lab2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 2: Facets, Plotting, Dplyr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Reminder that your first homework is due on Friday (2/5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review for the Homework\n",
    "\n",
    "- facets\n",
    "- geom_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n",
      "Registered S3 method overwritten by 'rvest':\n",
      "  method            from\n",
      "  read_xml.response xml2\n",
      "-- Attaching packages --------------------------------------- tidyverse 1.2.1 --\n",
      "v ggplot2 3.1.1       v purrr   0.3.2  \n",
      "v tibble  2.1.1       v dplyr   0.8.0.1\n",
      "v tidyr   0.8.3       v stringr 1.4.0  \n",
      "v readr   1.3.1       v forcats 0.4.0  \n",
      "-- Conflicts ------------------------------------------ tidyverse_conflicts() --\n",
      "x dplyr::filter() masks stats::filter()\n",
      "x dplyr::lag()    masks stats::lag()\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  Major_code = col_double(),\n",
      "  Major = col_character(),\n",
      "  Major_category = col_character(),\n",
      "  Total = col_double(),\n",
      "  Employed = col_double(),\n",
      "  Employed_full_time_year_round = col_double(),\n",
      "  Unemployed = col_double(),\n",
      "  Unemployment_rate = col_double(),\n",
      "  Median = col_double(),\n",
      "  P25th = col_double(),\n",
      "  P75th = col_double()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "college_df <- read_csv(\"https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/all-ages.csv\")\n",
    "# https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Major_code</th><th scope=col>Major</th><th scope=col>Major_category</th><th scope=col>Total</th><th scope=col>Employed</th><th scope=col>Employed_full_time_year_round</th><th scope=col>Unemployed</th><th scope=col>Unemployment_rate</th><th scope=col>Median</th><th scope=col>P25th</th><th scope=col>P75th</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1100                                                                         </td><td><span style=white-space:pre-wrap>GENERAL AGRICULTURE                  </span></td><td>Agriculture &amp; Natural Resources                                          </td><td>128148                                                                       </td><td>90245                                                                        </td><td>74078                                                                        </td><td>2423                                                                         </td><td>0.02614711                                                                   </td><td>50000                                                                        </td><td>34000                                                                        </td><td>80000                                                                        </td></tr>\n",
       "\t<tr><td>1101                                 </td><td>AGRICULTURE PRODUCTION AND MANAGEMENT</td><td>Agriculture &amp; Natural Resources  </td><td> 95326                               </td><td>76865                                </td><td>64240                                </td><td>2266                                 </td><td>0.02863606                           </td><td>54000                                </td><td>36000                                </td><td>80000                                </td></tr>\n",
       "\t<tr><td>1102                                                                         </td><td><span style=white-space:pre-wrap>AGRICULTURAL ECONOMICS               </span></td><td>Agriculture &amp; Natural Resources                                          </td><td> 33955                                                                       </td><td>26321                                                                        </td><td>22810                                                                        </td><td> 821                                                                         </td><td>0.03024832                                                                   </td><td>63000                                                                        </td><td>40000                                                                        </td><td>98000                                                                        </td></tr>\n",
       "\t<tr><td>1103                                                                         </td><td><span style=white-space:pre-wrap>ANIMAL SCIENCES                      </span></td><td>Agriculture &amp; Natural Resources                                          </td><td>103549                                                                       </td><td>81177                                                                        </td><td>64937                                                                        </td><td>3619                                                                         </td><td>0.04267890                                                                   </td><td>46000                                                                        </td><td>30000                                                                        </td><td>72000                                                                        </td></tr>\n",
       "\t<tr><td>1104                                                                         </td><td><span style=white-space:pre-wrap>FOOD SCIENCE                         </span></td><td>Agriculture &amp; Natural Resources                                          </td><td> 24280                                                                       </td><td>17281                                                                        </td><td>12722                                                                        </td><td> 894                                                                         </td><td>0.04918845                                                                   </td><td>62000                                                                        </td><td>38500                                                                        </td><td>90000                                                                        </td></tr>\n",
       "\t<tr><td>1105                                                                         </td><td><span style=white-space:pre-wrap>PLANT SCIENCE AND AGRONOMY           </span></td><td>Agriculture &amp; Natural Resources                                          </td><td> 79409                                                                       </td><td>63043                                                                        </td><td>51077                                                                        </td><td>2070                                                                         </td><td>0.03179089                                                                   </td><td>50000                                                                        </td><td>35000                                                                        </td><td>75000                                                                        </td></tr>\n",
       "\t<tr><td>1106                                                                         </td><td><span style=white-space:pre-wrap>SOIL SCIENCE                         </span></td><td>Agriculture &amp; Natural Resources                                          </td><td><span style=white-space:pre-wrap>  6586</span>                               </td><td> 4926                                                                        </td><td> 4042                                                                        </td><td> 264                                                                         </td><td>0.05086705                                                                   </td><td>63000                                                                        </td><td>39400                                                                        </td><td>88000                                                                        </td></tr>\n",
       "\t<tr><td>1199                                                                         </td><td><span style=white-space:pre-wrap>MISCELLANEOUS AGRICULTURE            </span></td><td>Agriculture &amp; Natural Resources                                          </td><td><span style=white-space:pre-wrap>  8549</span>                               </td><td> 6392                                                                        </td><td> 5074                                                                        </td><td> 261                                                                         </td><td>0.03923042                                                                   </td><td>52000                                                                        </td><td>35000                                                                        </td><td>75000                                                                        </td></tr>\n",
       "\t<tr><td>1301                                                                         </td><td><span style=white-space:pre-wrap>ENVIRONMENTAL SCIENCE                </span></td><td><span style=white-space:pre-wrap>Biology &amp; Life Science         </span>  </td><td>106106                                                                       </td><td>87602                                                                        </td><td>65238                                                                        </td><td>4736                                                                         </td><td>0.05128983                                                                   </td><td>52000                                                                        </td><td>38000                                                                        </td><td>75000                                                                        </td></tr>\n",
       "\t<tr><td>1302                                                                         </td><td><span style=white-space:pre-wrap>FORESTRY                             </span></td><td>Agriculture &amp; Natural Resources                                          </td><td> 69447                                                                       </td><td>48228                                                                        </td><td>39613                                                                        </td><td>2144                                                                         </td><td>0.04256333                                                                   </td><td>58000                                                                        </td><td>40500                                                                        </td><td>80000                                                                        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       " Major\\_code & Major & Major\\_category & Total & Employed & Employed\\_full\\_time\\_year\\_round & Unemployed & Unemployment\\_rate & Median & P25th & P75th\\\\\n",
       "\\hline\n",
       "\t 1100                                  & GENERAL AGRICULTURE                   & Agriculture \\& Natural Resources     & 128148                                & 90245                                 & 74078                                 & 2423                                  & 0.02614711                            & 50000                                 & 34000                                 & 80000                                \\\\\n",
       "\t 1101                                  & AGRICULTURE PRODUCTION AND MANAGEMENT & Agriculture \\& Natural Resources     &  95326                                & 76865                                 & 64240                                 & 2266                                  & 0.02863606                            & 54000                                 & 36000                                 & 80000                                \\\\\n",
       "\t 1102                                  & AGRICULTURAL ECONOMICS                & Agriculture \\& Natural Resources     &  33955                                & 26321                                 & 22810                                 &  821                                  & 0.03024832                            & 63000                                 & 40000                                 & 98000                                \\\\\n",
       "\t 1103                                  & ANIMAL SCIENCES                       & Agriculture \\& Natural Resources     & 103549                                & 81177                                 & 64937                                 & 3619                                  & 0.04267890                            & 46000                                 & 30000                                 & 72000                                \\\\\n",
       "\t 1104                                  & FOOD SCIENCE                          & Agriculture \\& Natural Resources     &  24280                                & 17281                                 & 12722                                 &  894                                  & 0.04918845                            & 62000                                 & 38500                                 & 90000                                \\\\\n",
       "\t 1105                                  & PLANT SCIENCE AND AGRONOMY            & Agriculture \\& Natural Resources     &  79409                                & 63043                                 & 51077                                 & 2070                                  & 0.03179089                            & 50000                                 & 35000                                 & 75000                                \\\\\n",
       "\t 1106                                  & SOIL SCIENCE                          & Agriculture \\& Natural Resources     &   6586                                &  4926                                 &  4042                                 &  264                                  & 0.05086705                            & 63000                                 & 39400                                 & 88000                                \\\\\n",
       "\t 1199                                  & MISCELLANEOUS AGRICULTURE             & Agriculture \\& Natural Resources     &   8549                                &  6392                                 &  5074                                 &  261                                  & 0.03923042                            & 52000                                 & 35000                                 & 75000                                \\\\\n",
       "\t 1301                                  & ENVIRONMENTAL SCIENCE                 & Biology \\& Life Science              & 106106                                & 87602                                 & 65238                                 & 4736                                  & 0.05128983                            & 52000                                 & 38000                                 & 75000                                \\\\\n",
       "\t 1302                                  & FORESTRY                              & Agriculture \\& Natural Resources     &  69447                                & 48228                                 & 39613                                 & 2144                                  & 0.04256333                            & 58000                                 & 40500                                 & 80000                                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Major_code | Major | Major_category | Total | Employed | Employed_full_time_year_round | Unemployed | Unemployment_rate | Median | P25th | P75th |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1100                                  | GENERAL AGRICULTURE                   | Agriculture & Natural Resources       | 128148                                | 90245                                 | 74078                                 | 2423                                  | 0.02614711                            | 50000                                 | 34000                                 | 80000                                 |\n",
       "| 1101                                  | AGRICULTURE PRODUCTION AND MANAGEMENT | Agriculture & Natural Resources       |  95326                                | 76865                                 | 64240                                 | 2266                                  | 0.02863606                            | 54000                                 | 36000                                 | 80000                                 |\n",
       "| 1102                                  | AGRICULTURAL ECONOMICS                | Agriculture & Natural Resources       |  33955                                | 26321                                 | 22810                                 |  821                                  | 0.03024832                            | 63000                                 | 40000                                 | 98000                                 |\n",
       "| 1103                                  | ANIMAL SCIENCES                       | Agriculture & Natural Resources       | 103549                                | 81177                                 | 64937                                 | 3619                                  | 0.04267890                            | 46000                                 | 30000                                 | 72000                                 |\n",
       "| 1104                                  | FOOD SCIENCE                          | Agriculture & Natural Resources       |  24280                                | 17281                                 | 12722                                 |  894                                  | 0.04918845                            | 62000                                 | 38500                                 | 90000                                 |\n",
       "| 1105                                  | PLANT SCIENCE AND AGRONOMY            | Agriculture & Natural Resources       |  79409                                | 63043                                 | 51077                                 | 2070                                  | 0.03179089                            | 50000                                 | 35000                                 | 75000                                 |\n",
       "| 1106                                  | SOIL SCIENCE                          | Agriculture & Natural Resources       |   6586                                |  4926                                 |  4042                                 |  264                                  | 0.05086705                            | 63000                                 | 39400                                 | 88000                                 |\n",
       "| 1199                                  | MISCELLANEOUS AGRICULTURE             | Agriculture & Natural Resources       |   8549                                |  6392                                 |  5074                                 |  261                                  | 0.03923042                            | 52000                                 | 35000                                 | 75000                                 |\n",
       "| 1301                                  | ENVIRONMENTAL SCIENCE                 | Biology & Life Science                | 106106                                | 87602                                 | 65238                                 | 4736                                  | 0.05128983                            | 52000                                 | 38000                                 | 75000                                 |\n",
       "| 1302                                  | FORESTRY                              | Agriculture & Natural Resources       |  69447                                | 48228                                 | 39613                                 | 2144                                  | 0.04256333                            | 58000                                 | 40500                                 | 80000                                 |\n",
       "\n"
      ],
      "text/plain": [
       "   Major_code Major                                \n",
       "1  1100       GENERAL AGRICULTURE                  \n",
       "2  1101       AGRICULTURE PRODUCTION AND MANAGEMENT\n",
       "3  1102       AGRICULTURAL ECONOMICS               \n",
       "4  1103       ANIMAL SCIENCES                      \n",
       "5  1104       FOOD SCIENCE                         \n",
       "6  1105       PLANT SCIENCE AND AGRONOMY           \n",
       "7  1106       SOIL SCIENCE                         \n",
       "8  1199       MISCELLANEOUS AGRICULTURE            \n",
       "9  1301       ENVIRONMENTAL SCIENCE                \n",
       "10 1302       FORESTRY                             \n",
       "   Major_category                  Total  Employed\n",
       "1  Agriculture & Natural Resources 128148 90245   \n",
       "2  Agriculture & Natural Resources  95326 76865   \n",
       "3  Agriculture & Natural Resources  33955 26321   \n",
       "4  Agriculture & Natural Resources 103549 81177   \n",
       "5  Agriculture & Natural Resources  24280 17281   \n",
       "6  Agriculture & Natural Resources  79409 63043   \n",
       "7  Agriculture & Natural Resources   6586  4926   \n",
       "8  Agriculture & Natural Resources   8549  6392   \n",
       "9  Biology & Life Science          106106 87602   \n",
       "10 Agriculture & Natural Resources  69447 48228   \n",
       "   Employed_full_time_year_round Unemployed Unemployment_rate Median P25th\n",
       "1  74078                         2423       0.02614711        50000  34000\n",
       "2  64240                         2266       0.02863606        54000  36000\n",
       "3  22810                          821       0.03024832        63000  40000\n",
       "4  64937                         3619       0.04267890        46000  30000\n",
       "5  12722                          894       0.04918845        62000  38500\n",
       "6  51077                         2070       0.03179089        50000  35000\n",
       "7   4042                          264       0.05086705        63000  39400\n",
       "8   5074                          261       0.03923042        52000  35000\n",
       "9  65238                         4736       0.05128983        52000  38000\n",
       "10 39613                         2144       0.04256333        58000  40500\n",
       "   P75th\n",
       "1  80000\n",
       "2  80000\n",
       "3  98000\n",
       "4  72000\n",
       "5  90000\n",
       "6  75000\n",
       "7  88000\n",
       "8  75000\n",
       "9  75000\n",
       "10 80000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(college_df, n = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using method = 'loess' and formula 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAP1BMVEUAAAAzMzMzZv9NTU1o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\n/B9J7OjpMpeBawAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAOVBMVEUAAAAzMzMzZv9NTU1o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\nZEyBKJIxBaJIxhSIIhlTIIpkTIH8H6y8UWDLPr6aAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = college_df) +\n",
    "    geom_smooth(mapping = aes(x = Unemployment_rate, y = Median))\n",
    "\n",
    "ggplot(data = college_df) +\n",
    "    geom_smooth(mapping = aes(x = Unemployment_rate, y = Median), method = 'lm', se = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using method = 'loess' and formula 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAP1BMVEUAAAAzMzMzZv9NTU1o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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot <- ggplot(data = college_df) +\n",
    "    geom_smooth(mapping = aes(x = Total, y = Employed))\n",
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAElBMVEUaGhozMzNNTU3Z2dnr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\nkYGAJDIQkEQG8n9zbC2gqm0sCgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://ggplot2-book.org/facet.html\n",
    "base <- ggplot(mpg, aes(displ, hwy)) + \n",
    "  geom_blank() + \n",
    "  xlab(NULL) + \n",
    "  ylab(NULL)\n",
    "\n",
    "base + facet_wrap(. ~class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAElBMVEUaGhozMzNNTU3Z2dnr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\nknMTDiTnJhxIzk04kJybcCA5N+H+D/UYvp+n8F8iAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base + facet_grid(drv ~ cyl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Transformations and Positional Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "popn <- tribble(\n",
    "~city, ~population,\n",
    "\"Istanbul\", 15029231,\n",
    "\"Moscow\", 12615279,\n",
    "\"Saint Petersburg\", 9126366,\n",
    "\"Berlin\", 5383890,\n",
    "\"Madrid\", 3748148\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAOVBMVEUAAAAzMzNNTU1ZWVlo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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(popn) +\n",
    "    geom_bar(mapping = aes(x = city, y = population), stat = \"identity\") +\n",
    "    ggtitle(\"Most Populated Cities in Europe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAOVBMVEUAAAAzMzNNTU1ZWVlo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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(popn) +\n",
    "    geom_bar(mapping = aes(x = city, y = population / 1000), stat = \"identity\") +\n",
    "    ggtitle(\"Most Populated Cities in Europe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAUVBMVEUAAAAfoYcnf44zMzM2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\nbEW7yD94ZSYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = diamonds) + \n",
    "  geom_bar(mapping = aes(x = cut, fill = clarity), position = \"fill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAUVBMVEUAAAAfoYcnf44zMzM2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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = diamonds) + \n",
    "  geom_bar(mapping = aes(x = cut, fill = clarity), position = \"dodge\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAANlBMVEUAAAAzMzNNTU1oaGh8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\nPJ4djiF5PDscQ/J4djj/A0mJ6nebsZ3QAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAANlBMVEUAAAAzMzNNTU1oaGh8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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = mpg) + \n",
    "  geom_point(mapping = aes(x = displ, y = hwy))\n",
    "\n",
    "ggplot(data = mpg) + \n",
    "  geom_point(mapping = aes(x = displ, y = hwy), position = \"jitter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dplyr\n",
    "\n",
    "There are five functions in dplyr: filter, arrange, select, mutate and summarise. all of them have the following properties:\n",
    "\n",
    "The first argument is a data frame.\n",
    "The subsequent arguments describe what to do with the data frame, using the variable names (without quotes).\n",
    "The result is a new data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1.05</td><td>Fair</td><td>J   </td><td>SI2 </td><td>65.8</td><td>59  </td><td>2789</td><td>6.41</td><td>6.27</td><td>4.18</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>VS2 </td><td>65.7</td><td>59  </td><td>2811</td><td>6.14</td><td>6.07</td><td>4.01</td></tr>\n",
       "\t<tr><td>0.99</td><td>Fair</td><td>J   </td><td>SI1 </td><td>55.0</td><td>61  </td><td>2812</td><td>6.72</td><td>6.67</td><td>3.68</td></tr>\n",
       "\t<tr><td>0.90</td><td>Fair</td><td>J   </td><td>VS2 </td><td>65.0</td><td>56  </td><td>2815</td><td>6.08</td><td>6.04</td><td>3.94</td></tr>\n",
       "\t<tr><td>0.91</td><td>Fair</td><td>J   </td><td>VS2 </td><td>64.4</td><td>62  </td><td>2854</td><td>6.06</td><td>6.03</td><td>3.89</td></tr>\n",
       "\t<tr><td>0.91</td><td>Fair</td><td>J   </td><td>VS2 </td><td>65.4</td><td>60  </td><td>2854</td><td>6.04</td><td>6.00</td><td>3.94</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>VS1 </td><td>65.5</td><td>55  </td><td>2875</td><td>6.30</td><td>6.25</td><td>4.11</td></tr>\n",
       "\t<tr><td>0.99</td><td>Fair</td><td>J   </td><td>SI1 </td><td>58.0</td><td>67  </td><td>2949</td><td>6.57</td><td>6.50</td><td>3.79</td></tr>\n",
       "\t<tr><td>0.90</td><td>Fair</td><td>J   </td><td>VS1 </td><td>65.4</td><td>60  </td><td>2964</td><td>6.02</td><td>5.93</td><td>3.91</td></tr>\n",
       "\t<tr><td>0.90</td><td>Fair</td><td>J   </td><td>VS1 </td><td>64.6</td><td>58  </td><td>2964</td><td>6.12</td><td>6.06</td><td>3.93</td></tr>\n",
       "\t<tr><td>1.06</td><td>Fair</td><td>J   </td><td>SI2 </td><td>61.0</td><td>66  </td><td>2992</td><td>6.54</td><td>6.35</td><td>3.93</td></tr>\n",
       "\t<tr><td>1.01</td><td>Fair</td><td>J   </td><td>VVS2</td><td>66.0</td><td>56  </td><td>2998</td><td>6.29</td><td>6.22</td><td>4.13</td></tr>\n",
       "\t<tr><td>1.20</td><td>Fair</td><td>J   </td><td>I1  </td><td>64.9</td><td>59  </td><td>3011</td><td>6.61</td><td>6.54</td><td>4.27</td></tr>\n",
       "\t<tr><td>1.02</td><td>Fair</td><td>J   </td><td>SI1 </td><td>66.8</td><td>55  </td><td>3027</td><td>6.25</td><td>6.18</td><td>4.15</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>VS1 </td><td>66.6</td><td>57  </td><td>3080</td><td>6.07</td><td>6.03</td><td>4.03</td></tr>\n",
       "\t<tr><td>1.02</td><td>Fair</td><td>J   </td><td>SI2 </td><td>65.0</td><td>59  </td><td>3119</td><td>6.34</td><td>6.24</td><td>4.08</td></tr>\n",
       "\t<tr><td>1.03</td><td>Fair</td><td>J   </td><td>SI2 </td><td>64.6</td><td>62  </td><td>3149</td><td>6.30</td><td>6.21</td><td>4.06</td></tr>\n",
       "\t<tr><td>0.95</td><td>Fair</td><td>J   </td><td>SI1 </td><td>58.0</td><td>66  </td><td>3154</td><td>6.50</td><td>6.46</td><td>3.76</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>SI1 </td><td>65.3</td><td>57  </td><td>3175</td><td>6.33</td><td>6.12</td><td>4.08</td></tr>\n",
       "\t<tr><td>1.01</td><td>Fair</td><td>J   </td><td>VS2 </td><td>65.8</td><td>55  </td><td>3190</td><td>6.29</td><td>6.24</td><td>4.12</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>SI2 </td><td>56.3</td><td>64  </td><td>3199</td><td>6.58</td><td>6.54</td><td>3.69</td></tr>\n",
       "\t<tr><td>1.03</td><td>Fair</td><td>J   </td><td>SI2 </td><td>56.2</td><td>62  </td><td>3246</td><td>6.73</td><td>6.69</td><td>3.77</td></tr>\n",
       "\t<tr><td>1.08</td><td>Fair</td><td>J   </td><td>SI2 </td><td>64.9</td><td>60  </td><td>3302</td><td>6.46</td><td>6.32</td><td>4.15</td></tr>\n",
       "\t<tr><td>1.14</td><td>Fair</td><td>J   </td><td>SI2 </td><td>64.4</td><td>55  </td><td>3403</td><td>6.56</td><td>6.54</td><td>4.22</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>VS2 </td><td>65.3</td><td>61  </td><td>3422</td><td>6.33</td><td>6.13</td><td>4.03</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>SI1 </td><td>64.9</td><td>58  </td><td>3427</td><td>6.29</td><td>6.23</td><td>4.06</td></tr>\n",
       "\t<tr><td>1.09</td><td>Fair</td><td>J   </td><td>VS2 </td><td>64.6</td><td>58  </td><td>3443</td><td>6.48</td><td>6.41</td><td>4.16</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>SI1 </td><td>64.7</td><td>52  </td><td>3780</td><td>6.35</td><td>6.30</td><td>4.09</td></tr>\n",
       "\t<tr><td>1.01</td><td>Fair</td><td>J   </td><td>VS2 </td><td>59.6</td><td>62  </td><td>3886</td><td>6.54</td><td>6.49</td><td>3.88</td></tr>\n",
       "\t<tr><td>1.08</td><td>Fair</td><td>J   </td><td>VS2 </td><td>65.1</td><td>54  </td><td>3980</td><td>6.47</td><td>6.44</td><td>4.20</td></tr>\n",
       "\t<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><td>0.61</td><td>Fair</td><td>J   </td><td>VS2 </td><td>58.2</td><td>56  </td><td>1174</td><td>5.74</td><td>5.49</td><td>3.27</td></tr>\n",
       "\t<tr><td>0.85</td><td>Fair</td><td>J   </td><td>I1  </td><td>65.5</td><td>59  </td><td>1334</td><td>5.95</td><td>5.82</td><td>3.86</td></tr>\n",
       "\t<tr><td>0.71</td><td>Fair</td><td>J   </td><td>SI2 </td><td>66.1</td><td>58  </td><td>1362</td><td>5.53</td><td>5.43</td><td>3.62</td></tr>\n",
       "\t<tr><td>0.71</td><td>Fair</td><td>J   </td><td>SI2 </td><td>65.5</td><td>61  </td><td>1409</td><td>5.54</td><td>5.49</td><td>3.61</td></tr>\n",
       "\t<tr><td>0.71</td><td>Fair</td><td>J   </td><td>VS2 </td><td>64.9</td><td>54  </td><td>1435</td><td>5.63</td><td>5.53</td><td>3.62</td></tr>\n",
       "\t<tr><td>0.70</td><td>Fair</td><td>J   </td><td>VS2 </td><td>64.6</td><td>58  </td><td>1672</td><td>5.58</td><td>5.56</td><td>3.60</td></tr>\n",
       "\t<tr><td>0.70</td><td>Fair</td><td>J   </td><td>VVS1</td><td>67.6</td><td>54  </td><td>1691</td><td>5.56</td><td>5.41</td><td>3.71</td></tr>\n",
       "\t<tr><td>0.70</td><td>Fair</td><td>J   </td><td>SI1 </td><td>63.0</td><td>57  </td><td>1723</td><td>5.55</td><td>5.60</td><td>3.51</td></tr>\n",
       "\t<tr><td>0.72</td><td>Fair</td><td>J   </td><td>SI2 </td><td>66.1</td><td>55  </td><td>1762</td><td>5.51</td><td>5.56</td><td>3.66</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>I1  </td><td>65.0</td><td>59  </td><td>1784</td><td>6.27</td><td>6.17</td><td>4.05</td></tr>\n",
       "\t<tr><td>0.99</td><td>Fair</td><td>J   </td><td>I1  </td><td>73.6</td><td>60  </td><td>1789</td><td>6.01</td><td>5.80</td><td>4.35</td></tr>\n",
       "\t<tr><td>0.73</td><td>Fair</td><td>J   </td><td>VS1 </td><td>64.6</td><td>60  </td><td>1802</td><td>5.68</td><td>5.59</td><td>3.64</td></tr>\n",
       "\t<tr><td>0.71</td><td>Fair</td><td>J   </td><td>VS2 </td><td>64.4</td><td>56  </td><td>1826</td><td>5.64</td><td>5.60</td><td>3.62</td></tr>\n",
       "\t<tr><td>0.75</td><td>Fair</td><td>J   </td><td>SI1 </td><td>55.0</td><td>66  </td><td>1827</td><td>6.20</td><td>6.04</td><td>3.37</td></tr>\n",
       "\t<tr><td>0.75</td><td>Fair</td><td>J   </td><td>VS1 </td><td>61.9</td><td>67  </td><td>1869</td><td>5.80</td><td>5.70</td><td>3.56</td></tr>\n",
       "\t<tr><td>0.70</td><td>Fair</td><td>J   </td><td>VS2 </td><td>67.4</td><td>58  </td><td>1896</td><td>5.34</td><td>5.37</td><td>3.61</td></tr>\n",
       "\t<tr><td>0.99</td><td>Fair</td><td>J   </td><td>I1  </td><td>62.9</td><td>58  </td><td>1901</td><td>6.33</td><td>6.10</td><td>3.91</td></tr>\n",
       "\t<tr><td>0.75</td><td>Fair</td><td>J   </td><td>VS2 </td><td>59.1</td><td>66  </td><td>1944</td><td>5.99</td><td>5.82</td><td>3.49</td></tr>\n",
       "\t<tr><td>0.77</td><td>Fair</td><td>J   </td><td>SI1 </td><td>60.2</td><td>66  </td><td>1956</td><td>5.97</td><td>5.81</td><td>3.55</td></tr>\n",
       "\t<tr><td>0.74</td><td>Fair</td><td>J   </td><td>VS1 </td><td>58.8</td><td>68  </td><td>1982</td><td>5.91</td><td>5.80</td><td>3.44</td></tr>\n",
       "\t<tr><td>0.77</td><td>Fair</td><td>J   </td><td>VS1 </td><td>61.2</td><td>66  </td><td>2005</td><td>5.92</td><td>5.83</td><td>3.60</td></tr>\n",
       "\t<tr><td>1.00</td><td>Fair</td><td>J   </td><td>SI2 </td><td>69.9</td><td>61  </td><td>2117</td><td>6.13</td><td>5.86</td><td>4.23</td></tr>\n",
       "\t<tr><td>0.96</td><td>Fair</td><td>J   </td><td>I1  </td><td>59.5</td><td>60  </td><td>2304</td><td>6.28</td><td>6.23</td><td>3.72</td></tr>\n",
       "\t<tr><td>0.95</td><td>Fair</td><td>J   </td><td>I1  </td><td>64.9</td><td>56  </td><td>2322</td><td>6.17</td><td>6.12</td><td>3.99</td></tr>\n",
       "\t<tr><td>0.91</td><td>Fair</td><td>J   </td><td>SI1 </td><td>66.3</td><td>56  </td><td>2390</td><td>5.92</td><td>5.99</td><td>3.95</td></tr>\n",
       "\t<tr><td>0.90</td><td>Fair</td><td>J   </td><td>SI1 </td><td>64.6</td><td>61  </td><td>2438</td><td>5.92</td><td>5.87</td><td>3.81</td></tr>\n",
       "\t<tr><td>0.96</td><td>Fair</td><td>J   </td><td>SI1 </td><td>67.3</td><td>56  </td><td>2517</td><td>6.06</td><td>6.01</td><td>4.06</td></tr>\n",
       "\t<tr><td>0.90</td><td>Fair</td><td>J   </td><td>SI2 </td><td>66.6</td><td>54  </td><td>2536</td><td>6.05</td><td>5.99</td><td>4.01</td></tr>\n",
       "\t<tr><td>0.97</td><td>Fair</td><td>J   </td><td>SI2 </td><td>60.8</td><td>67  </td><td>2538</td><td>6.41</td><td>6.32</td><td>3.87</td></tr>\n",
       "\t<tr><td>1.01</td><td>Fair</td><td>J   </td><td>SI2 </td><td>66.9</td><td>58  </td><td>2683</td><td>6.13</td><td>6.07</td><td>4.08</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       "\\hline\n",
       "\t 1.05 & Fair & J    & SI2  & 65.8 & 59   & 2789 & 6.41 & 6.27 & 4.18\\\\\n",
       "\t 1.00 & Fair & J    & VS2  & 65.7 & 59   & 2811 & 6.14 & 6.07 & 4.01\\\\\n",
       "\t 0.99 & Fair & J    & SI1  & 55.0 & 61   & 2812 & 6.72 & 6.67 & 3.68\\\\\n",
       "\t 0.90 & Fair & J    & VS2  & 65.0 & 56   & 2815 & 6.08 & 6.04 & 3.94\\\\\n",
       "\t 0.91 & Fair & J    & VS2  & 64.4 & 62   & 2854 & 6.06 & 6.03 & 3.89\\\\\n",
       "\t 0.91 & Fair & J    & VS2  & 65.4 & 60   & 2854 & 6.04 & 6.00 & 3.94\\\\\n",
       "\t 1.00 & Fair & J    & VS1  & 65.5 & 55   & 2875 & 6.30 & 6.25 & 4.11\\\\\n",
       "\t 0.99 & Fair & J    & SI1  & 58.0 & 67   & 2949 & 6.57 & 6.50 & 3.79\\\\\n",
       "\t 0.90 & Fair & J    & VS1  & 65.4 & 60   & 2964 & 6.02 & 5.93 & 3.91\\\\\n",
       "\t 0.90 & Fair & J    & VS1  & 64.6 & 58   & 2964 & 6.12 & 6.06 & 3.93\\\\\n",
       "\t 1.06 & Fair & J    & SI2  & 61.0 & 66   & 2992 & 6.54 & 6.35 & 3.93\\\\\n",
       "\t 1.01 & Fair & J    & VVS2 & 66.0 & 56   & 2998 & 6.29 & 6.22 & 4.13\\\\\n",
       "\t 1.20 & Fair & J    & I1   & 64.9 & 59   & 3011 & 6.61 & 6.54 & 4.27\\\\\n",
       "\t 1.02 & Fair & J    & SI1  & 66.8 & 55   & 3027 & 6.25 & 6.18 & 4.15\\\\\n",
       "\t 1.00 & Fair & J    & VS1  & 66.6 & 57   & 3080 & 6.07 & 6.03 & 4.03\\\\\n",
       "\t 1.02 & Fair & J    & SI2  & 65.0 & 59   & 3119 & 6.34 & 6.24 & 4.08\\\\\n",
       "\t 1.03 & Fair & J    & SI2  & 64.6 & 62   & 3149 & 6.30 & 6.21 & 4.06\\\\\n",
       "\t 0.95 & Fair & J    & SI1  & 58.0 & 66   & 3154 & 6.50 & 6.46 & 3.76\\\\\n",
       "\t 1.00 & Fair & J    & SI1  & 65.3 & 57   & 3175 & 6.33 & 6.12 & 4.08\\\\\n",
       "\t 1.01 & Fair & J    & VS2  & 65.8 & 55   & 3190 & 6.29 & 6.24 & 4.12\\\\\n",
       "\t 1.00 & Fair & J    & SI2  & 56.3 & 64   & 3199 & 6.58 & 6.54 & 3.69\\\\\n",
       "\t 1.03 & Fair & J    & SI2  & 56.2 & 62   & 3246 & 6.73 & 6.69 & 3.77\\\\\n",
       "\t 1.08 & Fair & J    & SI2  & 64.9 & 60   & 3302 & 6.46 & 6.32 & 4.15\\\\\n",
       "\t 1.14 & Fair & J    & SI2  & 64.4 & 55   & 3403 & 6.56 & 6.54 & 4.22\\\\\n",
       "\t 1.00 & Fair & J    & VS2  & 65.3 & 61   & 3422 & 6.33 & 6.13 & 4.03\\\\\n",
       "\t 1.00 & Fair & J    & SI1  & 64.9 & 58   & 3427 & 6.29 & 6.23 & 4.06\\\\\n",
       "\t 1.09 & Fair & J    & VS2  & 64.6 & 58   & 3443 & 6.48 & 6.41 & 4.16\\\\\n",
       "\t 1.00 & Fair & J    & SI1  & 64.7 & 52   & 3780 & 6.35 & 6.30 & 4.09\\\\\n",
       "\t 1.01 & Fair & J    & VS2  & 59.6 & 62   & 3886 & 6.54 & 6.49 & 3.88\\\\\n",
       "\t 1.08 & Fair & J    & VS2  & 65.1 & 54   & 3980 & 6.47 & 6.44 & 4.20\\\\\n",
       "\t ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
       "\t 0.61 & Fair & J    & VS2  & 58.2 & 56   & 1174 & 5.74 & 5.49 & 3.27\\\\\n",
       "\t 0.85 & Fair & J    & I1   & 65.5 & 59   & 1334 & 5.95 & 5.82 & 3.86\\\\\n",
       "\t 0.71 & Fair & J    & SI2  & 66.1 & 58   & 1362 & 5.53 & 5.43 & 3.62\\\\\n",
       "\t 0.71 & Fair & J    & SI2  & 65.5 & 61   & 1409 & 5.54 & 5.49 & 3.61\\\\\n",
       "\t 0.71 & Fair & J    & VS2  & 64.9 & 54   & 1435 & 5.63 & 5.53 & 3.62\\\\\n",
       "\t 0.70 & Fair & J    & VS2  & 64.6 & 58   & 1672 & 5.58 & 5.56 & 3.60\\\\\n",
       "\t 0.70 & Fair & J    & VVS1 & 67.6 & 54   & 1691 & 5.56 & 5.41 & 3.71\\\\\n",
       "\t 0.70 & Fair & J    & SI1  & 63.0 & 57   & 1723 & 5.55 & 5.60 & 3.51\\\\\n",
       "\t 0.72 & Fair & J    & SI2  & 66.1 & 55   & 1762 & 5.51 & 5.56 & 3.66\\\\\n",
       "\t 1.00 & Fair & J    & I1   & 65.0 & 59   & 1784 & 6.27 & 6.17 & 4.05\\\\\n",
       "\t 0.99 & Fair & J    & I1   & 73.6 & 60   & 1789 & 6.01 & 5.80 & 4.35\\\\\n",
       "\t 0.73 & Fair & J    & VS1  & 64.6 & 60   & 1802 & 5.68 & 5.59 & 3.64\\\\\n",
       "\t 0.71 & Fair & J    & VS2  & 64.4 & 56   & 1826 & 5.64 & 5.60 & 3.62\\\\\n",
       "\t 0.75 & Fair & J    & SI1  & 55.0 & 66   & 1827 & 6.20 & 6.04 & 3.37\\\\\n",
       "\t 0.75 & Fair & J    & VS1  & 61.9 & 67   & 1869 & 5.80 & 5.70 & 3.56\\\\\n",
       "\t 0.70 & Fair & J    & VS2  & 67.4 & 58   & 1896 & 5.34 & 5.37 & 3.61\\\\\n",
       "\t 0.99 & Fair & J    & I1   & 62.9 & 58   & 1901 & 6.33 & 6.10 & 3.91\\\\\n",
       "\t 0.75 & Fair & J    & VS2  & 59.1 & 66   & 1944 & 5.99 & 5.82 & 3.49\\\\\n",
       "\t 0.77 & Fair & J    & SI1  & 60.2 & 66   & 1956 & 5.97 & 5.81 & 3.55\\\\\n",
       "\t 0.74 & Fair & J    & VS1  & 58.8 & 68   & 1982 & 5.91 & 5.80 & 3.44\\\\\n",
       "\t 0.77 & Fair & J    & VS1  & 61.2 & 66   & 2005 & 5.92 & 5.83 & 3.60\\\\\n",
       "\t 1.00 & Fair & J    & SI2  & 69.9 & 61   & 2117 & 6.13 & 5.86 & 4.23\\\\\n",
       "\t 0.96 & Fair & J    & I1   & 59.5 & 60   & 2304 & 6.28 & 6.23 & 3.72\\\\\n",
       "\t 0.95 & Fair & J    & I1   & 64.9 & 56   & 2322 & 6.17 & 6.12 & 3.99\\\\\n",
       "\t 0.91 & Fair & J    & SI1  & 66.3 & 56   & 2390 & 5.92 & 5.99 & 3.95\\\\\n",
       "\t 0.90 & Fair & J    & SI1  & 64.6 & 61   & 2438 & 5.92 & 5.87 & 3.81\\\\\n",
       "\t 0.96 & Fair & J    & SI1  & 67.3 & 56   & 2517 & 6.06 & 6.01 & 4.06\\\\\n",
       "\t 0.90 & Fair & J    & SI2  & 66.6 & 54   & 2536 & 6.05 & 5.99 & 4.01\\\\\n",
       "\t 0.97 & Fair & J    & SI2  & 60.8 & 67   & 2538 & 6.41 & 6.32 & 3.87\\\\\n",
       "\t 1.01 & Fair & J    & SI2  & 66.9 & 58   & 2683 & 6.13 & 6.07 & 4.08\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| carat | cut | color | clarity | depth | table | price | x | y | z |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1.05 | Fair | J    | SI2  | 65.8 | 59   | 2789 | 6.41 | 6.27 | 4.18 |\n",
       "| 1.00 | Fair | J    | VS2  | 65.7 | 59   | 2811 | 6.14 | 6.07 | 4.01 |\n",
       "| 0.99 | Fair | J    | SI1  | 55.0 | 61   | 2812 | 6.72 | 6.67 | 3.68 |\n",
       "| 0.90 | Fair | J    | VS2  | 65.0 | 56   | 2815 | 6.08 | 6.04 | 3.94 |\n",
       "| 0.91 | Fair | J    | VS2  | 64.4 | 62   | 2854 | 6.06 | 6.03 | 3.89 |\n",
       "| 0.91 | Fair | J    | VS2  | 65.4 | 60   | 2854 | 6.04 | 6.00 | 3.94 |\n",
       "| 1.00 | Fair | J    | VS1  | 65.5 | 55   | 2875 | 6.30 | 6.25 | 4.11 |\n",
       "| 0.99 | Fair | J    | SI1  | 58.0 | 67   | 2949 | 6.57 | 6.50 | 3.79 |\n",
       "| 0.90 | Fair | J    | VS1  | 65.4 | 60   | 2964 | 6.02 | 5.93 | 3.91 |\n",
       "| 0.90 | Fair | J    | VS1  | 64.6 | 58   | 2964 | 6.12 | 6.06 | 3.93 |\n",
       "| 1.06 | Fair | J    | SI2  | 61.0 | 66   | 2992 | 6.54 | 6.35 | 3.93 |\n",
       "| 1.01 | Fair | J    | VVS2 | 66.0 | 56   | 2998 | 6.29 | 6.22 | 4.13 |\n",
       "| 1.20 | Fair | J    | I1   | 64.9 | 59   | 3011 | 6.61 | 6.54 | 4.27 |\n",
       "| 1.02 | Fair | J    | SI1  | 66.8 | 55   | 3027 | 6.25 | 6.18 | 4.15 |\n",
       "| 1.00 | Fair | J    | VS1  | 66.6 | 57   | 3080 | 6.07 | 6.03 | 4.03 |\n",
       "| 1.02 | Fair | J    | SI2  | 65.0 | 59   | 3119 | 6.34 | 6.24 | 4.08 |\n",
       "| 1.03 | Fair | J    | SI2  | 64.6 | 62   | 3149 | 6.30 | 6.21 | 4.06 |\n",
       "| 0.95 | Fair | J    | SI1  | 58.0 | 66   | 3154 | 6.50 | 6.46 | 3.76 |\n",
       "| 1.00 | Fair | J    | SI1  | 65.3 | 57   | 3175 | 6.33 | 6.12 | 4.08 |\n",
       "| 1.01 | Fair | J    | VS2  | 65.8 | 55   | 3190 | 6.29 | 6.24 | 4.12 |\n",
       "| 1.00 | Fair | J    | SI2  | 56.3 | 64   | 3199 | 6.58 | 6.54 | 3.69 |\n",
       "| 1.03 | Fair | J    | SI2  | 56.2 | 62   | 3246 | 6.73 | 6.69 | 3.77 |\n",
       "| 1.08 | Fair | J    | SI2  | 64.9 | 60   | 3302 | 6.46 | 6.32 | 4.15 |\n",
       "| 1.14 | Fair | J    | SI2  | 64.4 | 55   | 3403 | 6.56 | 6.54 | 4.22 |\n",
       "| 1.00 | Fair | J    | VS2  | 65.3 | 61   | 3422 | 6.33 | 6.13 | 4.03 |\n",
       "| 1.00 | Fair | J    | SI1  | 64.9 | 58   | 3427 | 6.29 | 6.23 | 4.06 |\n",
       "| 1.09 | Fair | J    | VS2  | 64.6 | 58   | 3443 | 6.48 | 6.41 | 4.16 |\n",
       "| 1.00 | Fair | J    | SI1  | 64.7 | 52   | 3780 | 6.35 | 6.30 | 4.09 |\n",
       "| 1.01 | Fair | J    | VS2  | 59.6 | 62   | 3886 | 6.54 | 6.49 | 3.88 |\n",
       "| 1.08 | Fair | J    | VS2  | 65.1 | 54   | 3980 | 6.47 | 6.44 | 4.20 |\n",
       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
       "| 0.61 | Fair | J    | VS2  | 58.2 | 56   | 1174 | 5.74 | 5.49 | 3.27 |\n",
       "| 0.85 | Fair | J    | I1   | 65.5 | 59   | 1334 | 5.95 | 5.82 | 3.86 |\n",
       "| 0.71 | Fair | J    | SI2  | 66.1 | 58   | 1362 | 5.53 | 5.43 | 3.62 |\n",
       "| 0.71 | Fair | J    | SI2  | 65.5 | 61   | 1409 | 5.54 | 5.49 | 3.61 |\n",
       "| 0.71 | Fair | J    | VS2  | 64.9 | 54   | 1435 | 5.63 | 5.53 | 3.62 |\n",
       "| 0.70 | Fair | J    | VS2  | 64.6 | 58   | 1672 | 5.58 | 5.56 | 3.60 |\n",
       "| 0.70 | Fair | J    | VVS1 | 67.6 | 54   | 1691 | 5.56 | 5.41 | 3.71 |\n",
       "| 0.70 | Fair | J    | SI1  | 63.0 | 57   | 1723 | 5.55 | 5.60 | 3.51 |\n",
       "| 0.72 | Fair | J    | SI2  | 66.1 | 55   | 1762 | 5.51 | 5.56 | 3.66 |\n",
       "| 1.00 | Fair | J    | I1   | 65.0 | 59   | 1784 | 6.27 | 6.17 | 4.05 |\n",
       "| 0.99 | Fair | J    | I1   | 73.6 | 60   | 1789 | 6.01 | 5.80 | 4.35 |\n",
       "| 0.73 | Fair | J    | VS1  | 64.6 | 60   | 1802 | 5.68 | 5.59 | 3.64 |\n",
       "| 0.71 | Fair | J    | VS2  | 64.4 | 56   | 1826 | 5.64 | 5.60 | 3.62 |\n",
       "| 0.75 | Fair | J    | SI1  | 55.0 | 66   | 1827 | 6.20 | 6.04 | 3.37 |\n",
       "| 0.75 | Fair | J    | VS1  | 61.9 | 67   | 1869 | 5.80 | 5.70 | 3.56 |\n",
       "| 0.70 | Fair | J    | VS2  | 67.4 | 58   | 1896 | 5.34 | 5.37 | 3.61 |\n",
       "| 0.99 | Fair | J    | I1   | 62.9 | 58   | 1901 | 6.33 | 6.10 | 3.91 |\n",
       "| 0.75 | Fair | J    | VS2  | 59.1 | 66   | 1944 | 5.99 | 5.82 | 3.49 |\n",
       "| 0.77 | Fair | J    | SI1  | 60.2 | 66   | 1956 | 5.97 | 5.81 | 3.55 |\n",
       "| 0.74 | Fair | J    | VS1  | 58.8 | 68   | 1982 | 5.91 | 5.80 | 3.44 |\n",
       "| 0.77 | Fair | J    | VS1  | 61.2 | 66   | 2005 | 5.92 | 5.83 | 3.60 |\n",
       "| 1.00 | Fair | J    | SI2  | 69.9 | 61   | 2117 | 6.13 | 5.86 | 4.23 |\n",
       "| 0.96 | Fair | J    | I1   | 59.5 | 60   | 2304 | 6.28 | 6.23 | 3.72 |\n",
       "| 0.95 | Fair | J    | I1   | 64.9 | 56   | 2322 | 6.17 | 6.12 | 3.99 |\n",
       "| 0.91 | Fair | J    | SI1  | 66.3 | 56   | 2390 | 5.92 | 5.99 | 3.95 |\n",
       "| 0.90 | Fair | J    | SI1  | 64.6 | 61   | 2438 | 5.92 | 5.87 | 3.81 |\n",
       "| 0.96 | Fair | J    | SI1  | 67.3 | 56   | 2517 | 6.06 | 6.01 | 4.06 |\n",
       "| 0.90 | Fair | J    | SI2  | 66.6 | 54   | 2536 | 6.05 | 5.99 | 4.01 |\n",
       "| 0.97 | Fair | J    | SI2  | 60.8 | 67   | 2538 | 6.41 | 6.32 | 3.87 |\n",
       "| 1.01 | Fair | J    | SI2  | 66.9 | 58   | 2683 | 6.13 | 6.07 | 4.08 |\n",
       "\n"
      ],
      "text/plain": [
       "    carat cut  color clarity depth table price x    y    z   \n",
       "1   1.05  Fair J     SI2     65.8  59    2789  6.41 6.27 4.18\n",
       "2   1.00  Fair J     VS2     65.7  59    2811  6.14 6.07 4.01\n",
       "3   0.99  Fair J     SI1     55.0  61    2812  6.72 6.67 3.68\n",
       "4   0.90  Fair J     VS2     65.0  56    2815  6.08 6.04 3.94\n",
       "5   0.91  Fair J     VS2     64.4  62    2854  6.06 6.03 3.89\n",
       "6   0.91  Fair J     VS2     65.4  60    2854  6.04 6.00 3.94\n",
       "7   1.00  Fair J     VS1     65.5  55    2875  6.30 6.25 4.11\n",
       "8   0.99  Fair J     SI1     58.0  67    2949  6.57 6.50 3.79\n",
       "9   0.90  Fair J     VS1     65.4  60    2964  6.02 5.93 3.91\n",
       "10  0.90  Fair J     VS1     64.6  58    2964  6.12 6.06 3.93\n",
       "11  1.06  Fair J     SI2     61.0  66    2992  6.54 6.35 3.93\n",
       "12  1.01  Fair J     VVS2    66.0  56    2998  6.29 6.22 4.13\n",
       "13  1.20  Fair J     I1      64.9  59    3011  6.61 6.54 4.27\n",
       "14  1.02  Fair J     SI1     66.8  55    3027  6.25 6.18 4.15\n",
       "15  1.00  Fair J     VS1     66.6  57    3080  6.07 6.03 4.03\n",
       "16  1.02  Fair J     SI2     65.0  59    3119  6.34 6.24 4.08\n",
       "17  1.03  Fair J     SI2     64.6  62    3149  6.30 6.21 4.06\n",
       "18  0.95  Fair J     SI1     58.0  66    3154  6.50 6.46 3.76\n",
       "19  1.00  Fair J     SI1     65.3  57    3175  6.33 6.12 4.08\n",
       "20  1.01  Fair J     VS2     65.8  55    3190  6.29 6.24 4.12\n",
       "21  1.00  Fair J     SI2     56.3  64    3199  6.58 6.54 3.69\n",
       "22  1.03  Fair J     SI2     56.2  62    3246  6.73 6.69 3.77\n",
       "23  1.08  Fair J     SI2     64.9  60    3302  6.46 6.32 4.15\n",
       "24  1.14  Fair J     SI2     64.4  55    3403  6.56 6.54 4.22\n",
       "25  1.00  Fair J     VS2     65.3  61    3422  6.33 6.13 4.03\n",
       "26  1.00  Fair J     SI1     64.9  58    3427  6.29 6.23 4.06\n",
       "27  1.09  Fair J     VS2     64.6  58    3443  6.48 6.41 4.16\n",
       "28  1.00  Fair J     SI1     64.7  52    3780  6.35 6.30 4.09\n",
       "29  1.01  Fair J     VS2     59.6  62    3886  6.54 6.49 3.88\n",
       "30  1.08  Fair J     VS2     65.1  54    3980  6.47 6.44 4.20\n",
       "... ...   ...  ...   ...     ...   ...   ...   ...  ...  ... \n",
       "90  0.61  Fair J     VS2     58.2  56    1174  5.74 5.49 3.27\n",
       "91  0.85  Fair J     I1      65.5  59    1334  5.95 5.82 3.86\n",
       "92  0.71  Fair J     SI2     66.1  58    1362  5.53 5.43 3.62\n",
       "93  0.71  Fair J     SI2     65.5  61    1409  5.54 5.49 3.61\n",
       "94  0.71  Fair J     VS2     64.9  54    1435  5.63 5.53 3.62\n",
       "95  0.70  Fair J     VS2     64.6  58    1672  5.58 5.56 3.60\n",
       "96  0.70  Fair J     VVS1    67.6  54    1691  5.56 5.41 3.71\n",
       "97  0.70  Fair J     SI1     63.0  57    1723  5.55 5.60 3.51\n",
       "98  0.72  Fair J     SI2     66.1  55    1762  5.51 5.56 3.66\n",
       "99  1.00  Fair J     I1      65.0  59    1784  6.27 6.17 4.05\n",
       "100 0.99  Fair J     I1      73.6  60    1789  6.01 5.80 4.35\n",
       "101 0.73  Fair J     VS1     64.6  60    1802  5.68 5.59 3.64\n",
       "102 0.71  Fair J     VS2     64.4  56    1826  5.64 5.60 3.62\n",
       "103 0.75  Fair J     SI1     55.0  66    1827  6.20 6.04 3.37\n",
       "104 0.75  Fair J     VS1     61.9  67    1869  5.80 5.70 3.56\n",
       "105 0.70  Fair J     VS2     67.4  58    1896  5.34 5.37 3.61\n",
       "106 0.99  Fair J     I1      62.9  58    1901  6.33 6.10 3.91\n",
       "107 0.75  Fair J     VS2     59.1  66    1944  5.99 5.82 3.49\n",
       "108 0.77  Fair J     SI1     60.2  66    1956  5.97 5.81 3.55\n",
       "109 0.74  Fair J     VS1     58.8  68    1982  5.91 5.80 3.44\n",
       "110 0.77  Fair J     VS1     61.2  66    2005  5.92 5.83 3.60\n",
       "111 1.00  Fair J     SI2     69.9  61    2117  6.13 5.86 4.23\n",
       "112 0.96  Fair J     I1      59.5  60    2304  6.28 6.23 3.72\n",
       "113 0.95  Fair J     I1      64.9  56    2322  6.17 6.12 3.99\n",
       "114 0.91  Fair J     SI1     66.3  56    2390  5.92 5.99 3.95\n",
       "115 0.90  Fair J     SI1     64.6  61    2438  5.92 5.87 3.81\n",
       "116 0.96  Fair J     SI1     67.3  56    2517  6.06 6.01 4.06\n",
       "117 0.90  Fair J     SI2     66.6  54    2536  6.05 5.99 4.01\n",
       "118 0.97  Fair J     SI2     60.8  67    2538  6.41 6.32 3.87\n",
       "119 1.01  Fair J     SI2     66.9  58    2683  6.13 6.07 4.08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter(diamonds,  color == 'J', cut == 'Fair')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## a = filter(diamonds, color == 'D' | clarity=='VS2') \n",
    "# filtering for rows that satisfy one or both of the conditions\n",
    "\n",
    "b = filter(diamonds, color == 'D' & clarity == 'VS2') \n",
    "# filtering for rows that satisfy both conditions\n",
    "\n",
    "best_cuts = filter(diamonds, cut == 'Ideal') \n",
    "# filtering using membership condition\n",
    "\n",
    "not_worst_cuts = filter(diamonds, cut > 'Fair') \n",
    "# can do this because cut is an ordinal variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "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></tr></thead>\n",
       "<tbody>\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></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></tr>\n",
       "\t<tr><td>0.23     </td><td>Very Good</td><td>E        </td><td>VS2      </td><td>63.8     </td><td>55       </td><td>352      </td><td>3.85     </td><td>3.92     </td><td>2.48     </td></tr>\n",
       "\t<tr><td>0.30     </td><td>Very Good</td><td>J        </td><td>VS2      </td><td>62.2     </td><td>57       </td><td>357      </td><td>4.28     </td><td>4.30     </td><td>2.67     </td></tr>\n",
       "\t<tr><td>0.23     </td><td>Very Good</td><td>D        </td><td>VS2      </td><td>60.5     </td><td>61       </td><td>357      </td><td>3.96     </td><td>3.97     </td><td>2.40     </td></tr>\n",
       "\t<tr><td>0.23     </td><td>Very Good</td><td>D        </td><td>VS1      </td><td>61.9     </td><td>58       </td><td>402      </td><td>3.92     </td><td>3.96     </td><td>2.44     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       "\\hline\n",
       "\t 0.29      & Premium   & I         & VS2       & 62.4      & 58        & 334       & 4.20      & 4.23      & 2.63     \\\\\n",
       "\t 0.22      & Fair      & E         & VS2       & 65.1      & 61        & 337       & 3.87      & 3.78      & 2.49     \\\\\n",
       "\t 0.23      & Very Good & E         & VS2       & 63.8      & 55        & 352       & 3.85      & 3.92      & 2.48     \\\\\n",
       "\t 0.30      & Very Good & J         & VS2       & 62.2      & 57        & 357       & 4.28      & 4.30      & 2.67     \\\\\n",
       "\t 0.23      & Very Good & D         & VS2       & 60.5      & 61        & 357       & 3.96      & 3.97      & 2.40     \\\\\n",
       "\t 0.23      & Very Good & D         & VS1       & 61.9      & 58        & 402       & 3.92      & 3.96      & 2.44     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| carat | cut | color | clarity | depth | table | price | x | y | z |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.29      | Premium   | I         | VS2       | 62.4      | 58        | 334       | 4.20      | 4.23      | 2.63      |\n",
       "| 0.22      | Fair      | E         | VS2       | 65.1      | 61        | 337       | 3.87      | 3.78      | 2.49      |\n",
       "| 0.23      | Very Good | E         | VS2       | 63.8      | 55        | 352       | 3.85      | 3.92      | 2.48      |\n",
       "| 0.30      | Very Good | J         | VS2       | 62.2      | 57        | 357       | 4.28      | 4.30      | 2.67      |\n",
       "| 0.23      | Very Good | D         | VS2       | 60.5      | 61        | 357       | 3.96      | 3.97      | 2.40      |\n",
       "| 0.23      | Very Good | D         | VS1       | 61.9      | 58        | 402       | 3.92      | 3.96      | 2.44      |\n",
       "\n"
      ],
      "text/plain": [
       "  carat cut       color clarity depth table price x    y    z   \n",
       "1 0.29  Premium   I     VS2     62.4  58    334   4.20 4.23 2.63\n",
       "2 0.22  Fair      E     VS2     65.1  61    337   3.87 3.78 2.49\n",
       "3 0.23  Very Good E     VS2     63.8  55    352   3.85 3.92 2.48\n",
       "4 0.30  Very Good J     VS2     62.2  57    357   4.28 4.30 2.67\n",
       "5 0.23  Very Good D     VS2     60.5  61    357   3.96 3.97 2.40\n",
       "6 0.23  Very Good D     VS1     61.9  58    402   3.92 3.96 2.44"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice(a, 1:6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "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></tr></thead>\n",
       "<tbody>\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></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></tr>\n",
       "\t<tr><td>0.23     </td><td>Very Good</td><td>E        </td><td>VS2      </td><td>63.8     </td><td>55       </td><td>352      </td><td>3.85     </td><td>3.92     </td><td>2.48     </td></tr>\n",
       "\t<tr><td>0.30     </td><td>Very Good</td><td>J        </td><td>VS2      </td><td>62.2     </td><td>57       </td><td>357      </td><td>4.28     </td><td>4.30     </td><td>2.67     </td></tr>\n",
       "\t<tr><td>0.23     </td><td>Very Good</td><td>D        </td><td>VS2      </td><td>60.5     </td><td>61       </td><td>357      </td><td>3.96     </td><td>3.97     </td><td>2.40     </td></tr>\n",
       "\t<tr><td>0.23     </td><td>Very Good</td><td>D        </td><td>VS1      </td><td>61.9     </td><td>58       </td><td>402      </td><td>3.92     </td><td>3.96     </td><td>2.44     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       "\\hline\n",
       "\t 0.29      & Premium   & I         & VS2       & 62.4      & 58        & 334       & 4.20      & 4.23      & 2.63     \\\\\n",
       "\t 0.22      & Fair      & E         & VS2       & 65.1      & 61        & 337       & 3.87      & 3.78      & 2.49     \\\\\n",
       "\t 0.23      & Very Good & E         & VS2       & 63.8      & 55        & 352       & 3.85      & 3.92      & 2.48     \\\\\n",
       "\t 0.30      & Very Good & J         & VS2       & 62.2      & 57        & 357       & 4.28      & 4.30      & 2.67     \\\\\n",
       "\t 0.23      & Very Good & D         & VS2       & 60.5      & 61        & 357       & 3.96      & 3.97      & 2.40     \\\\\n",
       "\t 0.23      & Very Good & D         & VS1       & 61.9      & 58        & 402       & 3.92      & 3.96      & 2.44     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| carat | cut | color | clarity | depth | table | price | x | y | z |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.29      | Premium   | I         | VS2       | 62.4      | 58        | 334       | 4.20      | 4.23      | 2.63      |\n",
       "| 0.22      | Fair      | E         | VS2       | 65.1      | 61        | 337       | 3.87      | 3.78      | 2.49      |\n",
       "| 0.23      | Very Good | E         | VS2       | 63.8      | 55        | 352       | 3.85      | 3.92      | 2.48      |\n",
       "| 0.30      | Very Good | J         | VS2       | 62.2      | 57        | 357       | 4.28      | 4.30      | 2.67      |\n",
       "| 0.23      | Very Good | D         | VS2       | 60.5      | 61        | 357       | 3.96      | 3.97      | 2.40      |\n",
       "| 0.23      | Very Good | D         | VS1       | 61.9      | 58        | 402       | 3.92      | 3.96      | 2.44      |\n",
       "\n"
      ],
      "text/plain": [
       "  carat cut       color clarity depth table price x    y    z   \n",
       "1 0.29  Premium   I     VS2     62.4  58    334   4.20 4.23 2.63\n",
       "2 0.22  Fair      E     VS2     65.1  61    337   3.87 3.78 2.49\n",
       "3 0.23  Very Good E     VS2     63.8  55    352   3.85 3.92 2.48\n",
       "4 0.30  Very Good J     VS2     62.2  57    357   4.28 4.30 2.67\n",
       "5 0.23  Very Good D     VS2     60.5  61    357   3.96 3.97 2.40\n",
       "6 0.23  Very Good D     VS1     61.9  58    402   3.92 3.96 2.44"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>cut</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Premium  </td></tr>\n",
       "\t<tr><td>Fair     </td></tr>\n",
       "\t<tr><td>Very Good</td></tr>\n",
       "\t<tr><td>Very Good</td></tr>\n",
       "\t<tr><td>Very Good</td></tr>\n",
       "\t<tr><td>Very Good</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " cut\\\\\n",
       "\\hline\n",
       "\t Premium  \\\\\n",
       "\t Fair     \\\\\n",
       "\t Very Good\\\\\n",
       "\t Very Good\\\\\n",
       "\t Very Good\\\\\n",
       "\t Very Good\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| cut |\n",
       "|---|\n",
       "| Premium   |\n",
       "| Fair      |\n",
       "| Very Good |\n",
       "| Very Good |\n",
       "| Very Good |\n",
       "| Very Good |\n",
       "\n"
      ],
      "text/plain": [
       "  cut      \n",
       "1 Premium  \n",
       "2 Fair     \n",
       "3 Very Good\n",
       "4 Very Good\n",
       "5 Very Good\n",
       "6 Very Good"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a[1:6, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'carat'</li>\n",
       "\t<li>'cut'</li>\n",
       "\t<li>'color'</li>\n",
       "\t<li>'clarity'</li>\n",
       "\t<li>'depth'</li>\n",
       "\t<li>'table'</li>\n",
       "\t<li>'price'</li>\n",
       "\t<li>'x'</li>\n",
       "\t<li>'y'</li>\n",
       "\t<li>'z'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'carat'\n",
       "\\item 'cut'\n",
       "\\item 'color'\n",
       "\\item 'clarity'\n",
       "\\item 'depth'\n",
       "\\item 'table'\n",
       "\\item 'price'\n",
       "\\item 'x'\n",
       "\\item 'y'\n",
       "\\item 'z'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'carat'\n",
       "2. 'cut'\n",
       "3. 'color'\n",
       "4. 'clarity'\n",
       "5. 'depth'\n",
       "6. 'table'\n",
       "7. 'price'\n",
       "8. 'x'\n",
       "9. 'y'\n",
       "10. 'z'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"carat\"   \"cut\"     \"color\"   \"clarity\" \"depth\"   \"table\"   \"price\"  \n",
       " [8] \"x\"       \"y\"       \"z\"      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names(diamonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>depth</th><th scope=col>clarity</th><th scope=col>color</th><th scope=col>cut</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>61.5     </td><td>SI2      </td><td>E        </td><td>Ideal    </td></tr>\n",
       "\t<tr><td>59.8     </td><td>SI1      </td><td>E        </td><td>Premium  </td></tr>\n",
       "\t<tr><td>56.9     </td><td>VS1      </td><td>E        </td><td>Good     </td></tr>\n",
       "\t<tr><td>62.4     </td><td>VS2      </td><td>I        </td><td>Premium  </td></tr>\n",
       "\t<tr><td>63.3     </td><td>SI2      </td><td>J        </td><td>Good     </td></tr>\n",
       "\t<tr><td>62.8     </td><td>VVS2     </td><td>J        </td><td>Very Good</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " depth & clarity & color & cut\\\\\n",
       "\\hline\n",
       "\t 61.5      & SI2       & E         & Ideal    \\\\\n",
       "\t 59.8      & SI1       & E         & Premium  \\\\\n",
       "\t 56.9      & VS1       & E         & Good     \\\\\n",
       "\t 62.4      & VS2       & I         & Premium  \\\\\n",
       "\t 63.3      & SI2       & J         & Good     \\\\\n",
       "\t 62.8      & VVS2      & J         & Very Good\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| depth | clarity | color | cut |\n",
       "|---|---|---|---|\n",
       "| 61.5      | SI2       | E         | Ideal     |\n",
       "| 59.8      | SI1       | E         | Premium   |\n",
       "| 56.9      | VS1       | E         | Good      |\n",
       "| 62.4      | VS2       | I         | Premium   |\n",
       "| 63.3      | SI2       | J         | Good      |\n",
       "| 62.8      | VVS2      | J         | Very Good |\n",
       "\n"
      ],
      "text/plain": [
       "  depth clarity color cut      \n",
       "1 61.5  SI2     E     Ideal    \n",
       "2 59.8  SI1     E     Premium  \n",
       "3 56.9  VS1     E     Good     \n",
       "4 62.4  VS2     I     Premium  \n",
       "5 63.3  SI2     J     Good     \n",
       "6 62.8  VVS2    J     Very Good"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(select(diamonds, depth:cut))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head(select(diamonds, c(carat, table, price)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>cut</th><th scope=col>color</th><th scope=col>clarity</th><th scope=col>depth</th><th scope=col>x</th><th scope=col>y</th><th scope=col>z</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Ideal    </td><td>E        </td><td>SI2      </td><td>61.5     </td><td>3.95     </td><td>3.98     </td><td>2.43     </td></tr>\n",
       "\t<tr><td>Premium  </td><td>E        </td><td>SI1      </td><td>59.8     </td><td>3.89     </td><td>3.84     </td><td>2.31     </td></tr>\n",
       "\t<tr><td>Good     </td><td>E        </td><td>VS1      </td><td>56.9     </td><td>4.05     </td><td>4.07     </td><td>2.31     </td></tr>\n",
       "\t<tr><td>Premium  </td><td>I        </td><td>VS2      </td><td>62.4     </td><td>4.20     </td><td>4.23     </td><td>2.63     </td></tr>\n",
       "\t<tr><td>Good     </td><td>J        </td><td>SI2      </td><td>63.3     </td><td>4.34     </td><td>4.35     </td><td>2.75     </td></tr>\n",
       "\t<tr><td>Very Good</td><td>J        </td><td>VVS2     </td><td>62.8     </td><td>3.94     </td><td>3.96     </td><td>2.48     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " cut & color & clarity & depth & x & y & z\\\\\n",
       "\\hline\n",
       "\t Ideal     & E         & SI2       & 61.5      & 3.95      & 3.98      & 2.43     \\\\\n",
       "\t Premium   & E         & SI1       & 59.8      & 3.89      & 3.84      & 2.31     \\\\\n",
       "\t Good      & E         & VS1       & 56.9      & 4.05      & 4.07      & 2.31     \\\\\n",
       "\t Premium   & I         & VS2       & 62.4      & 4.20      & 4.23      & 2.63     \\\\\n",
       "\t Good      & J         & SI2       & 63.3      & 4.34      & 4.35      & 2.75     \\\\\n",
       "\t Very Good & J         & VVS2      & 62.8      & 3.94      & 3.96      & 2.48     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| cut | color | clarity | depth | x | y | z |\n",
       "|---|---|---|---|---|---|---|\n",
       "| Ideal     | E         | SI2       | 61.5      | 3.95      | 3.98      | 2.43      |\n",
       "| Premium   | E         | SI1       | 59.8      | 3.89      | 3.84      | 2.31      |\n",
       "| Good      | E         | VS1       | 56.9      | 4.05      | 4.07      | 2.31      |\n",
       "| Premium   | I         | VS2       | 62.4      | 4.20      | 4.23      | 2.63      |\n",
       "| Good      | J         | SI2       | 63.3      | 4.34      | 4.35      | 2.75      |\n",
       "| Very Good | J         | VVS2      | 62.8      | 3.94      | 3.96      | 2.48      |\n",
       "\n"
      ],
      "text/plain": [
       "  cut       color clarity depth x    y    z   \n",
       "1 Ideal     E     SI2     61.5  3.95 3.98 2.43\n",
       "2 Premium   E     SI1     59.8  3.89 3.84 2.31\n",
       "3 Good      E     VS1     56.9  4.05 4.07 2.31\n",
       "4 Premium   I     VS2     62.4  4.20 4.23 2.63\n",
       "5 Good      J     SI2     63.3  4.34 4.35 2.75\n",
       "6 Very Good J     VVS2    62.8  3.94 3.96 2.48"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(select(diamonds, -c(carat, table, price)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "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>width</th><th scope=col>y</th><th scope=col>z</th></tr></thead>\n",
       "<tbody>\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></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></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></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></tr>\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></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></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></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></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></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></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & width & y & z\\\\\n",
       "\\hline\n",
       "\t 0.23      & Ideal     & E         & SI2       & 61.5      & 55        & 326       & 3.95      & 3.98      & 2.43     \\\\\n",
       "\t 0.21      & Premium   & E         & SI1       & 59.8      & 61        & 326       & 3.89      & 3.84      & 2.31     \\\\\n",
       "\t 0.23      & Good      & E         & VS1       & 56.9      & 65        & 327       & 4.05      & 4.07      & 2.31     \\\\\n",
       "\t 0.29      & Premium   & I         & VS2       & 62.4      & 58        & 334       & 4.20      & 4.23      & 2.63     \\\\\n",
       "\t 0.31      & Good      & J         & SI2       & 63.3      & 58        & 335       & 4.34      & 4.35      & 2.75     \\\\\n",
       "\t 0.24      & Very Good & J         & VVS2      & 62.8      & 57        & 336       & 3.94      & 3.96      & 2.48     \\\\\n",
       "\t 0.24      & Very Good & I         & VVS1      & 62.3      & 57        & 336       & 3.95      & 3.98      & 2.47     \\\\\n",
       "\t 0.26      & Very Good & H         & SI1       & 61.9      & 55        & 337       & 4.07      & 4.11      & 2.53     \\\\\n",
       "\t 0.22      & Fair      & E         & VS2       & 65.1      & 61        & 337       & 3.87      & 3.78      & 2.49     \\\\\n",
       "\t 0.23      & Very Good & H         & VS1       & 59.4      & 61        & 338       & 4.00      & 4.05      & 2.39     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| carat | cut | color | clarity | depth | table | price | width | y | z |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.23      | Ideal     | E         | SI2       | 61.5      | 55        | 326       | 3.95      | 3.98      | 2.43      |\n",
       "| 0.21      | Premium   | E         | SI1       | 59.8      | 61        | 326       | 3.89      | 3.84      | 2.31      |\n",
       "| 0.23      | Good      | E         | VS1       | 56.9      | 65        | 327       | 4.05      | 4.07      | 2.31      |\n",
       "| 0.29      | Premium   | I         | VS2       | 62.4      | 58        | 334       | 4.20      | 4.23      | 2.63      |\n",
       "| 0.31      | Good      | J         | SI2       | 63.3      | 58        | 335       | 4.34      | 4.35      | 2.75      |\n",
       "| 0.24      | Very Good | J         | VVS2      | 62.8      | 57        | 336       | 3.94      | 3.96      | 2.48      |\n",
       "| 0.24      | Very Good | I         | VVS1      | 62.3      | 57        | 336       | 3.95      | 3.98      | 2.47      |\n",
       "| 0.26      | Very Good | H         | SI1       | 61.9      | 55        | 337       | 4.07      | 4.11      | 2.53      |\n",
       "| 0.22      | Fair      | E         | VS2       | 65.1      | 61        | 337       | 3.87      | 3.78      | 2.49      |\n",
       "| 0.23      | Very Good | H         | VS1       | 59.4      | 61        | 338       | 4.00      | 4.05      | 2.39      |\n",
       "\n"
      ],
      "text/plain": [
       "   carat cut       color clarity depth table price width y    z   \n",
       "1  0.23  Ideal     E     SI2     61.5  55    326   3.95  3.98 2.43\n",
       "2  0.21  Premium   E     SI1     59.8  61    326   3.89  3.84 2.31\n",
       "3  0.23  Good      E     VS1     56.9  65    327   4.05  4.07 2.31\n",
       "4  0.29  Premium   I     VS2     62.4  58    334   4.20  4.23 2.63\n",
       "5  0.31  Good      J     SI2     63.3  58    335   4.34  4.35 2.75\n",
       "6  0.24  Very Good J     VVS2    62.8  57    336   3.94  3.96 2.48\n",
       "7  0.24  Very Good I     VVS1    62.3  57    336   3.95  3.98 2.47\n",
       "8  0.26  Very Good H     SI1     61.9  55    337   4.07  4.11 2.53\n",
       "9  0.22  Fair      E     VS2     65.1  61    337   3.87  3.78 2.49\n",
       "10 0.23  Very Good H     VS1     59.4  61    338   4.00  4.05 2.39"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rename(diamonds, width=x)[1:10,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>price</th><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>x</th><th scope=col>y</th><th scope=col>z</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>326      </td><td>0.23     </td><td>Ideal    </td><td>E        </td><td>SI2      </td><td>61.5     </td><td>55       </td><td>3.95     </td><td>3.98     </td><td>2.43     </td></tr>\n",
       "\t<tr><td>326      </td><td>0.21     </td><td>Premium  </td><td>E        </td><td>SI1      </td><td>59.8     </td><td>61       </td><td>3.89     </td><td>3.84     </td><td>2.31     </td></tr>\n",
       "\t<tr><td>327      </td><td>0.23     </td><td>Good     </td><td>E        </td><td>VS1      </td><td>56.9     </td><td>65       </td><td>4.05     </td><td>4.07     </td><td>2.31     </td></tr>\n",
       "\t<tr><td>334      </td><td>0.29     </td><td>Premium  </td><td>I        </td><td>VS2      </td><td>62.4     </td><td>58       </td><td>4.20     </td><td>4.23     </td><td>2.63     </td></tr>\n",
       "\t<tr><td>335      </td><td>0.31     </td><td>Good     </td><td>J        </td><td>SI2      </td><td>63.3     </td><td>58       </td><td>4.34     </td><td>4.35     </td><td>2.75     </td></tr>\n",
       "\t<tr><td>336      </td><td>0.24     </td><td>Very Good</td><td>J        </td><td>VVS2     </td><td>62.8     </td><td>57       </td><td>3.94     </td><td>3.96     </td><td>2.48     </td></tr>\n",
       "\t<tr><td>336      </td><td>0.24     </td><td>Very Good</td><td>I        </td><td>VVS1     </td><td>62.3     </td><td>57       </td><td>3.95     </td><td>3.98     </td><td>2.47     </td></tr>\n",
       "\t<tr><td>337      </td><td>0.26     </td><td>Very Good</td><td>H        </td><td>SI1      </td><td>61.9     </td><td>55       </td><td>4.07     </td><td>4.11     </td><td>2.53     </td></tr>\n",
       "\t<tr><td>337      </td><td>0.22     </td><td>Fair     </td><td>E        </td><td>VS2      </td><td>65.1     </td><td>61       </td><td>3.87     </td><td>3.78     </td><td>2.49     </td></tr>\n",
       "\t<tr><td>338      </td><td>0.23     </td><td>Very Good</td><td>H        </td><td>VS1      </td><td>59.4     </td><td>61       </td><td>4.00     </td><td>4.05     </td><td>2.39     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " price & carat & cut & color & clarity & depth & table & x & y & z\\\\\n",
       "\\hline\n",
       "\t 326       & 0.23      & Ideal     & E         & SI2       & 61.5      & 55        & 3.95      & 3.98      & 2.43     \\\\\n",
       "\t 326       & 0.21      & Premium   & E         & SI1       & 59.8      & 61        & 3.89      & 3.84      & 2.31     \\\\\n",
       "\t 327       & 0.23      & Good      & E         & VS1       & 56.9      & 65        & 4.05      & 4.07      & 2.31     \\\\\n",
       "\t 334       & 0.29      & Premium   & I         & VS2       & 62.4      & 58        & 4.20      & 4.23      & 2.63     \\\\\n",
       "\t 335       & 0.31      & Good      & J         & SI2       & 63.3      & 58        & 4.34      & 4.35      & 2.75     \\\\\n",
       "\t 336       & 0.24      & Very Good & J         & VVS2      & 62.8      & 57        & 3.94      & 3.96      & 2.48     \\\\\n",
       "\t 336       & 0.24      & Very Good & I         & VVS1      & 62.3      & 57        & 3.95      & 3.98      & 2.47     \\\\\n",
       "\t 337       & 0.26      & Very Good & H         & SI1       & 61.9      & 55        & 4.07      & 4.11      & 2.53     \\\\\n",
       "\t 337       & 0.22      & Fair      & E         & VS2       & 65.1      & 61        & 3.87      & 3.78      & 2.49     \\\\\n",
       "\t 338       & 0.23      & Very Good & H         & VS1       & 59.4      & 61        & 4.00      & 4.05      & 2.39     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| price | carat | cut | color | clarity | depth | table | x | y | z |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 326       | 0.23      | Ideal     | E         | SI2       | 61.5      | 55        | 3.95      | 3.98      | 2.43      |\n",
       "| 326       | 0.21      | Premium   | E         | SI1       | 59.8      | 61        | 3.89      | 3.84      | 2.31      |\n",
       "| 327       | 0.23      | Good      | E         | VS1       | 56.9      | 65        | 4.05      | 4.07      | 2.31      |\n",
       "| 334       | 0.29      | Premium   | I         | VS2       | 62.4      | 58        | 4.20      | 4.23      | 2.63      |\n",
       "| 335       | 0.31      | Good      | J         | SI2       | 63.3      | 58        | 4.34      | 4.35      | 2.75      |\n",
       "| 336       | 0.24      | Very Good | J         | VVS2      | 62.8      | 57        | 3.94      | 3.96      | 2.48      |\n",
       "| 336       | 0.24      | Very Good | I         | VVS1      | 62.3      | 57        | 3.95      | 3.98      | 2.47      |\n",
       "| 337       | 0.26      | Very Good | H         | SI1       | 61.9      | 55        | 4.07      | 4.11      | 2.53      |\n",
       "| 337       | 0.22      | Fair      | E         | VS2       | 65.1      | 61        | 3.87      | 3.78      | 2.49      |\n",
       "| 338       | 0.23      | Very Good | H         | VS1       | 59.4      | 61        | 4.00      | 4.05      | 2.39      |\n",
       "\n"
      ],
      "text/plain": [
       "   price carat cut       color clarity depth table x    y    z   \n",
       "1  326   0.23  Ideal     E     SI2     61.5  55    3.95 3.98 2.43\n",
       "2  326   0.21  Premium   E     SI1     59.8  61    3.89 3.84 2.31\n",
       "3  327   0.23  Good      E     VS1     56.9  65    4.05 4.07 2.31\n",
       "4  334   0.29  Premium   I     VS2     62.4  58    4.20 4.23 2.63\n",
       "5  335   0.31  Good      J     SI2     63.3  58    4.34 4.35 2.75\n",
       "6  336   0.24  Very Good J     VVS2    62.8  57    3.94 3.96 2.48\n",
       "7  336   0.24  Very Good I     VVS1    62.3  57    3.95 3.98 2.47\n",
       "8  337   0.26  Very Good H     SI1     61.9  55    4.07 4.11 2.53\n",
       "9  337   0.22  Fair      E     VS2     65.1  61    3.87 3.78 2.49\n",
       "10 338   0.23  Very Good H     VS1     59.4  61    4.00 4.05 2.39"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "select(diamonds, price, carat, everything())[1:10,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "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></tr></thead>\n",
       "<tbody>\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></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></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></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></tr>\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></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       "\\hline\n",
       "\t 0.23    & Ideal   & E       & SI2     & 61.5    & 55      & 326     & 3.95    & 3.98    & 2.43   \\\\\n",
       "\t 0.21    & Premium & E       & SI1     & 59.8    & 61      & 326     & 3.89    & 3.84    & 2.31   \\\\\n",
       "\t 0.23    & Good    & E       & VS1     & 56.9    & 65      & 327     & 4.05    & 4.07    & 2.31   \\\\\n",
       "\t 0.29    & Premium & I       & VS2     & 62.4    & 58      & 334     & 4.20    & 4.23    & 2.63   \\\\\n",
       "\t 0.31    & Good    & J       & SI2     & 63.3    & 58      & 335     & 4.34    & 4.35    & 2.75   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| carat | cut | color | clarity | depth | table | price | x | y | z |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.23    | Ideal   | E       | SI2     | 61.5    | 55      | 326     | 3.95    | 3.98    | 2.43    |\n",
       "| 0.21    | Premium | E       | SI1     | 59.8    | 61      | 326     | 3.89    | 3.84    | 2.31    |\n",
       "| 0.23    | Good    | E       | VS1     | 56.9    | 65      | 327     | 4.05    | 4.07    | 2.31    |\n",
       "| 0.29    | Premium | I       | VS2     | 62.4    | 58      | 334     | 4.20    | 4.23    | 2.63    |\n",
       "| 0.31    | Good    | J       | SI2     | 63.3    | 58      | 335     | 4.34    | 4.35    | 2.75    |\n",
       "\n"
      ],
      "text/plain": [
       "  carat cut     color clarity depth table price x    y    z   \n",
       "1 0.23  Ideal   E     SI2     61.5  55    326   3.95 3.98 2.43\n",
       "2 0.21  Premium E     SI1     59.8  61    326   3.89 3.84 2.31\n",
       "3 0.23  Good    E     VS1     56.9  65    327   4.05 4.07 2.31\n",
       "4 0.29  Premium I     VS2     62.4  58    334   4.20 4.23 2.63\n",
       "5 0.31  Good    J     SI2     63.3  58    335   4.34 4.35 2.75"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arrange(diamonds, price)[1:5, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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></tr></thead>\n",
       "<tbody>\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></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></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></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></tr>\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></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       "\\hline\n",
       "\t 0.21    & Premium & E       & SI1     & 59.8    & 61      & 326     & 3.89    & 3.84    & 2.31   \\\\\n",
       "\t 0.23    & Ideal   & E       & SI2     & 61.5    & 55      & 326     & 3.95    & 3.98    & 2.43   \\\\\n",
       "\t 0.23    & Good    & E       & VS1     & 56.9    & 65      & 327     & 4.05    & 4.07    & 2.31   \\\\\n",
       "\t 0.29    & Premium & I       & VS2     & 62.4    & 58      & 334     & 4.20    & 4.23    & 2.63   \\\\\n",
       "\t 0.31    & Good    & J       & SI2     & 63.3    & 58      & 335     & 4.34    & 4.35    & 2.75   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| carat | cut | color | clarity | depth | table | price | x | y | z |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.21    | Premium | E       | SI1     | 59.8    | 61      | 326     | 3.89    | 3.84    | 2.31    |\n",
       "| 0.23    | Ideal   | E       | SI2     | 61.5    | 55      | 326     | 3.95    | 3.98    | 2.43    |\n",
       "| 0.23    | Good    | E       | VS1     | 56.9    | 65      | 327     | 4.05    | 4.07    | 2.31    |\n",
       "| 0.29    | Premium | I       | VS2     | 62.4    | 58      | 334     | 4.20    | 4.23    | 2.63    |\n",
       "| 0.31    | Good    | J       | SI2     | 63.3    | 58      | 335     | 4.34    | 4.35    | 2.75    |\n",
       "\n"
      ],
      "text/plain": [
       "  carat cut     color clarity depth table price x    y    z   \n",
       "1 0.21  Premium E     SI1     59.8  61    326   3.89 3.84 2.31\n",
       "2 0.23  Ideal   E     SI2     61.5  55    326   3.95 3.98 2.43\n",
       "3 0.23  Good    E     VS1     56.9  65    327   4.05 4.07 2.31\n",
       "4 0.29  Premium I     VS2     62.4  58    334   4.20 4.23 2.63\n",
       "5 0.31  Good    J     SI2     63.3  58    335   4.34 4.35 2.75"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arrange(diamonds, price, desc(table))[1:5, ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brief talk on pipes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- %>%\n",
    "- 'Ctrl-shift-m' is a short cut\n",
    "- Passes the output of one function as the first input to the next function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df <- college_df  %>%\n",
    "    group_by(Major_category)  %>% \n",
    "    summarize(cat_median = mean(Median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Major_category</th><th scope=col>cat_median</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Agriculture &amp; Natural Resources    </span></td><td>55000.00                                                                       </td></tr>\n",
       "\t<tr><td>Arts                               </td><td>43525.00                           </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Biology &amp; Life Science             </span></td><td>50821.43                                                                       </td></tr>\n",
       "\t<tr><td>Business                           </td><td>60615.38                           </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Communications &amp; Journalism        </span></td><td>49500.00                                                                       </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Computers &amp; Mathematics            </span></td><td>66272.73                                                                       </td></tr>\n",
       "\t<tr><td>Education                          </td><td>43831.25                           </td></tr>\n",
       "\t<tr><td>Engineering                        </td><td>77758.62                           </td></tr>\n",
       "\t<tr><td>Health                             </td><td>56458.33                           </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Humanities &amp; Liberal Arts          </span></td><td>46080.00                                                                       </td></tr>\n",
       "\t<tr><td>Industrial Arts &amp; Consumer Services</td><td>52642.86                               </td></tr>\n",
       "\t<tr><td>Interdisciplinary                  </td><td>43000.00                           </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Law &amp; Public Policy                </span></td><td>52800.00                                                                       </td></tr>\n",
       "\t<tr><td>Physical Sciences                  </td><td>62400.00                           </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Psychology &amp; Social Work           </span></td><td>44555.56                                                                       </td></tr>\n",
       "\t<tr><td>Social Science                     </td><td>53222.22                           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " Major\\_category & cat\\_median\\\\\n",
       "\\hline\n",
       "\t Agriculture \\& Natural Resources     & 55000.00                             \\\\\n",
       "\t Arts                                & 43525.00                           \\\\\n",
       "\t Biology \\& Life Science              & 50821.43                             \\\\\n",
       "\t Business                            & 60615.38                           \\\\\n",
       "\t Communications \\& Journalism         & 49500.00                             \\\\\n",
       "\t Computers \\& Mathematics             & 66272.73                             \\\\\n",
       "\t Education                           & 43831.25                           \\\\\n",
       "\t Engineering                         & 77758.62                           \\\\\n",
       "\t Health                              & 56458.33                           \\\\\n",
       "\t Humanities \\& Liberal Arts           & 46080.00                             \\\\\n",
       "\t Industrial Arts \\& Consumer Services & 52642.86                             \\\\\n",
       "\t Interdisciplinary                   & 43000.00                           \\\\\n",
       "\t Law \\& Public Policy                 & 52800.00                             \\\\\n",
       "\t Physical Sciences                   & 62400.00                           \\\\\n",
       "\t Psychology \\& Social Work            & 44555.56                             \\\\\n",
       "\t Social Science                      & 53222.22                           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Major_category | cat_median |\n",
       "|---|---|\n",
       "| Agriculture & Natural Resources     | 55000.00                            |\n",
       "| Arts                                | 43525.00                            |\n",
       "| Biology & Life Science              | 50821.43                            |\n",
       "| Business                            | 60615.38                            |\n",
       "| Communications & Journalism         | 49500.00                            |\n",
       "| Computers & Mathematics             | 66272.73                            |\n",
       "| Education                           | 43831.25                            |\n",
       "| Engineering                         | 77758.62                            |\n",
       "| Health                              | 56458.33                            |\n",
       "| Humanities & Liberal Arts           | 46080.00                            |\n",
       "| Industrial Arts & Consumer Services | 52642.86                            |\n",
       "| Interdisciplinary                   | 43000.00                            |\n",
       "| Law & Public Policy                 | 52800.00                            |\n",
       "| Physical Sciences                   | 62400.00                            |\n",
       "| Psychology & Social Work            | 44555.56                            |\n",
       "| Social Science                      | 53222.22                            |\n",
       "\n"
      ],
      "text/plain": [
       "   Major_category                      cat_median\n",
       "1  Agriculture & Natural Resources     55000.00  \n",
       "2  Arts                                43525.00  \n",
       "3  Biology & Life Science              50821.43  \n",
       "4  Business                            60615.38  \n",
       "5  Communications & Journalism         49500.00  \n",
       "6  Computers & Mathematics             66272.73  \n",
       "7  Education                           43831.25  \n",
       "8  Engineering                         77758.62  \n",
       "9  Health                              56458.33  \n",
       "10 Humanities & Liberal Arts           46080.00  \n",
       "11 Industrial Arts & Consumer Services 52642.86  \n",
       "12 Interdisciplinary                   43000.00  \n",
       "13 Law & Public Policy                 52800.00  \n",
       "14 Physical Sciences                   62400.00  \n",
       "15 Psychology & Social Work            44555.56  \n",
       "16 Social Science                      53222.22  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Using the df I created just above, create a barplot with the Major_category on the x-axis, and the median on the y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAOVBMVEUAAAAzMzNNTU1ZWVlo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\nDCAxjGD+H1voZEM3ZXTqAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This looks horrible! Why? What are some possible solutions?\n",
    "- Here's one: Try filtering out some Major categories so that there is less clutter. If you thought of another, feel free to try that as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Agriculture &amp; Natural Resources'</li>\n",
       "\t<li>'Arts'</li>\n",
       "\t<li>'Biology &amp; Life Science'</li>\n",
       "\t<li>'Business'</li>\n",
       "\t<li>'Communications &amp; Journalism'</li>\n",
       "\t<li>'Computers &amp; Mathematics'</li>\n",
       "\t<li>'Education'</li>\n",
       "\t<li>'Engineering'</li>\n",
       "\t<li>'Health'</li>\n",
       "\t<li>'Humanities &amp; Liberal Arts'</li>\n",
       "\t<li>'Industrial Arts &amp; Consumer Services'</li>\n",
       "\t<li>'Interdisciplinary'</li>\n",
       "\t<li>'Law &amp; Public Policy'</li>\n",
       "\t<li>'Physical Sciences'</li>\n",
       "\t<li>'Psychology &amp; Social Work'</li>\n",
       "\t<li>'Social Science'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Agriculture \\& Natural Resources'\n",
       "\\item 'Arts'\n",
       "\\item 'Biology \\& Life Science'\n",
       "\\item 'Business'\n",
       "\\item 'Communications \\& Journalism'\n",
       "\\item 'Computers \\& Mathematics'\n",
       "\\item 'Education'\n",
       "\\item 'Engineering'\n",
       "\\item 'Health'\n",
       "\\item 'Humanities \\& Liberal Arts'\n",
       "\\item 'Industrial Arts \\& Consumer Services'\n",
       "\\item 'Interdisciplinary'\n",
       "\\item 'Law \\& Public Policy'\n",
       "\\item 'Physical Sciences'\n",
       "\\item 'Psychology \\& Social Work'\n",
       "\\item 'Social Science'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Agriculture &amp; Natural Resources'\n",
       "2. 'Arts'\n",
       "3. 'Biology &amp; Life Science'\n",
       "4. 'Business'\n",
       "5. 'Communications &amp; Journalism'\n",
       "6. 'Computers &amp; Mathematics'\n",
       "7. 'Education'\n",
       "8. 'Engineering'\n",
       "9. 'Health'\n",
       "10. 'Humanities &amp; Liberal Arts'\n",
       "11. 'Industrial Arts &amp; Consumer Services'\n",
       "12. 'Interdisciplinary'\n",
       "13. 'Law &amp; Public Policy'\n",
       "14. 'Physical Sciences'\n",
       "15. 'Psychology &amp; Social Work'\n",
       "16. 'Social Science'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Agriculture & Natural Resources\"     \"Arts\"                               \n",
       " [3] \"Biology & Life Science\"              \"Business\"                           \n",
       " [5] \"Communications & Journalism\"         \"Computers & Mathematics\"            \n",
       " [7] \"Education\"                           \"Engineering\"                        \n",
       " [9] \"Health\"                              \"Humanities & Liberal Arts\"          \n",
       "[11] \"Industrial Arts & Consumer Services\" \"Interdisciplinary\"                  \n",
       "[13] \"Law & Public Policy\"                 \"Physical Sciences\"                  \n",
       "[15] \"Psychology & Social Work\"            \"Social Science\"                     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(df$Major_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAOVBMVEUAAAAzMzNNTU1ZWVlo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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- That's a bit better! Note that the x-label and y-label are ugly. Look up how to change them. Also consider adding a title, and perhaps changing around the plot to look nicer in other ways. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAOVBMVEUAAAAzMzNNTU1ZWVlo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\nNR3bAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What if you need to include more columns than just 5? Look up what coord_flip does. Just like the polar transformation we saw in lecture, it is another way to revision our data! Try using coord_flip on the whole data and see what it looks like!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAOVBMVEUAAAAzMzNNTU1ZWVlo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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Major_code seems like a pretty useless variable right now. Create a new dataframe called no_code (or whatever else you want!) that doesn't  contain it.\n",
    "- How many majors are in the Education category? (use nrow with filter)\n",
    "- How many majors in the Education Category have more than 100,000 Total students?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16"
      ],
      "text/latex": [
       "16"
      ],
      "text/markdown": [
       "16"
      ],
      "text/plain": [
       "[1] 16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "10"
      ],
      "text/latex": [
       "10"
      ],
      "text/markdown": [
       "10"
      ],
      "text/plain": [
       "[1] 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}