{
  "nbformat": 4,
  "nbformat_minor": 0,
  "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"
    },
    "colab": {
      "name": "stats306_lab4_solution.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRkd4StMnCXK"
      },
      "source": [
        "# Lab 4: dplyr examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-h7i2qyGnCXN",
        "outputId": "6465e47b-a979-4251-af8f-615c434a22b9"
      },
      "source": [
        "require(tidyverse)\n",
        "options(repr.plot.height=4)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading required package: tidyverse\n",
            "\n",
            "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.0 ──\n",
            "\n",
            "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.3     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
            "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.0.6     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.4\n",
            "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.2     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
            "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 1.4.0     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n",
            "\n",
            "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
            "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
            "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5h4IPIOznCXO"
      },
      "source": [
        "# Review\n",
        "For now, we are going to work with the following dplyr functions:\n",
        "\n",
        "    * filter\n",
        "    * arrange\n",
        "    * select\n",
        "    * mutate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aabN5sqonCXP"
      },
      "source": [
        "Today we are going to work on `midwest` dataset. It has demographic information of midwest counties."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "W4rEhUGNnCXP",
        "outputId": "7fea3c47-d9bd-4675-b4c9-dc7895232944"
      },
      "source": [
        "head(midwest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead><tr><th scope=col>PID</th><th scope=col>county</th><th scope=col>state</th><th scope=col>area</th><th scope=col>poptotal</th><th scope=col>popdensity</th><th scope=col>popwhite</th><th scope=col>popblack</th><th scope=col>popamerindian</th><th scope=col>popasian</th><th scope=col>...</th><th scope=col>percollege</th><th scope=col>percprof</th><th scope=col>poppovertyknown</th><th scope=col>percpovertyknown</th><th scope=col>percbelowpoverty</th><th scope=col>percchildbelowpovert</th><th scope=col>percadultpoverty</th><th scope=col>percelderlypoverty</th><th scope=col>inmetro</th><th scope=col>category</th></tr></thead>\n",
              "<tbody>\n",
              "\t<tr><td>561      </td><td>ADAMS    </td><td>IL       </td><td>0.052    </td><td>66090    </td><td>1270.9615</td><td>63917    </td><td>1702     </td><td>98       </td><td>249      </td><td>...      </td><td>19.63139 </td><td>4.355859 </td><td>63628    </td><td>96.27478 </td><td>13.151443</td><td>18.01172 </td><td>11.009776</td><td>12.443812</td><td>0        </td><td>AAR      </td></tr>\n",
              "\t<tr><td>562      </td><td>ALEXANDER</td><td>IL       </td><td>0.014    </td><td>10626    </td><td> 759.0000</td><td> 7054    </td><td>3496     </td><td>19       </td><td> 48      </td><td>...      </td><td>11.24331 </td><td>2.870315 </td><td>10529    </td><td>99.08714 </td><td>32.244278</td><td>45.82651 </td><td>27.385647</td><td>25.228976</td><td>0        </td><td>LHR      </td></tr>\n",
              "\t<tr><td>563      </td><td>BOND     </td><td>IL       </td><td>0.022    </td><td>14991    </td><td> 681.4091</td><td>14477    </td><td> 429     </td><td>35       </td><td> 16      </td><td>...      </td><td>17.03382 </td><td>4.488572 </td><td>14235    </td><td>94.95697 </td><td>12.068844</td><td>14.03606 </td><td>10.852090</td><td>12.697410</td><td>0        </td><td>AAR      </td></tr>\n",
              "\t<tr><td>564      </td><td>BOONE    </td><td>IL       </td><td>0.017    </td><td>30806    </td><td>1812.1176</td><td>29344    </td><td> 127     </td><td>46       </td><td>150      </td><td>...      </td><td>17.27895 </td><td>4.197800 </td><td>30337    </td><td>98.47757 </td><td> 7.209019</td><td>11.17954 </td><td> 5.536013</td><td> 6.217047</td><td>1        </td><td>ALU      </td></tr>\n",
              "\t<tr><td>565      </td><td>BROWN    </td><td>IL       </td><td>0.018    </td><td> 5836    </td><td> 324.2222</td><td> 5264    </td><td> 547     </td><td>14       </td><td>  5      </td><td>...      </td><td>14.47600 </td><td>3.367680 </td><td> 4815    </td><td>82.50514 </td><td>13.520249</td><td>13.02289 </td><td>11.143211</td><td>19.200000</td><td>0        </td><td>AAR      </td></tr>\n",
              "\t<tr><td>566      </td><td>BUREAU   </td><td>IL       </td><td>0.050    </td><td>35688    </td><td> 713.7600</td><td>35157    </td><td>  50     </td><td>65       </td><td>195      </td><td>...      </td><td>18.90462 </td><td>3.275891 </td><td>35107    </td><td>98.37200 </td><td>10.399635</td><td>14.15882 </td><td> 8.179287</td><td>11.008586</td><td>0        </td><td>AAR      </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": "\\begin{tabular}{r|llllllllllllllllllllllllllll}\n PID & county & state & area & poptotal & popdensity & popwhite & popblack & popamerindian & popasian & ... & percollege & percprof & poppovertyknown & percpovertyknown & percbelowpoverty & percchildbelowpovert & percadultpoverty & percelderlypoverty & inmetro & category\\\\\n\\hline\n\t 561       & ADAMS     & IL        & 0.052     & 66090     & 1270.9615 & 63917     & 1702      & 98        & 249       & ...       & 19.63139  & 4.355859  & 63628     & 96.27478  & 13.151443 & 18.01172  & 11.009776 & 12.443812 & 0         & AAR      \\\\\n\t 562       & ALEXANDER & IL        & 0.014     & 10626     &  759.0000 &  7054     & 3496      & 19        &  48       & ...       & 11.24331  & 2.870315  & 10529     & 99.08714  & 32.244278 & 45.82651  & 27.385647 & 25.228976 & 0         & LHR      \\\\\n\t 563       & BOND      & IL        & 0.022     & 14991     &  681.4091 & 14477     &  429      & 35        &  16       & ...       & 17.03382  & 4.488572  & 14235     & 94.95697  & 12.068844 & 14.03606  & 10.852090 & 12.697410 & 0         & AAR      \\\\\n\t 564       & BOONE     & IL        & 0.017     & 30806     & 1812.1176 & 29344     &  127      & 46        & 150       & ...       & 17.27895  & 4.197800  & 30337     & 98.47757  &  7.209019 & 11.17954  &  5.536013 &  6.217047 & 1         & ALU      \\\\\n\t 565       & BROWN     & IL        & 0.018     &  5836     &  324.2222 &  5264     &  547      & 14        &   5       & ...       & 14.47600  & 3.367680  &  4815     & 82.50514  & 13.520249 & 13.02289  & 11.143211 & 19.200000 & 0         & AAR      \\\\\n\t 566       & BUREAU    & IL        & 0.050     & 35688     &  713.7600 & 35157     &   50      & 65        & 195       & ...       & 18.90462  & 3.275891  & 35107     & 98.37200  & 10.399635 & 14.15882  &  8.179287 & 11.008586 & 0         & AAR      \\\\\n\\end{tabular}\n",
            "text/markdown": "\n| PID | county | state | area | poptotal | popdensity | popwhite | popblack | popamerindian | popasian | ... | percollege | percprof | poppovertyknown | percpovertyknown | percbelowpoverty | percchildbelowpovert | percadultpoverty | percelderlypoverty | inmetro | category |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 561       | ADAMS     | IL        | 0.052     | 66090     | 1270.9615 | 63917     | 1702      | 98        | 249       | ...       | 19.63139  | 4.355859  | 63628     | 96.27478  | 13.151443 | 18.01172  | 11.009776 | 12.443812 | 0         | AAR       |\n| 562       | ALEXANDER | IL        | 0.014     | 10626     |  759.0000 |  7054     | 3496      | 19        |  48       | ...       | 11.24331  | 2.870315  | 10529     | 99.08714  | 32.244278 | 45.82651  | 27.385647 | 25.228976 | 0         | LHR       |\n| 563       | BOND      | IL        | 0.022     | 14991     |  681.4091 | 14477     |  429      | 35        |  16       | ...       | 17.03382  | 4.488572  | 14235     | 94.95697  | 12.068844 | 14.03606  | 10.852090 | 12.697410 | 0         | AAR       |\n| 564       | BOONE     | IL        | 0.017     | 30806     | 1812.1176 | 29344     |  127      | 46        | 150       | ...       | 17.27895  | 4.197800  | 30337     | 98.47757  |  7.209019 | 11.17954  |  5.536013 |  6.217047 | 1         | ALU       |\n| 565       | BROWN     | IL        | 0.018     |  5836     |  324.2222 |  5264     |  547      | 14        |   5       | ...       | 14.47600  | 3.367680  |  4815     | 82.50514  | 13.520249 | 13.02289  | 11.143211 | 19.200000 | 0         | AAR       |\n| 566       | BUREAU    | IL        | 0.050     | 35688     |  713.7600 | 35157     |   50      | 65        | 195       | ...       | 18.90462  | 3.275891  | 35107     | 98.37200  | 10.399635 | 14.15882  |  8.179287 | 11.008586 | 0         | AAR       |\n\n",
            "text/plain": [
              "  PID county    state area  poptotal popdensity popwhite popblack popamerindian\n",
              "1 561 ADAMS     IL    0.052 66090    1270.9615  63917    1702     98           \n",
              "2 562 ALEXANDER IL    0.014 10626     759.0000   7054    3496     19           \n",
              "3 563 BOND      IL    0.022 14991     681.4091  14477     429     35           \n",
              "4 564 BOONE     IL    0.017 30806    1812.1176  29344     127     46           \n",
              "5 565 BROWN     IL    0.018  5836     324.2222   5264     547     14           \n",
              "6 566 BUREAU    IL    0.050 35688     713.7600  35157      50     65           \n",
              "  popasian ... percollege percprof poppovertyknown percpovertyknown\n",
              "1 249      ... 19.63139   4.355859 63628           96.27478        \n",
              "2  48      ... 11.24331   2.870315 10529           99.08714        \n",
              "3  16      ... 17.03382   4.488572 14235           94.95697        \n",
              "4 150      ... 17.27895   4.197800 30337           98.47757        \n",
              "5   5      ... 14.47600   3.367680  4815           82.50514        \n",
              "6 195      ... 18.90462   3.275891 35107           98.37200        \n",
              "  percbelowpoverty percchildbelowpovert percadultpoverty percelderlypoverty\n",
              "1 13.151443        18.01172             11.009776        12.443812         \n",
              "2 32.244278        45.82651             27.385647        25.228976         \n",
              "3 12.068844        14.03606             10.852090        12.697410         \n",
              "4  7.209019        11.17954              5.536013         6.217047         \n",
              "5 13.520249        13.02289             11.143211        19.200000         \n",
              "6 10.399635        14.15882              8.179287        11.008586         \n",
              "  inmetro category\n",
              "1 0       AAR     \n",
              "2 0       LHR     \n",
              "3 0       AAR     \n",
              "4 1       ALU     \n",
              "5 0       AAR     \n",
              "6 0       AAR     "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIUHWrOdnCXQ"
      },
      "source": [
        "?midwest"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4smUzChnCXQ"
      },
      "source": [
        "#### Filter: Create a new dataset called `michigan` which only consist of michigan counties"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BO73mNr5nCXR"
      },
      "source": [
        "michigan = midwest %>% filter(state == 'MI')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRdqRbtonCXS"
      },
      "source": [
        "#### Arrange: Print top 10  Michigan counties by decsending area size "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8MsUlbqnCXT",
        "outputId": "e7c80b69-f874-4492-fe71-ee67b817ef51"
      },
      "source": [
        "michigan %>% arrange(desc(area)) %>% head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead><tr><th scope=col>PID</th><th scope=col>county</th><th scope=col>state</th><th scope=col>area</th><th scope=col>poptotal</th><th scope=col>popdensity</th><th scope=col>popwhite</th><th scope=col>popblack</th><th scope=col>popamerindian</th><th scope=col>popasian</th><th scope=col>...</th><th scope=col>percollege</th><th scope=col>percprof</th><th scope=col>poppovertyknown</th><th scope=col>percpovertyknown</th><th scope=col>percbelowpoverty</th><th scope=col>percchildbelowpovert</th><th scope=col>percadultpoverty</th><th scope=col>percelderlypoverty</th><th scope=col>inmetro</th><th scope=col>category</th></tr></thead>\n",
              "<tbody>\n",
              "\t<tr><td>1248       </td><td>MARQUETTE  </td><td>MI         </td><td>0.110      </td><td>70887      </td><td>644.4273   </td><td>68027      </td><td>1170       </td><td> 943       </td><td>538        </td><td>...        </td><td>26.76355   </td><td>6.799415   </td><td>66398      </td><td>93.66739   </td><td>12.60881   </td><td>14.26216   </td><td>11.84465   </td><td>12.52389   </td><td>0          </td><td>HAR        </td></tr>\n",
              "\t<tr><td>1213       </td><td>CHIPPEWA   </td><td>MI         </td><td>0.078      </td><td>34604      </td><td>443.6410   </td><td>28353      </td><td>2184       </td><td>3820       </td><td>152        </td><td>...        </td><td>16.93977   </td><td>3.808129   </td><td>29486      </td><td>85.20980   </td><td>17.07590   </td><td>20.95597   </td><td>16.10350   </td><td>14.33915   </td><td>0          </td><td>AAR        </td></tr>\n",
              "\t<tr><td>1262       </td><td>ONTONAGON  </td><td>MI         </td><td>0.078      </td><td> 8854      </td><td>113.5128   </td><td> 8723      </td><td>   4       </td><td> 109       </td><td> 15        </td><td>...        </td><td>15.95676   </td><td>2.662149   </td><td> 8712      </td><td>98.39621   </td><td>13.18871   </td><td>16.51637   </td><td>11.80804   </td><td>12.80000   </td><td>0          </td><td>AAR        </td></tr>\n",
              "\t<tr><td>1273       </td><td>SCHOOLCRAFT</td><td>MI         </td><td>0.075      </td><td> 8302      </td><td>110.6933   </td><td> 7755      </td><td>   7       </td><td> 519       </td><td> 13        </td><td>...        </td><td>14.56672   </td><td>2.764487   </td><td> 8218      </td><td>98.98820   </td><td>16.60988   </td><td>20.72882   </td><td>15.32121   </td><td>14.89899   </td><td>0          </td><td>AAR        </td></tr>\n",
              "\t<tr><td>1232       </td><td>IRON       </td><td>MI         </td><td>0.070      </td><td>13175      </td><td>188.2143   </td><td>13028      </td><td>   4       </td><td> 102       </td><td> 32        </td><td>...        </td><td>15.45758   </td><td>3.283302   </td><td>12886      </td><td>97.80645   </td><td>17.11935   </td><td>23.32412   </td><td>14.45616   </td><td>16.51224   </td><td>0          </td><td>AAR        </td></tr>\n",
              "\t<tr><td>1217       </td><td>DELTA      </td><td>MI         </td><td>0.069      </td><td>37780      </td><td>547.5362   </td><td>36819      </td><td>  16       </td><td> 809       </td><td> 99        </td><td>...        </td><td>18.91240   </td><td>3.525903   </td><td>37242      </td><td>98.57597   </td><td>14.61253   </td><td>18.59113   </td><td>13.12755   </td><td>13.10731   </td><td>0          </td><td>AAR        </td></tr>\n",
              "\t<tr><td>1223       </td><td>GOGEBIC    </td><td>MI         </td><td>0.068      </td><td>18052      </td><td>265.4706   </td><td>17486      </td><td> 243       </td><td> 283       </td><td> 26        </td><td>...        </td><td>21.17308   </td><td>3.352805   </td><td>17385      </td><td>96.30512   </td><td>14.88064   </td><td>19.54478   </td><td>13.59873   </td><td>13.25794   </td><td>0          </td><td>AAR        </td></tr>\n",
              "\t<tr><td>1251       </td><td>MENOMINEE  </td><td>MI         </td><td>0.064      </td><td>24920      </td><td>389.3750   </td><td>24464      </td><td>   7       </td><td> 382       </td><td> 60        </td><td>...        </td><td>15.08417   </td><td>2.652295   </td><td>24562      </td><td>98.56340   </td><td>12.75140   </td><td>14.06867   </td><td>11.30868   </td><td>14.55126   </td><td>0          </td><td>AAR        </td></tr>\n",
              "\t<tr><td>1227       </td><td>HOUGHTON   </td><td>MI         </td><td>0.060      </td><td>35446      </td><td>590.7667   </td><td>34469      </td><td> 158       </td><td> 153       </td><td>610        </td><td>...        </td><td>24.76509   </td><td>7.328296   </td><td>32448      </td><td>91.54206   </td><td>21.03674   </td><td>21.19898   </td><td>23.69715   </td><td>14.57849   </td><td>0          </td><td>AHR        </td></tr>\n",
              "\t<tr><td>1245       </td><td>MACKINAC   </td><td>MI         </td><td>0.059      </td><td>10674      </td><td>180.9153   </td><td> 8955      </td><td>   5       </td><td>1691       </td><td> 11        </td><td>...        </td><td>15.28787   </td><td>2.780883   </td><td>10570      </td><td>99.02567   </td><td>16.40492   </td><td>22.24231   </td><td>14.99346   </td><td>12.90717   </td><td>0          </td><td>AAR        </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": "\\begin{tabular}{r|llllllllllllllllllllllllllll}\n PID & county & state & area & poptotal & popdensity & popwhite & popblack & popamerindian & popasian & ... & percollege & percprof & poppovertyknown & percpovertyknown & percbelowpoverty & percchildbelowpovert & percadultpoverty & percelderlypoverty & inmetro & category\\\\\n\\hline\n\t 1248        & MARQUETTE   & MI          & 0.110       & 70887       & 644.4273    & 68027       & 1170        &  943        & 538         & ...         & 26.76355    & 6.799415    & 66398       & 93.66739    & 12.60881    & 14.26216    & 11.84465    & 12.52389    & 0           & HAR        \\\\\n\t 1213        & CHIPPEWA    & MI          & 0.078       & 34604       & 443.6410    & 28353       & 2184        & 3820        & 152         & ...         & 16.93977    & 3.808129    & 29486       & 85.20980    & 17.07590    & 20.95597    & 16.10350    & 14.33915    & 0           & AAR        \\\\\n\t 1262        & ONTONAGON   & MI          & 0.078       &  8854       & 113.5128    &  8723       &    4        &  109        &  15         & ...         & 15.95676    & 2.662149    &  8712       & 98.39621    & 13.18871    & 16.51637    & 11.80804    & 12.80000    & 0           & AAR        \\\\\n\t 1273        & SCHOOLCRAFT & MI          & 0.075       &  8302       & 110.6933    &  7755       &    7        &  519        &  13         & ...         & 14.56672    & 2.764487    &  8218       & 98.98820    & 16.60988    & 20.72882    & 15.32121    & 14.89899    & 0           & AAR        \\\\\n\t 1232        & IRON        & MI          & 0.070       & 13175       & 188.2143    & 13028       &    4        &  102        &  32         & ...         & 15.45758    & 3.283302    & 12886       & 97.80645    & 17.11935    & 23.32412    & 14.45616    & 16.51224    & 0           & AAR        \\\\\n\t 1217        & DELTA       & MI          & 0.069       & 37780       & 547.5362    & 36819       &   16        &  809        &  99         & ...         & 18.91240    & 3.525903    & 37242       & 98.57597    & 14.61253    & 18.59113    & 13.12755    & 13.10731    & 0           & AAR        \\\\\n\t 1223        & GOGEBIC     & MI          & 0.068       & 18052       & 265.4706    & 17486       &  243        &  283        &  26         & ...         & 21.17308    & 3.352805    & 17385       & 96.30512    & 14.88064    & 19.54478    & 13.59873    & 13.25794    & 0           & AAR        \\\\\n\t 1251        & MENOMINEE   & MI          & 0.064       & 24920       & 389.3750    & 24464       &    7        &  382        &  60         & ...         & 15.08417    & 2.652295    & 24562       & 98.56340    & 12.75140    & 14.06867    & 11.30868    & 14.55126    & 0           & AAR        \\\\\n\t 1227        & HOUGHTON    & MI          & 0.060       & 35446       & 590.7667    & 34469       &  158        &  153        & 610         & ...         & 24.76509    & 7.328296    & 32448       & 91.54206    & 21.03674    & 21.19898    & 23.69715    & 14.57849    & 0           & AHR        \\\\\n\t 1245        & MACKINAC    & MI          & 0.059       & 10674       & 180.9153    &  8955       &    5        & 1691        &  11         & ...         & 15.28787    & 2.780883    & 10570       & 99.02567    & 16.40492    & 22.24231    & 14.99346    & 12.90717    & 0           & AAR        \\\\\n\\end{tabular}\n",
            "text/markdown": "\n| PID | county | state | area | poptotal | popdensity | popwhite | popblack | popamerindian | popasian | ... | percollege | percprof | poppovertyknown | percpovertyknown | percbelowpoverty | percchildbelowpovert | percadultpoverty | percelderlypoverty | inmetro | category |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1248        | MARQUETTE   | MI          | 0.110       | 70887       | 644.4273    | 68027       | 1170        |  943        | 538         | ...         | 26.76355    | 6.799415    | 66398       | 93.66739    | 12.60881    | 14.26216    | 11.84465    | 12.52389    | 0           | HAR         |\n| 1213        | CHIPPEWA    | MI          | 0.078       | 34604       | 443.6410    | 28353       | 2184        | 3820        | 152         | ...         | 16.93977    | 3.808129    | 29486       | 85.20980    | 17.07590    | 20.95597    | 16.10350    | 14.33915    | 0           | AAR         |\n| 1262        | ONTONAGON   | MI          | 0.078       |  8854       | 113.5128    |  8723       |    4        |  109        |  15         | ...         | 15.95676    | 2.662149    |  8712       | 98.39621    | 13.18871    | 16.51637    | 11.80804    | 12.80000    | 0           | AAR         |\n| 1273        | SCHOOLCRAFT | MI          | 0.075       |  8302       | 110.6933    |  7755       |    7        |  519        |  13         | ...         | 14.56672    | 2.764487    |  8218       | 98.98820    | 16.60988    | 20.72882    | 15.32121    | 14.89899    | 0           | AAR         |\n| 1232        | IRON        | MI          | 0.070       | 13175       | 188.2143    | 13028       |    4        |  102        |  32         | ...         | 15.45758    | 3.283302    | 12886       | 97.80645    | 17.11935    | 23.32412    | 14.45616    | 16.51224    | 0           | AAR         |\n| 1217        | DELTA       | MI          | 0.069       | 37780       | 547.5362    | 36819       |   16        |  809        |  99         | ...         | 18.91240    | 3.525903    | 37242       | 98.57597    | 14.61253    | 18.59113    | 13.12755    | 13.10731    | 0           | AAR         |\n| 1223        | GOGEBIC     | MI          | 0.068       | 18052       | 265.4706    | 17486       |  243        |  283        |  26         | ...         | 21.17308    | 3.352805    | 17385       | 96.30512    | 14.88064    | 19.54478    | 13.59873    | 13.25794    | 0           | AAR         |\n| 1251        | MENOMINEE   | MI          | 0.064       | 24920       | 389.3750    | 24464       |    7        |  382        |  60         | ...         | 15.08417    | 2.652295    | 24562       | 98.56340    | 12.75140    | 14.06867    | 11.30868    | 14.55126    | 0           | AAR         |\n| 1227        | HOUGHTON    | MI          | 0.060       | 35446       | 590.7667    | 34469       |  158        |  153        | 610         | ...         | 24.76509    | 7.328296    | 32448       | 91.54206    | 21.03674    | 21.19898    | 23.69715    | 14.57849    | 0           | AHR         |\n| 1245        | MACKINAC    | MI          | 0.059       | 10674       | 180.9153    |  8955       |    5        | 1691        |  11         | ...         | 15.28787    | 2.780883    | 10570       | 99.02567    | 16.40492    | 22.24231    | 14.99346    | 12.90717    | 0           | AAR         |\n\n",
            "text/plain": [
              "   PID  county      state area  poptotal popdensity popwhite popblack\n",
              "1  1248 MARQUETTE   MI    0.110 70887    644.4273   68027    1170    \n",
              "2  1213 CHIPPEWA    MI    0.078 34604    443.6410   28353    2184    \n",
              "3  1262 ONTONAGON   MI    0.078  8854    113.5128    8723       4    \n",
              "4  1273 SCHOOLCRAFT MI    0.075  8302    110.6933    7755       7    \n",
              "5  1232 IRON        MI    0.070 13175    188.2143   13028       4    \n",
              "6  1217 DELTA       MI    0.069 37780    547.5362   36819      16    \n",
              "7  1223 GOGEBIC     MI    0.068 18052    265.4706   17486     243    \n",
              "8  1251 MENOMINEE   MI    0.064 24920    389.3750   24464       7    \n",
              "9  1227 HOUGHTON    MI    0.060 35446    590.7667   34469     158    \n",
              "10 1245 MACKINAC    MI    0.059 10674    180.9153    8955       5    \n",
              "   popamerindian popasian ... percollege percprof poppovertyknown\n",
              "1   943          538      ... 26.76355   6.799415 66398          \n",
              "2  3820          152      ... 16.93977   3.808129 29486          \n",
              "3   109           15      ... 15.95676   2.662149  8712          \n",
              "4   519           13      ... 14.56672   2.764487  8218          \n",
              "5   102           32      ... 15.45758   3.283302 12886          \n",
              "6   809           99      ... 18.91240   3.525903 37242          \n",
              "7   283           26      ... 21.17308   3.352805 17385          \n",
              "8   382           60      ... 15.08417   2.652295 24562          \n",
              "9   153          610      ... 24.76509   7.328296 32448          \n",
              "10 1691           11      ... 15.28787   2.780883 10570          \n",
              "   percpovertyknown percbelowpoverty percchildbelowpovert percadultpoverty\n",
              "1  93.66739         12.60881         14.26216             11.84465        \n",
              "2  85.20980         17.07590         20.95597             16.10350        \n",
              "3  98.39621         13.18871         16.51637             11.80804        \n",
              "4  98.98820         16.60988         20.72882             15.32121        \n",
              "5  97.80645         17.11935         23.32412             14.45616        \n",
              "6  98.57597         14.61253         18.59113             13.12755        \n",
              "7  96.30512         14.88064         19.54478             13.59873        \n",
              "8  98.56340         12.75140         14.06867             11.30868        \n",
              "9  91.54206         21.03674         21.19898             23.69715        \n",
              "10 99.02567         16.40492         22.24231             14.99346        \n",
              "   percelderlypoverty inmetro category\n",
              "1  12.52389           0       HAR     \n",
              "2  14.33915           0       AAR     \n",
              "3  12.80000           0       AAR     \n",
              "4  14.89899           0       AAR     \n",
              "5  16.51224           0       AAR     \n",
              "6  13.10731           0       AAR     \n",
              "7  13.25794           0       AAR     \n",
              "8  14.55126           0       AAR     \n",
              "9  14.57849           0       AHR     \n",
              "10 12.90717           0       AAR     "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1NrlPLpnCXU"
      },
      "source": [
        "#### Select: Print the same plot, this time only with county name and area"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKjHW4YAnCXU",
        "outputId": "e7d893d7-3954-41b3-d0aa-2b53e73ea7fb"
      },
      "source": [
        "michigan %>% arrange(desc(area)) %>% head(10) %>% select(county, area)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead><tr><th scope=col>county</th><th scope=col>area</th></tr></thead>\n",
              "<tbody>\n",
              "\t<tr><td>MARQUETTE  </td><td>0.110      </td></tr>\n",
              "\t<tr><td>CHIPPEWA   </td><td>0.078      </td></tr>\n",
              "\t<tr><td>ONTONAGON  </td><td>0.078      </td></tr>\n",
              "\t<tr><td>SCHOOLCRAFT</td><td>0.075      </td></tr>\n",
              "\t<tr><td>IRON       </td><td>0.070      </td></tr>\n",
              "\t<tr><td>DELTA      </td><td>0.069      </td></tr>\n",
              "\t<tr><td>GOGEBIC    </td><td>0.068      </td></tr>\n",
              "\t<tr><td>MENOMINEE  </td><td>0.064      </td></tr>\n",
              "\t<tr><td>HOUGHTON   </td><td>0.060      </td></tr>\n",
              "\t<tr><td>MACKINAC   </td><td>0.059      </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": "\\begin{tabular}{r|ll}\n county & area\\\\\n\\hline\n\t MARQUETTE   & 0.110      \\\\\n\t CHIPPEWA    & 0.078      \\\\\n\t ONTONAGON   & 0.078      \\\\\n\t SCHOOLCRAFT & 0.075      \\\\\n\t IRON        & 0.070      \\\\\n\t DELTA       & 0.069      \\\\\n\t GOGEBIC     & 0.068      \\\\\n\t MENOMINEE   & 0.064      \\\\\n\t HOUGHTON    & 0.060      \\\\\n\t MACKINAC    & 0.059      \\\\\n\\end{tabular}\n",
            "text/markdown": "\n| county | area |\n|---|---|\n| MARQUETTE   | 0.110       |\n| CHIPPEWA    | 0.078       |\n| ONTONAGON   | 0.078       |\n| SCHOOLCRAFT | 0.075       |\n| IRON        | 0.070       |\n| DELTA       | 0.069       |\n| GOGEBIC     | 0.068       |\n| MENOMINEE   | 0.064       |\n| HOUGHTON    | 0.060       |\n| MACKINAC    | 0.059       |\n\n",
            "text/plain": [
              "   county      area \n",
              "1  MARQUETTE   0.110\n",
              "2  CHIPPEWA    0.078\n",
              "3  ONTONAGON   0.078\n",
              "4  SCHOOLCRAFT 0.075\n",
              "5  IRON        0.070\n",
              "6  DELTA       0.069\n",
              "7  GOGEBIC     0.068\n",
              "8  MENOMINEE   0.064\n",
              "9  HOUGHTON    0.060\n",
              "10 MACKINAC    0.059"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95qVURuLnCXV"
      },
      "source": [
        "#### Mutate: This time use `diamonds` dataset and create a new variable called `volume`. Simply by multiplying `x`,`y` and `z` and print top 10 by volume"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnrRlmdenCXW",
        "outputId": "ac514b21-770c-420a-d761-080c509e7f2e"
      },
      "source": [
        "diamonds %>% \n",
        "mutate(volume = x*y*z) %>% \n",
        "head(10) %>% arrange(desc(volume))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead><tr><th scope=col>carat</th><th scope=col>cut</th><th scope=col>color</th><th scope=col>clarity</th><th scope=col>depth</th><th scope=col>table</th><th scope=col>price</th><th scope=col>x</th><th scope=col>y</th><th scope=col>z</th><th scope=col>volume</th></tr></thead>\n",
              "<tbody>\n",
              "\t<tr><td>0.31     </td><td>Good     </td><td>J        </td><td>SI2      </td><td>63.3     </td><td>58       </td><td>335      </td><td>4.34     </td><td>4.35     </td><td>2.75     </td><td>51.91725 </td></tr>\n",
              "\t<tr><td>0.29     </td><td>Premium  </td><td>I        </td><td>VS2      </td><td>62.4     </td><td>58       </td><td>334      </td><td>4.20     </td><td>4.23     </td><td>2.63     </td><td>46.72458 </td></tr>\n",
              "\t<tr><td>0.26     </td><td>Very Good</td><td>H        </td><td>SI1      </td><td>61.9     </td><td>55       </td><td>337      </td><td>4.07     </td><td>4.11     </td><td>2.53     </td><td>42.32108 </td></tr>\n",
              "\t<tr><td>0.24     </td><td>Very Good</td><td>I        </td><td>VVS1     </td><td>62.3     </td><td>57       </td><td>336      </td><td>3.95     </td><td>3.98     </td><td>2.47     </td><td>38.83087 </td></tr>\n",
              "\t<tr><td>0.23     </td><td>Very Good</td><td>H        </td><td>VS1      </td><td>59.4     </td><td>61       </td><td>338      </td><td>4.00     </td><td>4.05     </td><td>2.39     </td><td>38.71800 </td></tr>\n",
              "\t<tr><td>0.24     </td><td>Very Good</td><td>J        </td><td>VVS2     </td><td>62.8     </td><td>57       </td><td>336      </td><td>3.94     </td><td>3.96     </td><td>2.48     </td><td>38.69395 </td></tr>\n",
              "\t<tr><td>0.23     </td><td>Ideal    </td><td>E        </td><td>SI2      </td><td>61.5     </td><td>55       </td><td>326      </td><td>3.95     </td><td>3.98     </td><td>2.43     </td><td>38.20203 </td></tr>\n",
              "\t<tr><td>0.23     </td><td>Good     </td><td>E        </td><td>VS1      </td><td>56.9     </td><td>65       </td><td>327      </td><td>4.05     </td><td>4.07     </td><td>2.31     </td><td>38.07688 </td></tr>\n",
              "\t<tr><td>0.22     </td><td>Fair     </td><td>E        </td><td>VS2      </td><td>65.1     </td><td>61       </td><td>337      </td><td>3.87     </td><td>3.78     </td><td>2.49     </td><td>36.42521 </td></tr>\n",
              "\t<tr><td>0.21     </td><td>Premium  </td><td>E        </td><td>SI1      </td><td>59.8     </td><td>61       </td><td>326      </td><td>3.89     </td><td>3.84     </td><td>2.31     </td><td>34.50586 </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": "\\begin{tabular}{r|lllllllllll}\n carat & cut & color & clarity & depth & table & price & x & y & z & volume\\\\\n\\hline\n\t 0.31      & Good      & J         & SI2       & 63.3      & 58        & 335       & 4.34      & 4.35      & 2.75      & 51.91725 \\\\\n\t 0.29      & Premium   & I         & VS2       & 62.4      & 58        & 334       & 4.20      & 4.23      & 2.63      & 46.72458 \\\\\n\t 0.26      & Very Good & H         & SI1       & 61.9      & 55        & 337       & 4.07      & 4.11      & 2.53      & 42.32108 \\\\\n\t 0.24      & Very Good & I         & VVS1      & 62.3      & 57        & 336       & 3.95      & 3.98      & 2.47      & 38.83087 \\\\\n\t 0.23      & Very Good & H         & VS1       & 59.4      & 61        & 338       & 4.00      & 4.05      & 2.39      & 38.71800 \\\\\n\t 0.24      & Very Good & J         & VVS2      & 62.8      & 57        & 336       & 3.94      & 3.96      & 2.48      & 38.69395 \\\\\n\t 0.23      & Ideal     & E         & SI2       & 61.5      & 55        & 326       & 3.95      & 3.98      & 2.43      & 38.20203 \\\\\n\t 0.23      & Good      & E         & VS1       & 56.9      & 65        & 327       & 4.05      & 4.07      & 2.31      & 38.07688 \\\\\n\t 0.22      & Fair      & E         & VS2       & 65.1      & 61        & 337       & 3.87      & 3.78      & 2.49      & 36.42521 \\\\\n\t 0.21      & Premium   & E         & SI1       & 59.8      & 61        & 326       & 3.89      & 3.84      & 2.31      & 34.50586 \\\\\n\\end{tabular}\n",
            "text/markdown": "\n| carat | cut | color | clarity | depth | table | price | x | y | z | volume |\n|---|---|---|---|---|---|---|---|---|---|---|\n| 0.31      | Good      | J         | SI2       | 63.3      | 58        | 335       | 4.34      | 4.35      | 2.75      | 51.91725  |\n| 0.29      | Premium   | I         | VS2       | 62.4      | 58        | 334       | 4.20      | 4.23      | 2.63      | 46.72458  |\n| 0.26      | Very Good | H         | SI1       | 61.9      | 55        | 337       | 4.07      | 4.11      | 2.53      | 42.32108  |\n| 0.24      | Very Good | I         | VVS1      | 62.3      | 57        | 336       | 3.95      | 3.98      | 2.47      | 38.83087  |\n| 0.23      | Very Good | H         | VS1       | 59.4      | 61        | 338       | 4.00      | 4.05      | 2.39      | 38.71800  |\n| 0.24      | Very Good | J         | VVS2      | 62.8      | 57        | 336       | 3.94      | 3.96      | 2.48      | 38.69395  |\n| 0.23      | Ideal     | E         | SI2       | 61.5      | 55        | 326       | 3.95      | 3.98      | 2.43      | 38.20203  |\n| 0.23      | Good      | E         | VS1       | 56.9      | 65        | 327       | 4.05      | 4.07      | 2.31      | 38.07688  |\n| 0.22      | Fair      | E         | VS2       | 65.1      | 61        | 337       | 3.87      | 3.78      | 2.49      | 36.42521  |\n| 0.21      | Premium   | E         | SI1       | 59.8      | 61        | 326       | 3.89      | 3.84      | 2.31      | 34.50586  |\n\n",
            "text/plain": [
              "   carat cut       color clarity depth table price x    y    z    volume  \n",
              "1  0.31  Good      J     SI2     63.3  58    335   4.34 4.35 2.75 51.91725\n",
              "2  0.29  Premium   I     VS2     62.4  58    334   4.20 4.23 2.63 46.72458\n",
              "3  0.26  Very Good H     SI1     61.9  55    337   4.07 4.11 2.53 42.32108\n",
              "4  0.24  Very Good I     VVS1    62.3  57    336   3.95 3.98 2.47 38.83087\n",
              "5  0.23  Very Good H     VS1     59.4  61    338   4.00 4.05 2.39 38.71800\n",
              "6  0.24  Very Good J     VVS2    62.8  57    336   3.94 3.96 2.48 38.69395\n",
              "7  0.23  Ideal     E     SI2     61.5  55    326   3.95 3.98 2.43 38.20203\n",
              "8  0.23  Good      E     VS1     56.9  65    327   4.05 4.07 2.31 38.07688\n",
              "9  0.22  Fair      E     VS2     65.1  61    337   3.87 3.78 2.49 36.42521\n",
              "10 0.21  Premium   E     SI1     59.8  61    326   3.89 3.84 2.31 34.50586"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gFoXfT5nCXW"
      },
      "source": [
        "# Challenge exercises"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aB-gZailnCXX"
      },
      "source": [
        "#### 1. Create a new dataset called `michigan_diverse` which satisfies following conditions:\n",
        "    * Only has Michigan counties \n",
        "    * Counties which have more than 5 percent African-American population\n",
        "    * Also, at least 15 percent non-white races\n",
        "    * Only include the variables county and variables which has `pop` in its name "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "zn8A2IXznCXY",
        "outputId": "ec0829bc-3e90-4cd6-f7df-5de28bf8eea4"
      },
      "source": [
        "michigan_diverse = michigan %>% \n",
        "filter(popblack/poptotal > 0.05, popwhite/poptotal<0.85) %>% \n",
        "select(county, contains('pop'))\n",
        "michigan_diverse\n",
        "# You can also use percentage information in dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead><tr><th scope=col>county</th><th scope=col>poptotal</th><th scope=col>popdensity</th><th scope=col>popwhite</th><th scope=col>popblack</th><th scope=col>popamerindian</th><th scope=col>popasian</th><th scope=col>popother</th><th scope=col>popadults</th><th scope=col>poppovertyknown</th></tr></thead>\n",
              "<tbody>\n",
              "\t<tr><td>BERRIEN  </td><td> 161378  </td><td> 4890.242</td><td> 133259  </td><td> 24872   </td><td> 685     </td><td> 1487    </td><td> 1075    </td><td> 102485  </td><td> 158312  </td></tr>\n",
              "\t<tr><td>CHIPPEWA </td><td>  34604  </td><td>  443.641</td><td>  28353  </td><td>  2184   </td><td>3820     </td><td>  152    </td><td>   95    </td><td>  21848  </td><td>  29486  </td></tr>\n",
              "\t<tr><td>GENESEE  </td><td> 430459  </td><td>11634.027</td><td> 336651  </td><td> 84257   </td><td>3132     </td><td> 2902    </td><td> 3517    </td><td> 265430  </td><td> 425331  </td></tr>\n",
              "\t<tr><td>INGHAM   </td><td> 281912  </td><td> 8291.529</td><td> 237183  </td><td> 27837   </td><td>1941     </td><td> 7562    </td><td> 7389    </td><td> 158966  </td><td> 261491  </td></tr>\n",
              "\t<tr><td>MUSKEGON </td><td> 158983  </td><td> 5677.964</td><td> 133931  </td><td> 21617   </td><td>1338     </td><td>  555    </td><td> 1542    </td><td>  99720  </td><td> 154086  </td></tr>\n",
              "\t<tr><td>SAGINAW  </td><td> 211946  </td><td> 4415.542</td><td> 165430  </td><td> 36849   </td><td> 915     </td><td> 1272    </td><td> 7480    </td><td> 131154  </td><td> 208790  </td></tr>\n",
              "\t<tr><td>WASHTENAW</td><td> 282937  </td><td> 6900.902</td><td> 236390  </td><td> 31720   </td><td>1076     </td><td>11724    </td><td> 2027    </td><td> 167214  </td><td> 261261  </td></tr>\n",
              "\t<tr><td>WAYNE    </td><td>2111687  </td><td>60333.914</td><td>1212007  </td><td>849109   </td><td>8048     </td><td>21704    </td><td>20819    </td><td>1324635  </td><td>2084529  </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": "\\begin{tabular}{r|llllllllll}\n county & poptotal & popdensity & popwhite & popblack & popamerindian & popasian & popother & popadults & poppovertyknown\\\\\n\\hline\n\t BERRIEN   &  161378   &  4890.242 &  133259   &  24872    &  685      &  1487     &  1075     &  102485   &  158312  \\\\\n\t CHIPPEWA  &   34604   &   443.641 &   28353   &   2184    & 3820      &   152     &    95     &   21848   &   29486  \\\\\n\t GENESEE   &  430459   & 11634.027 &  336651   &  84257    & 3132      &  2902     &  3517     &  265430   &  425331  \\\\\n\t INGHAM    &  281912   &  8291.529 &  237183   &  27837    & 1941      &  7562     &  7389     &  158966   &  261491  \\\\\n\t MUSKEGON  &  158983   &  5677.964 &  133931   &  21617    & 1338      &   555     &  1542     &   99720   &  154086  \\\\\n\t SAGINAW   &  211946   &  4415.542 &  165430   &  36849    &  915      &  1272     &  7480     &  131154   &  208790  \\\\\n\t WASHTENAW &  282937   &  6900.902 &  236390   &  31720    & 1076      & 11724     &  2027     &  167214   &  261261  \\\\\n\t WAYNE     & 2111687   & 60333.914 & 1212007   & 849109    & 8048      & 21704     & 20819     & 1324635   & 2084529  \\\\\n\\end{tabular}\n",
            "text/markdown": "\n| county | poptotal | popdensity | popwhite | popblack | popamerindian | popasian | popother | popadults | poppovertyknown |\n|---|---|---|---|---|---|---|---|---|---|\n| BERRIEN   |  161378   |  4890.242 |  133259   |  24872    |  685      |  1487     |  1075     |  102485   |  158312   |\n| CHIPPEWA  |   34604   |   443.641 |   28353   |   2184    | 3820      |   152     |    95     |   21848   |   29486   |\n| GENESEE   |  430459   | 11634.027 |  336651   |  84257    | 3132      |  2902     |  3517     |  265430   |  425331   |\n| INGHAM    |  281912   |  8291.529 |  237183   |  27837    | 1941      |  7562     |  7389     |  158966   |  261491   |\n| MUSKEGON  |  158983   |  5677.964 |  133931   |  21617    | 1338      |   555     |  1542     |   99720   |  154086   |\n| SAGINAW   |  211946   |  4415.542 |  165430   |  36849    |  915      |  1272     |  7480     |  131154   |  208790   |\n| WASHTENAW |  282937   |  6900.902 |  236390   |  31720    | 1076      | 11724     |  2027     |  167214   |  261261   |\n| WAYNE     | 2111687   | 60333.914 | 1212007   | 849109    | 8048      | 21704     | 20819     | 1324635   | 2084529   |\n\n",
            "text/plain": [
              "  county    poptotal popdensity popwhite popblack popamerindian popasian\n",
              "1 BERRIEN    161378   4890.242   133259   24872    685           1487   \n",
              "2 CHIPPEWA    34604    443.641    28353    2184   3820            152   \n",
              "3 GENESEE    430459  11634.027   336651   84257   3132           2902   \n",
              "4 INGHAM     281912   8291.529   237183   27837   1941           7562   \n",
              "5 MUSKEGON   158983   5677.964   133931   21617   1338            555   \n",
              "6 SAGINAW    211946   4415.542   165430   36849    915           1272   \n",
              "7 WASHTENAW  282937   6900.902   236390   31720   1076          11724   \n",
              "8 WAYNE     2111687  60333.914  1212007  849109   8048          21704   \n",
              "  popother popadults poppovertyknown\n",
              "1  1075     102485    158312        \n",
              "2    95      21848     29486        \n",
              "3  3517     265430    425331        \n",
              "4  7389     158966    261491        \n",
              "5  1542      99720    154086        \n",
              "6  7480     131154    208790        \n",
              "7  2027     167214    261261        \n",
              "8 20819    1324635   2084529        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmXd0o9qnCXZ"
      },
      "source": [
        "#### 2. Show population variation of counties in a  state using box-plot. And limit the population by half a million"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AiARikxnCXd",
        "outputId": "55c10845-3d57-4305-8c74-f284d8f22b4e"
      },
      "source": [
        "ggplot(midwest %>% filter(poptotal<5e5), aes(x = state, y = poptotal)) + geom_boxplot() + \n",
        "ylim(NA, 5e5) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5KLGdvRz/itwwO0jzWpRxkrSKlga2eb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\n0rxKID38U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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOv5ATmtnCXe"
      },
      "source": [
        "#### 3. Find the Illinois counties which are top 15 by population as well as top 15 by area."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "Iu0VGomxnCXf",
        "outputId": "5a6f3758-7d58-41bf-bec9-6ad8ca50f116"
      },
      "source": [
        "n = 15\n",
        "State = \"IL\"\n",
        "mostpop = midwest %>% filter(state == State) %>% arrange(desc(poptotal)) %>% head(n) %>% select(county)\n",
        "midwest %>% filter(state == State) %>% arrange(desc(area)) %>% head(n) %>% select(county) %>% \n",
        "filter(county %in% mostpop$county)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  county   \n",
              "1 MCLEAN   \n",
              "2 CHAMPAIGN\n",
              "3 COOK     \n",
              "4 SANGAMON \n",
              "5 WILL     "
            ],
            "text/latex": "A tibble: 5 × 1\n\\begin{tabular}{l}\n county\\\\\n <chr>\\\\\n\\hline\n\t MCLEAN   \\\\\n\t CHAMPAIGN\\\\\n\t COOK     \\\\\n\t SANGAMON \\\\\n\t WILL     \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 5 × 1\n\n| county &lt;chr&gt; |\n|---|\n| MCLEAN    |\n| CHAMPAIGN |\n| COOK      |\n| SANGAMON  |\n| WILL      |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 5 × 1</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>county</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>MCLEAN   </td></tr>\n",
              "\t<tr><td>CHAMPAIGN</td></tr>\n",
              "\t<tr><td>COOK     </td></tr>\n",
              "\t<tr><td>SANGAMON </td></tr>\n",
              "\t<tr><td>WILL     </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-O1NaXCFnCXf",
        "outputId": "7691187b-09f0-4af5-944c-9af4527371e0"
      },
      "source": [
        "mostpop$county"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>'COOK'</li>\n",
              "\t<li>'DU PAGE'</li>\n",
              "\t<li>'LAKE'</li>\n",
              "\t<li>'WILL'</li>\n",
              "\t<li>'KANE'</li>\n",
              "\t<li>'ST CLAIR'</li>\n",
              "\t<li>'Winnebago'</li>\n",
              "\t<li>'MADISON'</li>\n",
              "\t<li>'MCHENRY'</li>\n",
              "\t<li>'PEORIA'</li>\n",
              "\t<li>'SANGAMON'</li>\n",
              "\t<li>'CHAMPAIGN'</li>\n",
              "\t<li>'ROCK ISLAND'</li>\n",
              "\t<li>'MCLEAN'</li>\n",
              "\t<li>'TAZEWELL'</li>\n",
              "</ol>\n"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 'COOK'\n\\item 'DU PAGE'\n\\item 'LAKE'\n\\item 'WILL'\n\\item 'KANE'\n\\item 'ST CLAIR'\n\\item 'Winnebago'\n\\item 'MADISON'\n\\item 'MCHENRY'\n\\item 'PEORIA'\n\\item 'SANGAMON'\n\\item 'CHAMPAIGN'\n\\item 'ROCK ISLAND'\n\\item 'MCLEAN'\n\\item 'TAZEWELL'\n\\end{enumerate*}\n",
            "text/markdown": "1. 'COOK'\n2. 'DU PAGE'\n3. 'LAKE'\n4. 'WILL'\n5. 'KANE'\n6. 'ST CLAIR'\n7. 'Winnebago'\n8. 'MADISON'\n9. 'MCHENRY'\n10. 'PEORIA'\n11. 'SANGAMON'\n12. 'CHAMPAIGN'\n13. 'ROCK ISLAND'\n14. 'MCLEAN'\n15. 'TAZEWELL'\n\n\n",
            "text/plain": [
              " [1] \"COOK\"        \"DU PAGE\"     \"LAKE\"        \"WILL\"        \"KANE\"       \n",
              " [6] \"ST CLAIR\"    \"Winnebago\"   \"MADISON\"     \"MCHENRY\"     \"PEORIA\"     \n",
              "[11] \"SANGAMON\"    \"CHAMPAIGN\"   \"ROCK ISLAND\" \"MCLEAN\"      \"TAZEWELL\"   "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqxaeYZRnCXg"
      },
      "source": [
        "#### 4. Show the relationship between percentage of adult poverty vs. percentage of college grads on the `log10` scale, conditioned on state (color  by). Also plot the best linear line (without \"se\" error margin)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTZ2q4WpnCXh",
        "outputId": "4e570c89-9661-418d-862a-3bb1c26065d9"
      },
      "source": [
        "ggplot(midwest, aes(x = percollege, y = percadultpoverty, color = state)) + \n",
        "geom_point() + geom_smooth(method = 'lm', se = F) + \n",
        "scale_y_continuous(trans='log10')+scale_x_continuous(trans='log10')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wTBM22WZ9qs8x5IJ6skgfO9Am8CEg1kmkLiQ7fFFPkTTpcJlae5TqemZ7DogRhV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PP/7DH/4wOzv7/PMj5Z3iOGMhme6gVJ1o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\naBeWaORNVnmfPWSEHQV4ewHsKEAHjKAiIAAfWeAjC36gClgKM8KwYoAs74c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8TkF4s2naksVvxONdkuQ1GhtY1njo8C/a2h9KyJ1YzDOn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\nAgojdFC4niEGh9Pp1DTtv//97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xWA2F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\ngg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zr-Cif2LnCXh"
      },
      "source": [
        "#### 5. Create a new variable called `poverty`, which is `Critical` if `percbelowpoverty` greater than 25, `percadultpoverty` greater than 15 and  `percelderlypoverty` greater than 10, otherwise it is `Normal`. After you create it, print out the critical Michigan counties."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xt9S4TufnCXi",
        "outputId": "a7318d8b-d221-4403-df54-28a33f98e028"
      },
      "source": [
        "midwest %>% \n",
        "mutate(poverty = ifelse(percbelowpoverty>25 & percadultpoverty>15 & percelderlypoverty>10, \n",
        "                       \"Critical\", \"Normal\")) %>% filter(poverty == \"Critical\", state == 'MI')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead><tr><th scope=col>PID</th><th scope=col>county</th><th scope=col>state</th><th scope=col>area</th><th scope=col>poptotal</th><th scope=col>popdensity</th><th scope=col>popwhite</th><th scope=col>popblack</th><th scope=col>popamerindian</th><th scope=col>popasian</th><th scope=col>...</th><th scope=col>percprof</th><th scope=col>poppovertyknown</th><th scope=col>percpovertyknown</th><th scope=col>percbelowpoverty</th><th scope=col>percchildbelowpovert</th><th scope=col>percadultpoverty</th><th scope=col>percelderlypoverty</th><th scope=col>inmetro</th><th scope=col>category</th><th scope=col>poverty</th></tr></thead>\n",
              "<tbody>\n",
              "\t<tr><td>1239     </td><td>LAKE     </td><td>MI       </td><td>0.035    </td><td> 8583    </td><td> 245.2286</td><td> 7337    </td><td>1146     </td><td> 81      </td><td>  9      </td><td>...      </td><td>2.006407 </td><td> 8387    </td><td>97.71642 </td><td>26.40992 </td><td>37.69860 </td><td>25.07071 </td><td>18.87650 </td><td>0        </td><td>LHR      </td><td>Critical </td></tr>\n",
              "\t<tr><td>1250     </td><td>MECOSTA  </td><td>MI       </td><td>0.034    </td><td>37308    </td><td>1097.2941</td><td>35739    </td><td> 978     </td><td>258      </td><td>187      </td><td>...      </td><td>7.845304 </td><td>32627    </td><td>87.45309 </td><td>25.06820 </td><td>25.54361 </td><td>28.22996 </td><td>13.16857 </td><td>0        </td><td>AHR      </td><td>Critical </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": "\\begin{tabular}{r|lllllllllllllllllllllllllllll}\n PID & county & state & area & poptotal & popdensity & popwhite & popblack & popamerindian & popasian & ... & percprof & poppovertyknown & percpovertyknown & percbelowpoverty & percchildbelowpovert & percadultpoverty & percelderlypoverty & inmetro & category & poverty\\\\\n\\hline\n\t 1239      & LAKE      & MI        & 0.035     &  8583     &  245.2286 &  7337     & 1146      &  81       &   9       & ...       & 2.006407  &  8387     & 97.71642  & 26.40992  & 37.69860  & 25.07071  & 18.87650  & 0         & LHR       & Critical \\\\\n\t 1250      & MECOSTA   & MI        & 0.034     & 37308     & 1097.2941 & 35739     &  978      & 258       & 187       & ...       & 7.845304  & 32627     & 87.45309  & 25.06820  & 25.54361  & 28.22996  & 13.16857  & 0         & AHR       & Critical \\\\\n\\end{tabular}\n",
            "text/markdown": "\n| PID | county | state | area | poptotal | popdensity | popwhite | popblack | popamerindian | popasian | ... | percprof | poppovertyknown | percpovertyknown | percbelowpoverty | percchildbelowpovert | percadultpoverty | percelderlypoverty | inmetro | category | poverty |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1239      | LAKE      | MI        | 0.035     |  8583     |  245.2286 |  7337     | 1146      |  81       |   9       | ...       | 2.006407  |  8387     | 97.71642  | 26.40992  | 37.69860  | 25.07071  | 18.87650  | 0         | LHR       | Critical  |\n| 1250      | MECOSTA   | MI        | 0.034     | 37308     | 1097.2941 | 35739     |  978      | 258       | 187       | ...       | 7.845304  | 32627     | 87.45309  | 25.06820  | 25.54361  | 28.22996  | 13.16857  | 0         | AHR       | Critical  |\n\n",
            "text/plain": [
              "  PID  county  state area  poptotal popdensity popwhite popblack popamerindian\n",
              "1 1239 LAKE    MI    0.035  8583     245.2286   7337    1146      81          \n",
              "2 1250 MECOSTA MI    0.034 37308    1097.2941  35739     978     258          \n",
              "  popasian ... percprof poppovertyknown percpovertyknown percbelowpoverty\n",
              "1   9      ... 2.006407  8387           97.71642         26.40992        \n",
              "2 187      ... 7.845304 32627           87.45309         25.06820        \n",
              "  percchildbelowpovert percadultpoverty percelderlypoverty inmetro category\n",
              "1 37.69860             25.07071         18.87650           0       LHR     \n",
              "2 25.54361             28.22996         13.16857           0       AHR     \n",
              "  poverty \n",
              "1 Critical\n",
              "2 Critical"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}