gapminder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gapminder dataset\n",
    "\n",
    "using dplyr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(gapminder)\n",
    "library(dplyr)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>country</th><th scope=col>continent</th><th scope=col>year</th><th scope=col>lifeExp</th><th scope=col>pop</th><th scope=col>gdpPercap</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Afghanistan</td><td>Asia       </td><td>1952       </td><td>28.801     </td><td> 8425333   </td><td>779.4453   </td></tr>\n",
       "\t<tr><td>Afghanistan</td><td>Asia       </td><td>1957       </td><td>30.332     </td><td> 9240934   </td><td>820.8530   </td></tr>\n",
       "\t<tr><td>Afghanistan</td><td>Asia       </td><td>1962       </td><td>31.997     </td><td>10267083   </td><td>853.1007   </td></tr>\n",
       "\t<tr><td>Afghanistan</td><td>Asia       </td><td>1967       </td><td>34.020     </td><td>11537966   </td><td>836.1971   </td></tr>\n",
       "\t<tr><td>Afghanistan</td><td>Asia       </td><td>1972       </td><td>36.088     </td><td>13079460   </td><td>739.9811   </td></tr>\n",
       "\t<tr><td>Afghanistan</td><td>Asia       </td><td>1977       </td><td>38.438     </td><td>14880372   </td><td>786.1134   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " country & continent & year & lifeExp & pop & gdpPercap\\\\\n",
       "\\hline\n",
       "\t Afghanistan & Asia        & 1952        & 28.801      &  8425333    & 779.4453   \\\\\n",
       "\t Afghanistan & Asia        & 1957        & 30.332      &  9240934    & 820.8530   \\\\\n",
       "\t Afghanistan & Asia        & 1962        & 31.997      & 10267083    & 853.1007   \\\\\n",
       "\t Afghanistan & Asia        & 1967        & 34.020      & 11537966    & 836.1971   \\\\\n",
       "\t Afghanistan & Asia        & 1972        & 36.088      & 13079460    & 739.9811   \\\\\n",
       "\t Afghanistan & Asia        & 1977        & 38.438      & 14880372    & 786.1134   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "country | continent | year | lifeExp | pop | gdpPercap | \n",
       "|---|---|---|---|---|---|\n",
       "| Afghanistan | Asia        | 1952        | 28.801      |  8425333    | 779.4453    | \n",
       "| Afghanistan | Asia        | 1957        | 30.332      |  9240934    | 820.8530    | \n",
       "| Afghanistan | Asia        | 1962        | 31.997      | 10267083    | 853.1007    | \n",
       "| Afghanistan | Asia        | 1967        | 34.020      | 11537966    | 836.1971    | \n",
       "| Afghanistan | Asia        | 1972        | 36.088      | 13079460    | 739.9811    | \n",
       "| Afghanistan | Asia        | 1977        | 38.438      | 14880372    | 786.1134    | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  country     continent year lifeExp pop      gdpPercap\n",
       "1 Afghanistan Asia      1952 28.801   8425333 779.4453 \n",
       "2 Afghanistan Asia      1957 30.332   9240934 820.8530 \n",
       "3 Afghanistan Asia      1962 31.997  10267083 853.1007 \n",
       "4 Afghanistan Asia      1967 34.020  11537966 836.1971 \n",
       "5 Afghanistan Asia      1972 36.088  13079460 739.9811 \n",
       "6 Afghanistan Asia      1977 38.438  14880372 786.1134 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gapminder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>country</th><th scope=col>continent</th><th scope=col>year</th><th scope=col>lifeExp</th><th scope=col>pop</th><th scope=col>gdpPercap</th><th scope=col>gdp</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>United States </td><td>Americas      </td><td>2007          </td><td>78.242        </td><td> 301139947    </td><td>42951.653     </td><td>12934459      </td></tr>\n",
       "\t<tr><td>China         </td><td>Asia          </td><td>2007          </td><td>72.961        </td><td>1318683096    </td><td> 4959.115     </td><td> 6539501      </td></tr>\n",
       "\t<tr><td>Japan         </td><td>Asia          </td><td>2007          </td><td>82.603        </td><td> 127467972    </td><td>31656.068     </td><td> 4035135      </td></tr>\n",
       "\t<tr><td>India         </td><td>Asia          </td><td>2007          </td><td>64.698        </td><td>1110396331    </td><td> 2452.210     </td><td> 2722925      </td></tr>\n",
       "\t<tr><td>Germany       </td><td>Europe        </td><td>2007          </td><td>79.406        </td><td>  82400996    </td><td>32170.374     </td><td> 2650871      </td></tr>\n",
       "\t<tr><td>United Kingdom</td><td>Europe        </td><td>2007          </td><td>79.425        </td><td>  60776238    </td><td>33203.261     </td><td> 2017969      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " country & continent & year & lifeExp & pop & gdpPercap & gdp\\\\\n",
       "\\hline\n",
       "\t United States  & Americas       & 2007           & 78.242         &  301139947     & 42951.653      & 12934459      \\\\\n",
       "\t China          & Asia           & 2007           & 72.961         & 1318683096     &  4959.115      &  6539501      \\\\\n",
       "\t Japan          & Asia           & 2007           & 82.603         &  127467972     & 31656.068      &  4035135      \\\\\n",
       "\t India          & Asia           & 2007           & 64.698         & 1110396331     &  2452.210      &  2722925      \\\\\n",
       "\t Germany        & Europe         & 2007           & 79.406         &   82400996     & 32170.374      &  2650871      \\\\\n",
       "\t United Kingdom & Europe         & 2007           & 79.425         &   60776238     & 33203.261      &  2017969      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "country | continent | year | lifeExp | pop | gdpPercap | gdp | \n",
       "|---|---|---|---|---|---|\n",
       "| United States  | Americas       | 2007           | 78.242         |  301139947     | 42951.653      | 12934459       | \n",
       "| China          | Asia           | 2007           | 72.961         | 1318683096     |  4959.115      |  6539501       | \n",
       "| Japan          | Asia           | 2007           | 82.603         |  127467972     | 31656.068      |  4035135       | \n",
       "| India          | Asia           | 2007           | 64.698         | 1110396331     |  2452.210      |  2722925       | \n",
       "| Germany        | Europe         | 2007           | 79.406         |   82400996     | 32170.374      |  2650871       | \n",
       "| United Kingdom | Europe         | 2007           | 79.425         |   60776238     | 33203.261      |  2017969       | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  country        continent year lifeExp pop        gdpPercap gdp     \n",
       "1 United States  Americas  2007 78.242   301139947 42951.653 12934459\n",
       "2 China          Asia      2007 72.961  1318683096  4959.115  6539501\n",
       "3 Japan          Asia      2007 82.603   127467972 31656.068  4035135\n",
       "4 India          Asia      2007 64.698  1110396331  2452.210  2722925\n",
       "5 Germany        Europe    2007 79.406    82400996 32170.374  2650871\n",
       "6 United Kingdom Europe    2007 79.425    60776238 33203.261  2017969"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(gapminder %>%\n",
    "    mutate(gdp = gdpPercap * pop / 1000000) %>%\n",
    "        filter(year == 2007) %>%\n",
    "            arrange(desc(gdp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width=8, repr.plot.height=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jg3/OUB1lVAqA00DMBOAooM47bvA+cAPFwTeoUv1p6gCd1eFfrA9sMkGoDgJXQ4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\nD+Zs/jrV2zChAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) +\n",
    "    geom_point() +\n",
    "    scale_x_log10() +\n",
    "    facet_wrap(~ year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>continent</th><th scope=col>year</th><th scope=col>medianGdpPercap</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Africa   </td><td>1952     </td><td> 987.0256</td></tr>\n",
       "\t<tr><td>Africa   </td><td>1957     </td><td>1024.0230</td></tr>\n",
       "\t<tr><td>Africa   </td><td>1962     </td><td>1133.7837</td></tr>\n",
       "\t<tr><td>Africa   </td><td>1967     </td><td>1210.3764</td></tr>\n",
       "\t<tr><td>Africa   </td><td>1972     </td><td>1443.3725</td></tr>\n",
       "\t<tr><td>Africa   </td><td>1977     </td><td>1399.6388</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " continent & year & medianGdpPercap\\\\\n",
       "\\hline\n",
       "\t Africa    & 1952      &  987.0256\\\\\n",
       "\t Africa    & 1957      & 1024.0230\\\\\n",
       "\t Africa    & 1962      & 1133.7837\\\\\n",
       "\t Africa    & 1967      & 1210.3764\\\\\n",
       "\t Africa    & 1972      & 1443.3725\\\\\n",
       "\t Africa    & 1977      & 1399.6388\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "continent | year | medianGdpPercap | \n",
       "|---|---|---|---|---|---|\n",
       "| Africa    | 1952      |  987.0256 | \n",
       "| Africa    | 1957      | 1024.0230 | \n",
       "| Africa    | 1962      | 1133.7837 | \n",
       "| Africa    | 1967      | 1210.3764 | \n",
       "| Africa    | 1972      | 1443.3725 | \n",
       "| Africa    | 1977      | 1399.6388 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  continent year medianGdpPercap\n",
       "1 Africa    1952  987.0256      \n",
       "2 Africa    1957 1024.0230      \n",
       "3 Africa    1962 1133.7837      \n",
       "4 Africa    1967 1210.3764      \n",
       "5 Africa    1972 1443.3725      \n",
       "6 Africa    1977 1399.6388      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_continent <- gapminder %>%\n",
    "    group_by(continent, year) %>%\n",
    "    summarize(medianGdpPercap = median(gdpPercap))\n",
    "head(by_continent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width=6, repr.plot.height=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F/CY0mlYwWgnXYONPEZp9KxAtBOuwWa/riedmQTI4DutUegHcrVYAWgnfYF9Khb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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(by_continent, aes(x = year, y = medianGdpPercap, color = continent)) +\n",
    "    geom_line()"
   ]
  },
  {
   "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}