02_Data_Visualization.ipynb

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      "include_colab_link": true
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      "name": "ir",
      "display_name": "R"
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    "language_info": {
      "name": "R"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luuloi/GWAS_Introduction_2023/blob/main/02_Data_Visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Bar plot**"
      ],
      "metadata": {
        "id": "v1sYvhBpIAGh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Import data\n",
        "data <- read.csv(\"https://raw.githubusercontent.com/luuloi/GWAS_Introduction_2023/main/data/StudentsPerformance.csv\")"
      ],
      "metadata": {
        "id": "k9d3nSpV56ZD"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "head(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "1Ri4c7yZ6Gdu",
        "outputId": "98947fc8-9059-442b-844e-92b03c8cd6f6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>gender</th><th scope=col>race.ethnicity</th><th scope=col>parental.level.of.education</th><th scope=col>lunch</th><th scope=col>test.preparation.course</th><th scope=col>math.score</th><th scope=col>reading.score</th><th scope=col>writing.score</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;chr&gt;</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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>female</td><td>group B</td><td>bachelor's degree </td><td>standard    </td><td>none     </td><td>72</td><td>72</td><td>74</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>female</td><td>group C</td><td>some college      </td><td>standard    </td><td>completed</td><td>69</td><td>90</td><td>88</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>female</td><td>group B</td><td>master's degree   </td><td>standard    </td><td>none     </td><td>90</td><td>95</td><td>93</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>male  </td><td>group A</td><td>associate's degree</td><td>free/reduced</td><td>none     </td><td>47</td><td>57</td><td>44</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>male  </td><td>group C</td><td>some college      </td><td>standard    </td><td>none     </td><td>76</td><td>78</td><td>75</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>female</td><td>group B</td><td>associate's degree</td><td>standard    </td><td>none     </td><td>71</td><td>83</td><td>78</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 6 × 8\n\n| <!--/--> | gender &lt;chr&gt; | race.ethnicity &lt;chr&gt; | parental.level.of.education &lt;chr&gt; | lunch &lt;chr&gt; | test.preparation.course &lt;chr&gt; | math.score &lt;int&gt; | reading.score &lt;int&gt; | writing.score &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|\n| 1 | female | group B | bachelor's degree  | standard     | none      | 72 | 72 | 74 |\n| 2 | female | group C | some college       | standard     | completed | 69 | 90 | 88 |\n| 3 | female | group B | master's degree    | standard     | none      | 90 | 95 | 93 |\n| 4 | male   | group A | associate's degree | free/reduced | none      | 47 | 57 | 44 |\n| 5 | male   | group C | some college       | standard     | none      | 76 | 78 | 75 |\n| 6 | female | group B | associate's degree | standard     | none      | 71 | 83 | 78 |\n\n",
            "text/latex": "A data.frame: 6 × 8\n\\begin{tabular}{r|llllllll}\n  & gender & race.ethnicity & parental.level.of.education & lunch & test.preparation.course & math.score & reading.score & writing.score\\\\\n  & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & female & group B & bachelor's degree  & standard     & none      & 72 & 72 & 74\\\\\n\t2 & female & group C & some college       & standard     & completed & 69 & 90 & 88\\\\\n\t3 & female & group B & master's degree    & standard     & none      & 90 & 95 & 93\\\\\n\t4 & male   & group A & associate's degree & free/reduced & none      & 47 & 57 & 44\\\\\n\t5 & male   & group C & some college       & standard     & none      & 76 & 78 & 75\\\\\n\t6 & female & group B & associate's degree & standard     & none      & 71 & 83 & 78\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gender race.ethnicity parental.level.of.education lunch       \n",
              "1 female group B        bachelor's degree           standard    \n",
              "2 female group C        some college                standard    \n",
              "3 female group B        master's degree             standard    \n",
              "4 male   group A        associate's degree          free/reduced\n",
              "5 male   group C        some college                standard    \n",
              "6 female group B        associate's degree          standard    \n",
              "  test.preparation.course math.score reading.score writing.score\n",
              "1 none                    72         72            74           \n",
              "2 completed               69         90            88           \n",
              "3 none                    90         95            93           \n",
              "4 none                    47         57            44           \n",
              "5 none                    76         78            75           \n",
              "6 none                    71         83            78           "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Examine the structure of the data\n",
        "str(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDSw0bBJ6PER",
        "outputId": "91e00111-54c1-44d1-b282-9371afcc66b9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'data.frame':\t1000 obs. of  8 variables:\n",
            " $ gender                     : chr  \"female\" \"female\" \"female\" \"male\" ...\n",
            " $ race.ethnicity             : chr  \"group B\" \"group C\" \"group B\" \"group A\" ...\n",
            " $ parental.level.of.education: chr  \"bachelor's degree\" \"some college\" \"master's degree\" \"associate's degree\" ...\n",
            " $ lunch                      : chr  \"standard\" \"standard\" \"standard\" \"free/reduced\" ...\n",
            " $ test.preparation.course    : chr  \"none\" \"completed\" \"none\" \"none\" ...\n",
            " $ math.score                 : int  72 69 90 47 76 71 88 40 64 38 ...\n",
            " $ reading.score              : int  72 90 95 57 78 83 95 43 64 60 ...\n",
            " $ writing.score              : int  74 88 93 44 75 78 92 39 67 50 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Check the number of columns and rows\n",
        "dim(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "pqJJQomP7mr1",
        "outputId": "9a7a29c5-00ab-4082-ef29-a6ac97eba14e"
      },
      "execution_count": null,
      "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>1000</li><li>8</li></ol>\n"
            ],
            "text/markdown": "1. 1000\n2. 8\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 1000\n\\item 8\n\\end{enumerate*}\n",
            "text/plain": [
              "[1] 1000    8"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Show the first 6 rows of the dataset\n",
        "head(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "BftMKCld7rPS",
        "outputId": "5aecc150-bff8-4e12-89ad-de847d9b79a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>gender</th><th scope=col>race.ethnicity</th><th scope=col>parental.level.of.education</th><th scope=col>lunch</th><th scope=col>test.preparation.course</th><th scope=col>math.score</th><th scope=col>reading.score</th><th scope=col>writing.score</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;chr&gt;</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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>female</td><td>group B</td><td>bachelor's degree </td><td>standard    </td><td>none     </td><td>72</td><td>72</td><td>74</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>female</td><td>group C</td><td>some college      </td><td>standard    </td><td>completed</td><td>69</td><td>90</td><td>88</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>female</td><td>group B</td><td>master's degree   </td><td>standard    </td><td>none     </td><td>90</td><td>95</td><td>93</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>male  </td><td>group A</td><td>associate's degree</td><td>free/reduced</td><td>none     </td><td>47</td><td>57</td><td>44</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>male  </td><td>group C</td><td>some college      </td><td>standard    </td><td>none     </td><td>76</td><td>78</td><td>75</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>female</td><td>group B</td><td>associate's degree</td><td>standard    </td><td>none     </td><td>71</td><td>83</td><td>78</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 6 × 8\n\n| <!--/--> | gender &lt;chr&gt; | race.ethnicity &lt;chr&gt; | parental.level.of.education &lt;chr&gt; | lunch &lt;chr&gt; | test.preparation.course &lt;chr&gt; | math.score &lt;int&gt; | reading.score &lt;int&gt; | writing.score &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|\n| 1 | female | group B | bachelor's degree  | standard     | none      | 72 | 72 | 74 |\n| 2 | female | group C | some college       | standard     | completed | 69 | 90 | 88 |\n| 3 | female | group B | master's degree    | standard     | none      | 90 | 95 | 93 |\n| 4 | male   | group A | associate's degree | free/reduced | none      | 47 | 57 | 44 |\n| 5 | male   | group C | some college       | standard     | none      | 76 | 78 | 75 |\n| 6 | female | group B | associate's degree | standard     | none      | 71 | 83 | 78 |\n\n",
            "text/latex": "A data.frame: 6 × 8\n\\begin{tabular}{r|llllllll}\n  & gender & race.ethnicity & parental.level.of.education & lunch & test.preparation.course & math.score & reading.score & writing.score\\\\\n  & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & female & group B & bachelor's degree  & standard     & none      & 72 & 72 & 74\\\\\n\t2 & female & group C & some college       & standard     & completed & 69 & 90 & 88\\\\\n\t3 & female & group B & master's degree    & standard     & none      & 90 & 95 & 93\\\\\n\t4 & male   & group A & associate's degree & free/reduced & none      & 47 & 57 & 44\\\\\n\t5 & male   & group C & some college       & standard     & none      & 76 & 78 & 75\\\\\n\t6 & female & group B & associate's degree & standard     & none      & 71 & 83 & 78\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gender race.ethnicity parental.level.of.education lunch       \n",
              "1 female group B        bachelor's degree           standard    \n",
              "2 female group C        some college                standard    \n",
              "3 female group B        master's degree             standard    \n",
              "4 male   group A        associate's degree          free/reduced\n",
              "5 male   group C        some college                standard    \n",
              "6 female group B        associate's degree          standard    \n",
              "  test.preparation.course math.score reading.score writing.score\n",
              "1 none                    72         72            74           \n",
              "2 completed               69         90            88           \n",
              "3 none                    90         95            93           \n",
              "4 none                    47         57            44           \n",
              "5 none                    76         78            75           \n",
              "6 none                    71         83            78           "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Check for NA values\n",
        "table(is.na(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "XikZpEUd7vO6",
        "outputId": "665bdaae-6a68-4acd-d038-4e10354c9893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "FALSE \n",
              " 8000 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Remove rows with NA values, if any exist\n",
        "data <- data[rowSums(is.na(data)) == 0, ]\n",
        "\n",
        "## Remove columns with NA values, if any exist (This can be potentially dangerous, I commented it out. Uncomment only if necessary.)\n",
        "# data <- data[, colSums(is.na(data)) == 0]"
      ],
      "metadata": {
        "id": "ROX-JdfG7yHJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Visualization\n",
        "## Load required library\n",
        "library(ggplot2)\n",
        "\n",
        "## Basic ggplot2 barplot\n",
        "ggplot(data, aes(x = gender)) +\n",
        "    geom_bar()"
      ],
      "metadata": {
        "id": "p1HuVk-m_z72",
        "outputId": "37263c83-1986-4238-965c-a9e1d906e8d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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JmQMnFcI2XKhJSJ4xopUyakTBzXSJky\nIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInjGilTJqRM\nHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMa\nKVMmpEwc10iZcv8h/WLpnLPf9+Oq2nn1/DlLNh1+ElITOK6RMuW+Q9o3/2MbH1hx1mPV0kUb\n7r9q4YHJJyE1geMaKVPuO6RtX3qsqjaOrt88c339q9Gstd0nITWC4xopU57S90g7PvX2vatn\nj9efXXh999n52z+qbXpE0PTFcnFcI2XK2/sP6cCpo3/3cLXqvM7nl1/TfdYfbp1Ru+OpO3yi\npi+Wi+MaKVOe/B6nj1+RfvmD5W/duWpB59M6pEPP+sM9V9R+ukvQ9MVycVwjZcq7phBSnd2Z\nN645+JZuZffZfYnvkQaK4xopU+77e6TvXbC7qsbn3rhl5r1VtX1sXfdJSI3guEbKlPsOaee5\nf/+LB6+Z/WC1/KINGxdfPD75JKQmcFwjZcr9/67dfR86/Yz3rq2qR1fMm7ts6+EnITWB4xop\nU+ZfEcrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKm\nTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkp\nE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInjGilTJqRMHNdImTIhZeK4\nRsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZ\nco+QZvzo4PMLLyWkVuK4RsqUe4Q0ctfEY9+SZxJSK3FcI2XKRwxp5LBXEFIrcVwjZcpHDGnt\nP4yMnd/xlg/+kpBaieMaKVPu8dbuTT/tNyBCagTHNVKmzO/aZeK4RsqUe4S0af7xTz/4TRIh\ntRLHNVKm3COk03/nlPkT3yWdT0itxHGNlCn3COl3v9JvQITUCI5rpEy5R0jHPERIrcZxjZQp\n9wjpdf9FSK3GcY2UKfcI6TsnryakNuO4RsqUe4T0mheOHPOiCYTUShzXSJlyr7d2p3QRUitx\nXCNlyvyBbCaOa6RMmZAycVwjZcq9/hyp6zhCaiWOa6RMuUdIYxNOPvrEhYTUShzXSJnyk7+1\ne/D1NxJSK3FcI2XKT/E90l0zCKmVOK6RMuWnCOnBowmplTiukTLlJw9p/IoTCKmVOK6RMuUe\nIf3phBOfO/JeQmoljmukTPlJQzrpjf+wh5BaieMaKVPmD2QzcVwjZco9Q3r4xms+s2oHIbUU\nxzVSptwjpAOXHNX5DzYceyUhtRPHNVKm3COkK0f+9tqv3fjpN41cR0itxHGNlCn3COmlFx98\nvpX/0mo7cVwjZco9QnrWNw8+b+IPZNuJ4xopU+4R0rE3HHx+5dmE1Eoc10iZco+QXvuGiT9A\n2vVXf05IrcRxjZQp9wjppqf9/tuWfviC459+CyG1Esc1Uqbc68+RvvySzm9/v+ymfjuqfr1D\n0PTFcnFcI2XKO3uEVFX333nXr/rOqKoeUzR9sVwc10jaco+QHvxE/eGhJZv6Dom3dgPFcY2U\nKfd4a/eT53f+f17eN/L89YTUShzXSJlyj5BmvfjOzuNHLz6VkFqJ4xopU+4R0vP+5eDz0/xX\nhNqJ4xopU+4R0tH/fvD5H8cQUitxXCNlyj1CevWb9nceO175GkJqJY5rpEy5R0irnvaHCxd/\ncMHznr6KkFqJ4xopU+71B7I3z+j8gezL+/8DWUIaKI5rpEy59//U/OEf/HAK/wNZQhosjmuk\nTJn/ZkMmjmukTJmQMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEy\nZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInjGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxI\nmTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPH\nNVKmTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbK\nlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInjGilTJqRMHNdImTIh\nZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc\n10iZMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xop\nUyakTBzXSJkyIWXiuEbKlPsPactV55xx2T1VtfPq+XOWbDr8JKQmcFwjZcr9h/SeResf+Ojc\nXdXSRRvuv2rhgcknITWB4xopU+47pB3LflFVD43+dPPM9fWvRrPWdp+E1AiOa6RMeWrfI/14\nbOvq2eP1Jxde330SUiM4rpEy5SmFtOMdn61Wndf57PJrus/6w/1frP18p6Dpi+XiuEbKlH89\nhZB++dZPjVerFhwKacFkSLfOqN3Rxy9oT9D0xXJxXCNlypO/WfDUIa2dc0P9cc3Bt3Qru8/6\nw6Zbaht3CJq+WC6Oa6RMeWffIf3w7O90Hltm3ltV28fWdZ/dl/keaaA4rpEy5b6/R9pzwX92\n/vld1fKLNmxcfPH45JOQmsBxjZQp9x3S2tEJN1aPrpg3d1n9w7pPQmoCxzVSpsy/IpSJ4xop\nUyakTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqE\nlInjGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJx\nXCNlyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVCysRxjZQpE1Imjmuk\nTJmQMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkT\nUiaOa6RMmZAycVwjZcqElInjGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrE\ncY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKmTEiZOK6R\nMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkpE8c1UqZM\nSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInjGilTJqRMHNdImTIhZeK4RsqUCSkT\nxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEy5WEi7FE1fLBfHNZK2XCqkndsETV8sF8c1Uqa8\no1RIvLUbKI5rpEyZ75EycVwjZcqElInjGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxImTiu\nkTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKm\nTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkp\nE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInjGilTJqRMHNdImTIhZeK4\nRsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZ\nMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xopUyak\nTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInj\nGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNl\nyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKmPIWQNl4y1nnsvHr+nCWbDj8JqQkc10iZ\ncv8h3TZvxURISxdtuP+qhQcmn4TUBI5rpEy5/5C++dDtnZA2z1xf/2o0a233SUiN4LhGypSn\n8j3SREirZ4/XHy+8vvskpEZwXCNlylMOadV5nU8vv6b7rD+sW1T78W5B0xfLxXGNlCnvnnJI\nCw6FtGAypFtn1O54yh9+BE1fLBfHNVKmPPmbBf2GtObgW7qV3Wf94bGNtc1bBU1fLBfHNVKm\nvG2qIW2ZeW9VbR9b1312X+R7pIHiuEbKlPv/Hmnr5pvHNm/eVS2/aMPGxRePTz4JqQkc10iZ\ncv8hnT/a8dXq0RXz5i6rf1j3SUhN4LhGypT5V4QycVwjZcqElInjGilTJqRMHNdImTIhZeK4\nRsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZ\nMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xopUyak\nTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInj\nGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNl\nyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQ\nMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaO\na6RMmZAycVwjZcqElInjGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2U\nKRNSJo5rpEyZkDJxXCNlyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVC\nysRxjZQpFwtpzz5B0xfLxXGNlCnvLRXS9ocFTV8sF8c1Uqb8SKmQeGs3UBzXSJky3yNl4rhG\nypQJKRPHNVKmTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XKhJSJ4xopUyakTBzXSJky\nIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAycVwjZcqElInjGilTJqRM\nHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5rpEyZkDJxXCNlyoSUieMa\nKVMmpEwc10iZMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVCysRxjZQpE1ImjmukTJmQMnFcI2XK\nhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULKxHGNlCkTUiaOa6RMmZAy\ncVwjZcqElInjGilTJqRMHNdImTIhZeK4RsqUCSkTxzVSpkxImTiukTJlQsrEcY2UKRNSJo5r\npEyZkDJxXCNlyoSUieMaKVMmpEwc10iZMiFl4rhGypQJKRPHNVKmTEiZOK6RMmVCysRxjZQp\nE1ImjmukTJmQMnFcI2XKhJSJ4xopUyakTBzXSJkyIWXiuEbKlAkpE8c1UqZMSJk4rpEyZULK\nxHGNlCkTUiaOa6RMWQ9p59Xz5yzZREjN4LhGypT1kJYu2nD/VQsPEFIjOK6RMmU5pM0z19e/\nKs1aS0iN4LhGypTlkFbPHq8/Xng9ITWC4xopU5ZDWnVe5+Pl19Qfbp9Zu3u/oOmL5eK4RsqU\n98khLSgQUptV401/BdPYeNX0V1CaHNKag2/tVnb/Wnpr12bV3qa/gmlsd7W16S+hMPmt3ZaZ\n91bV9rF1hISpI6TDll+0YePii8cJCVNHSIc9umLe3GWHfzghoX+E1BMhoX+EREgogJAICQUQ\nEiGhAEIiJBRASISEAgiJkFAAIRESCiAkQkIBhERIKICQCAkFEBIhoQBCIiQUQEiEhAIIiZBQ\nACEREgogJEJCAYRESCiAkAgJBRASIaEAQiIkFEBIhIQCCImQUAAhERIKICRCQgGEREgogJAI\nCQUQEiGhAEIiJBRASISEAghpaBy44rqmv4Rp7CtXbH3qfygUIT3O/hnnN/0lTGPvn/HLpr8E\nG0J6HEJyIqShQUhOhDQ0CMmJkAA8KUICCiAkoIBhDumeC07d9iQv7x/97sC+lGEyPe86zCFd\nfdnO8Sd5eXr+hDdvet51mEP68Cef9OXp+RPevOl51yEO6bKxWadv23rlvNMu+1k1PvrN953/\njvXXvmveF6vqvg+cfeYHH5j4CT/0MkTDc9chDqlaUv+KdMmVO/b82zl7qrFFuw5cdtbq6rtj\n26q3rdj16PJLJ37Cuy9DNDR3HfKQfja6tf4/m2fdVo19rar+dUFV7Rr9SbVzd1WtnjVe/4RP\nvgzR0Nx1yEO6bXTCymrsjqr6/CWdN/A/qL7/vnPPPWt0f/0TPvkyRENz1yEPac3oobcXY3dO\n/oQ/cOrKPfULnZ/wyZchGpq7DnlIv6jfcVTVg4/7Cb9tbH9VXTfxEz75MkRDc9chD6m6/NKH\n9n/ttC2/+RP+k9F1e//7stGHOt8Ud1+GaGjuOuwhbf3ImWdcuu5xP+HVZ8+e84md7znrgc5v\n0x56GaKhueswhwQUQ0hAAYQEFEBIQAGEBBRASEABhAQUQEhAAYQ0TZx5bNNfwXAjpGmCkJpF\nSNMEITWLkAIc+NAJz3rFzRceVX/6rb847uiTrq0/ed1rv/fG45531qaqGl9ywrNOXDkR0uSr\nr3ndDSe8qtkvesgQUoArRs74+mdecHKdyjee8fobbn7byEer6pQXvvKWTV94xvyq+sjI3Fuu\nP/FPHvfqG1/+kk/e2PSXPVQIqf3Gf+/E8apaM1KnctKLH63/xszjdlWnjPxP/dkpx1fjx59Y\nf/LAUf/v1S81+zUPHUJqvwdG3tN5nHhstWnk3btq/zxyZ3XKMWvs5TwAAAFPSURBVJ2/N//p\n1c9H3tX57FWPf/WZe5v8iocQIbXf3SNXdh6zj60/O+RL1Skv6vy980eqO0aWHuHV45v8gocR\nIbXfms53PVV1WieVN98+YfPhkNYcDGnWEV7F4BBS+/105NLO42XHVltG5nf/5mRI60cWdj77\nsyO8isEhpPbb95zObyfc2fnNhpOf80j96XWX7zsc0oHn/tGBqrrnaUd4FYNDSAEuHjnv65/+\ng9fUqXzrqJdf9/X3H3Xeb/yKVH1g5NQv/tOLZhzhVQwOIQXY/c7nHvu6O+Y8u/7023953FF/\nfOW+3wxp/2XPf+bLvnzhM5/4KgaHkGKc8oKmvwL0RkgBVpxa/yLzyHP+uumvA70RUoDPjfzN\nV69/1dO+0fTXgd4IKcHnTjr2mFff1PRXgSdBSEABhAQUQEhAAYQEFEBIQAGEBBRASEABhAQU\n8H+tcFtIUZ8ShgAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ggplot(data, aes(x = race.ethnicity)) +\n",
        "    geom_bar()"
      ],
      "metadata": {
        "id": "G4NRdMw3_5d_",
        "outputId": "88b553b2-5b80-4363-83c9-4f479db923a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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ycT0qZ3frZZcnz/R2dePPrYvrl+Zutm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\nrkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Coloring the bar plot**"
      ],
      "metadata": {
        "id": "4pXPDIn0LS8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Import data\n",
        "## Load required library\n",
        "library(ggplot2)\n",
        "\n",
        "data <- read.csv(\"https://raw.githubusercontent.com/luuloi/GWAS_Introduction_2023/main/data/StudentsPerformance.csv\")\n",
        "head(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "ZJtkR28TLt1S",
        "outputId": "98179990-8272-4f19-8ba5-424b50cbc5b9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>gender</th><th scope=col>race.ethnicity</th><th scope=col>parental.level.of.education</th><th scope=col>lunch</th><th scope=col>test.preparation.course</th><th scope=col>math.score</th><th scope=col>reading.score</th><th scope=col>writing.score</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;chr&gt;</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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>female</td><td>group B</td><td>bachelor's degree </td><td>standard    </td><td>none     </td><td>72</td><td>72</td><td>74</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>female</td><td>group C</td><td>some college      </td><td>standard    </td><td>completed</td><td>69</td><td>90</td><td>88</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>female</td><td>group B</td><td>master's degree   </td><td>standard    </td><td>none     </td><td>90</td><td>95</td><td>93</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>male  </td><td>group A</td><td>associate's degree</td><td>free/reduced</td><td>none     </td><td>47</td><td>57</td><td>44</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>male  </td><td>group C</td><td>some college      </td><td>standard    </td><td>none     </td><td>76</td><td>78</td><td>75</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>female</td><td>group B</td><td>associate's degree</td><td>standard    </td><td>none     </td><td>71</td><td>83</td><td>78</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 6 × 8\n\n| <!--/--> | gender &lt;chr&gt; | race.ethnicity &lt;chr&gt; | parental.level.of.education &lt;chr&gt; | lunch &lt;chr&gt; | test.preparation.course &lt;chr&gt; | math.score &lt;int&gt; | reading.score &lt;int&gt; | writing.score &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|\n| 1 | female | group B | bachelor's degree  | standard     | none      | 72 | 72 | 74 |\n| 2 | female | group C | some college       | standard     | completed | 69 | 90 | 88 |\n| 3 | female | group B | master's degree    | standard     | none      | 90 | 95 | 93 |\n| 4 | male   | group A | associate's degree | free/reduced | none      | 47 | 57 | 44 |\n| 5 | male   | group C | some college       | standard     | none      | 76 | 78 | 75 |\n| 6 | female | group B | associate's degree | standard     | none      | 71 | 83 | 78 |\n\n",
            "text/latex": "A data.frame: 6 × 8\n\\begin{tabular}{r|llllllll}\n  & gender & race.ethnicity & parental.level.of.education & lunch & test.preparation.course & math.score & reading.score & writing.score\\\\\n  & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & female & group B & bachelor's degree  & standard     & none      & 72 & 72 & 74\\\\\n\t2 & female & group C & some college       & standard     & completed & 69 & 90 & 88\\\\\n\t3 & female & group B & master's degree    & standard     & none      & 90 & 95 & 93\\\\\n\t4 & male   & group A & associate's degree & free/reduced & none      & 47 & 57 & 44\\\\\n\t5 & male   & group C & some college       & standard     & none      & 76 & 78 & 75\\\\\n\t6 & female & group B & associate's degree & standard     & none      & 71 & 83 & 78\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gender race.ethnicity parental.level.of.education lunch       \n",
              "1 female group B        bachelor's degree           standard    \n",
              "2 female group C        some college                standard    \n",
              "3 female group B        master's degree             standard    \n",
              "4 male   group A        associate's degree          free/reduced\n",
              "5 male   group C        some college                standard    \n",
              "6 female group B        associate's degree          standard    \n",
              "  test.preparation.course math.score reading.score writing.score\n",
              "1 none                    72         72            74           \n",
              "2 completed               69         90            88           \n",
              "3 none                    90         95            93           \n",
              "4 none                    47         57            44           \n",
              "5 none                    76         78            75           \n",
              "6 none                    71         83            78           "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Create a data frame with the count of genders\n",
        "data_new <- data.frame(table(data$gender))\n",
        "colnames(data_new) <- c(\"gender\", \"freq\") # Rename the columns\n",
        "head(data_new)"
      ],
      "metadata": {
        "id": "wZcDWQOO_8Rf",
        "outputId": "0dd10e72-5167-496d-cc3c-7a7438830516",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 2 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>gender</th><th scope=col>freq</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>female</td><td>518</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>male  </td><td>482</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 2 × 2\n\n| <!--/--> | gender &lt;fct&gt; | freq &lt;int&gt; |\n|---|---|---|\n| 1 | female | 518 |\n| 2 | male   | 482 |\n\n",
            "text/latex": "A data.frame: 2 × 2\n\\begin{tabular}{r|ll}\n  & gender & freq\\\\\n  & <fct> & <int>\\\\\n\\hline\n\t1 & female & 518\\\\\n\t2 & male   & 482\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gender freq\n",
              "1 female 518 \n",
              "2 male   482 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.Improved barplot design 1\n",
        "ggplot(data_new, aes(x = gender, y = freq, fill = gender)) +\n",
        "    theme_bw() +\n",
        "    geom_bar(stat = \"identity\") +\n",
        "    theme(text = element_text(size = 20)) +\n",
        "    theme(legend.position = \"none\") + # remove legend\n",
        "    labs(x = \"Gender\", y = \"Number of Students\", title = \"Number of Female and Male students\") +\n",
        "    theme(plot.title = element_text(hjust = 0.5, face = \"bold\")) +\n",
        "    scale_fill_manual(values = c(\"lightblue\", \"pink\")) # Custom colors"
      ],
      "metadata": {
        "id": "58XSmbeLAAv_",
        "outputId": "1926729d-2776-40be-89b4-ef354982790b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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j4+1d0LAFcpKysrKiry8vLy9PSs7l6gHmVlZUuWLCHYVUpMTMyTTz5Z3V1APYxG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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Improved barplot design 2\n",
        "ggplot(data_new, aes(x = gender, y = freq, fill = gender)) +\n",
        "    theme_bw() +\n",
        "    geom_bar(stat = \"identity\") +\n",
        "    geom_text(aes(label = freq), vjust = 1.6, color = \"black\", size = 8) +\n",
        "    theme(text = element_text(size = 20)) +\n",
        "    theme(legend.position = \"none\") +\n",
        "    labs(x = \"Gender\", y = \"Number of Students\", title = \"Number of Female and Male students\") +\n",
        "    scale_fill_manual(values = c(\"lightblue\", \"pink\"))"
      ],
      "metadata": {
        "id": "TRqhXjz1ADpo",
        "outputId": "fe0f4b37-e7a3-48b0-fc2c-3cb77c1ee10d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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l6v12q1Li4uajVtKLAnX1/fTp06FbSU35VCnDhxYv/+/c2aNSvuROA89Hp9Zmam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"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Bar plot with grouped data**"
      ],
      "metadata": {
        "id": "kH21uWm3NtXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Import data\n",
        "## Load required library\n",
        "library(ggplot2)\n",
        "\n",
        "data <- read.csv(\"https://raw.githubusercontent.com/luuloi/GWAS_Introduction_2023/main/data/StudentsPerformance.csv\")\n",
        "head(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "1ec233d4-33c3-4812-afca-c49a0696d05f",
        "id": "D7qiC5GSN_XY"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>gender</th><th scope=col>race.ethnicity</th><th scope=col>parental.level.of.education</th><th scope=col>lunch</th><th scope=col>test.preparation.course</th><th scope=col>math.score</th><th scope=col>reading.score</th><th scope=col>writing.score</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;chr&gt;</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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>female</td><td>group B</td><td>bachelor's degree </td><td>standard    </td><td>none     </td><td>72</td><td>72</td><td>74</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>female</td><td>group C</td><td>some college      </td><td>standard    </td><td>completed</td><td>69</td><td>90</td><td>88</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>female</td><td>group B</td><td>master's degree   </td><td>standard    </td><td>none     </td><td>90</td><td>95</td><td>93</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>male  </td><td>group A</td><td>associate's degree</td><td>free/reduced</td><td>none     </td><td>47</td><td>57</td><td>44</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>male  </td><td>group C</td><td>some college      </td><td>standard    </td><td>none     </td><td>76</td><td>78</td><td>75</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>female</td><td>group B</td><td>associate's degree</td><td>standard    </td><td>none     </td><td>71</td><td>83</td><td>78</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 6 × 8\n\n| <!--/--> | gender &lt;chr&gt; | race.ethnicity &lt;chr&gt; | parental.level.of.education &lt;chr&gt; | lunch &lt;chr&gt; | test.preparation.course &lt;chr&gt; | math.score &lt;int&gt; | reading.score &lt;int&gt; | writing.score &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|\n| 1 | female | group B | bachelor's degree  | standard     | none      | 72 | 72 | 74 |\n| 2 | female | group C | some college       | standard     | completed | 69 | 90 | 88 |\n| 3 | female | group B | master's degree    | standard     | none      | 90 | 95 | 93 |\n| 4 | male   | group A | associate's degree | free/reduced | none      | 47 | 57 | 44 |\n| 5 | male   | group C | some college       | standard     | none      | 76 | 78 | 75 |\n| 6 | female | group B | associate's degree | standard     | none      | 71 | 83 | 78 |\n\n",
            "text/latex": "A data.frame: 6 × 8\n\\begin{tabular}{r|llllllll}\n  & gender & race.ethnicity & parental.level.of.education & lunch & test.preparation.course & math.score & reading.score & writing.score\\\\\n  & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & female & group B & bachelor's degree  & standard     & none      & 72 & 72 & 74\\\\\n\t2 & female & group C & some college       & standard     & completed & 69 & 90 & 88\\\\\n\t3 & female & group B & master's degree    & standard     & none      & 90 & 95 & 93\\\\\n\t4 & male   & group A & associate's degree & free/reduced & none      & 47 & 57 & 44\\\\\n\t5 & male   & group C & some college       & standard     & none      & 76 & 78 & 75\\\\\n\t6 & female & group B & associate's degree & standard     & none      & 71 & 83 & 78\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gender race.ethnicity parental.level.of.education lunch       \n",
              "1 female group B        bachelor's degree           standard    \n",
              "2 female group C        some college                standard    \n",
              "3 female group B        master's degree             standard    \n",
              "4 male   group A        associate's degree          free/reduced\n",
              "5 male   group C        some college                standard    \n",
              "6 female group B        associate's degree          standard    \n",
              "  test.preparation.course math.score reading.score writing.score\n",
              "1 none                    72         72            74           \n",
              "2 completed               69         90            88           \n",
              "3 none                    90         95            93           \n",
              "4 none                    47         57            44           \n",
              "5 none                    76         78            75           \n",
              "6 none                    71         83            78           "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Create grouped data\n",
        "data_new_2 <- data.frame(table(data$gender, data$test.preparation.course))\n",
        "colnames(data_new_2) <- c(\"gender\", \"test.preparation.course\", \"freq\")\n",
        "head(data_new_2)"
      ],
      "metadata": {
        "id": "MrgtZQKdAHVm",
        "outputId": "8c5e85a5-0412-44a8-99af-0442d193f9e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 4 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>gender</th><th scope=col>test.preparation.course</th><th scope=col>freq</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>female</td><td>completed</td><td>184</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>male  </td><td>completed</td><td>174</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>female</td><td>none     </td><td>334</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>male  </td><td>none     </td><td>308</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 4 × 3\n\n| <!--/--> | gender &lt;fct&gt; | test.preparation.course &lt;fct&gt; | freq &lt;int&gt; |\n|---|---|---|---|\n| 1 | female | completed | 184 |\n| 2 | male   | completed | 174 |\n| 3 | female | none      | 334 |\n| 4 | male   | none      | 308 |\n\n",
            "text/latex": "A data.frame: 4 × 3\n\\begin{tabular}{r|lll}\n  & gender & test.preparation.course & freq\\\\\n  & <fct> & <fct> & <int>\\\\\n\\hline\n\t1 & female & completed & 184\\\\\n\t2 & male   & completed & 174\\\\\n\t3 & female & none      & 334\\\\\n\t4 & male   & none      & 308\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gender test.preparation.course freq\n",
              "1 female completed               184 \n",
              "2 male   completed               174 \n",
              "3 female none                    334 \n",
              "4 male   none                    308 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Create grouped barplots\n",
        "ggplot(data_new_2, aes(x = gender, y = freq, fill = test.preparation.course)) +\n",
        "    geom_bar(stat = \"identity\", position = position_dodge()) +\n",
        "    geom_text(aes(label = freq),\n",
        "        vjust = 1.2,\n",
        "        color = \"black\",\n",
        "        position = position_dodge(0.9),\n",
        "        size = 5\n",
        "    ) +\n",
        "    theme_minimal() +\n",
        "    theme(text = element_text(size = 14)) +\n",
        "    labs(fill = \"Test Preparation\", y = \"Counts\", x = \"Gender\") +\n",
        "    scale_fill_brewer(palette = \"Reds\")"
      ],
      "metadata": {
        "id": "AOhUZuTmANWB",
        "outputId": "80f1126d-3b88-4922-ad38-dabcc84cf074",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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92+PCEhQQjh4uKiHHV9WG+xS1GpVPwh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\nEsIOACASwg4AIBLCDgAgEv8FU9us5EG+noUAAAAASUVORK5CYII="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Table plot**"
      ],
      "metadata": {
        "id": "BBxR7GpkOjuw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Import data\n",
        "## Load required library\n",
        "library(ggplot2)\n",
        "\n",
        "data <- read.csv(\"https://raw.githubusercontent.com/luuloi/GWAS_Introduction_2023/main/data/StudentsPerformance.csv\")\n",
        "head(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "ZFVnHZcROymv",
        "outputId": "96fbcdd6-8dd8-4955-a753-69525f5cc976"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>gender</th><th scope=col>race.ethnicity</th><th scope=col>parental.level.of.education</th><th scope=col>lunch</th><th scope=col>test.preparation.course</th><th scope=col>math.score</th><th scope=col>reading.score</th><th scope=col>writing.score</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;chr&gt;</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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>female</td><td>group B</td><td>bachelor's degree </td><td>standard    </td><td>none     </td><td>72</td><td>72</td><td>74</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>female</td><td>group C</td><td>some college      </td><td>standard    </td><td>completed</td><td>69</td><td>90</td><td>88</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>female</td><td>group B</td><td>master's degree   </td><td>standard    </td><td>none     </td><td>90</td><td>95</td><td>93</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>male  </td><td>group A</td><td>associate's degree</td><td>free/reduced</td><td>none     </td><td>47</td><td>57</td><td>44</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>male  </td><td>group C</td><td>some college      </td><td>standard    </td><td>none     </td><td>76</td><td>78</td><td>75</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>female</td><td>group B</td><td>associate's degree</td><td>standard    </td><td>none     </td><td>71</td><td>83</td><td>78</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 6 × 8\n\n| <!--/--> | gender &lt;chr&gt; | race.ethnicity &lt;chr&gt; | parental.level.of.education &lt;chr&gt; | lunch &lt;chr&gt; | test.preparation.course &lt;chr&gt; | math.score &lt;int&gt; | reading.score &lt;int&gt; | writing.score &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|\n| 1 | female | group B | bachelor's degree  | standard     | none      | 72 | 72 | 74 |\n| 2 | female | group C | some college       | standard     | completed | 69 | 90 | 88 |\n| 3 | female | group B | master's degree    | standard     | none      | 90 | 95 | 93 |\n| 4 | male   | group A | associate's degree | free/reduced | none      | 47 | 57 | 44 |\n| 5 | male   | group C | some college       | standard     | none      | 76 | 78 | 75 |\n| 6 | female | group B | associate's degree | standard     | none      | 71 | 83 | 78 |\n\n",
            "text/latex": "A data.frame: 6 × 8\n\\begin{tabular}{r|llllllll}\n  & gender & race.ethnicity & parental.level.of.education & lunch & test.preparation.course & math.score & reading.score & writing.score\\\\\n  & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <int> & <int>\\\\\n\\hline\n\t1 & female & group B & bachelor's degree  & standard     & none      & 72 & 72 & 74\\\\\n\t2 & female & group C & some college       & standard     & completed & 69 & 90 & 88\\\\\n\t3 & female & group B & master's degree    & standard     & none      & 90 & 95 & 93\\\\\n\t4 & male   & group A & associate's degree & free/reduced & none      & 47 & 57 & 44\\\\\n\t5 & male   & group C & some college       & standard     & none      & 76 & 78 & 75\\\\\n\t6 & female & group B & associate's degree & standard     & none      & 71 & 83 & 78\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gender race.ethnicity parental.level.of.education lunch       \n",
              "1 female group B        bachelor's degree           standard    \n",
              "2 female group C        some college                standard    \n",
              "3 female group B        master's degree             standard    \n",
              "4 male   group A        associate's degree          free/reduced\n",
              "5 male   group C        some college                standard    \n",
              "6 female group B        associate's degree          standard    \n",
              "  test.preparation.course math.score reading.score writing.score\n",
              "1 none                    72         72            74           \n",
              "2 completed               69         90            88           \n",
              "3 none                    90         95            93           \n",
              "4 none                    47         57            44           \n",
              "5 none                    76         78            75           \n",
              "6 none                    71         83            78           "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Create table plot with Mosaicplot\n",
        "mosaicplot(table(data$gender, data$test.preparation.course), cex = 2)"
      ],
      "metadata": {
        "id": "lH70SzsFAP9C",
        "outputId": "f2a15d75-a04a-4dc7-ce3b-0b8fe1515513",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “table(data$gender, data$test.preparation.course)”"
            ],
            "image/png": 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iQkF4SkEFRIrWVb3SAhuSAkhaBC6ipDdYOE5IKQFIIK6UDZUTdISC4ISSGokG4T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\nRJCs/6UAAAAASUVORK5CYII="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "?mosaicplot"
      ],
      "metadata": {
        "id": "3wOI0QaDO95K"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Create table plot with Mosaicplot with nice title\n",
        "mosaicplot(table(data$gender, data$test.preparation.course), main = \"Overview of students completed the course by gender\", cex = 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "FDbYTROzPHHH",
        "outputId": "40ab6555-fc6a-4d5b-cc9a-bb988d3f1952"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “Overview of students completed the course by gender”"
            ],
            "image/png": 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hNQ567fvn167/K1SbL3iG+sOjTuMedH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\nHCH5ICSFuEJaPF5GP6OYIyQfhKQQVUhN7712XF3d+KnX/fqOKpZ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}