| Vermont |   472 | 3907 | 0.6 | 71.64 |  5.5 | 57.1 | 168 |   9267 |\n",
       "| Virginia |  4981 | 4701 | 1.4 | 70.08 |  9.5 | 47.8 |  85 |  39780 |\n",
       "| Washington |  3559 | 4864 | 0.6 | 71.72 |  4.3 | 63.5 |  32 |  66570 |\n",
       "| West Virginia |  1799 | 3617 | 1.4 | 69.48 |  6.7 | 41.6 | 100 |  24070 |\n",
       "| Wisconsin |  4589 | 4468 | 0.7 | 72.48 |  3.0 | 54.5 | 149 |  54464 |\n",
       "| Wyoming |   376 | 4566 | 0.6 | 70.29 |  6.9 | 62.9 | 173 |  97203 |\n",
       "\n"
      ],
      "text/plain": [
       "               Population Income Illiteracy Life Exp Murder HS Grad Frost\n",
       "Alabama         3615      3624   2.1        69.05    15.1   41.3     20  \n",
       "Alaska           365      6315   1.5        69.31    11.3   66.7    152  \n",
       "Arizona         2212      4530   1.8        70.55     7.8   58.1     15  \n",
       "Arkansas        2110      3378   1.9        70.66    10.1   39.9     65  \n",
       "California     21198      5114   1.1        71.71    10.3   62.6     20  \n",
       "Colorado        2541      4884   0.7        72.06     6.8   63.9    166  \n",
       "Connecticut     3100      5348   1.1        72.48     3.1   56.0    139  \n",
       "Delaware         579      4809   0.9        70.06     6.2   54.6    103  \n",
       "Florida         8277      4815   1.3        70.66    10.7   52.6     11  \n",
       "Georgia         4931      4091   2.0        68.54    13.9   40.6     60  \n",
       "Hawaii           868      4963   1.9        73.60     6.2   61.9      0  \n",
       "Idaho            813      4119   0.6        71.87     5.3   59.5    126  \n",
       "Illinois       11197      5107   0.9        70.14    10.3   52.6    127  \n",
       "Indiana         5313      4458   0.7        70.88     7.1   52.9    122  \n",
       "Iowa            2861      4628   0.5        72.56     2.3   59.0    140  \n",
       "Kansas          2280      4669   0.6        72.58     4.5   59.9    114  \n",
       "Kentucky        3387      3712   1.6        70.10    10.6   38.5     95  \n",
       "Louisiana       3806      3545   2.8        68.76    13.2   42.2     12  \n",
       "Maine           1058      3694   0.7        70.39     2.7   54.7    161  \n",
       "Maryland        4122      5299   0.9        70.22     8.5   52.3    101  \n",
       "Massachusetts   5814      4755   1.1        71.83     3.3   58.5    103  \n",
       "Michigan        9111      4751   0.9        70.63    11.1   52.8    125  \n",
       "Minnesota       3921      4675   0.6        72.96     2.3   57.6    160  \n",
       "Mississippi     2341      3098   2.4        68.09    12.5   41.0     50  \n",
       "Missouri        4767      4254   0.8        70.69     9.3   48.8    108  \n",
       "Montana          746      4347   0.6        70.56     5.0   59.2    155  \n",
       "Nebraska        1544      4508   0.6        72.60     2.9   59.3    139  \n",
       "Nevada           590      5149   0.5        69.03    11.5   65.2    188  \n",
       "New Hampshire    812      4281   0.7        71.23     3.3   57.6    174  \n",
       "New Jersey      7333      5237   1.1        70.93     5.2   52.5    115  \n",
       "New Mexico      1144      3601   2.2        70.32     9.7   55.2    120  \n",
       "New York       18076      4903   1.4        70.55    10.9   52.7     82  \n",
       "North Carolina  5441      3875   1.8        69.21    11.1   38.5     80  \n",
       "North Dakota     637      5087   0.8        72.78     1.4   50.3    186  \n",
       "Ohio           10735      4561   0.8        70.82     7.4   53.2    124  \n",
       "Oklahoma        2715      3983   1.1        71.42     6.4   51.6     82  \n",
       "Oregon          2284      4660   0.6        72.13     4.2   60.0     44  \n",
       "Pennsylvania   11860      4449   1.0        70.43     6.1   50.2    126  \n",
       "Rhode Island     931      4558   1.3        71.90     2.4   46.4    127  \n",
       "South Carolina  2816      3635   2.3        67.96    11.6   37.8     65  \n",
       "South Dakota     681      4167   0.5        72.08     1.7   53.3    172  \n",
       "Tennessee       4173      3821   1.7        70.11    11.0   41.8     70  \n",
       "Texas          12237      4188   2.2        70.90    12.2   47.4     35  \n",
       "Utah            1203      4022   0.6        72.90     4.5   67.3    137  \n",
       "Vermont          472      3907   0.6        71.64     5.5   57.1    168  \n",
       "Virginia        4981      4701   1.4        70.08     9.5   47.8     85  \n",
       "Washington      3559      4864   0.6        71.72     4.3   63.5     32  \n",
       "West Virginia   1799      3617   1.4        69.48     6.7   41.6    100  \n",
       "Wisconsin       4589      4468   0.7        72.48     3.0   54.5    149  \n",
       "Wyoming          376      4566   0.6        70.29     6.9   62.9    173  \n",
       "               Area  \n",
       "Alabama         50708\n",
       "Alaska         566432\n",
       "Arizona        113417\n",
       "Arkansas        51945\n",
       "California     156361\n",
       "Colorado       103766\n",
       "Connecticut      4862\n",
       "Delaware         1982\n",
       "Florida         54090\n",
       "Georgia         58073\n",
       "Hawaii           6425\n",
       "Idaho           82677\n",
       "Illinois        55748\n",
       "Indiana         36097\n",
       "Iowa            55941\n",
       "Kansas          81787\n",
       "Kentucky        39650\n",
       "Louisiana       44930\n",
       "Maine           30920\n",
       "Maryland         9891\n",
       "Massachusetts    7826\n",
       "Michigan        56817\n",
       "Minnesota       79289\n",
       "Mississippi     47296\n",
       "Missouri        68995\n",
       "Montana        145587\n",
       "Nebraska        76483\n",
       "Nevada         109889\n",
       "New Hampshire    9027\n",
       "New Jersey       7521\n",
       "New Mexico     121412\n",
       "New York        47831\n",
       "North Carolina  48798\n",
       "North Dakota    69273\n",
       "Ohio            40975\n",
       "Oklahoma        68782\n",
       "Oregon          96184\n",
       "Pennsylvania    44966\n",
       "Rhode Island     1049\n",
       "South Carolina  30225\n",
       "South Dakota    75955\n",
       "Tennessee       41328\n",
       "Texas          262134\n",
       "Utah            82096\n",
       "Vermont          9267\n",
       "Virginia        39780\n",
       "Washington      66570\n",
       "West Virginia   24070\n",
       "Wisconsin       54464\n",
       "Wyoming         97203"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state.x77 #### this is a built in dataframe which contains information on the 50 states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 5 × 5 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>1940</th><th scope=col>1945</th><th scope=col>1950</th><th scope=col>1955</th><th scope=col>1960</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Food and Tobacco</th><td>22.200</td><td>44.500</td><td>59.60</td><td>73.2</td><td>86.80</td></tr>\n",
       "\t<tr><th scope=row>Household Operation</th><td>10.500</td><td>15.500</td><td>29.00</td><td>36.5</td><td>46.20</td></tr>\n",
       "\t<tr><th scope=row>Medical and Health</th><td> 3.530</td><td> 5.760</td><td> 9.71</td><td>14.0</td><td>21.10</td></tr>\n",
       "\t<tr><th scope=row>Personal Care</th><td> 1.040</td><td> 1.980</td><td> 2.45</td><td> 3.4</td><td> 5.40</td></tr>\n",
       "\t<tr><th scope=row>Private Education</th><td> 0.341</td><td> 0.974</td><td> 1.80</td><td> 2.6</td><td> 3.64</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 5 × 5 of type dbl\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & 1940 & 1945 & 1950 & 1955 & 1960\\\\\n",
       "\\hline\n",
       "\tFood and Tobacco & 22.200 & 44.500 & 59.60 & 73.2 & 86.80\\\\\n",
       "\tHousehold Operation & 10.500 & 15.500 & 29.00 & 36.5 & 46.20\\\\\n",
       "\tMedical and Health &  3.530 &  5.760 &  9.71 & 14.0 & 21.10\\\\\n",
       "\tPersonal Care &  1.040 &  1.980 &  2.45 &  3.4 &  5.40\\\\\n",
       "\tPrivate Education &  0.341 &  0.974 &  1.80 &  2.6 &  3.64\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 5 × 5 of type dbl\n",
       "\n",
       "| <!--/--> | 1940 | 1945 | 1950 | 1955 | 1960 |\n",
       "|---|---|---|---|---|---|\n",
       "| Food and Tobacco | 22.200 | 44.500 | 59.60 | 73.2 | 86.80 |\n",
       "| Household Operation | 10.500 | 15.500 | 29.00 | 36.5 | 46.20 |\n",
       "| Medical and Health |  3.530 |  5.760 |  9.71 | 14.0 | 21.10 |\n",
       "| Personal Care |  1.040 |  1.980 |  2.45 |  3.4 |  5.40 |\n",
       "| Private Education |  0.341 |  0.974 |  1.80 |  2.6 |  3.64 |\n",
       "\n"
      ],
      "text/plain": [
       "                    1940   1945   1950  1955 1960 \n",
       "Food and Tobacco    22.200 44.500 59.60 73.2 86.80\n",
       "Household Operation 10.500 15.500 29.00 36.5 46.20\n",
       "Medical and Health   3.530  5.760  9.71 14.0 21.10\n",
       "Personal Care        1.040  1.980  2.45  3.4  5.40\n",
       "Private Education    0.341  0.974  1.80  2.6  3.64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "USPersonalExpenditure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 15 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>height</th><th scope=col>weight</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>58</td><td>115</td></tr>\n",
       "\t<tr><td>59</td><td>117</td></tr>\n",
       "\t<tr><td>60</td><td>120</td></tr>\n",
       "\t<tr><td>61</td><td>123</td></tr>\n",
       "\t<tr><td>62</td><td>126</td></tr>\n",
       "\t<tr><td>63</td><td>129</td></tr>\n",
       "\t<tr><td>64</td><td>132</td></tr>\n",
       "\t<tr><td>65</td><td>135</td></tr>\n",
       "\t<tr><td>66</td><td>139</td></tr>\n",
       "\t<tr><td>67</td><td>142</td></tr>\n",
       "\t<tr><td>68</td><td>146</td></tr>\n",
       "\t<tr><td>69</td><td>150</td></tr>\n",
       "\t<tr><td>70</td><td>154</td></tr>\n",
       "\t<tr><td>71</td><td>159</td></tr>\n",
       "\t<tr><td>72</td><td>164</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 15 × 2\n",
       "\\begin{tabular}{ll}\n",
       " height & weight\\\\\n",
       " <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 58 & 115\\\\\n",
       "\t 59 & 117\\\\\n",
       "\t 60 & 120\\\\\n",
       "\t 61 & 123\\\\\n",
       "\t 62 & 126\\\\\n",
       "\t 63 & 129\\\\\n",
       "\t 64 & 132\\\\\n",
       "\t 65 & 135\\\\\n",
       "\t 66 & 139\\\\\n",
       "\t 67 & 142\\\\\n",
       "\t 68 & 146\\\\\n",
       "\t 69 & 150\\\\\n",
       "\t 70 & 154\\\\\n",
       "\t 71 & 159\\\\\n",
       "\t 72 & 164\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 15 × 2\n",
       "\n",
       "| height &lt;dbl&gt; | weight &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 58 | 115 |\n",
       "| 59 | 117 |\n",
       "| 60 | 120 |\n",
       "| 61 | 123 |\n",
       "| 62 | 126 |\n",
       "| 63 | 129 |\n",
       "| 64 | 132 |\n",
       "| 65 | 135 |\n",
       "| 66 | 139 |\n",
       "| 67 | 142 |\n",
       "| 68 | 146 |\n",
       "| 69 | 150 |\n",
       "| 70 | 154 |\n",
       "| 71 | 159 |\n",
       "| 72 | 164 |\n",
       "\n"
      ],
      "text/plain": [
       "   height weight\n",
       "1  58     115   \n",
       "2  59     117   \n",
       "3  60     120   \n",
       "4  61     123   \n",
       "5  62     126   \n",
       "6  63     129   \n",
       "7  64     132   \n",
       "8  65     135   \n",
       "9  66     139   \n",
       "10 67     142   \n",
       "11 68     146   \n",
       "12 69     150   \n",
       "13 70     154   \n",
       "14 71     159   \n",
       "15 72     164   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data() #shows all the datasets that come prepackages with R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Data Frames\n",
    "\n",
    "You'll notice some Data Frames are really big. We can use the head() and tail() functions to view the first and last 6 rows. Let's try it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Population</th><th scope=col>Income</th><th scope=col>Illiteracy</th><th scope=col>Life Exp</th><th scope=col>Murder</th><th scope=col>HS Grad</th><th scope=col>Frost</th><th scope=col>Area</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Alabama</th><td> 3615</td><td>3624</td><td>2.1</td><td>69.05</td><td>15.1</td><td>41.3</td><td> 20</td><td> 50708</td></tr>\n",
       "\t<tr><th scope=row>Alaska</th><td>  365</td><td>6315</td><td>1.5</td><td>69.31</td><td>11.3</td><td>66.7</td><td>152</td><td>566432</td></tr>\n",
       "\t<tr><th scope=row>Arizona</th><td> 2212</td><td>4530</td><td>1.8</td><td>70.55</td><td> 7.8</td><td>58.1</td><td> 15</td><td>113417</td></tr>\n",
       "\t<tr><th scope=row>Arkansas</th><td> 2110</td><td>3378</td><td>1.9</td><td>70.66</td><td>10.1</td><td>39.9</td><td> 65</td><td> 51945</td></tr>\n",
       "\t<tr><th scope=row>California</th><td>21198</td><td>5114</td><td>1.1</td><td>71.71</td><td>10.3</td><td>62.6</td><td> 20</td><td>156361</td></tr>\n",
       "\t<tr><th scope=row>Colorado</th><td> 2541</td><td>4884</td><td>0.7</td><td>72.06</td><td> 6.8</td><td>63.9</td><td>166</td><td>103766</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Population & Income & Illiteracy & Life Exp & Murder & HS Grad & Frost & Area\\\\\n",
       "\\hline\n",
       "\tAlabama &  3615 & 3624 & 2.1 & 69.05 & 15.1 & 41.3 &  20 &  50708\\\\\n",
       "\tAlaska &   365 & 6315 & 1.5 & 69.31 & 11.3 & 66.7 & 152 & 566432\\\\\n",
       "\tArizona &  2212 & 4530 & 1.8 & 70.55 &  7.8 & 58.1 &  15 & 113417\\\\\n",
       "\tArkansas &  2110 & 3378 & 1.9 & 70.66 & 10.1 & 39.9 &  65 &  51945\\\\\n",
       "\tCalifornia & 21198 & 5114 & 1.1 & 71.71 & 10.3 & 62.6 &  20 & 156361\\\\\n",
       "\tColorado &  2541 & 4884 & 0.7 & 72.06 &  6.8 & 63.9 & 166 & 103766\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Population | Income | Illiteracy | Life Exp | Murder | HS Grad | Frost | Area |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Alabama |  3615 | 3624 | 2.1 | 69.05 | 15.1 | 41.3 |  20 |  50708 |\n",
       "| Alaska |   365 | 6315 | 1.5 | 69.31 | 11.3 | 66.7 | 152 | 566432 |\n",
       "| Arizona |  2212 | 4530 | 1.8 | 70.55 |  7.8 | 58.1 |  15 | 113417 |\n",
       "| Arkansas |  2110 | 3378 | 1.9 | 70.66 | 10.1 | 39.9 |  65 |  51945 |\n",
       "| California | 21198 | 5114 | 1.1 | 71.71 | 10.3 | 62.6 |  20 | 156361 |\n",
       "| Colorado |  2541 | 4884 | 0.7 | 72.06 |  6.8 | 63.9 | 166 | 103766 |\n",
       "\n"
      ],
      "text/plain": [
       "           Population Income Illiteracy Life Exp Murder HS Grad Frost Area  \n",
       "Alabama     3615      3624   2.1        69.05    15.1   41.3     20    50708\n",
       "Alaska       365      6315   1.5        69.31    11.3   66.7    152   566432\n",
       "Arizona     2212      4530   1.8        70.55     7.8   58.1     15   113417\n",
       "Arkansas    2110      3378   1.9        70.66    10.1   39.9     65    51945\n",
       "California 21198      5114   1.1        71.71    10.3   62.6     20   156361\n",
       "Colorado    2541      4884   0.7        72.06     6.8   63.9    166   103766"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Population</th><th scope=col>Income</th><th scope=col>Illiteracy</th><th scope=col>Life Exp</th><th scope=col>Murder</th><th scope=col>HS Grad</th><th scope=col>Frost</th><th scope=col>Area</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Vermont</th><td> 472</td><td>3907</td><td>0.6</td><td>71.64</td><td>5.5</td><td>57.1</td><td>168</td><td> 9267</td></tr>\n",
       "\t<tr><th scope=row>Virginia</th><td>4981</td><td>4701</td><td>1.4</td><td>70.08</td><td>9.5</td><td>47.8</td><td> 85</td><td>39780</td></tr>\n",
       "\t<tr><th scope=row>Washington</th><td>3559</td><td>4864</td><td>0.6</td><td>71.72</td><td>4.3</td><td>63.5</td><td> 32</td><td>66570</td></tr>\n",
       "\t<tr><th scope=row>West Virginia</th><td>1799</td><td>3617</td><td>1.4</td><td>69.48</td><td>6.7</td><td>41.6</td><td>100</td><td>24070</td></tr>\n",
       "\t<tr><th scope=row>Wisconsin</th><td>4589</td><td>4468</td><td>0.7</td><td>72.48</td><td>3.0</td><td>54.5</td><td>149</td><td>54464</td></tr>\n",
       "\t<tr><th scope=row>Wyoming</th><td> 376</td><td>4566</td><td>0.6</td><td>70.29</td><td>6.9</td><td>62.9</td><td>173</td><td>97203</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Population & Income & Illiteracy & Life Exp & Murder & HS Grad & Frost & Area\\\\\n",
       "\\hline\n",
       "\tVermont &  472 & 3907 & 0.6 & 71.64 & 5.5 & 57.1 & 168 &  9267\\\\\n",
       "\tVirginia & 4981 & 4701 & 1.4 & 70.08 & 9.5 & 47.8 &  85 & 39780\\\\\n",
       "\tWashington & 3559 & 4864 & 0.6 & 71.72 & 4.3 & 63.5 &  32 & 66570\\\\\n",
       "\tWest Virginia & 1799 & 3617 & 1.4 & 69.48 & 6.7 & 41.6 & 100 & 24070\\\\\n",
       "\tWisconsin & 4589 & 4468 & 0.7 & 72.48 & 3.0 & 54.5 & 149 & 54464\\\\\n",
       "\tWyoming &  376 & 4566 & 0.6 & 70.29 & 6.9 & 62.9 & 173 & 97203\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Population | Income | Illiteracy | Life Exp | Murder | HS Grad | Frost | Area |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Vermont |  472 | 3907 | 0.6 | 71.64 | 5.5 | 57.1 | 168 |  9267 |\n",
       "| Virginia | 4981 | 4701 | 1.4 | 70.08 | 9.5 | 47.8 |  85 | 39780 |\n",
       "| Washington | 3559 | 4864 | 0.6 | 71.72 | 4.3 | 63.5 |  32 | 66570 |\n",
       "| West Virginia | 1799 | 3617 | 1.4 | 69.48 | 6.7 | 41.6 | 100 | 24070 |\n",
       "| Wisconsin | 4589 | 4468 | 0.7 | 72.48 | 3.0 | 54.5 | 149 | 54464 |\n",
       "| Wyoming |  376 | 4566 | 0.6 | 70.29 | 6.9 | 62.9 | 173 | 97203 |\n",
       "\n"
      ],
      "text/plain": [
       "              Population Income Illiteracy Life Exp Murder HS Grad Frost Area \n",
       "Vermont        472       3907   0.6        71.64    5.5    57.1    168    9267\n",
       "Virginia      4981       4701   1.4        70.08    9.5    47.8     85   39780\n",
       "Washington    3559       4864   0.6        71.72    4.3    63.5     32   66570\n",
       "West Virginia 1799       3617   1.4        69.48    6.7    41.6    100   24070\n",
       "Wisconsin     4589       4468   0.7        72.48    3.0    54.5    149   54464\n",
       "Wyoming        376       4566   0.6        70.29    6.9    62.9    173   97203"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(state.x77)\n",
    "tail(state.x77)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Frames - Overview of Information\n",
    "\n",
    "We can use the str() to get the structure of a data frame. which gives information about the data frame such as variable names and data types. We can use the summary() function to get a quick statistical summary of all the columns of a data frame. The usefulness of this data will vary case by case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " num [1:50, 1:8] 3615 365 2212 2110 21198 ...\n",
      " - attr(*, \"dimnames\")=List of 2\n",
      "  ..$ : chr [1:50] \"Alabama\" \"Alaska\" \"Arizona\" \"Arkansas\" ...\n",
      "  ..$ : chr [1:8] \"Population\" \"Income\" \"Illiteracy\" \"Life Exp\" ...\n"
     ]
    }
   ],
   "source": [
    "str(state.x77) ### str() shows the structure of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Population        Income       Illiteracy       Life Exp    \n",
       " Min.   :  365   Min.   :3098   Min.   :0.500   Min.   :67.96  \n",
       " 1st Qu.: 1080   1st Qu.:3993   1st Qu.:0.625   1st Qu.:70.12  \n",
       " Median : 2838   Median :4519   Median :0.950   Median :70.67  \n",
       " Mean   : 4246   Mean   :4436   Mean   :1.170   Mean   :70.88  \n",
       " 3rd Qu.: 4968   3rd Qu.:4814   3rd Qu.:1.575   3rd Qu.:71.89  \n",
       " Max.   :21198   Max.   :6315   Max.   :2.800   Max.   :73.60  \n",
       "     Murder          HS Grad          Frost             Area       \n",
       " Min.   : 1.400   Min.   :37.80   Min.   :  0.00   Min.   :  1049  \n",
       " 1st Qu.: 4.350   1st Qu.:48.05   1st Qu.: 66.25   1st Qu.: 36985  \n",
       " Median : 6.850   Median :53.25   Median :114.50   Median : 54277  \n",
       " Mean   : 7.378   Mean   :53.11   Mean   :104.46   Mean   : 70736  \n",
       " 3rd Qu.:10.675   3rd Qu.:59.15   3rd Qu.:139.75   3rd Qu.: 81163  \n",
       " Max.   :15.100   Max.   :67.30   Max.   :188.00   Max.   :566432  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(state.x77) #gives a summary of the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Data Frames\n",
    "\n",
    "We can create data frames using the data.frame() function and pass vectors as arguments, which will then convert the vectors into columns of the data frame. Let's see a simple example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 7 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>days</th><th scope=col>temp</th><th scope=col>rain</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Mon </td><td>78</td><td> TRUE</td></tr>\n",
       "\t<tr><td>Tues</td><td>79</td><td>FALSE</td></tr>\n",
       "\t<tr><td>Wed </td><td>65</td><td>FALSE</td></tr>\n",
       "\t<tr><td>Thur</td><td>68</td><td>FALSE</td></tr>\n",
       "\t<tr><td>Fri </td><td>65</td><td> TRUE</td></tr>\n",
       "\t<tr><td>Sat </td><td>70</td><td>FALSE</td></tr>\n",
       "\t<tr><td>Sun </td><td>71</td><td> TRUE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 3\n",
       "\\begin{tabular}{lll}\n",
       " days & temp & rain\\\\\n",
       " <fct> & <dbl> & <lgl>\\\\\n",
       "\\hline\n",
       "\t Mon  & 78 &  TRUE\\\\\n",
       "\t Tues & 79 & FALSE\\\\\n",
       "\t Wed  & 65 & FALSE\\\\\n",
       "\t Thur & 68 & FALSE\\\\\n",
       "\t Fri  & 65 &  TRUE\\\\\n",
       "\t Sat  & 70 & FALSE\\\\\n",
       "\t Sun  & 71 &  TRUE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 3\n",
       "\n",
       "| days &lt;fct&gt; | temp &lt;dbl&gt; | rain &lt;lgl&gt; |\n",
       "|---|---|---|\n",
       "| Mon  | 78 |  TRUE |\n",
       "| Tues | 79 | FALSE |\n",
       "| Wed  | 65 | FALSE |\n",
       "| Thur | 68 | FALSE |\n",
       "| Fri  | 65 |  TRUE |\n",
       "| Sat  | 70 | FALSE |\n",
       "| Sun  | 71 |  TRUE |\n",
       "\n"
      ],
      "text/plain": [
       "  days temp rain \n",
       "1 Mon  78    TRUE\n",
       "2 Tues 79   FALSE\n",
       "3 Wed  65   FALSE\n",
       "4 Thur 68   FALSE\n",
       "5 Fri  65    TRUE\n",
       "6 Sat  70   FALSE\n",
       "7 Sun  71    TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### lets make a dataframe!\n",
    "days <- c('Mon','Tues','Wed','Thur','Fri','Sat','Sun')\n",
    "temp <- c(78,79,65,68,65,70,71)\n",
    "rain <- c(TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE)\n",
    "weather <- data.frame(days,temp,rain)\n",
    "weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   days        temp          rain        \n",
       " Fri :1   Min.   :65.00   Mode :logical  \n",
       " Mon :1   1st Qu.:66.50   FALSE:4        \n",
       " Sat :1   Median :70.00   TRUE :3        \n",
       " Sun :1   Mean   :70.86                  \n",
       " Thur:1   3rd Qu.:74.50                  \n",
       " Tues:1   Max.   :79.00                  \n",
       " Wed :1                                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(weather)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t7 obs. of  3 variables:\n",
      " $ days: Factor w/ 7 levels \"Fri\",\"Mon\",\"Sat\",..: 2 6 7 5 1 3 4\n",
      " $ temp: num  78 79 65 68 65 70 71\n",
      " $ rain: logi  TRUE FALSE FALSE FALSE TRUE FALSE ...\n"
     ]
    }
   ],
   "source": [
    "str(weather)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it for the basics! "
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}