descriptive_statistics.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bernatsort/Statistics/blob/main/descriptive_statistics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c83d5ae8-3e3c-466a-8336-3e1c98d09b66",
      "metadata": {
        "id": "c83d5ae8-3e3c-466a-8336-3e1c98d09b66"
      },
      "source": [
        "Bernat Sort Rufat\n",
        "\n",
        "MD004_Advanced data analysis and visualization tools\n",
        "\n",
        "Máster en Data Science"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e61a7530-b14c-421d-b858-603a8e82f6a7",
      "metadata": {
        "id": "e61a7530-b14c-421d-b858-603a8e82f6a7"
      },
      "source": [
        "# Trabajo del Tema 2: Estadística Descriptiva"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "151fb08a-7cc7-49cc-959a-9d0fd9023a5b",
      "metadata": {
        "id": "151fb08a-7cc7-49cc-959a-9d0fd9023a5b"
      },
      "source": [
        "# Carga de packages "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c450785-8e56-4d95-bc9f-d525ead496bf",
      "metadata": {
        "id": "6c450785-8e56-4d95-bc9f-d525ead496bf"
      },
      "source": [
        "Para empezar, instalamos y cargamos todas las librerías necesarias para realizar el trabajo.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "193365a5-08d8-47ae-87bc-eda22502bbdb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "193365a5-08d8-47ae-87bc-eda22502bbdb",
        "outputId": "676b9477-0372-44da-9474-f290a516013e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘plyr’\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘stringr’\n",
            "\n",
            "\n",
            "\n",
            "Attaching package: ‘tidyr’\n",
            "\n",
            "\n",
            "The following object is masked from ‘package:reshape2’:\n",
            "\n",
            "    smiths\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "\n",
            "Attaching package: ‘dplyr’\n",
            "\n",
            "\n",
            "The following objects are masked from ‘package:stats’:\n",
            "\n",
            "    filter, lag\n",
            "\n",
            "\n",
            "The following objects are masked from ‘package:base’:\n",
            "\n",
            "    intersect, setdiff, setequal, union\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# install and load plotly\n",
        "install.packages(\"plotly\")\n",
        "library(plotly)\n",
        "# load ggplot2\n",
        "library(ggplot2)\n",
        "# load reshape2\n",
        "install.packages(\"reshape2\")\n",
        "library(reshape2)\n",
        "#load tidyr\n",
        "install.packages(\"tidyr\")\n",
        "library(tidyr)\n",
        "# install and load patchwork\n",
        "install.packages(\"patchwork\")\n",
        "library(patchwork)\n",
        "# load dplyr\n",
        "install.packages(\"dplyr\")\n",
        "library(dplyr)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2bd8af24-45aa-437f-b3c6-b978994507d6",
      "metadata": {
        "id": "2bd8af24-45aa-437f-b3c6-b978994507d6"
      },
      "source": [
        "# Problema 1 \n",
        "\n",
        "El fichero HAVD Exo02.csv contiene en formato csv datos demográficos de la ciudad de Barcelona obtenidos como datos abiertos proporcionados por la propia ciudad. Se trata del número de habitantes, por edades y por distrito. El último valor representa las personas de más de cien años. \n",
        "\n",
        "1.-Representar un histograma con la distribución de la población de la ciudad por edades. \n",
        "\n",
        "2.-Representar un boxplot para la distribución de edades de la ciudad. \n",
        "\n",
        "3.-Calcular la media y la desviación estándar de la distribución de edades de la ciudad. Comentar y analizar los resultados."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "055f4857-5c9f-4843-b585-526c92da6a90",
      "metadata": {
        "id": "055f4857-5c9f-4843-b585-526c92da6a90"
      },
      "source": [
        "## Carga de datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "05586f63-59d2-4086-9105-61de1e3e07b3",
      "metadata": {
        "id": "05586f63-59d2-4086-9105-61de1e3e07b3"
      },
      "outputs": [],
      "source": [
        "# cargamos nuestro dataset en demogr_bcn_data y checkeamos los datos cargados\n",
        "demogr_bcn_data <- read.csv(file='HAVD_Exo02.csv', header=TRUE, sep=',', dec='.')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2be3f776-9984-4496-ad45-10bfaa925fba",
      "metadata": {
        "id": "2be3f776-9984-4496-ad45-10bfaa925fba"
      },
      "source": [
        "## Exploración básica de los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "f0bb85d4-873f-4a82-959a-bd9ee4e923a0",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "f0bb85d4-873f-4a82-959a-bd9ee4e923a0",
        "outputId": "a24348d3-a123-4191-a5d6-1f4e3ca922cd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 10 × 104</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Dte.</th><th scope=col>Barris</th><th scope=col>TOTAL</th><th scope=col>X0</th><th scope=col>X1</th><th scope=col>X2</th><th scope=col>X3</th><th scope=col>X4</th><th scope=col>X5</th><th scope=col>X6</th><th scope=col>⋯</th><th scope=col>X91</th><th scope=col>X92</th><th scope=col>X93</th><th scope=col>X94</th><th scope=col>X95</th><th scope=col>X96</th><th scope=col>X97</th><th scope=col>X98</th><th scope=col>X99</th><th scope=col>X100</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>BARCELONA</td><td>                                        </td><td>1625137</td><td>13633</td><td>13918</td><td>13712</td><td>13533</td><td>14018</td><td>13968</td><td>13801</td><td>⋯</td><td>4528</td><td>3761</td><td>2923</td><td>2309</td><td>1658</td><td>1223</td><td>827</td><td>565</td><td>398</td><td>767</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>1        </td><td>1. el Raval                             </td><td>  47986</td><td>  449</td><td>  431</td><td>  409</td><td>  396</td><td>  457</td><td>  475</td><td>  462</td><td>⋯</td><td>  91</td><td>  67</td><td>  50</td><td>  30</td><td>  30</td><td>  20</td><td> 12</td><td>  9</td><td>  2</td><td> 12</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>1        </td><td>2. el Barri Gòtic                       </td><td>  16240</td><td>   99</td><td>   97</td><td>   93</td><td>   88</td><td>   99</td><td>   97</td><td>   89</td><td>⋯</td><td>  38</td><td>  36</td><td>  27</td><td>  25</td><td>  11</td><td>   9</td><td> 11</td><td>  3</td><td>  0</td><td> 11</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>1        </td><td>3. la Barceloneta                       </td><td>  15101</td><td>   94</td><td>  100</td><td>   86</td><td>  101</td><td>   80</td><td>   71</td><td>   86</td><td>⋯</td><td>  30</td><td>  37</td><td>  29</td><td>  15</td><td>  15</td><td>  12</td><td>  6</td><td>  3</td><td>  5</td><td>  5</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>1        </td><td>4. Sant Pere, Santa Caterina i la Ribera</td><td>  22923</td><td>  177</td><td>  168</td><td>  166</td><td>  145</td><td>  166</td><td>  164</td><td>  145</td><td>⋯</td><td>  38</td><td>  42</td><td>  31</td><td>  28</td><td>  12</td><td>   9</td><td>  6</td><td>  8</td><td>  5</td><td> 14</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>2        </td><td>5. el Fort Pienc                        </td><td>  32048</td><td>  251</td><td>  266</td><td>  268</td><td>  240</td><td>  281</td><td>  238</td><td>  262</td><td>⋯</td><td> 106</td><td>  73</td><td>  75</td><td>  49</td><td>  44</td><td>  31</td><td> 17</td><td> 16</td><td>  7</td><td> 14</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>2        </td><td>6. la Sagrada Família                   </td><td>  51651</td><td>  395</td><td>  406</td><td>  405</td><td>  343</td><td>  375</td><td>  361</td><td>  345</td><td>⋯</td><td> 195</td><td> 166</td><td> 115</td><td>  80</td><td>  84</td><td>  53</td><td> 34</td><td> 22</td><td> 13</td><td> 24</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>2        </td><td>7. la Dreta de l'Eixample               </td><td>  44246</td><td>  365</td><td>  402</td><td>  358</td><td>  354</td><td>  358</td><td>  371</td><td>  354</td><td>⋯</td><td> 155</td><td> 147</td><td> 108</td><td> 103</td><td>  67</td><td>  68</td><td> 40</td><td> 37</td><td> 23</td><td> 50</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>2        </td><td>8. l'Antiga Esquerra de l'Eixample      </td><td>  42512</td><td>  324</td><td>  342</td><td>  328</td><td>  316</td><td>  323</td><td>  323</td><td>  287</td><td>⋯</td><td> 145</td><td> 137</td><td>  93</td><td>  80</td><td>  59</td><td>  47</td><td> 26</td><td> 34</td><td> 20</td><td> 45</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>2        </td><td>9. la Nova Esquerra de l'Eixample       </td><td>  58315</td><td>  439</td><td>  454</td><td>  452</td><td>  389</td><td>  441</td><td>  394</td><td>  423</td><td>⋯</td><td> 175</td><td> 154</td><td> 115</td><td> 104</td><td>  74</td><td>  51</td><td> 35</td><td> 18</td><td> 17</td><td> 33</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 10 × 104\n\n| <!--/--> | Dte. &lt;chr&gt; | Barris &lt;chr&gt; | TOTAL &lt;int&gt; | X0 &lt;int&gt; | X1 &lt;int&gt; | X2 &lt;int&gt; | X3 &lt;int&gt; | X4 &lt;int&gt; | X5 &lt;int&gt; | X6 &lt;int&gt; | ⋯ ⋯ | X91 &lt;int&gt; | X92 &lt;int&gt; | X93 &lt;int&gt; | X94 &lt;int&gt; | X95 &lt;int&gt; | X96 &lt;int&gt; | X97 &lt;int&gt; | X98 &lt;int&gt; | X99 &lt;int&gt; | X100 &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | BARCELONA | <!----> | 1625137 | 13633 | 13918 | 13712 | 13533 | 14018 | 13968 | 13801 | ⋯ | 4528 | 3761 | 2923 | 2309 | 1658 | 1223 | 827 | 565 | 398 | 767 |\n| 2 | 1         | 1. el Raval                              |   47986 |   449 |   431 |   409 |   396 |   457 |   475 |   462 | ⋯ |   91 |   67 |   50 |   30 |   30 |   20 |  12 |   9 |   2 |  12 |\n| 3 | 1         | 2. el Barri Gòtic                        |   16240 |    99 |    97 |    93 |    88 |    99 |    97 |    89 | ⋯ |   38 |   36 |   27 |   25 |   11 |    9 |  11 |   3 |   0 |  11 |\n| 4 | 1         | 3. la Barceloneta                        |   15101 |    94 |   100 |    86 |   101 |    80 |    71 |    86 | ⋯ |   30 |   37 |   29 |   15 |   15 |   12 |   6 |   3 |   5 |   5 |\n| 5 | 1         | 4. Sant Pere, Santa Caterina i la Ribera |   22923 |   177 |   168 |   166 |   145 |   166 |   164 |   145 | ⋯ |   38 |   42 |   31 |   28 |   12 |    9 |   6 |   8 |   5 |  14 |\n| 6 | 2         | 5. el Fort Pienc                         |   32048 |   251 |   266 |   268 |   240 |   281 |   238 |   262 | ⋯ |  106 |   73 |   75 |   49 |   44 |   31 |  17 |  16 |   7 |  14 |\n| 7 | 2         | 6. la Sagrada Família                    |   51651 |   395 |   406 |   405 |   343 |   375 |   361 |   345 | ⋯ |  195 |  166 |  115 |   80 |   84 |   53 |  34 |  22 |  13 |  24 |\n| 8 | 2         | 7. la Dreta de l'Eixample                |   44246 |   365 |   402 |   358 |   354 |   358 |   371 |   354 | ⋯ |  155 |  147 |  108 |  103 |   67 |   68 |  40 |  37 |  23 |  50 |\n| 9 | 2         | 8. l'Antiga Esquerra de l'Eixample       |   42512 |   324 |   342 |   328 |   316 |   323 |   323 |   287 | ⋯ |  145 |  137 |   93 |   80 |   59 |   47 |  26 |  34 |  20 |  45 |\n| 10 | 2         | 9. la Nova Esquerra de l'Eixample        |   58315 |   439 |   454 |   452 |   389 |   441 |   394 |   423 | ⋯ |  175 |  154 |  115 |  104 |   74 |   51 |  35 |  18 |  17 |  33 |\n\n",
            "text/latex": "A data.frame: 10 × 104\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & Dte. & Barris & TOTAL & X0 & X1 & X2 & X3 & X4 & X5 & X6 & ⋯ & X91 & X92 & X93 & X94 & X95 & X96 & X97 & X98 & X99 & X100\\\\\n  & <chr> & <chr> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & BARCELONA &                                          & 1625137 & 13633 & 13918 & 13712 & 13533 & 14018 & 13968 & 13801 & ⋯ & 4528 & 3761 & 2923 & 2309 & 1658 & 1223 & 827 & 565 & 398 & 767\\\\\n\t2 & 1         & 1. el Raval                              &   47986 &   449 &   431 &   409 &   396 &   457 &   475 &   462 & ⋯ &   91 &   67 &   50 &   30 &   30 &   20 &  12 &   9 &   2 &  12\\\\\n\t3 & 1         & 2. el Barri Gòtic                        &   16240 &    99 &    97 &    93 &    88 &    99 &    97 &    89 & ⋯ &   38 &   36 &   27 &   25 &   11 &    9 &  11 &   3 &   0 &  11\\\\\n\t4 & 1         & 3. la Barceloneta                        &   15101 &    94 &   100 &    86 &   101 &    80 &    71 &    86 & ⋯ &   30 &   37 &   29 &   15 &   15 &   12 &   6 &   3 &   5 &   5\\\\\n\t5 & 1         & 4. Sant Pere, Santa Caterina i la Ribera &   22923 &   177 &   168 &   166 &   145 &   166 &   164 &   145 & ⋯ &   38 &   42 &   31 &   28 &   12 &    9 &   6 &   8 &   5 &  14\\\\\n\t6 & 2         & 5. el Fort Pienc                         &   32048 &   251 &   266 &   268 &   240 &   281 &   238 &   262 & ⋯ &  106 &   73 &   75 &   49 &   44 &   31 &  17 &  16 &   7 &  14\\\\\n\t7 & 2         & 6. la Sagrada Família                    &   51651 &   395 &   406 &   405 &   343 &   375 &   361 &   345 & ⋯ &  195 &  166 &  115 &   80 &   84 &   53 &  34 &  22 &  13 &  24\\\\\n\t8 & 2         & 7. la Dreta de l'Eixample                &   44246 &   365 &   402 &   358 &   354 &   358 &   371 &   354 & ⋯ &  155 &  147 &  108 &  103 &   67 &   68 &  40 &  37 &  23 &  50\\\\\n\t9 & 2         & 8. l'Antiga Esquerra de l'Eixample       &   42512 &   324 &   342 &   328 &   316 &   323 &   323 &   287 & ⋯ &  145 &  137 &   93 &   80 &   59 &   47 &  26 &  34 &  20 &  45\\\\\n\t10 & 2         & 9. la Nova Esquerra de l'Eixample        &   58315 &   439 &   454 &   452 &   389 &   441 &   394 &   423 & ⋯ &  175 &  154 &  115 &  104 &   74 &   51 &  35 &  18 &  17 &  33\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   Dte.      Barris                                   TOTAL   X0    X1    X2   \n",
              "1  BARCELONA                                          1625137 13633 13918 13712\n",
              "2  1         1. el Raval                                47986   449   431   409\n",
              "3  1         2. el Barri Gòtic                          16240    99    97    93\n",
              "4  1         3. la Barceloneta                          15101    94   100    86\n",
              "5  1         4. Sant Pere, Santa Caterina i la Ribera   22923   177   168   166\n",
              "6  2         5. el Fort Pienc                           32048   251   266   268\n",
              "7  2         6. la Sagrada Família                      51651   395   406   405\n",
              "8  2         7. la Dreta de l'Eixample                  44246   365   402   358\n",
              "9  2         8. l'Antiga Esquerra de l'Eixample         42512   324   342   328\n",
              "10 2         9. la Nova Esquerra de l'Eixample          58315   439   454   452\n",
              "   X3    X4    X5    X6    ⋯ X91  X92  X93  X94  X95  X96  X97 X98 X99 X100\n",
              "1  13533 14018 13968 13801 ⋯ 4528 3761 2923 2309 1658 1223 827 565 398 767 \n",
              "2    396   457   475   462 ⋯   91   67   50   30   30   20  12   9   2  12 \n",
              "3     88    99    97    89 ⋯   38   36   27   25   11    9  11   3   0  11 \n",
              "4    101    80    71    86 ⋯   30   37   29   15   15   12   6   3   5   5 \n",
              "5    145   166   164   145 ⋯   38   42   31   28   12    9   6   8   5  14 \n",
              "6    240   281   238   262 ⋯  106   73   75   49   44   31  17  16   7  14 \n",
              "7    343   375   361   345 ⋯  195  166  115   80   84   53  34  22  13  24 \n",
              "8    354   358   371   354 ⋯  155  147  108  103   67   68  40  37  23  50 \n",
              "9    316   323   323   287 ⋯  145  137   93   80   59   47  26  34  20  45 \n",
              "10   389   441   394   423 ⋯  175  154  115  104   74   51  35  18  17  33 "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 10 primeras observaciones del df\n",
        "head(demogr_bcn_data,10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8a7592ac-2f41-4a05-a4ee-d2ee56e294f2",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "8a7592ac-2f41-4a05-a4ee-d2ee56e294f2",
        "outputId": "4867afcf-84e4-4f2f-c822-f35956250129"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>'Dte.'</li><li>'Barris'</li><li>'TOTAL'</li><li>'X0'</li><li>'X1'</li><li>'X2'</li><li>'X3'</li><li>'X4'</li><li>'X5'</li><li>'X6'</li><li>'X7'</li><li>'X8'</li><li>'X9'</li><li>'X10'</li><li>'X11'</li><li>'X12'</li><li>'X13'</li><li>'X14'</li><li>'X15'</li><li>'X16'</li><li>'X17'</li><li>'X18'</li><li>'X19'</li><li>'X20'</li><li>'X21'</li><li>'X22'</li><li>'X23'</li><li>'X24'</li><li>'X25'</li><li>'X26'</li><li>'X27'</li><li>'X28'</li><li>'X29'</li><li>'X30'</li><li>'X31'</li><li>'X32'</li><li>'X33'</li><li>'X34'</li><li>'X35'</li><li>'X36'</li><li>'X37'</li><li>'X38'</li><li>'X39'</li><li>'X40'</li><li>'X41'</li><li>'X42'</li><li>'X43'</li><li>'X44'</li><li>'X45'</li><li>'X46'</li><li>'X47'</li><li>'X48'</li><li>'X49'</li><li>'X50'</li><li>'X51'</li><li>'X52'</li><li>'X53'</li><li>'X54'</li><li>'X55'</li><li>'X56'</li><li>'X57'</li><li>'X58'</li><li>'X59'</li><li>'X60'</li><li>'X61'</li><li>'X62'</li><li>'X63'</li><li>'X64'</li><li>'X65'</li><li>'X66'</li><li>'X67'</li><li>'X68'</li><li>'X69'</li><li>'X70'</li><li>'X71'</li><li>'X72'</li><li>'X73'</li><li>'X74'</li><li>'X75'</li><li>'X76'</li><li>'X77'</li><li>'X78'</li><li>'X79'</li><li>'X80'</li><li>'X81'</li><li>'X82'</li><li>'X83'</li><li>'X84'</li><li>'X85'</li><li>'X86'</li><li>'X87'</li><li>'X88'</li><li>'X89'</li><li>'X90'</li><li>'X91'</li><li>'X92'</li><li>'X93'</li><li>'X94'</li><li>'X95'</li><li>'X96'</li><li>'X97'</li><li>'X98'</li><li>'X99'</li><li>'X100'</li></ol>\n"
            ],
            "text/markdown": "1. 'Dte.'\n2. 'Barris'\n3. 'TOTAL'\n4. 'X0'\n5. 'X1'\n6. 'X2'\n7. 'X3'\n8. 'X4'\n9. 'X5'\n10. 'X6'\n11. 'X7'\n12. 'X8'\n13. 'X9'\n14. 'X10'\n15. 'X11'\n16. 'X12'\n17. 'X13'\n18. 'X14'\n19. 'X15'\n20. 'X16'\n21. 'X17'\n22. 'X18'\n23. 'X19'\n24. 'X20'\n25. 'X21'\n26. 'X22'\n27. 'X23'\n28. 'X24'\n29. 'X25'\n30. 'X26'\n31. 'X27'\n32. 'X28'\n33. 'X29'\n34. 'X30'\n35. 'X31'\n36. 'X32'\n37. 'X33'\n38. 'X34'\n39. 'X35'\n40. 'X36'\n41. 'X37'\n42. 'X38'\n43. 'X39'\n44. 'X40'\n45. 'X41'\n46. 'X42'\n47. 'X43'\n48. 'X44'\n49. 'X45'\n50. 'X46'\n51. 'X47'\n52. 'X48'\n53. 'X49'\n54. 'X50'\n55. 'X51'\n56. 'X52'\n57. 'X53'\n58. 'X54'\n59. 'X55'\n60. 'X56'\n61. 'X57'\n62. 'X58'\n63. 'X59'\n64. 'X60'\n65. 'X61'\n66. 'X62'\n67. 'X63'\n68. 'X64'\n69. 'X65'\n70. 'X66'\n71. 'X67'\n72. 'X68'\n73. 'X69'\n74. 'X70'\n75. 'X71'\n76. 'X72'\n77. 'X73'\n78. 'X74'\n79. 'X75'\n80. 'X76'\n81. 'X77'\n82. 'X78'\n83. 'X79'\n84. 'X80'\n85. 'X81'\n86. 'X82'\n87. 'X83'\n88. 'X84'\n89. 'X85'\n90. 'X86'\n91. 'X87'\n92. 'X88'\n93. 'X89'\n94. 'X90'\n95. 'X91'\n96. 'X92'\n97. 'X93'\n98. 'X94'\n99. 'X95'\n100. 'X96'\n101. 'X97'\n102. 'X98'\n103. 'X99'\n104. 'X100'\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 'Dte.'\n\\item 'Barris'\n\\item 'TOTAL'\n\\item 'X0'\n\\item 'X1'\n\\item 'X2'\n\\item 'X3'\n\\item 'X4'\n\\item 'X5'\n\\item 'X6'\n\\item 'X7'\n\\item 'X8'\n\\item 'X9'\n\\item 'X10'\n\\item 'X11'\n\\item 'X12'\n\\item 'X13'\n\\item 'X14'\n\\item 'X15'\n\\item 'X16'\n\\item 'X17'\n\\item 'X18'\n\\item 'X19'\n\\item 'X20'\n\\item 'X21'\n\\item 'X22'\n\\item 'X23'\n\\item 'X24'\n\\item 'X25'\n\\item 'X26'\n\\item 'X27'\n\\item 'X28'\n\\item 'X29'\n\\item 'X30'\n\\item 'X31'\n\\item 'X32'\n\\item 'X33'\n\\item 'X34'\n\\item 'X35'\n\\item 'X36'\n\\item 'X37'\n\\item 'X38'\n\\item 'X39'\n\\item 'X40'\n\\item 'X41'\n\\item 'X42'\n\\item 'X43'\n\\item 'X44'\n\\item 'X45'\n\\item 'X46'\n\\item 'X47'\n\\item 'X48'\n\\item 'X49'\n\\item 'X50'\n\\item 'X51'\n\\item 'X52'\n\\item 'X53'\n\\item 'X54'\n\\item 'X55'\n\\item 'X56'\n\\item 'X57'\n\\item 'X58'\n\\item 'X59'\n\\item 'X60'\n\\item 'X61'\n\\item 'X62'\n\\item 'X63'\n\\item 'X64'\n\\item 'X65'\n\\item 'X66'\n\\item 'X67'\n\\item 'X68'\n\\item 'X69'\n\\item 'X70'\n\\item 'X71'\n\\item 'X72'\n\\item 'X73'\n\\item 'X74'\n\\item 'X75'\n\\item 'X76'\n\\item 'X77'\n\\item 'X78'\n\\item 'X79'\n\\item 'X80'\n\\item 'X81'\n\\item 'X82'\n\\item 'X83'\n\\item 'X84'\n\\item 'X85'\n\\item 'X86'\n\\item 'X87'\n\\item 'X88'\n\\item 'X89'\n\\item 'X90'\n\\item 'X91'\n\\item 'X92'\n\\item 'X93'\n\\item 'X94'\n\\item 'X95'\n\\item 'X96'\n\\item 'X97'\n\\item 'X98'\n\\item 'X99'\n\\item 'X100'\n\\end{enumerate*}\n",
            "text/plain": [
              "  [1] \"Dte.\"   \"Barris\" \"TOTAL\"  \"X0\"     \"X1\"     \"X2\"     \"X3\"     \"X4\"    \n",
              "  [9] \"X5\"     \"X6\"     \"X7\"     \"X8\"     \"X9\"     \"X10\"    \"X11\"    \"X12\"   \n",
              " [17] \"X13\"    \"X14\"    \"X15\"    \"X16\"    \"X17\"    \"X18\"    \"X19\"    \"X20\"   \n",
              " [25] \"X21\"    \"X22\"    \"X23\"    \"X24\"    \"X25\"    \"X26\"    \"X27\"    \"X28\"   \n",
              " [33] \"X29\"    \"X30\"    \"X31\"    \"X32\"    \"X33\"    \"X34\"    \"X35\"    \"X36\"   \n",
              " [41] \"X37\"    \"X38\"    \"X39\"    \"X40\"    \"X41\"    \"X42\"    \"X43\"    \"X44\"   \n",
              " [49] \"X45\"    \"X46\"    \"X47\"    \"X48\"    \"X49\"    \"X50\"    \"X51\"    \"X52\"   \n",
              " [57] \"X53\"    \"X54\"    \"X55\"    \"X56\"    \"X57\"    \"X58\"    \"X59\"    \"X60\"   \n",
              " [65] \"X61\"    \"X62\"    \"X63\"    \"X64\"    \"X65\"    \"X66\"    \"X67\"    \"X68\"   \n",
              " [73] \"X69\"    \"X70\"    \"X71\"    \"X72\"    \"X73\"    \"X74\"    \"X75\"    \"X76\"   \n",
              " [81] \"X77\"    \"X78\"    \"X79\"    \"X80\"    \"X81\"    \"X82\"    \"X83\"    \"X84\"   \n",
              " [89] \"X85\"    \"X86\"    \"X87\"    \"X88\"    \"X89\"    \"X90\"    \"X91\"    \"X92\"   \n",
              " [97] \"X93\"    \"X94\"    \"X95\"    \"X96\"    \"X97\"    \"X98\"    \"X99\"    \"X100\"  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "104"
            ],
            "text/markdown": "104",
            "text/latex": "104",
            "text/plain": [
              "[1] 104"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "74"
            ],
            "text/markdown": "74",
            "text/latex": "74",
            "text/plain": [
              "[1] 74"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# columnas \n",
        "colnames(demogr_bcn_data)\n",
        "# num columnas\n",
        "ncol(demogr_bcn_data)\n",
        "# num filas\n",
        "nrow(demogr_bcn_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "59efa435-c77e-438c-bab5-907608c40c31",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "59efa435-c77e-438c-bab5-907608c40c31",
        "outputId": "8ca2a86d-b752-40a7-8190-96d94c06044d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     Dte.              Barris              TOTAL               X0          \n",
              " Length:74          Length:74          Min.   :    611   Min.   :    2.00  \n",
              " Class :character   Class :character   1st Qu.:  10725   1st Qu.:   94.25  \n",
              " Mode  :character   Mode  :character   Median :  21410   Median :  177.00  \n",
              "                                       Mean   :  43923   Mean   :  368.46  \n",
              "                                       3rd Qu.:  31343   3rd Qu.:  276.50  \n",
              "                                       Max.   :1625137   Max.   :13633.00  \n",
              "       X1                X2                X3                X4          \n",
              " Min.   :   11.0   Min.   :   10.0   Min.   :    7.0   Min.   :    6.00  \n",
              " 1st Qu.:   90.0   1st Qu.:   93.5   1st Qu.:  101.5   1st Qu.:   95.25  \n",
              " Median :  182.0   Median :  174.0   Median :  177.5   Median :  187.00  \n",
              " Mean   :  376.2   Mean   :  370.6   Mean   :  365.8   Mean   :  378.87  \n",
              " 3rd Qu.:  264.5   3rd Qu.:  260.0   3rd Qu.:  259.2   3rd Qu.:  275.00  \n",
              " Max.   :13918.0   Max.   :13712.0   Max.   :13533.0   Max.   :14018.00  \n",
              "       X5                X6                 X7                X8         \n",
              " Min.   :    5.0   Min.   :    5.00   Min.   :    5.0   Min.   :    6.0  \n",
              " 1st Qu.:  101.8   1st Qu.:   91.25   1st Qu.:  103.2   1st Qu.:  103.8  \n",
              " Median :  181.5   Median :  179.50   Median :  179.5   Median :  176.0  \n",
              " Mean   :  377.5   Mean   :  373.00   Mean   :  371.5   Mean   :  386.2  \n",
              " 3rd Qu.:  271.8   3rd Qu.:  270.00   3rd Qu.:  287.2   3rd Qu.:  278.5  \n",
              " Max.   :13968.0   Max.   :13801.00   Max.   :13747.0   Max.   :14288.0  \n",
              "       X9                X10               X11                X12         \n",
              " Min.   :    5.00   Min.   :    5.0   Min.   :    3.00   Min.   :    4.0  \n",
              " 1st Qu.:   89.75   1st Qu.:   93.0   1st Qu.:   97.25   1st Qu.:  104.5  \n",
              " Median :  175.50   Median :  183.5   Median :  169.00   Median :  166.5  \n",
              " Mean   :  367.24   Mean   :  374.3   Mean   :  367.89   Mean   :  368.4  \n",
              " 3rd Qu.:  274.75   3rd Qu.:  274.0   3rd Qu.:  263.00   3rd Qu.:  276.8  \n",
              " Max.   :13588.00   Max.   :13849.0   Max.   :13612.00   Max.   :13630.0  \n",
              "      X13                X14               X15               X16         \n",
              " Min.   :    4.00   Min.   :    3.0   Min.   :    3.0   Min.   :    4.0  \n",
              " 1st Qu.:   98.25   1st Qu.:   91.0   1st Qu.:   91.5   1st Qu.:   91.5  \n",
              " Median :  167.50   Median :  156.5   Median :  161.5   Median :  173.5  \n",
              " Mean   :  363.68   Mean   :  354.5   Mean   :  345.3   Mean   :  359.0  \n",
              " 3rd Qu.:  262.50   3rd Qu.:  273.5   3rd Qu.:  260.0   3rd Qu.:  268.8  \n",
              " Max.   :13456.00   Max.   :13118.0   Max.   :12776.0   Max.   :13284.0  \n",
              "      X17                X18                X19                X20         \n",
              " Min.   :    6.00   Min.   :    7.00   Min.   :    5.00   Min.   :    3.0  \n",
              " 1st Qu.:   99.75   1st Qu.:   90.25   1st Qu.:   98.75   1st Qu.:  106.2  \n",
              " Median :  170.00   Median :  170.50   Median :  184.00   Median :  188.5  \n",
              " Mean   :  357.11   Mean   :  352.73   Mean   :  372.19   Mean   :  375.8  \n",
              " 3rd Qu.:  259.00   3rd Qu.:  265.00   3rd Qu.:  268.50   3rd Qu.:  277.8  \n",
              " Max.   :13213.00   Max.   :13051.00   Max.   :13771.00   Max.   :13903.0  \n",
              "      X21               X22               X23               X24         \n",
              " Min.   :    5.0   Min.   :    6.0   Min.   :    5.0   Min.   :    8.0  \n",
              " 1st Qu.:  101.8   1st Qu.:  110.0   1st Qu.:  116.2   1st Qu.:  119.0  \n",
              " Median :  187.5   Median :  212.5   Median :  224.5   Median :  235.0  \n",
              " Mean   :  390.5   Mean   :  420.1   Mean   :  451.7   Mean   :  503.9  \n",
              " 3rd Qu.:  278.8   3rd Qu.:  297.0   3rd Qu.:  314.2   3rd Qu.:  341.5  \n",
              " Max.   :14450.0   Max.   :15543.0   Max.   :16714.0   Max.   :18644.0  \n",
              "      X25               X26               X27               X28         \n",
              " Min.   :    6.0   Min.   :    2.0   Min.   :    3.0   Min.   :    8.0  \n",
              " 1st Qu.:  117.2   1st Qu.:  125.8   1st Qu.:  129.2   1st Qu.:  129.8  \n",
              " Median :  236.0   Median :  257.5   Median :  261.0   Median :  268.0  \n",
              " Mean   :  522.4   Mean   :  560.2   Mean   :  589.5   Mean   :  603.3  \n",
              " 3rd Qu.:  368.8   3rd Qu.:  396.0   3rd Qu.:  416.0   3rd Qu.:  402.0  \n",
              " Max.   :19330.0   Max.   :20726.0   Max.   :21812.0   Max.   :22323.0  \n",
              "      X29               X30               X31               X32         \n",
              " Min.   :   10.0   Min.   :   15.0   Min.   :   10.0   Min.   :   18.0  \n",
              " 1st Qu.:  121.0   1st Qu.:  134.8   1st Qu.:  144.2   1st Qu.:  136.5  \n",
              " Median :  282.0   Median :  282.5   Median :  292.5   Median :  298.5  \n",
              " Mean   :  616.2   Mean   :  636.0   Mean   :  647.4   Mean   :  664.9  \n",
              " 3rd Qu.:  418.0   3rd Qu.:  454.2   3rd Qu.:  458.8   3rd Qu.:  474.0  \n",
              " Max.   :22801.0   Max.   :23531.0   Max.   :23952.0   Max.   :24602.0  \n",
              "      X33               X34               X35               X36         \n",
              " Min.   :   15.0   Min.   :   14.0   Min.   :   14.0   Min.   :   19.0  \n",
              " 1st Qu.:  141.0   1st Qu.:  164.2   1st Qu.:  172.0   1st Qu.:  173.2  \n",
              " Median :  316.0   Median :  315.5   Median :  314.0   Median :  324.0  \n",
              " Mean   :  676.8   Mean   :  699.6   Mean   :  710.2   Mean   :  727.5  \n",
              " 3rd Qu.:  493.5   3rd Qu.:  521.8   3rd Qu.:  524.2   3rd Qu.:  560.8  \n",
              " Max.   :25041.0   Max.   :25887.0   Max.   :26276.0   Max.   :26918.0  \n",
              "      X37               X38               X39               X40         \n",
              " Min.   :   13.0   Min.   :    9.0   Min.   :   12.0   Min.   :    9.0  \n",
              " 1st Qu.:  165.2   1st Qu.:  194.5   1st Qu.:  193.2   1st Qu.:  195.0  \n",
              " Median :  327.0   Median :  336.0   Median :  346.5   Median :  351.5  \n",
              " Mean   :  726.3   Mean   :  746.2   Mean   :  743.3   Mean   :  750.2  \n",
              " 3rd Qu.:  516.5   3rd Qu.:  514.8   3rd Qu.:  520.5   3rd Qu.:  517.8  \n",
              " Max.   :26872.0   Max.   :27608.0   Max.   :27503.0   Max.   :27756.0  \n",
              "      X41               X42               X43               X44         \n",
              " Min.   :   17.0   Min.   :   13.0   Min.   :   15.0   Min.   :    8.0  \n",
              " 1st Qu.:  185.0   1st Qu.:  191.5   1st Qu.:  183.2   1st Qu.:  180.5  \n",
              " Median :  352.0   Median :  334.5   Median :  330.0   Median :  313.0  \n",
              " Mean   :  747.4   Mean   :  729.6   Mean   :  709.2   Mean   :  685.5  \n",
              " 3rd Qu.:  535.2   3rd Qu.:  510.8   3rd Qu.:  486.0   3rd Qu.:  484.8  \n",
              " Max.   :27653.0   Max.   :26994.0   Max.   :26239.0   Max.   :25364.0  \n",
              "      X45               X46               X47               X48         \n",
              " Min.   :    8.0   Min.   :    9.0   Min.   :   11.0   Min.   :   15.0  \n",
              " 1st Qu.:  182.0   1st Qu.:  173.2   1st Qu.:  156.2   1st Qu.:  167.8  \n",
              " Median :  314.5   Median :  307.5   Median :  293.5   Median :  303.5  \n",
              " Mean   :  670.8   Mean   :  653.9   Mean   :  633.3   Mean   :  625.7  \n",
              " 3rd Qu.:  484.5   3rd Qu.:  459.2   3rd Qu.:  458.8   3rd Qu.:  458.8  \n",
              " Max.   :24818.0   Max.   :24194.0   Max.   :23432.0   Max.   :23152.0  \n",
              "      X49               X50               X51               X52         \n",
              " Min.   :   10.0   Min.   :    7.0   Min.   :   10.0   Min.   :    7.0  \n",
              " 1st Qu.:  151.2   1st Qu.:  162.8   1st Qu.:  158.2   1st Qu.:  173.0  \n",
              " Median :  295.0   Median :  286.5   Median :  302.0   Median :  308.5  \n",
              " Mean   :  623.6   Mean   :  624.0   Mean   :  626.8   Mean   :  628.0  \n",
              " 3rd Qu.:  442.2   3rd Qu.:  431.5   3rd Qu.:  452.2   3rd Qu.:  435.5  \n",
              " Max.   :23074.0   Max.   :23088.0   Max.   :23193.0   Max.   :23236.0  \n",
              "      X53               X54               X55               X56         \n",
              " Min.   :    7.0   Min.   :    6.0   Min.   :   12.0   Min.   :    6.0  \n",
              " 1st Qu.:  145.0   1st Qu.:  148.8   1st Qu.:  137.8   1st Qu.:  140.0  \n",
              " Median :  277.0   Median :  284.0   Median :  268.5   Median :  283.5  \n",
              " Mean   :  596.3   Mean   :  585.5   Mean   :  565.2   Mean   :  567.2  \n",
              " 3rd Qu.:  453.0   3rd Qu.:  431.5   3rd Qu.:  395.5   3rd Qu.:  409.8  \n",
              " Max.   :22063.0   Max.   :21664.0   Max.   :20913.0   Max.   :20985.0  \n",
              "      X57               X58               X59               X60         \n",
              " Min.   :    7.0   Min.   :    3.0   Min.   :    2.0   Min.   :    3.0  \n",
              " 1st Qu.:  136.2   1st Qu.:  139.8   1st Qu.:  123.2   1st Qu.:  121.2  \n",
              " Median :  289.5   Median :  268.0   Median :  242.5   Median :  249.0  \n",
              " Mean   :  562.1   Mean   :  554.3   Mean   :  538.2   Mean   :  512.5  \n",
              " 3rd Qu.:  395.2   3rd Qu.:  399.0   3rd Qu.:  398.5   3rd Qu.:  370.8  \n",
              " Max.   :20796.0   Max.   :20510.0   Max.   :19913.0   Max.   :18963.0  \n",
              "      X61               X62               X63               X64         \n",
              " Min.   :    6.0   Min.   :    1.0   Min.   :    1.0   Min.   :    4.0  \n",
              " 1st Qu.:  121.2   1st Qu.:  115.2   1st Qu.:  118.0   1st Qu.:  111.2  \n",
              " Median :  232.5   Median :  214.0   Median :  238.0   Median :  224.0  \n",
              " Mean   :  503.5   Mean   :  475.9   Mean   :  481.8   Mean   :  474.1  \n",
              " 3rd Qu.:  383.2   3rd Qu.:  349.0   3rd Qu.:  351.5   3rd Qu.:  355.8  \n",
              " Max.   :18629.0   Max.   :17607.0   Max.   :17827.0   Max.   :17540.0  \n",
              "      X65               X66               X67               X68         \n",
              " Min.   :    7.0   Min.   :    1.0   Min.   :    4.0   Min.   :    3.0  \n",
              " 1st Qu.:  113.2   1st Qu.:  103.2   1st Qu.:  111.2   1st Qu.:  111.5  \n",
              " Median :  211.0   Median :  191.5   Median :  207.5   Median :  220.5  \n",
              " Mean   :  454.3   Mean   :  453.4   Mean   :  478.1   Mean   :  491.7  \n",
              " 3rd Qu.:  320.5   3rd Qu.:  330.5   3rd Qu.:  348.5   3rd Qu.:  346.8  \n",
              " Max.   :16808.0   Max.   :16774.0   Max.   :17689.0   Max.   :18192.0  \n",
              "      X69               X70               X71               X72         \n",
              " Min.   :    2.0   Min.   :    3.0   Min.   :    4.0   Min.   :    3.0  \n",
              " 1st Qu.:  112.5   1st Qu.:  104.0   1st Qu.:  110.8   1st Qu.:  101.8  \n",
              " Median :  196.5   Median :  188.5   Median :  198.5   Median :  178.5  \n",
              " Mean   :  457.4   Mean   :  436.3   Mean   :  459.1   Mean   :  433.4  \n",
              " 3rd Qu.:  323.2   3rd Qu.:  322.0   3rd Qu.:  346.5   3rd Qu.:  335.2  \n",
              " Max.   :16922.0   Max.   :16142.0   Max.   :16985.0   Max.   :16034.0  \n",
              "      X73               X74               X75                X76         \n",
              " Min.   :    6.0   Min.   :    1.0   Min.   :    1.00   Min.   :    3.0  \n",
              " 1st Qu.:  105.2   1st Qu.:   89.5   1st Qu.:   80.25   1st Qu.:   98.5  \n",
              " Median :  171.0   Median :  139.5   Median :  148.00   Median :  180.0  \n",
              " Mean   :  413.7   Mean   :  354.4   Mean   :  343.22   Mean   :  415.2  \n",
              " 3rd Qu.:  320.8   3rd Qu.:  263.2   3rd Qu.:  270.75   3rd Qu.:  300.5  \n",
              " Max.   :15307.0   Max.   :13112.0   Max.   :12699.00   Max.   :15364.0  \n",
              "      X77              X78               X79               X80         \n",
              " Min.   :   1.0   Min.   :    4.0   Min.   :    5.0   Min.   :   6.00  \n",
              " 1st Qu.:  60.5   1st Qu.:   74.0   1st Qu.:   77.0   1st Qu.:  81.25  \n",
              " Median : 107.5   Median :  132.5   Median :  157.0   Median : 169.00  \n",
              " Mean   : 243.2   Mean   :  285.6   Mean   :  334.7   Mean   : 214.05  \n",
              " 3rd Qu.: 195.2   3rd Qu.:  224.5   3rd Qu.:  263.8   3rd Qu.: 261.25  \n",
              " Max.   :8999.0   Max.   :10569.0   Max.   :12385.0   Max.   :2920.00  \n",
              "      X81                X82               X83                X84         \n",
              " Min.   :    7.00   Min.   :    2.0   Min.   :    2.00   Min.   :    2.0  \n",
              " 1st Qu.:   79.25   1st Qu.:   71.5   1st Qu.:   74.75   1st Qu.:   66.0  \n",
              " Median :  171.50   Median :  162.5   Median :  148.50   Median :  148.0  \n",
              " Mean   :  332.46   Mean   :  319.0   Mean   :  314.30   Mean   :  292.6  \n",
              " 3rd Qu.:  252.50   3rd Qu.:  242.2   3rd Qu.:  240.25   3rd Qu.:  220.5  \n",
              " Max.   :12301.00   Max.   :11802.0   Max.   :11629.00   Max.   :10828.0  \n",
              "      X85                X86               X87               X88         \n",
              " Min.   :    1.00   Min.   :   1.00   Min.   :   3.00   Min.   :   3.00  \n",
              " 1st Qu.:   59.25   1st Qu.:  61.25   1st Qu.:  48.75   1st Qu.:  42.25  \n",
              " Median :  134.50   Median : 132.50   Median : 109.50   Median :  90.50  \n",
              " Mean   :  272.92   Mean   : 257.65   Mean   : 219.57   Mean   : 192.24  \n",
              " 3rd Qu.:  202.50   3rd Qu.: 178.00   3rd Qu.: 169.25   3rd Qu.: 135.50  \n",
              " Max.   :10098.00   Max.   :9533.00   Max.   :8124.00   Max.   :7113.00  \n",
              "      X89              X90               X91               X92        \n",
              " Min.   :   1.0   Min.   :   1.00   Min.   :   3.00   Min.   :   1.0  \n",
              " 1st Qu.:  33.0   1st Qu.:  32.25   1st Qu.:  22.25   1st Qu.:  19.0  \n",
              " Median :  71.5   Median :  67.00   Median :  50.00   Median :  43.0  \n",
              " Mean   : 170.7   Mean   : 149.38   Mean   : 122.38   Mean   : 101.6  \n",
              " 3rd Qu.: 124.8   3rd Qu.: 103.75   3rd Qu.:  91.75   3rd Qu.:  72.0  \n",
              " Max.   :6317.0   Max.   :5527.00   Max.   :4528.00   Max.   :3761.0  \n",
              "      X93              X94               X95               X96         \n",
              " Min.   :   0.0   Min.   :   0.00   Min.   :   0.00   Min.   :   0.00  \n",
              " 1st Qu.:  17.0   1st Qu.:  13.25   1st Qu.:   9.00   1st Qu.:   6.00  \n",
              " Median :  33.0   Median :  26.00   Median :  18.00   Median :  12.50  \n",
              " Mean   :  79.0   Mean   :  62.41   Mean   :  44.81   Mean   :  33.05  \n",
              " 3rd Qu.:  62.5   3rd Qu.:  47.25   3rd Qu.:  32.75   3rd Qu.:  22.50  \n",
              " Max.   :2923.0   Max.   :2309.00   Max.   :1658.00   Max.   :1223.00  \n",
              "      X97              X98              X99              X100       \n",
              " Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   :  0.00  \n",
              " 1st Qu.:  3.25   1st Qu.:  2.25   1st Qu.:  1.00   1st Qu.:  3.00  \n",
              " Median :  9.00   Median :  5.00   Median :  4.00   Median :  7.50  \n",
              " Mean   : 22.35   Mean   : 15.27   Mean   : 10.76   Mean   : 20.73  \n",
              " 3rd Qu.: 17.00   3rd Qu.: 10.75   3rd Qu.:  8.75   3rd Qu.: 14.00  \n",
              " Max.   :827.00   Max.   :565.00   Max.   :398.00   Max.   :767.00  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# visión general del df\n",
        "summary(demogr_bcn_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2fe1bc80-81ca-4f33-b472-22153db7c4c6",
      "metadata": {
        "id": "2fe1bc80-81ca-4f33-b472-22153db7c4c6"
      },
      "source": [
        "- Observamos que nuestro data frame no contiene valores nulos ni valores NA. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a5f27df6-8a3f-4b8d-9ac9-5743e7e0f15b",
      "metadata": {
        "id": "a5f27df6-8a3f-4b8d-9ac9-5743e7e0f15b"
      },
      "source": [
        "## 1. Histograma con la distribución de la población de la ciudad por edades"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "1d9f646d-8fbf-4f1c-8ffc-aee8be67c9bf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "id": "1d9f646d-8fbf-4f1c-8ffc-aee8be67c9bf",
        "outputId": "5ee4a3a2-ca05-4e09-e1d5-221f9ceb3789"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 1 × 101</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X0</th><th scope=col>X1</th><th scope=col>X2</th><th scope=col>X3</th><th scope=col>X4</th><th scope=col>X5</th><th scope=col>X6</th><th scope=col>X7</th><th scope=col>X8</th><th scope=col>X9</th><th scope=col>⋯</th><th scope=col>X91</th><th scope=col>X92</th><th scope=col>X93</th><th scope=col>X94</th><th scope=col>X95</th><th scope=col>X96</th><th scope=col>X97</th><th scope=col>X98</th><th scope=col>X99</th><th scope=col>X100</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>13633</td><td>13918</td><td>13712</td><td>13533</td><td>14018</td><td>13968</td><td>13801</td><td>13747</td><td>14288</td><td>13588</td><td>⋯</td><td>4528</td><td>3761</td><td>2923</td><td>2309</td><td>1658</td><td>1223</td><td>827</td><td>565</td><td>398</td><td>767</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 1 × 101\n\n| <!--/--> | X0 &lt;int&gt; | X1 &lt;int&gt; | X2 &lt;int&gt; | X3 &lt;int&gt; | X4 &lt;int&gt; | X5 &lt;int&gt; | X6 &lt;int&gt; | X7 &lt;int&gt; | X8 &lt;int&gt; | X9 &lt;int&gt; | ⋯ ⋯ | X91 &lt;int&gt; | X92 &lt;int&gt; | X93 &lt;int&gt; | X94 &lt;int&gt; | X95 &lt;int&gt; | X96 &lt;int&gt; | X97 &lt;int&gt; | X98 &lt;int&gt; | X99 &lt;int&gt; | X100 &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 13633 | 13918 | 13712 | 13533 | 14018 | 13968 | 13801 | 13747 | 14288 | 13588 | ⋯ | 4528 | 3761 | 2923 | 2309 | 1658 | 1223 | 827 | 565 | 398 | 767 |\n\n",
            "text/latex": "A data.frame: 1 × 101\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & X0 & X1 & X2 & X3 & X4 & X5 & X6 & X7 & X8 & X9 & ⋯ & X91 & X92 & X93 & X94 & X95 & X96 & X97 & X98 & X99 & X100\\\\\n  & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & 13633 & 13918 & 13712 & 13533 & 14018 & 13968 & 13801 & 13747 & 14288 & 13588 & ⋯ & 4528 & 3761 & 2923 & 2309 & 1658 & 1223 & 827 & 565 & 398 & 767\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  X0    X1    X2    X3    X4    X5    X6    X7    X8    X9    ⋯ X91  X92  X93 \n",
              "1 13633 13918 13712 13533 14018 13968 13801 13747 14288 13588 ⋯ 4528 3761 2923\n",
              "  X94  X95  X96  X97 X98 X99 X100\n",
              "1 2309 1658 1223 827 565 398 767 "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# subset edades: nos quedamos con la 1ª fila (Bcn) y las columnas que representan la edad (de la columna 4 a la 104). \n",
        "df_edades <- demogr_bcn_data[1,c(4:104)]\n",
        "head(df_edades)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "864421f0-61dc-4a80-8ab5-a4608f75acea",
      "metadata": {
        "tags": [],
        "id": "864421f0-61dc-4a80-8ab5-a4608f75acea"
      },
      "outputs": [],
      "source": [
        "# miramos la edad mínima y la máxima\n",
        "edad_min = names(df_edades[1]) # 0 \n",
        "edad_max = names(df_edades[101]) # 100\n",
        "\n",
        "# creamos un vector que contenga las edades\n",
        "v_edades<-c(0:100)\n",
        "\n",
        "# trasponemos el df edades \n",
        "df_edades_t<-t(df_edades)\n",
        "# renombramos la columna\n",
        "colnames(df_edades_t)[colnames(df_edades_t) == '1'] <- 'count'\n",
        "\n",
        "# creamos el df final\n",
        "df_distrib_pobl_bcn_edades <- data.frame(\"edad\" = v_edades, df_edades_t)\n",
        "# duplicamos las filas según el número de personas que tiene una determinada edad: si con 0 años hay 13633 personas, se crearán 13633 filas con un 0. \n",
        "df_distrib_pobl_bcn_edades_dup = uncount(df_distrib_pobl_bcn_edades, count, .remove = FALSE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "5e146ddb-25fb-4cad-a4ed-050941355825",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "5e146ddb-25fb-4cad-a4ed-050941355825",
        "outputId": "c58b05b5-2a44-4a88-d74b-38481e1da760"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “Distribución de la población de Barcelona por edades”"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ngEAIdgAAgRDsAAAC8d+G88IaxYShdQAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ],
      "source": [
        "# Creamos el histograma\n",
        "hist(df_distrib_pobl_bcn_edades_dup$edad,\n",
        "         main=\"Distribución de la población de Barcelona por edades\",\n",
        "         xlab=\"Edad\", \n",
        "         border=\"white\", \n",
        "         col=\"#7293cb\")  "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "997edb7f-fd8c-4142-9da5-39afd1243123",
      "metadata": {
        "id": "997edb7f-fd8c-4142-9da5-39afd1243123"
      },
      "source": [
        "Podemos observar que este histograma presenta una cola derecha más larga, formando una asimetría negativa. Aunque como veremos en el siguiente apartado la media y la mediana son muy similares (entorno los 43 años), estas están ligeramente a la derecha de la moda (entorno a los 40 años). De los 0 a los 20 años se puede apreciar que la frecuencia varía muy poco, y es a partir de los 25 años que empieza a ascender. Los valores más frecuentes están entorno a los 35-45. Por tanto, en Barcelona, la mayoría de las personas tienen entre 35 y 45 años. A partir de este rango de edades, la frecuencia de estas empieza a disminuir a medida que la edad avanza (cuantos más años, menos gente), con la excepción de la edad 80, que presenta una frecuencia mucho menor. Este hecho podría ser debido a una baja natalidad en los años 40 del siglo pasado. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2f4995a2-8221-4047-9514-722d9354c8a3",
      "metadata": {
        "id": "2f4995a2-8221-4047-9514-722d9354c8a3"
      },
      "source": [
        "## 2. Boxplot para la distribución de edades de la ciudad"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "087987db-565c-4535-b877-ab5ef6943e95",
      "metadata": {
        "id": "087987db-565c-4535-b877-ab5ef6943e95"
      },
      "source": [
        "- **Nota**: \n",
        "    - el boxplot realizado con la librería plotly no se puede visualizar en formato PDF pero sí en el entorno de Jupyter Notebook. Se han realizado dos boxplots, uno con plotly (interactivo) para cuando se visualice el código en Jupyter Notebook, y otro con ggplot2 para poder visualizarlo en el informe PDF. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "e4715df6-5409-45b7-b122-b317f7c96286",
      "metadata": {
        "id": "e4715df6-5409-45b7-b122-b317f7c96286"
      },
      "outputs": [],
      "source": [
        "# Boxplot interactivo usando plotly\n",
        "bp <-plot_ly(\n",
        "    data = df_distrib_pobl_bcn_edades_dup,\n",
        "    y = ~edad,\n",
        "    x = \"Población de Barcelona\",  \n",
        "    #boxmean = TRUE, \n",
        "    boxmean = 'sd', # mostramos la media y la desviación estándar (línea discontinua)\n",
        "    type = \"box\"\n",
        ") %>% \n",
        "    layout(title = 'Distribución de edades de Barcelona¶')\n",
        "\n",
        "bp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "d8495911-8c39-4ae2-b2bf-7d1b44701037",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "d8495911-8c39-4ae2-b2bf-7d1b44701037",
        "outputId": "cd6adff3-2ffa-454c-aa9c-ca69761eb004"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nBwAQhLADAAji/wM4YTmAqTFgRAAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ],
      "source": [
        "# Boxplot usando ggplot\n",
        "ggplot(df_distrib_pobl_bcn_edades_dup, aes(x='',y=edad)) + \n",
        "    geom_boxplot(fill=\"slateblue\", alpha=0.4) +\n",
        "    xlab(\"Población de Barcelona\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdec57d9-4c32-42d4-bc13-ee951084fc8d",
      "metadata": {
        "id": "fdec57d9-4c32-42d4-bc13-ee951084fc8d"
      },
      "source": [
        "En este boxplot, que representa la distribución de edades de Barcelona, podemos observar varias cosas. El IQR no es muy grande, ya que la altura del boxplot va des de los 27 años (Q1) hasta los 61 años (Q3), es decir, la mayoría de las personas de Barcelona se encuentran entre este rango de edad. Por tanto, no hay una dispersión muy elevada: está bastante condensado. Esto significa que varía poco; es más consistente y fácil de predecir. Además, este boxplot es bastante simétrico ya que el Q2 está en el centro. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c434429f-99fa-4c42-abde-fb813923a1da",
      "metadata": {
        "id": "c434429f-99fa-4c42-abde-fb813923a1da"
      },
      "source": [
        "## 3. Media y desviación estándar de la distribución de edades de la ciudad"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a26fee4-f9f4-4509-aaac-47341aeac2e5",
      "metadata": {
        "id": "5a26fee4-f9f4-4509-aaac-47341aeac2e5"
      },
      "source": [
        "### Media de la distribución de edades de Bcn¶"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "e4098757-2086-42f8-a250-5294cdfd5ad2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "e4098757-2086-42f8-a250-5294cdfd5ad2",
        "outputId": "c0c57801-fd09-43f7-ac62-66950b26fdbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "43.7269531934443"
            ],
            "text/markdown": "43.7269531934443",
            "text/latex": "43.7269531934443",
            "text/plain": [
              "[1] 43.72695"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "mean(df_distrib_pobl_bcn_edades_dup$edad)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03de4f56-0362-4285-939f-42306fbbe6b8",
      "metadata": {
        "id": "03de4f56-0362-4285-939f-42306fbbe6b8"
      },
      "source": [
        "Podemos observar que la edad media de los habitantes de Barcelona es 43.726 años. En el boxplot anterior realizado con la librería plotly, podemos observar como la media y la mediana son muy similares: 43.726 y 43 años, respectivamente."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4d6c814f-082d-401c-b606-0cb1ac03faae",
      "metadata": {
        "id": "4d6c814f-082d-401c-b606-0cb1ac03faae"
      },
      "source": [
        "### Desviación estándar de la distribución de edades de Bcn¶"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "fe0343ea-c56f-418b-b33c-7a3993391f2a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "fe0343ea-c56f-418b-b33c-7a3993391f2a",
        "outputId": "d5541485-6b37-4c84-fd88-74d99ef72f51"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "23.105792939766"
            ],
            "text/markdown": "23.105792939766",
            "text/latex": "23.105792939766",
            "text/plain": [
              "[1] 23.10579"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "sd(df_distrib_pobl_bcn_edades_dup$edad)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6183be6c-0511-49b7-94af-9b1ce20509ea",
      "metadata": {
        "id": "6183be6c-0511-49b7-94af-9b1ce20509ea"
      },
      "source": [
        "La desviación estándar es 23.105. Aproximadamente el 68% de los valores se ubican dentro de una desviación estándar de la media. Por tanto, aproximadamente el 68% de personas con edades más cercanas a la media tienen edades comprendidas entre entre 66.831 años y 20.621 años: 43.726 ± 23.105 = [66.831 – 20.621]."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "58351f56-5f25-4459-ba9c-2e2976f40424",
      "metadata": {
        "id": "58351f56-5f25-4459-ba9c-2e2976f40424"
      },
      "source": [
        "# Problema 2\n",
        "El fichero 20221110_DataFrutasHortalizasVerduras.csv contiene datos nutricionales de una selección de alimentos frescos (frutas, hortalizas y verduras)\n",
        "- Definid un posible caso de uso para este dataset y realizad un análisis exploratorio de los datos que aporte información relevante en relación a vuestro caso de uso."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0dfdde5-c8f4-4c19-bc7c-01340c347ccd",
      "metadata": {
        "id": "d0dfdde5-c8f4-4c19-bc7c-01340c347ccd"
      },
      "source": [
        "## Caso de uso: \n",
        "Una buena alimentación es esencial en deportistas para garantizar un buen rendimiento tanto en el entrenamiento como en la competición, así como para tener una buena recuperación. \n",
        "\n",
        "\n",
        "Las frutas, verduras y hortalizas forman parte de la dieta mediterránea y juegan un papel muy importante dentro de la dieta de los deportistas por todas las propiedades que tienen y todos los beneficios que ofrecen.\n",
        "\n",
        "\n",
        "A continuación, se van analizarán un conjunto de frutas, verduras y hortalizas para determinar cuáles presentan propiedades beneficiosas para los deportistas, ya sea antes o después de la competición. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ca0facb-6489-4b74-a2c6-9ad9f0c73c16",
      "metadata": {
        "id": "8ca0facb-6489-4b74-a2c6-9ad9f0c73c16"
      },
      "source": [
        "## Carga de datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "64c7fcc4-820f-49da-b905-7c43edd36d93",
      "metadata": {
        "id": "64c7fcc4-820f-49da-b905-7c43edd36d93"
      },
      "outputs": [],
      "source": [
        "data_fhv <- read.csv(file = \"20221110_DataFrutasHortalizasVerduras.csv\", header = TRUE, sep=';', dec=',')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d155a26-4afb-42f4-b172-6e1b95c024af",
      "metadata": {
        "id": "1d155a26-4afb-42f4-b172-6e1b95c024af"
      },
      "source": [
        "## Exploración básica de los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "c81c48cf-7f69-45dc-9a88-74b3f3dc15bb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "c81c48cf-7f69-45dc-9a88-74b3f3dc15bb",
        "outputId": "e7de2a10-087f-4e62-ff4b-782664375e27"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 3 × 19</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Id</th><th scope=col>Nombre</th><th scope=col>Grupo</th><th scope=col>SubGrupo</th><th scope=col>Energia_kcal</th><th scope=col>Grasas_g</th><th scope=col>AGS_g</th><th scope=col>AGM_g</th><th scope=col>AGP_g</th><th scope=col>HidratosC_g</th><th scope=col>Azucares_g</th><th scope=col>Proteina_g</th><th scope=col>Sodio_mg</th><th scope=col>Agua_g</th><th scope=col>Fibra_g</th><th scope=col>Calcio_mg</th><th scope=col>Yodo_mg</th><th scope=col>Potasio_mg</th><th scope=col>Fosforo_mg</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>1</td><td>Acedera        </td><td>Verduras y hortalizas</td><td>Tallo u hoja</td><td>33</td><td>0.7</td><td>0.000</td><td>0.000</td><td>0.000</td><td>3.2</td><td>0.0</td><td>2.0</td><td>  4</td><td>93.0</td><td>2.9</td><td> 44</td><td> 0</td><td>390</td><td>63</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>2</td><td>Acelgas frescas</td><td>Verduras y hortalizas</td><td>Tallo u hoja</td><td>41</td><td>0.4</td><td>0.030</td><td>0.040</td><td>0.070</td><td>4.5</td><td>1.0</td><td>2.0</td><td>147</td><td>87.5</td><td>5.6</td><td>113</td><td>35</td><td>550</td><td>40</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>3</td><td>Achicoria      </td><td>Verduras y hortalizas</td><td>Tallo u hoja</td><td>36</td><td>0.3</td><td>0.073</td><td>0.006</td><td>0.131</td><td>4.7</td><td>0.7</td><td>1.7</td><td> 45</td><td>92.0</td><td>4.0</td><td>100</td><td> 0</td><td>420</td><td>47</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 3 × 19\n\n| <!--/--> | Id &lt;int&gt; | Nombre &lt;chr&gt; | Grupo &lt;chr&gt; | SubGrupo &lt;chr&gt; | Energia_kcal &lt;dbl&gt; | Grasas_g &lt;dbl&gt; | AGS_g &lt;dbl&gt; | AGM_g &lt;dbl&gt; | AGP_g &lt;dbl&gt; | HidratosC_g &lt;dbl&gt; | Azucares_g &lt;dbl&gt; | Proteina_g &lt;dbl&gt; | Sodio_mg &lt;dbl&gt; | Agua_g &lt;dbl&gt; | Fibra_g &lt;dbl&gt; | Calcio_mg &lt;dbl&gt; | Yodo_mg &lt;dbl&gt; | Potasio_mg &lt;int&gt; | Fosforo_mg &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1 | Acedera         | Verduras y hortalizas | Tallo u hoja | 33 | 0.7 | 0.000 | 0.000 | 0.000 | 3.2 | 0.0 | 2.0 |   4 | 93.0 | 2.9 |  44 |  0 | 390 | 63 |\n| 2 | 2 | Acelgas frescas | Verduras y hortalizas | Tallo u hoja | 41 | 0.4 | 0.030 | 0.040 | 0.070 | 4.5 | 1.0 | 2.0 | 147 | 87.5 | 5.6 | 113 | 35 | 550 | 40 |\n| 3 | 3 | Achicoria       | Verduras y hortalizas | Tallo u hoja | 36 | 0.3 | 0.073 | 0.006 | 0.131 | 4.7 | 0.7 | 1.7 |  45 | 92.0 | 4.0 | 100 |  0 | 420 | 47 |\n\n",
            "text/latex": "A data.frame: 3 × 19\n\\begin{tabular}{r|lllllllllllllllllll}\n  & Id & Nombre & Grupo & SubGrupo & Energia\\_kcal & Grasas\\_g & AGS\\_g & AGM\\_g & AGP\\_g & HidratosC\\_g & Azucares\\_g & Proteina\\_g & Sodio\\_mg & Agua\\_g & Fibra\\_g & Calcio\\_mg & Yodo\\_mg & Potasio\\_mg & Fosforo\\_mg\\\\\n  & <int> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl>\\\\\n\\hline\n\t1 & 1 & Acedera         & Verduras y hortalizas & Tallo u hoja & 33 & 0.7 & 0.000 & 0.000 & 0.000 & 3.2 & 0.0 & 2.0 &   4 & 93.0 & 2.9 &  44 &  0 & 390 & 63\\\\\n\t2 & 2 & Acelgas frescas & Verduras y hortalizas & Tallo u hoja & 41 & 0.4 & 0.030 & 0.040 & 0.070 & 4.5 & 1.0 & 2.0 & 147 & 87.5 & 5.6 & 113 & 35 & 550 & 40\\\\\n\t3 & 3 & Achicoria       & Verduras y hortalizas & Tallo u hoja & 36 & 0.3 & 0.073 & 0.006 & 0.131 & 4.7 & 0.7 & 1.7 &  45 & 92.0 & 4.0 & 100 &  0 & 420 & 47\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Id Nombre          Grupo                 SubGrupo     Energia_kcal Grasas_g\n",
              "1 1  Acedera         Verduras y hortalizas Tallo u hoja 33           0.7     \n",
              "2 2  Acelgas frescas Verduras y hortalizas Tallo u hoja 41           0.4     \n",
              "3 3  Achicoria       Verduras y hortalizas Tallo u hoja 36           0.3     \n",
              "  AGS_g AGM_g AGP_g HidratosC_g Azucares_g Proteina_g Sodio_mg Agua_g Fibra_g\n",
              "1 0.000 0.000 0.000 3.2         0.0        2.0          4      93.0   2.9    \n",
              "2 0.030 0.040 0.070 4.5         1.0        2.0        147      87.5   5.6    \n",
              "3 0.073 0.006 0.131 4.7         0.7        1.7         45      92.0   4.0    \n",
              "  Calcio_mg Yodo_mg Potasio_mg Fosforo_mg\n",
              "1  44        0      390        63        \n",
              "2 113       35      550        40        \n",
              "3 100        0      420        47        "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "head(data_fhv,3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "e8010e6a-7c70-4e90-9fee-50aa177ed7af",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "e8010e6a-7c70-4e90-9fee-50aa177ed7af",
        "outputId": "68ef7106-f428-4f60-c42b-29a6fcf9b5dd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>'Id'</li><li>'Nombre'</li><li>'Grupo'</li><li>'SubGrupo'</li><li>'Energia_kcal'</li><li>'Grasas_g'</li><li>'AGS_g'</li><li>'AGM_g'</li><li>'AGP_g'</li><li>'HidratosC_g'</li><li>'Azucares_g'</li><li>'Proteina_g'</li><li>'Sodio_mg'</li><li>'Agua_g'</li><li>'Fibra_g'</li><li>'Calcio_mg'</li><li>'Yodo_mg'</li><li>'Potasio_mg'</li><li>'Fosforo_mg'</li></ol>\n"
            ],
            "text/markdown": "1. 'Id'\n2. 'Nombre'\n3. 'Grupo'\n4. 'SubGrupo'\n5. 'Energia_kcal'\n6. 'Grasas_g'\n7. 'AGS_g'\n8. 'AGM_g'\n9. 'AGP_g'\n10. 'HidratosC_g'\n11. 'Azucares_g'\n12. 'Proteina_g'\n13. 'Sodio_mg'\n14. 'Agua_g'\n15. 'Fibra_g'\n16. 'Calcio_mg'\n17. 'Yodo_mg'\n18. 'Potasio_mg'\n19. 'Fosforo_mg'\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 'Id'\n\\item 'Nombre'\n\\item 'Grupo'\n\\item 'SubGrupo'\n\\item 'Energia\\_kcal'\n\\item 'Grasas\\_g'\n\\item 'AGS\\_g'\n\\item 'AGM\\_g'\n\\item 'AGP\\_g'\n\\item 'HidratosC\\_g'\n\\item 'Azucares\\_g'\n\\item 'Proteina\\_g'\n\\item 'Sodio\\_mg'\n\\item 'Agua\\_g'\n\\item 'Fibra\\_g'\n\\item 'Calcio\\_mg'\n\\item 'Yodo\\_mg'\n\\item 'Potasio\\_mg'\n\\item 'Fosforo\\_mg'\n\\end{enumerate*}\n",
            "text/plain": [
              " [1] \"Id\"           \"Nombre\"       \"Grupo\"        \"SubGrupo\"     \"Energia_kcal\"\n",
              " [6] \"Grasas_g\"     \"AGS_g\"        \"AGM_g\"        \"AGP_g\"        \"HidratosC_g\" \n",
              "[11] \"Azucares_g\"   \"Proteina_g\"   \"Sodio_mg\"     \"Agua_g\"       \"Fibra_g\"     \n",
              "[16] \"Calcio_mg\"    \"Yodo_mg\"      \"Potasio_mg\"   \"Fosforo_mg\"  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "19"
            ],
            "text/markdown": "19",
            "text/latex": "19",
            "text/plain": [
              "[1] 19"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "106"
            ],
            "text/markdown": "106",
            "text/latex": "106",
            "text/plain": [
              "[1] 106"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# columnas \n",
        "colnames(data_fhv)\n",
        "# num columnas\n",
        "ncol(data_fhv)\n",
        "# num filas\n",
        "nrow(data_fhv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "3c617344-6bdc-4c87-a803-ce867c6e6644",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "3c617344-6bdc-4c87-a803-ce867c6e6644",
        "outputId": "541936df-ae72-4100-fa68-c82ed5f352e1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              ".dl-inline {width: auto; margin:0; padding: 0}\n",
              ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
              ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
              ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
              "</style><dl class=dl-inline><dt>Id</dt><dd>'integer'</dd><dt>Nombre</dt><dd>'character'</dd><dt>Grupo</dt><dd>'character'</dd><dt>SubGrupo</dt><dd>'character'</dd><dt>Energia_kcal</dt><dd>'double'</dd><dt>Grasas_g</dt><dd>'double'</dd><dt>AGS_g</dt><dd>'double'</dd><dt>AGM_g</dt><dd>'double'</dd><dt>AGP_g</dt><dd>'double'</dd><dt>HidratosC_g</dt><dd>'double'</dd><dt>Azucares_g</dt><dd>'double'</dd><dt>Proteina_g</dt><dd>'double'</dd><dt>Sodio_mg</dt><dd>'double'</dd><dt>Agua_g</dt><dd>'double'</dd><dt>Fibra_g</dt><dd>'double'</dd><dt>Calcio_mg</dt><dd>'double'</dd><dt>Yodo_mg</dt><dd>'double'</dd><dt>Potasio_mg</dt><dd>'integer'</dd><dt>Fosforo_mg</dt><dd>'double'</dd></dl>\n"
            ],
            "text/markdown": "Id\n:   'integer'Nombre\n:   'character'Grupo\n:   'character'SubGrupo\n:   'character'Energia_kcal\n:   'double'Grasas_g\n:   'double'AGS_g\n:   'double'AGM_g\n:   'double'AGP_g\n:   'double'HidratosC_g\n:   'double'Azucares_g\n:   'double'Proteina_g\n:   'double'Sodio_mg\n:   'double'Agua_g\n:   'double'Fibra_g\n:   'double'Calcio_mg\n:   'double'Yodo_mg\n:   'double'Potasio_mg\n:   'integer'Fosforo_mg\n:   'double'\n\n",
            "text/latex": "\\begin{description*}\n\\item[Id] 'integer'\n\\item[Nombre] 'character'\n\\item[Grupo] 'character'\n\\item[SubGrupo] 'character'\n\\item[Energia\\textbackslash{}\\_kcal] 'double'\n\\item[Grasas\\textbackslash{}\\_g] 'double'\n\\item[AGS\\textbackslash{}\\_g] 'double'\n\\item[AGM\\textbackslash{}\\_g] 'double'\n\\item[AGP\\textbackslash{}\\_g] 'double'\n\\item[HidratosC\\textbackslash{}\\_g] 'double'\n\\item[Azucares\\textbackslash{}\\_g] 'double'\n\\item[Proteina\\textbackslash{}\\_g] 'double'\n\\item[Sodio\\textbackslash{}\\_mg] 'double'\n\\item[Agua\\textbackslash{}\\_g] 'double'\n\\item[Fibra\\textbackslash{}\\_g] 'double'\n\\item[Calcio\\textbackslash{}\\_mg] 'double'\n\\item[Yodo\\textbackslash{}\\_mg] 'double'\n\\item[Potasio\\textbackslash{}\\_mg] 'integer'\n\\item[Fosforo\\textbackslash{}\\_mg] 'double'\n\\end{description*}\n",
            "text/plain": [
              "          Id       Nombre        Grupo     SubGrupo Energia_kcal     Grasas_g \n",
              "   \"integer\"  \"character\"  \"character\"  \"character\"     \"double\"     \"double\" \n",
              "       AGS_g        AGM_g        AGP_g  HidratosC_g   Azucares_g   Proteina_g \n",
              "    \"double\"     \"double\"     \"double\"     \"double\"     \"double\"     \"double\" \n",
              "    Sodio_mg       Agua_g      Fibra_g    Calcio_mg      Yodo_mg   Potasio_mg \n",
              "    \"double\"     \"double\"     \"double\"     \"double\"     \"double\"    \"integer\" \n",
              "  Fosforo_mg \n",
              "    \"double\" "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# data types del df\n",
        "sapply(data_fhv, typeof)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "90677107-6187-422b-bf9e-d6ead2d18c60",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90677107-6187-422b-bf9e-d6ead2d18c60",
        "outputId": "ba520df1-b458-4ef5-fc92-010dce856990"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'data.frame':\t106 obs. of  19 variables:\n",
            " $ Id          : int  1 2 3 4 5 6 7 8 9 10 ...\n",
            " $ Nombre      : chr  \"Acedera\" \"Acelgas frescas\" \"Achicoria\" \"Ajo\" ...\n",
            " $ Grupo       : chr  \"Verduras y hortalizas\" \"Verduras y hortalizas\" \"Verduras y hortalizas\" \"Verduras y hortalizas\" ...\n",
            " $ SubGrupo    : chr  \"Tallo u hoja\" \"Tallo u hoja\" \"Tallo u hoja\" \"Raices o bulbos\" ...\n",
            " $ Energia_kcal: num  33 41 36 118 44 32 54 16 101 27 ...\n",
            " $ Grasas_g    : num  0.7 0.4 0.3 0.3 0.1 0.2 0.64 0.2 0.6 0.2 ...\n",
            " $ AGS_g       : num  0 0.03 0.073 0.05 0.02 0.033 0.13 0 0.23 0.05 ...\n",
            " $ AGM_g       : num  0 0.04 0.006 0 0.01 0.015 0.06 0 0.04 0 ...\n",
            " $ AGP_g       : num  0 0.07 0.131 0.15 0.05 0.055 0.22 0.1 0.2 0.1 ...\n",
            " $ HidratosC_g : num  3.2 4.5 4.7 23 7.5 0 8.7 1.3 21.5 4.4 ...\n",
            " $ Azucares_g  : num  0 1 0.7 2.3 7.5 0 0.7 1.3 4.1 4 ...\n",
            " $ Proteina_g  : num  2 2 1.7 5.3 2.3 1.5 3 1.3 1.2 1.2 ...\n",
            " $ Sodio_mg    : num  4 147 45 19 47 67 872 126 19 2 ...\n",
            " $ Agua_g      : num  93 87.5 92 70.3 88.1 86 87.2 95.4 74.2 93 ...\n",
            " $ Fibra_g     : num  2.9 5.6 4 1.1 2 12.3 0.5 1.8 2.5 1.2 ...\n",
            " $ Calcio_mg   : num  44 113 100 14 45 72 150 55 22 11 ...\n",
            " $ Yodo_mg     : num  0 35 0 94 1 0 0 0 2 2 ...\n",
            " $ Potasio_mg  : int  390 550 420 529 430 63 50 341 320 214 ...\n",
            " $ Fosforo_mg  : num  63 40 47 134 130 160 80 32 60 21.4 ...\n"
          ]
        }
      ],
      "source": [
        "# data types de cada variable y otros datos adicionales, como los niveles de los factores y los primeros valores de cada variable:\n",
        "str(data_fhv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "bb02f636-be51-4561-828e-0c3ee4213c19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "bb02f636-be51-4561-828e-0c3ee4213c19",
        "outputId": "57bcba09-137e-4561-f3ca-647adf7dc37a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "0"
            ],
            "text/markdown": "0",
            "text/latex": "0",
            "text/plain": [
              "[1] 0"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# miramos si hay NA values\n",
        "sum(is.na(data_fhv))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "87645987-ea36-41c9-8e55-d1785b86aef0",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "87645987-ea36-41c9-8e55-d1785b86aef0",
        "outputId": "832bd507-5b2b-4747-93f6-0e00d6289857"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       Id            Nombre             Grupo             SubGrupo        \n",
              " Min.   :  1.00   Length:106         Length:106         Length:106        \n",
              " 1st Qu.: 27.25   Class :character   Class :character   Class :character  \n",
              " Median : 53.50   Mode  :character   Mode  :character   Mode  :character  \n",
              " Mean   : 53.50                                                           \n",
              " 3rd Qu.: 79.75                                                           \n",
              " Max.   :106.00                                                           \n",
              "  Energia_kcal       Grasas_g           AGS_g              AGM_g       \n",
              " Min.   : 13.00   Min.   : 0.0000   Min.   : 0.00000   Min.   :0.0000  \n",
              " 1st Qu.: 28.00   1st Qu.: 0.1025   1st Qu.: 0.00000   1st Qu.:0.0000  \n",
              " Median : 39.50   Median : 0.3000   Median : 0.02600   Median :0.0065  \n",
              " Mean   : 52.57   Mean   : 0.7663   Mean   : 0.35436   Mean   :0.1298  \n",
              " 3rd Qu.: 60.10   3rd Qu.: 0.4750   3rd Qu.: 0.08275   3rd Qu.:0.0280  \n",
              " Max.   :373.00   Max.   :36.0000   Max.   :30.90000   Max.   :9.0100  \n",
              "     AGP_g         HidratosC_g       Azucares_g       Proteina_g   \n",
              " Min.   :0.0000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n",
              " 1st Qu.:0.0000   1st Qu.: 3.425   1st Qu.: 1.825   1st Qu.:0.800  \n",
              " Median :0.0700   Median : 6.050   Median : 4.100   Median :1.200  \n",
              " Mean   :0.1078   Mean   : 8.542   Mean   : 5.917   Mean   :1.538  \n",
              " 3rd Qu.:0.1300   3rd Qu.:10.900   3rd Qu.: 8.418   3rd Qu.:2.075  \n",
              " Max.   :1.0650   Max.   :62.500   Max.   :57.400   Max.   :5.600  \n",
              "    Sodio_mg          Agua_g         Fibra_g         Calcio_mg     \n",
              " Min.   :  0.00   Min.   :29.00   Min.   : 0.200   Min.   :  0.00  \n",
              " 1st Qu.:  2.00   1st Qu.:83.70   1st Qu.: 1.325   1st Qu.: 12.25  \n",
              " Median :  4.50   Median :89.10   Median : 2.000   Median : 25.00  \n",
              " Mean   : 25.62   Mean   :86.66   Mean   : 2.641   Mean   : 41.12  \n",
              " 3rd Qu.: 14.00   3rd Qu.:92.55   3rd Qu.: 2.975   3rd Qu.: 51.25  \n",
              " Max.   :872.00   Max.   :96.70   Max.   :12.300   Max.   :363.00  \n",
              "    Yodo_mg         Potasio_mg      Fosforo_mg    \n",
              " Min.   : 0.000   Min.   :  0.0   Min.   :  0.00  \n",
              " 1st Qu.: 0.000   1st Qu.:200.0   1st Qu.: 21.55  \n",
              " Median : 1.000   Median :260.0   Median : 31.00  \n",
              " Mean   : 3.992   Mean   :276.2   Mean   : 39.08  \n",
              " 3rd Qu.: 2.000   3rd Qu.:339.2   3rd Qu.: 51.50  \n",
              " Max.   :94.000   Max.   :628.0   Max.   :160.00  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# visión general del df\n",
        "summary(data_fhv)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc1abb49-c9e9-4c59-9b88-d6dc73c3a13f",
      "metadata": {
        "id": "bc1abb49-c9e9-4c59-9b88-d6dc73c3a13f"
      },
      "source": [
        "- Observamos que nuestro data frame no contiene valores nulos ni valores NA."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "110a7134-efac-4e9c-88bd-053be21503e1",
      "metadata": {
        "id": "110a7134-efac-4e9c-88bd-053be21503e1"
      },
      "source": [
        "## 1. Verduras y hortalizas ricas en proteínas"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "98312c17-d063-4709-a697-a6e4f8972948",
      "metadata": {
        "id": "98312c17-d063-4709-a697-a6e4f8972948"
      },
      "source": [
        "Las proteínas son necesarias para poder construir el músculo después de un entrenamiento o competición. Es por ello que se ha seleccionado las verduras y hortalizas con más proteínas para que los deportistas las ingieran, por ejemplo, después de entrenar. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "eed5a335-34a9-48fb-93b0-5484826714fc",
      "metadata": {
        "tags": [],
        "id": "eed5a335-34a9-48fb-93b0-5484826714fc"
      },
      "outputs": [],
      "source": [
        "# df verduras y hortalizas\n",
        "verd_hort_df <- data_fhv[data_fhv$Grupo == \"Verduras y hortalizas\",]\n",
        "# df frutas\n",
        "frutas_df <- data_fhv[data_fhv$Grupo == \"Frutas\",]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "92b26592-49fe-4dbc-9a7f-ea10e45bceab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "92b26592-49fe-4dbc-9a7f-ea10e45bceab",
        "outputId": "3bd39568-b0cc-4d8a-8ebf-e4898d8bb91d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 10 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Nombre</th><th scope=col>Grupo</th><th scope=col>Proteina_g</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>Hojas de parra      </td><td>Verduras y hortalizas</td><td>5.6</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>Ajo                 </td><td>Verduras y hortalizas</td><td>5.3</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Wasabi, raiz        </td><td>Verduras y hortalizas</td><td>4.8</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Brecol              </td><td>Verduras y hortalizas</td><td>4.4</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>Coles de Bruselas   </td><td>Verduras y hortalizas</td><td>3.5</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>Coles               </td><td>Verduras y hortalizas</td><td>3.3</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>Repollo             </td><td>Verduras y hortalizas</td><td>3.3</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>Algas wakame frescas</td><td>Verduras y hortalizas</td><td>3.0</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>Berros              </td><td>Verduras y hortalizas</td><td>3.0</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>Cebollino           </td><td>Verduras y hortalizas</td><td>3.0</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 10 × 3\n\n| <!--/--> | Nombre &lt;chr&gt; | Grupo &lt;chr&gt; | Proteina_g &lt;dbl&gt; |\n|---|---|---|---|\n| 1 | Hojas de parra       | Verduras y hortalizas | 5.6 |\n| 2 | Ajo                  | Verduras y hortalizas | 5.3 |\n| 3 | Wasabi, raiz         | Verduras y hortalizas | 4.8 |\n| 4 | Brecol               | Verduras y hortalizas | 4.4 |\n| 5 | Coles de Bruselas    | Verduras y hortalizas | 3.5 |\n| 6 | Coles                | Verduras y hortalizas | 3.3 |\n| 7 | Repollo              | Verduras y hortalizas | 3.3 |\n| 8 | Algas wakame frescas | Verduras y hortalizas | 3.0 |\n| 9 | Berros               | Verduras y hortalizas | 3.0 |\n| 10 | Cebollino            | Verduras y hortalizas | 3.0 |\n\n",
            "text/latex": "A data.frame: 10 × 3\n\\begin{tabular}{r|lll}\n  & Nombre & Grupo & Proteina\\_g\\\\\n  & <chr> & <chr> & <dbl>\\\\\n\\hline\n\t1 & Hojas de parra       & Verduras y hortalizas & 5.6\\\\\n\t2 & Ajo                  & Verduras y hortalizas & 5.3\\\\\n\t3 & Wasabi, raiz         & Verduras y hortalizas & 4.8\\\\\n\t4 & Brecol               & Verduras y hortalizas & 4.4\\\\\n\t5 & Coles de Bruselas    & Verduras y hortalizas & 3.5\\\\\n\t6 & Coles                & Verduras y hortalizas & 3.3\\\\\n\t7 & Repollo              & Verduras y hortalizas & 3.3\\\\\n\t8 & Algas wakame frescas & Verduras y hortalizas & 3.0\\\\\n\t9 & Berros               & Verduras y hortalizas & 3.0\\\\\n\t10 & Cebollino            & Verduras y hortalizas & 3.0\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   Nombre               Grupo                 Proteina_g\n",
              "1  Hojas de parra       Verduras y hortalizas 5.6       \n",
              "2  Ajo                  Verduras y hortalizas 5.3       \n",
              "3  Wasabi, raiz         Verduras y hortalizas 4.8       \n",
              "4  Brecol               Verduras y hortalizas 4.4       \n",
              "5  Coles de Bruselas    Verduras y hortalizas 3.5       \n",
              "6  Coles                Verduras y hortalizas 3.3       \n",
              "7  Repollo              Verduras y hortalizas 3.3       \n",
              "8  Algas wakame frescas Verduras y hortalizas 3.0       \n",
              "9  Berros               Verduras y hortalizas 3.0       \n",
              "10 Cebollino            Verduras y hortalizas 3.0       "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# subset con el nombre de la verdura u hortaliza, el grupo y el valor proteico \n",
        "verd_hort_protein <- select(verd_hort_df, Nombre, Grupo, Proteina_g)\n",
        "# ordenamos de manera descendente \n",
        "verd_hort_protein_desc <- arrange(verd_hort_protein, desc(Proteina_g))\n",
        "# top 10 verduras y hortalizas ricas en proteínas\n",
        "top_10_verd_hort_ricas_prot <- head(verd_hort_protein_desc,10)\n",
        "top_10_verd_hort_ricas_prot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "e578f9ba-48b0-43f4-a74b-4da9d69adf66",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "e578f9ba-48b0-43f4-a74b-4da9d69adf66",
        "outputId": "fbaa5856-36b4-44c2-f25a-dcdf8150fadf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAJYCAMAAABFOO8oAAADAFBMVEUAAAABAQECAgIDAwME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"
          },
          "metadata": {
            "image/png": {
              "width": 600,
              "height": 300
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 10, repr.plot.height = 5)\n",
        "# barplot \n",
        "v_h_prot_p <- ggplot(data = top_10_verd_hort_ricas_prot, aes(x = Nombre, y = Proteina_g)) + \n",
        "                    geom_bar(stat = \"identity\", fill='burlywood3') +\n",
        "                    labs(title = \"Top 10 de verduras y hortalizas ricas en proteína\", x = \"Verduras y hortalizas\", y = \"Proteína (g por cada 100 g)\")\n",
        "# ggplotly(v_h_prot_p)\n",
        "v_h_prot_p"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d360d7ac-fdab-4d2a-b598-87afb3f86ad0",
      "metadata": {
        "id": "d360d7ac-fdab-4d2a-b598-87afb3f86ad0"
      },
      "source": [
        "Podemos observar que las hojas de parra son las hortalizas que mayor cantidad de proteínas tienen, seguidas del ajo, de la raíz de wasabi y del brécol. Por tanto, sería conveniente incluir hojas de parra, raíz de wasabi, brécol y coles de Bruselas en la dieta para favorecer la creación de músculo. Aunque el ajo haya salido en las primeras posiciones, este se debe consumir con moderación para evitar problemas intestinales. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "947b43e5-19b8-4ca4-bdde-25242e9dbf72",
      "metadata": {
        "tags": [],
        "id": "947b43e5-19b8-4ca4-bdde-25242e9dbf72"
      },
      "source": [
        "## 2. Verduras y hortalizas ricas en potasio"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d2142fd7-ee8a-4596-b38c-43f093fc48d6",
      "metadata": {
        "id": "d2142fd7-ee8a-4596-b38c-43f093fc48d6"
      },
      "source": [
        "El potasio es un electrolito que participa en los procesos de relajación muscular, y su carencia puede producir debilidad muscular. Por tanto, los deportistas deben incluirlo en su dieta de manera regular. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "2083a561-9b90-42b9-bec8-f0093eeb7dbd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "2083a561-9b90-42b9-bec8-f0093eeb7dbd",
        "outputId": "ff45b686-35df-4521-b9a0-4c77dc511823"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 10 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Nombre</th><th scope=col>Grupo</th><th scope=col>Potasio_mg</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>Patata                  </td><td>Verduras y hortalizas</td><td>570</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>Wasabi, raiz            </td><td>Verduras y hortalizas</td><td>568</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Acelgas frescas         </td><td>Verduras y hortalizas</td><td>550</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Brotes de bambu, frescos</td><td>Verduras y hortalizas</td><td>533</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>Ajo                     </td><td>Verduras y hortalizas</td><td>529</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>Borraja                 </td><td>Verduras y hortalizas</td><td>470</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>Chirivia                </td><td>Verduras y hortalizas</td><td>450</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>Alcachofas frescas      </td><td>Verduras y hortalizas</td><td>430</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>Espinacas               </td><td>Verduras y hortalizas</td><td>423</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>Canonigos               </td><td>Verduras y hortalizas</td><td>421</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 10 × 3\n\n| <!--/--> | Nombre &lt;chr&gt; | Grupo &lt;chr&gt; | Potasio_mg &lt;int&gt; |\n|---|---|---|---|\n| 1 | Patata                   | Verduras y hortalizas | 570 |\n| 2 | Wasabi, raiz             | Verduras y hortalizas | 568 |\n| 3 | Acelgas frescas          | Verduras y hortalizas | 550 |\n| 4 | Brotes de bambu, frescos | Verduras y hortalizas | 533 |\n| 5 | Ajo                      | Verduras y hortalizas | 529 |\n| 6 | Borraja                  | Verduras y hortalizas | 470 |\n| 7 | Chirivia                 | Verduras y hortalizas | 450 |\n| 8 | Alcachofas frescas       | Verduras y hortalizas | 430 |\n| 9 | Espinacas                | Verduras y hortalizas | 423 |\n| 10 | Canonigos                | Verduras y hortalizas | 421 |\n\n",
            "text/latex": "A data.frame: 10 × 3\n\\begin{tabular}{r|lll}\n  & Nombre & Grupo & Potasio\\_mg\\\\\n  & <chr> & <chr> & <int>\\\\\n\\hline\n\t1 & Patata                   & Verduras y hortalizas & 570\\\\\n\t2 & Wasabi, raiz             & Verduras y hortalizas & 568\\\\\n\t3 & Acelgas frescas          & Verduras y hortalizas & 550\\\\\n\t4 & Brotes de bambu, frescos & Verduras y hortalizas & 533\\\\\n\t5 & Ajo                      & Verduras y hortalizas & 529\\\\\n\t6 & Borraja                  & Verduras y hortalizas & 470\\\\\n\t7 & Chirivia                 & Verduras y hortalizas & 450\\\\\n\t8 & Alcachofas frescas       & Verduras y hortalizas & 430\\\\\n\t9 & Espinacas                & Verduras y hortalizas & 423\\\\\n\t10 & Canonigos                & Verduras y hortalizas & 421\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   Nombre                   Grupo                 Potasio_mg\n",
              "1  Patata                   Verduras y hortalizas 570       \n",
              "2  Wasabi, raiz             Verduras y hortalizas 568       \n",
              "3  Acelgas frescas          Verduras y hortalizas 550       \n",
              "4  Brotes de bambu, frescos Verduras y hortalizas 533       \n",
              "5  Ajo                      Verduras y hortalizas 529       \n",
              "6  Borraja                  Verduras y hortalizas 470       \n",
              "7  Chirivia                 Verduras y hortalizas 450       \n",
              "8  Alcachofas frescas       Verduras y hortalizas 430       \n",
              "9  Espinacas                Verduras y hortalizas 423       \n",
              "10 Canonigos                Verduras y hortalizas 421       "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# subset con el nombre de la verdura u hortaliza, el grupo y el potasio\n",
        "verd_hort_potasio <- select(verd_hort_df, Nombre, Grupo, Potasio_mg)\n",
        "# ordenamos de manera descendente \n",
        "verd_hort_potasio_desc <- arrange(verd_hort_potasio, desc(Potasio_mg))\n",
        "# top 10 verduras y hortalizas ricas en potasio\n",
        "top_10_verd_hort_ricas_potasio <- head(verd_hort_potasio_desc,10)\n",
        "top_10_verd_hort_ricas_potasio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "0419688d-6da8-4f0d-a040-ea0a8b8b55e8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "0419688d-6da8-4f0d-a040-ea0a8b8b55e8",
        "outputId": "e5f8dbe0-1c4d-4888-9e6f-21ec7d0608ea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAJYCAMAAABFOO8oAAADAFBMVEUAAAABAQECAgIDAwME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\n"
          },
          "metadata": {
            "image/png": {
              "width": 600,
              "height": 300
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 10, repr.plot.height = 5)\n",
        "# barplot \n",
        "v_h_pot_p <- ggplot(data = top_10_verd_hort_ricas_potasio, aes(y = Nombre, x = Potasio_mg)) + \n",
        "                    geom_bar(stat = \"identity\", fill='darkorchid') +\n",
        "                    labs(title = \"Top 10 de verduras y hortalizas ricas en potasio\", y = \"Verduras y hortalizas\", x = \"Potasio (mg por cada 100 g)\")\n",
        "v_h_pot_p"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2591e8be-e72d-4b9b-9673-f84c9e7e6ac4",
      "metadata": {
        "id": "2591e8be-e72d-4b9b-9673-f84c9e7e6ac4"
      },
      "source": [
        "Sería interesante que verduras fáciles de ingerir, como la patata, acelgas y las espinacas, fueran incluidas de manera regular en la dieta para garantizar unos niveles de potasio adecuados. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7c4f25d8-6a61-4ae1-be3e-fe75b1c5079d",
      "metadata": {
        "tags": [],
        "id": "7c4f25d8-6a61-4ae1-be3e-fe75b1c5079d"
      },
      "source": [
        "## 3. Verduras y hortalizas ricas en fósforo"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0eb5bbd9-3c34-489a-b941-d63ce4dc19e1",
      "metadata": {
        "id": "0eb5bbd9-3c34-489a-b941-d63ce4dc19e1"
      },
      "source": [
        "Micronutrientes como el fósforo es muy beneficioso en los deportistas porque ayuda al corazón, mejora la contractilidad del músculo, y participa en los procesos de creación de energía. Además, reduce el ácido láctico mientras se practica ejercicio."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "19aafc98-815d-41cf-b2ee-c2e672da08ca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "19aafc98-815d-41cf-b2ee-c2e672da08ca",
        "outputId": "1d503b2b-19eb-4d13-dc05-f5bd022b95ad"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 10 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Nombre</th><th scope=col>Grupo</th><th scope=col>Fosforo_mg</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>Algas musgo de Irlanda frescas</td><td>Verduras y hortalizas</td><td>160</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>Ajo                           </td><td>Verduras y hortalizas</td><td>134</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Alcachofas frescas            </td><td>Verduras y hortalizas</td><td>130</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Hojas de parra                </td><td>Verduras y hortalizas</td><td> 91</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>Brecol                        </td><td>Verduras y hortalizas</td><td> 87</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>Algas wakame frescas          </td><td>Verduras y hortalizas</td><td> 80</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>Wasabi, raiz                  </td><td>Verduras y hortalizas</td><td> 80</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>Chirivia                      </td><td>Verduras y hortalizas</td><td> 74</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>Acedera                       </td><td>Verduras y hortalizas</td><td> 63</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>Batata                        </td><td>Verduras y hortalizas</td><td> 60</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 10 × 3\n\n| <!--/--> | Nombre &lt;chr&gt; | Grupo &lt;chr&gt; | Fosforo_mg &lt;dbl&gt; |\n|---|---|---|---|\n| 1 | Algas musgo de Irlanda frescas | Verduras y hortalizas | 160 |\n| 2 | Ajo                            | Verduras y hortalizas | 134 |\n| 3 | Alcachofas frescas             | Verduras y hortalizas | 130 |\n| 4 | Hojas de parra                 | Verduras y hortalizas |  91 |\n| 5 | Brecol                         | Verduras y hortalizas |  87 |\n| 6 | Algas wakame frescas           | Verduras y hortalizas |  80 |\n| 7 | Wasabi, raiz                   | Verduras y hortalizas |  80 |\n| 8 | Chirivia                       | Verduras y hortalizas |  74 |\n| 9 | Acedera                        | Verduras y hortalizas |  63 |\n| 10 | Batata                         | Verduras y hortalizas |  60 |\n\n",
            "text/latex": "A data.frame: 10 × 3\n\\begin{tabular}{r|lll}\n  & Nombre & Grupo & Fosforo\\_mg\\\\\n  & <chr> & <chr> & <dbl>\\\\\n\\hline\n\t1 & Algas musgo de Irlanda frescas & Verduras y hortalizas & 160\\\\\n\t2 & Ajo                            & Verduras y hortalizas & 134\\\\\n\t3 & Alcachofas frescas             & Verduras y hortalizas & 130\\\\\n\t4 & Hojas de parra                 & Verduras y hortalizas &  91\\\\\n\t5 & Brecol                         & Verduras y hortalizas &  87\\\\\n\t6 & Algas wakame frescas           & Verduras y hortalizas &  80\\\\\n\t7 & Wasabi, raiz                   & Verduras y hortalizas &  80\\\\\n\t8 & Chirivia                       & Verduras y hortalizas &  74\\\\\n\t9 & Acedera                        & Verduras y hortalizas &  63\\\\\n\t10 & Batata                         & Verduras y hortalizas &  60\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   Nombre                         Grupo                 Fosforo_mg\n",
              "1  Algas musgo de Irlanda frescas Verduras y hortalizas 160       \n",
              "2  Ajo                            Verduras y hortalizas 134       \n",
              "3  Alcachofas frescas             Verduras y hortalizas 130       \n",
              "4  Hojas de parra                 Verduras y hortalizas  91       \n",
              "5  Brecol                         Verduras y hortalizas  87       \n",
              "6  Algas wakame frescas           Verduras y hortalizas  80       \n",
              "7  Wasabi, raiz                   Verduras y hortalizas  80       \n",
              "8  Chirivia                       Verduras y hortalizas  74       \n",
              "9  Acedera                        Verduras y hortalizas  63       \n",
              "10 Batata                         Verduras y hortalizas  60       "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# subset con el nombre de la verdura u hortaliza, el grupo y el fósforo\n",
        "verd_hort_fosforo <- select(verd_hort_df, Nombre, Grupo, Fosforo_mg)\n",
        "# ordenamos de manera descendente \n",
        "verd_hort_fosforo_desc <- arrange(verd_hort_fosforo , desc(Fosforo_mg))\n",
        "# top 10 verduras y hortalizas ricas en fósforo\n",
        "top_10_verd_hort_ricas_fosforo <- head(verd_hort_fosforo_desc,10)\n",
        "top_10_verd_hort_ricas_fosforo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "5333a528-147e-49a7-b8b1-b52823b6f724",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "5333a528-147e-49a7-b8b1-b52823b6f724",
        "outputId": "d227f234-f45b-4671-93e0-8ecd94a04931"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABLAAAAJYCAIAAAD9hIhNAAAACXBIWXMAABJ0AAASdAHeZh94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\nZQAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 600,
              "height": 300
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 10, repr.plot.height = 5)\n",
        "# barplot \n",
        "v_h_pot_p <- ggplot(data = top_10_verd_hort_ricas_fosforo, aes(y = Nombre, x = Fosforo_mg)) + \n",
        "                    geom_bar(stat = \"identity\", fill='#a5d0c6', width=0.8) + \n",
        "                    labs(title = \"Top 10 de verduras y hortalizas ricas en fósforo\", y = \"Verduras y hortalizas\", x = \"Fósforo (mg por cada 100 g)\")\n",
        "v_h_pot_p"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a0beb0dd-84da-4876-a6ce-eb6b6afe60ef",
      "metadata": {
        "id": "a0beb0dd-84da-4876-a6ce-eb6b6afe60ef"
      },
      "source": [
        "Teniendo en cuenta lo anterior, es conveniente que deportistas ingieran alimentos ricos en fósforo como pueden ser las alcachofas frescas o el brécol."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "af6cc62b-d9e5-4a89-86fd-1b8479e3c7e1",
      "metadata": {
        "id": "af6cc62b-d9e5-4a89-86fd-1b8479e3c7e1"
      },
      "source": [
        "## 4. Frutas, verduras y hortalizas con más agua"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4fd1ad50-5d4f-4d99-a33f-40acf733ca11",
      "metadata": {
        "id": "4fd1ad50-5d4f-4d99-a33f-40acf733ca11"
      },
      "source": [
        "La deshidratación supone un grave problema para aquellos que la sufren. Los deportistas deben estar bien hidratados en todo momento para optimizar su rendimiento y no tener problemas de salud. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "95cd9b33-acbe-445c-a659-f6db450acb17",
      "metadata": {
        "id": "95cd9b33-acbe-445c-a659-f6db450acb17"
      },
      "outputs": [],
      "source": [
        "# subset con el nombre de la verdura u hortaliza, el grupo y la cantidad de agua en g\n",
        "verd_hort_h2o <- select(verd_hort_df, Nombre, Grupo, Agua_g)\n",
        "# subset con el nombre de la fruta, el grupo y la cantidad de agua en g\n",
        "frutas_h2o <- select(frutas_df, Nombre, Grupo, Agua_g)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "2e877dd8-1169-4eef-b7e6-43606889a423",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "2e877dd8-1169-4eef-b7e6-43606889a423",
        "outputId": "b6628283-1534-45e1-9fda-5fd938c0f6e6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 5 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Nombre</th><th scope=col>Grupo</th><th scope=col>Agua_g</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>Pepino              </td><td>Verduras y hortalizas</td><td>96.7</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>Calabacin           </td><td>Verduras y hortalizas</td><td>96.5</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Calabaza            </td><td>Verduras y hortalizas</td><td>95.9</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Lechuga tipo iceberg</td><td>Verduras y hortalizas</td><td>95.8</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>Canonigos           </td><td>Verduras y hortalizas</td><td>95.6</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 5 × 3\n\n| <!--/--> | Nombre &lt;chr&gt; | Grupo &lt;chr&gt; | Agua_g &lt;dbl&gt; |\n|---|---|---|---|\n| 1 | Pepino               | Verduras y hortalizas | 96.7 |\n| 2 | Calabacin            | Verduras y hortalizas | 96.5 |\n| 3 | Calabaza             | Verduras y hortalizas | 95.9 |\n| 4 | Lechuga tipo iceberg | Verduras y hortalizas | 95.8 |\n| 5 | Canonigos            | Verduras y hortalizas | 95.6 |\n\n",
            "text/latex": "A data.frame: 5 × 3\n\\begin{tabular}{r|lll}\n  & Nombre & Grupo & Agua\\_g\\\\\n  & <chr> & <chr> & <dbl>\\\\\n\\hline\n\t1 & Pepino               & Verduras y hortalizas & 96.7\\\\\n\t2 & Calabacin            & Verduras y hortalizas & 96.5\\\\\n\t3 & Calabaza             & Verduras y hortalizas & 95.9\\\\\n\t4 & Lechuga tipo iceberg & Verduras y hortalizas & 95.8\\\\\n\t5 & Canonigos            & Verduras y hortalizas & 95.6\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Nombre               Grupo                 Agua_g\n",
              "1 Pepino               Verduras y hortalizas 96.7  \n",
              "2 Calabacin            Verduras y hortalizas 96.5  \n",
              "3 Calabaza             Verduras y hortalizas 95.9  \n",
              "4 Lechuga tipo iceberg Verduras y hortalizas 95.8  \n",
              "5 Canonigos            Verduras y hortalizas 95.6  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 5 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Nombre</th><th scope=col>Grupo</th><th scope=col>Agua_g</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>Lima     </td><td>Frutas</td><td>94.6</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>Sandia   </td><td>Frutas</td><td>94.6</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Melon    </td><td>Frutas</td><td>92.4</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Granada  </td><td>Frutas</td><td>91.5</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>Carambola</td><td>Frutas</td><td>91.0</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 5 × 3\n\n| <!--/--> | Nombre &lt;chr&gt; | Grupo &lt;chr&gt; | Agua_g &lt;dbl&gt; |\n|---|---|---|---|\n| 1 | Lima      | Frutas | 94.6 |\n| 2 | Sandia    | Frutas | 94.6 |\n| 3 | Melon     | Frutas | 92.4 |\n| 4 | Granada   | Frutas | 91.5 |\n| 5 | Carambola | Frutas | 91.0 |\n\n",
            "text/latex": "A data.frame: 5 × 3\n\\begin{tabular}{r|lll}\n  & Nombre & Grupo & Agua\\_g\\\\\n  & <chr> & <chr> & <dbl>\\\\\n\\hline\n\t1 & Lima      & Frutas & 94.6\\\\\n\t2 & Sandia    & Frutas & 94.6\\\\\n\t3 & Melon     & Frutas & 92.4\\\\\n\t4 & Granada   & Frutas & 91.5\\\\\n\t5 & Carambola & Frutas & 91.0\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Nombre    Grupo  Agua_g\n",
              "1 Lima      Frutas 94.6  \n",
              "2 Sandia    Frutas 94.6  \n",
              "3 Melon     Frutas 92.4  \n",
              "4 Granada   Frutas 91.5  \n",
              "5 Carambola Frutas 91.0  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ordenamos de manera descendente \n",
        "verd_hort_h2o_desc <- arrange(verd_hort_h2o, desc(Agua_g))\n",
        "frutas_h2o_desc <- arrange(frutas_h2o, desc(Agua_g))\n",
        "\n",
        "# top 5 verduras y hortalizas ricas en agua\n",
        "top_5_verd_hort_ricas_h20 <- head(verd_hort_h2o_desc,5)\n",
        "# top 5 frutas ricas en agua\n",
        "top_5_frutas_ricas_h20 <- head(frutas_h2o_desc,5)\n",
        "\n",
        "top_5_verd_hort_ricas_h20\n",
        "top_5_frutas_ricas_h20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "41eebaf3-a9cf-44de-9d10-3eb33e28a631",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "41eebaf3-a9cf-44de-9d10-3eb33e28a631",
        "outputId": "5fae80d6-3031-4150-bc2f-8d371366ce00"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAAJYCAIAAAAbpNZlAAAACXBIWXMAABJ0AAASdAHeZh94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\nAAAAAMgeBQ4AAAAAACB7/w+j6WNNmuoPTgAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 720,
              "height": 300
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 12, repr.plot.height = 5)\n",
        "# barplot \n",
        "bp_vh_h20 <- ggplot(data = top_5_verd_hort_ricas_h20, aes(x = Nombre, y = Agua_g)) + \n",
        "    geom_bar(stat = \"identity\", fill='#7293cb') +\n",
        "    labs(title = \"Top 5 de verduras y hortalizas ricas en agua\", x = \"Verduras y hortalizas\", y = \"Agua (g por cada 100 g)\")\n",
        "bp_f_h20 <- ggplot(data = top_5_frutas_ricas_h20, aes(x = Nombre, y = Agua_g)) + \n",
        "    geom_bar(stat = \"identity\", fill='#7293cb') +\n",
        "    labs(title = \"Top 5 de frutas ricas en agua\", x = \"Frutas\", y = \"Agua (g por cada 100 g)\")\n",
        "\n",
        "bp_vh_h20 + bp_f_h20\n",
        "# ggplotly(bp_vh_h20)\n",
        "# ggplotly(bp_f_h20)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5e02eedd-ee46-489b-8a49-36dadba0a2f5",
      "metadata": {
        "id": "5e02eedd-ee46-489b-8a49-36dadba0a2f5"
      },
      "source": [
        "Por ello, es recomendable que, sobre todo en días de mucho calor, tanto antes como después de realizar el ejercicio físico se ingieran alimentos ricos en agua. Por ejemplo, calabacines, pepinos o frutas como la sandía y el melón. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83bfdc4d-acad-4f0a-97e7-d71c46b8b961",
      "metadata": {
        "id": "83bfdc4d-acad-4f0a-97e7-d71c46b8b961"
      },
      "source": [
        "## 5. Fruta con más kcal"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ea8fccbd-6e15-47c6-a148-0c6f959dca2b",
      "metadata": {
        "id": "ea8fccbd-6e15-47c6-a148-0c6f959dca2b"
      },
      "source": [
        "Ingerir las suficientes calorías es necesario sobre todo para aquellos deportistas cuyo objetivo sea aumentar la masa muscular. Muchos de los alimentos con más calorías no son saludables, y es por ello por lo que se han seleccionado las frutas que contienen más kcal y pueden ayudar a llegar al objetivo en cuestión de una forma más saludable. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "6524f5b3-a4ca-40a0-9a6f-e0d0874b9db7",
      "metadata": {
        "tags": [],
        "id": "6524f5b3-a4ca-40a0-9a6f-e0d0874b9db7"
      },
      "outputs": [],
      "source": [
        "# ordenamos por las frutas que tienen más kcal (orden descendente)\n",
        "frutas_kcal_desc <- arrange(frutas_df, desc(Energia_kcal))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "975804ec-53a1-4548-9dfa-450d2a961c0b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "975804ec-53a1-4548-9dfa-450d2a961c0b",
        "outputId": "348c0e2c-70eb-4358-8051-21a019773db5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "'Coco fresco'"
            ],
            "text/markdown": "'Coco fresco'",
            "text/latex": "'Coco fresco'",
            "text/plain": [
              "[1] \"Coco fresco\""
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# fruta con más kcal\n",
        "head(frutas_kcal_desc$Nombre,1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dbbd6bd6-3d17-4191-b1ba-2e213688aea1",
      "metadata": {
        "id": "dbbd6bd6-3d17-4191-b1ba-2e213688aea1"
      },
      "source": [
        "Observamos que el coco fresco es la fruta que tiene más kcal. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "a5b9a871-ea0c-45ed-b598-077db79ce0c4",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "a5b9a871-ea0c-45ed-b598-077db79ce0c4",
        "outputId": "3c54cccc-31a0-45b1-a157-83f18375ca3a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABpAAAAJYCAMAAABSCVU5AAADAFBMVEUAAAABAQECAgIDAwME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\nWvj/J3TJ70rTXdgAAAAASUVORK5CYII="
          },
          "metadata": {
            "image/png": {
              "width": 840,
              "height": 300
            }
          }
        }
      ],
      "source": [
        "# Top 10 de frutas con más kcal\n",
        "top10_frutas_kcal <- head(frutas_kcal_desc,10)\n",
        "\n",
        "# tamaño plot\n",
        "options(repr.plot.width = 14, repr.plot.height = 5)\n",
        "# barplot \n",
        "f_kcal_p <- ggplot(data = top10_frutas_kcal, aes(x = Nombre, y = Energia_kcal)) + \n",
        "                geom_bar(stat = \"identity\", fill='darksalmon') +\n",
        "                labs(title = \"Top 10 de frutas con más kcal\", x = \"Frutas\", y = \"kcal\")\n",
        "f_kcal_p"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cab7eaa2-2a09-48e7-a2b1-9c8c0198c72f",
      "metadata": {
        "id": "cab7eaa2-2a09-48e7-a2b1-9c8c0198c72f"
      },
      "source": [
        "Podemos observar que el coco fresco es una alternativa muy interesante para ingerir calorías de manera saludable. Otras buenas opciones son el tamarindo y el aguacate. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "06a88035-4855-4305-8c12-8d2d434b3425",
      "metadata": {
        "id": "06a88035-4855-4305-8c12-8d2d434b3425"
      },
      "source": [
        "### 5.1 Cantidad y proporción de cada tipo de grasa (Grasas, AGS, AGP, AGM) que tienen las 10 frutas con más kcal"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8b49c51a-2cee-43fd-a92a-cf22a3e9c7ea",
      "metadata": {
        "id": "8b49c51a-2cee-43fd-a92a-cf22a3e9c7ea"
      },
      "source": [
        "Es muy posible, pero, que los alimentos con más kcal también sean los que tengan más grasas. Es por ello que se han analizado las cantidades y proporciones de los diferentes tipos de grasas que contienen las 10 frutas con más kcal. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top10_frut_kcal_grasas_ags_agp_agm <- top10_frutas_kcal[, c(\"Nombre\", \"Grasas_g\", \"AGS_g\", \"AGM_g\", \"AGP_g\")]"
      ],
      "metadata": {
        "id": "DnhDL7EK7pPi"
      },
      "id": "DnhDL7EK7pPi",
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top10_frut_kcal_grasas_ags_agp_agm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "AwpIdJEG79d-",
        "outputId": "77ef2c34-ccf0-4871-a82f-ead5964dc0e4"
      },
      "id": "AwpIdJEG79d-",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 10 × 5</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Nombre</th><th scope=col>Grasas_g</th><th scope=col>AGS_g</th><th scope=col>AGM_g</th><th scope=col>AGP_g</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>Coco fresco           </td><td>36.00</td><td>30.900</td><td>1.920</td><td>0.565</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>Tamarindo             </td><td> 0.60</td><td> 0.270</td><td>0.180</td><td>0.060</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>Aguacate              </td><td>12.00</td><td> 1.410</td><td>9.010</td><td>1.040</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>Platano macho o harton</td><td> 0.37</td><td> 0.143</td><td>0.032</td><td>0.069</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>Platano               </td><td> 0.30</td><td> 0.110</td><td>0.040</td><td>0.090</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>Chirimoya             </td><td> 0.20</td><td> 0.000</td><td>0.000</td><td>0.000</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>Lichi                 </td><td> 0.40</td><td> 0.100</td><td>0.120</td><td>0.130</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>Higos, brevas         </td><td> 0.00</td><td> 0.000</td><td>0.000</td><td>0.000</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>Palosanto             </td><td> 0.30</td><td> 0.000</td><td>0.000</td><td>0.000</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>Nispero               </td><td> 0.50</td><td> 0.000</td><td>0.000</td><td>0.000</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 10 × 5\n\n| <!--/--> | Nombre &lt;chr&gt; | Grasas_g &lt;dbl&gt; | AGS_g &lt;dbl&gt; | AGM_g &lt;dbl&gt; | AGP_g &lt;dbl&gt; |\n|---|---|---|---|---|---|\n| 1 | Coco fresco            | 36.00 | 30.900 | 1.920 | 0.565 |\n| 2 | Tamarindo              |  0.60 |  0.270 | 0.180 | 0.060 |\n| 3 | Aguacate               | 12.00 |  1.410 | 9.010 | 1.040 |\n| 4 | Platano macho o harton |  0.37 |  0.143 | 0.032 | 0.069 |\n| 5 | Platano                |  0.30 |  0.110 | 0.040 | 0.090 |\n| 6 | Chirimoya              |  0.20 |  0.000 | 0.000 | 0.000 |\n| 7 | Lichi                  |  0.40 |  0.100 | 0.120 | 0.130 |\n| 8 | Higos, brevas          |  0.00 |  0.000 | 0.000 | 0.000 |\n| 9 | Palosanto              |  0.30 |  0.000 | 0.000 | 0.000 |\n| 10 | Nispero                |  0.50 |  0.000 | 0.000 | 0.000 |\n\n",
            "text/latex": "A data.frame: 10 × 5\n\\begin{tabular}{r|lllll}\n  & Nombre & Grasas\\_g & AGS\\_g & AGM\\_g & AGP\\_g\\\\\n  & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t1 & Coco fresco            & 36.00 & 30.900 & 1.920 & 0.565\\\\\n\t2 & Tamarindo              &  0.60 &  0.270 & 0.180 & 0.060\\\\\n\t3 & Aguacate               & 12.00 &  1.410 & 9.010 & 1.040\\\\\n\t4 & Platano macho o harton &  0.37 &  0.143 & 0.032 & 0.069\\\\\n\t5 & Platano                &  0.30 &  0.110 & 0.040 & 0.090\\\\\n\t6 & Chirimoya              &  0.20 &  0.000 & 0.000 & 0.000\\\\\n\t7 & Lichi                  &  0.40 &  0.100 & 0.120 & 0.130\\\\\n\t8 & Higos, brevas          &  0.00 &  0.000 & 0.000 & 0.000\\\\\n\t9 & Palosanto              &  0.30 &  0.000 & 0.000 & 0.000\\\\\n\t10 & Nispero                &  0.50 &  0.000 & 0.000 & 0.000\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   Nombre                 Grasas_g AGS_g  AGM_g AGP_g\n",
              "1  Coco fresco            36.00    30.900 1.920 0.565\n",
              "2  Tamarindo               0.60     0.270 0.180 0.060\n",
              "3  Aguacate               12.00     1.410 9.010 1.040\n",
              "4  Platano macho o harton  0.37     0.143 0.032 0.069\n",
              "5  Platano                 0.30     0.110 0.040 0.090\n",
              "6  Chirimoya               0.20     0.000 0.000 0.000\n",
              "7  Lichi                   0.40     0.100 0.120 0.130\n",
              "8  Higos, brevas           0.00     0.000 0.000 0.000\n",
              "9  Palosanto               0.30     0.000 0.000 0.000\n",
              "10 Nispero                 0.50     0.000 0.000 0.000"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "3be682c6-863c-4c0d-a4a3-6d624b899810",
      "metadata": {
        "tags": [],
        "id": "3be682c6-863c-4c0d-a4a3-6d624b899810"
      },
      "outputs": [],
      "source": [
        "# pasamos de wide a long format \n",
        "top10_frut_kcal_grasas_ags_agp_agm_long <- melt(top10_frut_kcal_grasas_ags_agp_agm, id.vars = c(\"Nombre\"))\n",
        "# renombramos la columna \"variable\" por \"Tipo de grasa\"\n",
        "names(top10_frut_kcal_grasas_ags_agp_agm_long)[names(top10_frut_kcal_grasas_ags_agp_agm_long)==\"variable\"] <- \"Tipo_de_grasa\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4780690a-ecb9-41ea-ba68-eb3b5ae1dabc",
      "metadata": {
        "id": "4780690a-ecb9-41ea-ba68-eb3b5ae1dabc"
      },
      "source": [
        "#### Cantidad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "1350bd49-7cfe-44a5-bdaa-456ea424f3a2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "1350bd49-7cfe-44a5-bdaa-456ea424f3a2",
        "outputId": "769195b8-b67e-4090-8dac-2c46fc1654f4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABpAAAASwCAIAAABzcp6JAAAACXBIWXMAABJ0AAASdAHeZh94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\nBwAAAAABEdgBAAAAQED+H7gG1sCibytSAAAAAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 840,
              "height": 600
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 14, repr.plot.height = 10)\n",
        "# Grouped\n",
        "fat_p <- ggplot(top10_frut_kcal_grasas_ags_agp_agm_long, aes(fill= Tipo_de_grasa, y=value, x=Nombre)) + \n",
        "                geom_bar(position=\"dodge\", stat=\"identity\") +\n",
        "                ggtitle(\"Cantidad de los diferentes tipos de grasa que contienen las 10 frutas con más kcal\") +\n",
        "                xlab(\"Frutas\") + \n",
        "                ylab(\"g por cada 100g\")\n",
        "# ggplotly(fat_p)\n",
        "fat_p"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c219ffe-e6e1-4cef-8531-f44d7cbbeb9c",
      "metadata": {
        "id": "9c219ffe-e6e1-4cef-8531-f44d7cbbeb9c"
      },
      "source": [
        "#### Proporción"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "ec1b8c6c-67b2-4011-9116-f1921b876c64",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "ec1b8c6c-67b2-4011-9116-f1921b876c64",
        "outputId": "5cbed9f5-3068-4094-ddfa-76b95c36a604"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_bar()`).”\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABpAAAASwCAIAAABzcp6JAAAACXBIWXMAABJ0AAASdAHeZh94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\nAELk/wPgZfu64T2LRQAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 840,
              "height": 600
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 14, repr.plot.height = 10)\n",
        "# Grouped\n",
        "fat_p <- ggplot(top10_frut_kcal_grasas_ags_agp_agm_long, aes(fill=Tipo_de_grasa, y=value, x=Nombre)) + \n",
        "                geom_bar(position=\"fill\", stat=\"identity\") +\n",
        "                ggtitle(\"Proporción de los diferentes tipos de grasa que contienen las 10 frutas con más kcal\") +\n",
        "                xlab(\"Frutas\")\n",
        "# ggplotly(fat_p)\n",
        "fat_p"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83e60189-8b01-442c-bc72-b6e20cad6802",
      "metadata": {
        "id": "83e60189-8b01-442c-bc72-b6e20cad6802"
      },
      "source": [
        "Podemos observar, por ejemplo, que el coco fresco contiene 36 g de grasas por cada 100 g, y 30 g de ácidos grasos saturados por cada 100 g. La proporción de grasas y ácidos grasos saturados, por lo tanto, es elevada en esta fruta. \n",
        "\n",
        "\n",
        "Como la ingesta de grasas saturadas es de unos 13 g al día, el coco es un alimento que, aunque hayamos visto que presente beneficios para la salud del deportista, no se debe abusar de él. Otras frutas como el plátano son alternativas muy interesantes, ya que presentan un menor porcentaje de ácidos grasos saturados."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29b23c0d-24fd-49e2-bfef-116b30ea0c35",
      "metadata": {
        "id": "29b23c0d-24fd-49e2-bfef-116b30ea0c35"
      },
      "source": [
        "## 6. Verduras, hortalizas y frutas con más hidratos de carbono"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec3ee2f9-232c-43f1-9c82-804fc0c7dc3b",
      "metadata": {
        "id": "ec3ee2f9-232c-43f1-9c82-804fc0c7dc3b"
      },
      "source": [
        "Los hidratos de carbono son la base de la alimentación e ingerir las cantidades necesarias es fundamental para reponer los niveles de glucógeno sobre todo después de realizar actividad física intensa. \n",
        "\n",
        "Hoy en día, muchos hidratos de carbono se ingieren de alimentos preprocesados y poco saludables, como la bollería industrial y algunas harinas refinadas, que, entre otras cosas, pueden causar inflamación en el cuerpo. Esto es lo último que un deportista quiere, ya que después de realizar ejercicio físico el cuerpo ya está lo suficientemente inflamado. Una fuente de hidratos de carbono saludable son las verduras, hortalizas y frutas. Es por ello que se han seleccionado las 10 que contienen más carbohidratos. \n",
        "\n",
        "En el diguiente gráfico, podemos observar que entre ellas se encuentran el tamarindo, la batata, la yuca y el plátano macho o hartón. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "57c16f05-78ce-4f22-9bf3-3525ef61e613",
      "metadata": {
        "tags": [],
        "id": "57c16f05-78ce-4f22-9bf3-3525ef61e613"
      },
      "outputs": [],
      "source": [
        "# Las 10 verduras, hortalizas y frutas con más HC\n",
        "data_fhv_hc <- arrange(data_fhv, desc(HidratosC_g))\n",
        "top_10_fhv_hc <- head(data_fhv_hc,10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "9c90ccbc-ac80-4f23-a8e5-2ec5b109921c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "9c90ccbc-ac80-4f23-a8e5-2ec5b109921c",
        "outputId": "e7504bbd-8e06-4132-a5bd-1aaa653c7162"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABpAAAAJYCAIAAADqtTJcAAAACXBIWXMAABJ0AAASdAHeZh94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\ngg=="
          },
          "metadata": {
            "image/png": {
              "width": 840,
              "height": 300
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 14, repr.plot.height = 5)\n",
        "# barplot \n",
        "ags_p <- ggplot(data = top_10_fhv_hc, aes(x = Nombre, y = HidratosC_g, fill=Grupo)) + \n",
        "    geom_bar(stat = \"identity\") +\n",
        "    labs(title = \"Top 10 de frutas, verduras y hortalizas con más hidratos de carbono\", x = \"Frutas, verduras y hortalizas\", y = \"HC (g cada 100g)\")\n",
        "ags_p"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "246ed4dc-e45b-4f48-9d6c-32730c8f232d",
      "metadata": {
        "id": "246ed4dc-e45b-4f48-9d6c-32730c8f232d"
      },
      "source": [
        "### 6.1 Proporción de azúcar en las 10 de frutas, verduras y hortalizas con más hidratos de carbono"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "97d9b646-7ee2-4bb0-83a1-d69b2f1e2bf3",
      "metadata": {
        "id": "97d9b646-7ee2-4bb0-83a1-d69b2f1e2bf3"
      },
      "source": [
        "El azúcar proveniente de las frutas y verduras es interesante en cuanto a deportistas ya que proporciona una fuente de energía rápida y saludable. De las 10 piezas seleccionadas anteriormente, se ha buscado la proporción de azúcar que contienen. \n",
        "\n",
        "En el gráfico siguiente, podemos observar que, en frutas como el plátano y la chirimoya, el azúcar representa una importante proporción dentro de los hidratos de carbono, por tanto, nos reafirmamos en que son frutas nutricionalmente interesantes para el deportista. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "1fdb704d-edca-49f2-8ea2-9e4a48d52e5b",
      "metadata": {
        "tags": [],
        "id": "1fdb704d-edca-49f2-8ea2-9e4a48d52e5b"
      },
      "outputs": [],
      "source": [
        "top_10_fhv_hc_azuc <- select(top_10_fhv_hc, Nombre, HidratosC_g, Azucares_g)\n",
        "# pasamos de wide a long format \n",
        "top_10_fhv_hc_azuc_long <- melt(top_10_fhv_hc_azuc, id.vars = c(\"Nombre\"))\n",
        "# renombramos la columna \"variable\" por \"Tipo de HC\"\n",
        "names(top_10_fhv_hc_azuc_long)[names(top_10_fhv_hc_azuc_long)==\"variable\"] <- \"Tipo_de_HC\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "bb4adfa9-7b56-42fe-bbdf-0a85366c5c89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "bb4adfa9-7b56-42fe-bbdf-0a85366c5c89",
        "outputId": "d0e6fcac-dc69-47cd-937a-caf0e3b92ac6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABpAAAAJYCAIAAADqtTJcAAAACXBIWXMAABJ0AAASdAHeZh94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\nAAAAAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 840,
              "height": 300
            }
          }
        }
      ],
      "source": [
        "# tamaño plot\n",
        "options(repr.plot.width = 14, repr.plot.height = 5)\n",
        "# Grouped\n",
        "hc_p <- ggplot(top_10_fhv_hc_azuc_long, aes(fill=Tipo_de_HC, y=value, x=Nombre)) + \n",
        "                geom_bar(position=\"fill\", stat=\"identity\") +\n",
        "                ggtitle(\"Proporción de azúcar y HC que contienen las 10 frutas, verduras y hortalizas con más hidratos de carbono\") +\n",
        "                xlab(\"Frutas, verduras y hortalizas\")\n",
        "hc_p"
      ]
    }
  ],
  "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": "3.6.3"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}