5_EDA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOaQXS0R+gXOZokmyQr65f2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jayarnim/study-RBasicSyntax/blob/main/5_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"readxl\")\n",
        "library(readxl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKP6cI0NimE6",
        "outputId": "e8443288-00a6-4a6a-83ab-3b3f7f22a1c8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Warning message:\n",
            "“package ‘data/readxl’ is not available for this version of R\n",
            "\n",
            "A version of this package for your version of R might be available elsewhere,\n",
            "see the ideas at\n",
            "https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages”\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df <- read_excel(\"data/Sample1.xlsx\")"
      ],
      "metadata": {
        "id": "iurU4AZ3jR5A"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0. Glance"
      ],
      "metadata": {
        "id": "WCk_zoUKkVFE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 세트 내용 확인\n",
        "View(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "mHeXQVF5i1vC",
        "outputId": "61fe9c72-88bf-4494-ca71-fc3111c1650b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   ID SEX AGE AREA AMT17   Y17_CNT AMT16  Y16_CNT\n",
              "1   1 F   50  서울 1300000 50      100000 40     \n",
              "2   2 M   40  경기  450000 25      700000 30     \n",
              "3   3 F   28  제주  275000 10       50000  5     \n",
              "4   4 M   50  서울  400000  8      125000  3     \n",
              "5   5 M   27  서울  845000 30      760000 28     \n",
              "6   6 F   23  서울   42900  1      300000  6     \n",
              "7   7 F   56  경기  150000  2      130000  2     \n",
              "8   8 F   47  서울  570000 10      400000  7     \n",
              "9   9 M   20  인천  930000  4      250000  2     \n",
              "10 10 F   38  경기  520000 17      550000 16     "
            ],
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>Y17_CNT</th><th scope=col>AMT16</th><th scope=col>Y16_CNT</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 8\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | Y17_CNT &lt;dbl&gt; | AMT16 &lt;dbl&gt; | Y16_CNT &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 |\n\n",
            "text/latex": "A tibble: 10 × 8\n\\begin{tabular}{llllllll}\n ID & SEX & AGE & AREA & AMT17 & Y17\\_CNT & AMT16 & Y16\\_CNT\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16\\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 세트 변수 확인\n",
        "ls(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "ij-BO5XTi9M_",
        "outputId": "b90f7419-3ad9-4040-b35a-a4ba2d810a38"
      },
      "execution_count": 3,
      "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>'AGE'</li><li>'AMT16'</li><li>'AMT17'</li><li>'AREA'</li><li>'ID'</li><li>'SEX'</li><li>'Y16_CNT'</li><li>'Y17_CNT'</li></ol>\n"
            ],
            "text/markdown": "1. 'AGE'\n2. 'AMT16'\n3. 'AMT17'\n4. 'AREA'\n5. 'ID'\n6. 'SEX'\n7. 'Y16_CNT'\n8. 'Y17_CNT'\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 'AGE'\n\\item 'AMT16'\n\\item 'AMT17'\n\\item 'AREA'\n\\item 'ID'\n\\item 'SEX'\n\\item 'Y16\\_CNT'\n\\item 'Y17\\_CNT'\n\\end{enumerate*}\n",
            "text/plain": [
              "[1] \"AGE\"     \"AMT16\"   \"AMT17\"   \"AREA\"    \"ID\"      \"SEX\"     \"Y16_CNT\"\n",
              "[8] \"Y17_CNT\""
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 세트 차원 확인\n",
        "dim(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "Clkgm_EyjEdo",
        "outputId": "cfcdfb46-eefd-46a7-cb73-a83b6100e64c"
      },
      "execution_count": 4,
      "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>10</li><li>8</li></ol>\n"
            ],
            "text/markdown": "1. 10\n2. 8\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 10\n\\item 8\n\\end{enumerate*}\n",
            "text/plain": [
              "[1] 10  8"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 세트 변수 속성 확인\n",
        "str(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K29PHHrBjI2G",
        "outputId": "f8816366-ee23-4459-fad6-4edbbd0486f7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tibble [10 × 8] (S3: tbl_df/tbl/data.frame)\n",
            " $ ID     : num [1:10] 1 2 3 4 5 6 7 8 9 10\n",
            " $ SEX    : chr [1:10] \"F\" \"M\" \"F\" \"M\" ...\n",
            " $ AGE    : num [1:10] 50 40 28 50 27 23 56 47 20 38\n",
            " $ AREA   : chr [1:10] \"서울\" \"경기\" \"제주\" \"서울\" ...\n",
            " $ AMT17  : num [1:10] 1300000 450000 275000 400000 845000 42900 150000 570000 930000 520000\n",
            " $ Y17_CNT: num [1:10] 50 25 10 8 30 1 2 10 4 17\n",
            " $ AMT16  : num [1:10] 100000 700000 50000 125000 760000 300000 130000 400000 250000 550000\n",
            " $ Y16_CNT: num [1:10] 40 30 5 3 28 6 2 7 2 16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. rename"
      ],
      "metadata": {
        "id": "8Kc6pSIDk76O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df <- rename(\n",
        "    df,\n",
        "    CNT17 = Y17_CNT,\n",
        "    CNT16 = Y16_CNT\n",
        "    )\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "i6kSyZGEjk60",
        "outputId": "fa91f54d-ed5b-4b8d-c00a-95e8ec5e86a8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 8\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 |\n\n",
            "text/latex": "A tibble: 10 × 8\n\\begin{tabular}{llllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16\n",
              "1   1 F   50  서울 1300000 50    100000 40   \n",
              "2   2 M   40  경기  450000 25    700000 30   \n",
              "3   3 F   28  제주  275000 10     50000  5   \n",
              "4   4 M   50  서울  400000  8    125000  3   \n",
              "5   5 M   27  서울  845000 30    760000 28   \n",
              "6   6 F   23  서울   42900  1    300000  6   \n",
              "7   7 F   56  경기  150000  2    130000  2   \n",
              "8   8 F   47  서울  570000 10    400000  7   \n",
              "9   9 M   20  인천  930000  4    250000  2   \n",
              "10 10 F   38  경기  520000 17    550000 16   "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Derived Variables"
      ],
      "metadata": {
        "id": "YEpvpoLPk9W9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df <- mutate(\n",
        "    df,\n",
        "    AMT = AMT17 + AMT16,\n",
        "    CNT = CNT17 + CNT16\n",
        "    )\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "QjE94DDtjxRz",
        "outputId": "4d65ab28-1ec2-44ee-d78c-71c3937932bb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 10</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td><td>1150000</td><td>55</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td><td> 325000</td><td>15</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td><td> 525000</td><td>11</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td><td> 342900</td><td> 7</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td><td> 280000</td><td> 4</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td><td>1180000</td><td> 6</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td><td>1070000</td><td>33</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 10\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 | 1150000 | 55 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |  325000 | 15 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |  525000 | 11 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |  342900 |  7 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |  280000 |  4 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 | 1180000 |  6 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 | 1070000 | 33 |\n\n",
            "text/latex": "A tibble: 10 × 10\n\\begin{tabular}{llllllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30 & 1150000 & 55\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5 &  325000 & 15\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3 &  525000 & 11\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6 &  342900 &  7\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2 &  280000 &  4\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2 & 1180000 &  6\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16 & 1070000 & 33\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT\n",
              "1   1 F   50  서울 1300000 50    100000 40    1400000 90 \n",
              "2   2 M   40  경기  450000 25    700000 30    1150000 55 \n",
              "3   3 F   28  제주  275000 10     50000  5     325000 15 \n",
              "4   4 M   50  서울  400000  8    125000  3     525000 11 \n",
              "5   5 M   27  서울  845000 30    760000 28    1605000 58 \n",
              "6   6 F   23  서울   42900  1    300000  6     342900  7 \n",
              "7   7 F   56  경기  150000  2    130000  2     280000  4 \n",
              "8   8 F   47  서울  570000 10    400000  7     970000 17 \n",
              "9   9 M   20  인천  930000  4    250000  2    1180000  6 \n",
              "10 10 F   38  경기  520000 17    550000 16    1070000 33 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df$AVG_AMT <- df$AMT / df$CNT\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "q41BbuuKk6-C",
        "outputId": "5c38b2b3-e2f5-4392-9a9b-21de301abf9e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 11</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th><th scope=col>AVG_AMT</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td><td> 15555.56</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td><td>1150000</td><td>55</td><td> 20909.09</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td><td> 325000</td><td>15</td><td> 21666.67</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td><td> 525000</td><td>11</td><td> 47727.27</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td><td> 27672.41</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td><td> 342900</td><td> 7</td><td> 48985.71</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td><td> 280000</td><td> 4</td><td> 70000.00</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td><td> 57058.82</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td><td>1180000</td><td> 6</td><td>196666.67</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td><td>1070000</td><td>33</td><td> 32424.24</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 11\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; | AVG_AMT &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 |  15555.56 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 | 1150000 | 55 |  20909.09 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |  325000 | 15 |  21666.67 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |  525000 | 11 |  47727.27 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 |  27672.41 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |  342900 |  7 |  48985.71 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |  280000 |  4 |  70000.00 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 |  57058.82 |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 | 1180000 |  6 | 196666.67 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 | 1070000 | 33 |  32424.24 |\n\n",
            "text/latex": "A tibble: 10 × 11\n\\begin{tabular}{lllllllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT & AVG\\_AMT\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90 &  15555.56\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30 & 1150000 & 55 &  20909.09\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5 &  325000 & 15 &  21666.67\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3 &  525000 & 11 &  47727.27\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58 &  27672.41\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6 &  342900 &  7 &  48985.71\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2 &  280000 &  4 &  70000.00\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17 &  57058.82\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2 & 1180000 &  6 & 196666.67\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16 & 1070000 & 33 &  32424.24\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT AVG_AMT  \n",
              "1   1 F   50  서울 1300000 50    100000 40    1400000 90   15555.56\n",
              "2   2 M   40  경기  450000 25    700000 30    1150000 55   20909.09\n",
              "3   3 F   28  제주  275000 10     50000  5     325000 15   21666.67\n",
              "4   4 M   50  서울  400000  8    125000  3     525000 11   47727.27\n",
              "5   5 M   27  서울  845000 30    760000 28    1605000 58   27672.41\n",
              "6   6 F   23  서울   42900  1    300000  6     342900  7   48985.71\n",
              "7   7 F   56  경기  150000  2    130000  2     280000  4   70000.00\n",
              "8   8 F   47  서울  570000 10    400000  7     970000 17   57058.82\n",
              "9   9 M   20  인천  930000  4    250000  2    1180000  6  196666.67\n",
              "10 10 F   38  경기  520000 17    550000 16    1070000 33   32424.24"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df$AGE50_YN <- ifelse(\n",
        "    df$AGE >= 50,\n",
        "    \"Y\",\n",
        "    \"N\"\n",
        "    )\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "DoRTxNgjl7Wl",
        "outputId": "9bf2f988-b9e5-45bb-c053-b1d455657b61"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 12</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th><th scope=col>AVG_AMT</th><th scope=col>AGE50_YN</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td><td> 15555.56</td><td>Y</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td><td>1150000</td><td>55</td><td> 20909.09</td><td>N</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td><td> 325000</td><td>15</td><td> 21666.67</td><td>N</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td><td> 525000</td><td>11</td><td> 47727.27</td><td>Y</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td><td> 27672.41</td><td>N</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td><td> 342900</td><td> 7</td><td> 48985.71</td><td>N</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td><td> 280000</td><td> 4</td><td> 70000.00</td><td>Y</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td><td> 57058.82</td><td>N</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td><td>1180000</td><td> 6</td><td>196666.67</td><td>N</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td><td>1070000</td><td>33</td><td> 32424.24</td><td>N</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 12\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; | AVG_AMT &lt;dbl&gt; | AGE50_YN &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 |  15555.56 | Y |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 | 1150000 | 55 |  20909.09 | N |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |  325000 | 15 |  21666.67 | N |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |  525000 | 11 |  47727.27 | Y |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 |  27672.41 | N |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |  342900 |  7 |  48985.71 | N |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |  280000 |  4 |  70000.00 | Y |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 |  57058.82 | N |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 | 1180000 |  6 | 196666.67 | N |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 | 1070000 | 33 |  32424.24 | N |\n\n",
            "text/latex": "A tibble: 10 × 12\n\\begin{tabular}{llllllllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT & AVG\\_AMT & AGE50\\_YN\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90 &  15555.56 & Y\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30 & 1150000 & 55 &  20909.09 & N\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5 &  325000 & 15 &  21666.67 & N\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3 &  525000 & 11 &  47727.27 & Y\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58 &  27672.41 & N\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6 &  342900 &  7 &  48985.71 & N\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2 &  280000 &  4 &  70000.00 & Y\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17 &  57058.82 & N\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2 & 1180000 &  6 & 196666.67 & N\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16 & 1070000 & 33 &  32424.24 & N\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT AVG_AMT   AGE50_YN\n",
              "1   1 F   50  서울 1300000 50    100000 40    1400000 90   15555.56 Y       \n",
              "2   2 M   40  경기  450000 25    700000 30    1150000 55   20909.09 N       \n",
              "3   3 F   28  제주  275000 10     50000  5     325000 15   21666.67 N       \n",
              "4   4 M   50  서울  400000  8    125000  3     525000 11   47727.27 Y       \n",
              "5   5 M   27  서울  845000 30    760000 28    1605000 58   27672.41 N       \n",
              "6   6 F   23  서울   42900  1    300000  6     342900  7   48985.71 N       \n",
              "7   7 F   56  경기  150000  2    130000  2     280000  4   70000.00 Y       \n",
              "8   8 F   47  서울  570000 10    400000  7     970000 17   57058.82 N       \n",
              "9   9 M   20  인천  930000  4    250000  2    1180000  6  196666.67 N       \n",
              "10 10 F   38  경기  520000 17    550000 16    1070000 33   32424.24 N       "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df$AGE_GR10 <- ifelse(\n",
        "    df$AGE >= 50,\n",
        "    \"A1.50++\",\n",
        "    ifelse(\n",
        "        df$AGE >= 40,\n",
        "        \"A2.4049\",\n",
        "        ifelse(\n",
        "            df$AGE >= 30,\n",
        "            \"A3.3039\",\n",
        "            ifelse(\n",
        "                df$AGE >= 20,\n",
        "                \"A4.2029\",\n",
        "                \"A5.0019\"\n",
        "                )\n",
        "            )\n",
        "        )\n",
        "    )\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "m_bcKnvxogL5",
        "outputId": "a06f92b5-1b52-4231-9966-de8bfb273f55"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 13</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th><th scope=col>AVG_AMT</th><th scope=col>AGE50_YN</th><th scope=col>AGE_GR10</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td><td> 15555.56</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td><td>1150000</td><td>55</td><td> 20909.09</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td><td> 325000</td><td>15</td><td> 21666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td><td> 525000</td><td>11</td><td> 47727.27</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td><td> 27672.41</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td><td> 342900</td><td> 7</td><td> 48985.71</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td><td> 280000</td><td> 4</td><td> 70000.00</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td><td> 57058.82</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td><td>1180000</td><td> 6</td><td>196666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td><td>1070000</td><td>33</td><td> 32424.24</td><td>N</td><td>A3.3039</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 13\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; | AVG_AMT &lt;dbl&gt; | AGE50_YN &lt;chr&gt; | AGE_GR10 &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 |  15555.56 | Y | A1.50++ |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 | 1150000 | 55 |  20909.09 | N | A2.4049 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |  325000 | 15 |  21666.67 | N | A4.2029 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |  525000 | 11 |  47727.27 | Y | A1.50++ |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 |  27672.41 | N | A4.2029 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |  342900 |  7 |  48985.71 | N | A4.2029 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |  280000 |  4 |  70000.00 | Y | A1.50++ |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 |  57058.82 | N | A2.4049 |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 | 1180000 |  6 | 196666.67 | N | A4.2029 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 | 1070000 | 33 |  32424.24 | N | A3.3039 |\n\n",
            "text/latex": "A tibble: 10 × 13\n\\begin{tabular}{lllllllllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT & AVG\\_AMT & AGE50\\_YN & AGE\\_GR10\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90 &  15555.56 & Y & A1.50++\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30 & 1150000 & 55 &  20909.09 & N & A2.4049\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5 &  325000 & 15 &  21666.67 & N & A4.2029\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3 &  525000 & 11 &  47727.27 & Y & A1.50++\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58 &  27672.41 & N & A4.2029\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6 &  342900 &  7 &  48985.71 & N & A4.2029\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2 &  280000 &  4 &  70000.00 & Y & A1.50++\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17 &  57058.82 & N & A2.4049\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2 & 1180000 &  6 & 196666.67 & N & A4.2029\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16 & 1070000 & 33 &  32424.24 & N & A3.3039\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT AVG_AMT   AGE50_YN\n",
              "1   1 F   50  서울 1300000 50    100000 40    1400000 90   15555.56 Y       \n",
              "2   2 M   40  경기  450000 25    700000 30    1150000 55   20909.09 N       \n",
              "3   3 F   28  제주  275000 10     50000  5     325000 15   21666.67 N       \n",
              "4   4 M   50  서울  400000  8    125000  3     525000 11   47727.27 Y       \n",
              "5   5 M   27  서울  845000 30    760000 28    1605000 58   27672.41 N       \n",
              "6   6 F   23  서울   42900  1    300000  6     342900  7   48985.71 N       \n",
              "7   7 F   56  경기  150000  2    130000  2     280000  4   70000.00 Y       \n",
              "8   8 F   47  서울  570000 10    400000  7     970000 17   57058.82 N       \n",
              "9   9 M   20  인천  930000  4    250000  2    1180000  6  196666.67 N       \n",
              "10 10 F   38  경기  520000 17    550000 16    1070000 33   32424.24 N       \n",
              "   AGE_GR10\n",
              "1  A1.50++ \n",
              "2  A2.4049 \n",
              "3  A4.2029 \n",
              "4  A1.50++ \n",
              "5  A4.2029 \n",
              "6  A4.2029 \n",
              "7  A1.50++ \n",
              "8  A2.4049 \n",
              "9  A4.2029 \n",
              "10 A3.3039 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Filtering"
      ],
      "metadata": {
        "id": "VcP6-AgRCUj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"dplyr\")\n",
        "library(dplyr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrpU7XTGjZ54",
        "outputId": "195ef9b1-7c4b-4c72-81ae-389dc4fe3fb7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## select"
      ],
      "metadata": {
        "id": "SemUzc8cCbdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df %>% select(ID, AREA)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "vN09sCXxqC6R",
        "outputId": "ecd9fddb-40e8-467c-b126-d0b1ce259a84"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>AREA</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>서울</td></tr>\n",
              "\t<tr><td> 2</td><td>경기</td></tr>\n",
              "\t<tr><td> 3</td><td>제주</td></tr>\n",
              "\t<tr><td> 4</td><td>서울</td></tr>\n",
              "\t<tr><td> 5</td><td>서울</td></tr>\n",
              "\t<tr><td> 6</td><td>서울</td></tr>\n",
              "\t<tr><td> 7</td><td>경기</td></tr>\n",
              "\t<tr><td> 8</td><td>서울</td></tr>\n",
              "\t<tr><td> 9</td><td>인천</td></tr>\n",
              "\t<tr><td>10</td><td>경기</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 2\n\n| ID &lt;dbl&gt; | AREA &lt;chr&gt; |\n|---|---|\n|  1 | 서울 |\n|  2 | 경기 |\n|  3 | 제주 |\n|  4 | 서울 |\n|  5 | 서울 |\n|  6 | 서울 |\n|  7 | 경기 |\n|  8 | 서울 |\n|  9 | 인천 |\n| 10 | 경기 |\n\n",
            "text/latex": "A tibble: 10 × 2\n\\begin{tabular}{ll}\n ID & AREA\\\\\n <dbl> & <chr>\\\\\n\\hline\n\t  1 & 서울\\\\\n\t  2 & 경기\\\\\n\t  3 & 제주\\\\\n\t  4 & 서울\\\\\n\t  5 & 서울\\\\\n\t  6 & 서울\\\\\n\t  7 & 경기\\\\\n\t  8 & 서울\\\\\n\t  9 & 인천\\\\\n\t 10 & 경기\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID AREA\n",
              "1   1 서울\n",
              "2   2 경기\n",
              "3   3 제주\n",
              "4   4 서울\n",
              "5   5 서울\n",
              "6   6 서울\n",
              "7   7 경기\n",
              "8   8 서울\n",
              "9   9 인천\n",
              "10 10 경기"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df %>% select(-SEX, -AGE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "Kob1uMjLqX5p",
        "outputId": "0df78802-34fc-4253-d15f-a40023641faf"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 11</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th><th scope=col>AVG_AMT</th><th scope=col>AGE50_YN</th><th scope=col>AGE_GR10</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&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;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td><td> 15555.56</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 2</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td><td>1150000</td><td>55</td><td> 20909.09</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td> 3</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td><td> 325000</td><td>15</td><td> 21666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 4</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td><td> 525000</td><td>11</td><td> 47727.27</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 5</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td><td> 27672.41</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 6</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td><td> 342900</td><td> 7</td><td> 48985.71</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 7</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td><td> 280000</td><td> 4</td><td> 70000.00</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 8</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td><td> 57058.82</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td> 9</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td><td>1180000</td><td> 6</td><td>196666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td>10</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td><td>1070000</td><td>33</td><td> 32424.24</td><td>N</td><td>A3.3039</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 11\n\n| ID &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; | AVG_AMT &lt;dbl&gt; | AGE50_YN &lt;chr&gt; | AGE_GR10 &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|\n|  1 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 |  15555.56 | Y | A1.50++ |\n|  2 | 경기 |  450000 | 25 | 700000 | 30 | 1150000 | 55 |  20909.09 | N | A2.4049 |\n|  3 | 제주 |  275000 | 10 |  50000 |  5 |  325000 | 15 |  21666.67 | N | A4.2029 |\n|  4 | 서울 |  400000 |  8 | 125000 |  3 |  525000 | 11 |  47727.27 | Y | A1.50++ |\n|  5 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 |  27672.41 | N | A4.2029 |\n|  6 | 서울 |   42900 |  1 | 300000 |  6 |  342900 |  7 |  48985.71 | N | A4.2029 |\n|  7 | 경기 |  150000 |  2 | 130000 |  2 |  280000 |  4 |  70000.00 | Y | A1.50++ |\n|  8 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 |  57058.82 | N | A2.4049 |\n|  9 | 인천 |  930000 |  4 | 250000 |  2 | 1180000 |  6 | 196666.67 | N | A4.2029 |\n| 10 | 경기 |  520000 | 17 | 550000 | 16 | 1070000 | 33 |  32424.24 | N | A3.3039 |\n\n",
            "text/latex": "A tibble: 10 × 11\n\\begin{tabular}{lllllllllll}\n ID & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT & AVG\\_AMT & AGE50\\_YN & AGE\\_GR10\\\\\n <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n\\hline\n\t  1 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90 &  15555.56 & Y & A1.50++\\\\\n\t  2 & 경기 &  450000 & 25 & 700000 & 30 & 1150000 & 55 &  20909.09 & N & A2.4049\\\\\n\t  3 & 제주 &  275000 & 10 &  50000 &  5 &  325000 & 15 &  21666.67 & N & A4.2029\\\\\n\t  4 & 서울 &  400000 &  8 & 125000 &  3 &  525000 & 11 &  47727.27 & Y & A1.50++\\\\\n\t  5 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58 &  27672.41 & N & A4.2029\\\\\n\t  6 & 서울 &   42900 &  1 & 300000 &  6 &  342900 &  7 &  48985.71 & N & A4.2029\\\\\n\t  7 & 경기 &  150000 &  2 & 130000 &  2 &  280000 &  4 &  70000.00 & Y & A1.50++\\\\\n\t  8 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17 &  57058.82 & N & A2.4049\\\\\n\t  9 & 인천 &  930000 &  4 & 250000 &  2 & 1180000 &  6 & 196666.67 & N & A4.2029\\\\\n\t 10 & 경기 &  520000 & 17 & 550000 & 16 & 1070000 & 33 &  32424.24 & N & A3.3039\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT AVG_AMT   AGE50_YN AGE_GR10\n",
              "1   1 서울 1300000 50    100000 40    1400000 90   15555.56 Y        A1.50++ \n",
              "2   2 경기  450000 25    700000 30    1150000 55   20909.09 N        A2.4049 \n",
              "3   3 제주  275000 10     50000  5     325000 15   21666.67 N        A4.2029 \n",
              "4   4 서울  400000  8    125000  3     525000 11   47727.27 Y        A1.50++ \n",
              "5   5 서울  845000 30    760000 28    1605000 58   27672.41 N        A4.2029 \n",
              "6   6 서울   42900  1    300000  6     342900  7   48985.71 N        A4.2029 \n",
              "7   7 경기  150000  2    130000  2     280000  4   70000.00 Y        A1.50++ \n",
              "8   8 서울  570000 10    400000  7     970000 17   57058.82 N        A2.4049 \n",
              "9   9 인천  930000  4    250000  2    1180000  6  196666.67 N        A4.2029 \n",
              "10 10 경기  520000 17    550000 16    1070000 33   32424.24 N        A3.3039 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## filter"
      ],
      "metadata": {
        "id": "zULA_ky2qDJI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df %>% filter((AREA == \"서울\") & (CNT17 >= 10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "hbv9PaxYqEFl",
        "outputId": "94f2a23a-d907-42ca-b4de-924311005e80"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 3 × 13</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th><th scope=col>AVG_AMT</th><th scope=col>AGE50_YN</th><th scope=col>AGE_GR10</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td><td>15555.56</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td>5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td><td>27672.41</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td>8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td><td>57058.82</td><td>N</td><td>A2.4049</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 3 × 13\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; | AVG_AMT &lt;dbl&gt; | AGE50_YN &lt;chr&gt; | AGE_GR10 &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 | 15555.56 | Y | A1.50++ |\n| 5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 | 27672.41 | N | A4.2029 |\n| 8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 | 57058.82 | N | A2.4049 |\n\n",
            "text/latex": "A tibble: 3 × 13\n\\begin{tabular}{lllllllllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT & AVG\\_AMT & AGE50\\_YN & AGE\\_GR10\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n\\hline\n\t 1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90 & 15555.56 & Y & A1.50++\\\\\n\t 5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58 & 27672.41 & N & A4.2029\\\\\n\t 8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17 & 57058.82 & N & A2.4049\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT AVG_AMT  AGE50_YN\n",
              "1 1  F   50  서울 1300000 50    100000 40    1400000 90  15555.56 Y       \n",
              "2 5  M   27  서울  845000 30    760000 28    1605000 58  27672.41 N       \n",
              "3 8  F   47  서울  570000 10    400000  7     970000 17  57058.82 N       \n",
              "  AGE_GR10\n",
              "1 A1.50++ \n",
              "2 A4.2029 \n",
              "3 A2.4049 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## arrange"
      ],
      "metadata": {
        "id": "FQoNJTPMCb5e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df %>% arrange(AGE)"
      ],
      "metadata": {
        "id": "cSjDvcVYCf7R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "6462ba3d-bd53-457a-f202-614a1ed3e632"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 13</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th><th scope=col>AVG_AMT</th><th scope=col>AGE50_YN</th><th scope=col>AGE_GR10</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td><td>1180000</td><td> 6</td><td>196666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td><td> 342900</td><td> 7</td><td> 48985.71</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td><td> 27672.41</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td><td> 325000</td><td>15</td><td> 21666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td><td>1070000</td><td>33</td><td> 32424.24</td><td>N</td><td>A3.3039</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td><td>1150000</td><td>55</td><td> 20909.09</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td><td> 57058.82</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td><td> 15555.56</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td><td> 525000</td><td>11</td><td> 47727.27</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td><td> 280000</td><td> 4</td><td> 70000.00</td><td>Y</td><td>A1.50++</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 13\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; | AVG_AMT &lt;dbl&gt; | AGE50_YN &lt;chr&gt; | AGE_GR10 &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 | 1180000 |  6 | 196666.67 | N | A4.2029 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |  342900 |  7 |  48985.71 | N | A4.2029 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 |  27672.41 | N | A4.2029 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |  325000 | 15 |  21666.67 | N | A4.2029 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 | 1070000 | 33 |  32424.24 | N | A3.3039 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 | 1150000 | 55 |  20909.09 | N | A2.4049 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 |  57058.82 | N | A2.4049 |\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 |  15555.56 | Y | A1.50++ |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |  525000 | 11 |  47727.27 | Y | A1.50++ |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |  280000 |  4 |  70000.00 | Y | A1.50++ |\n\n",
            "text/latex": "A tibble: 10 × 13\n\\begin{tabular}{lllllllllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT & AVG\\_AMT & AGE50\\_YN & AGE\\_GR10\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n\\hline\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2 & 1180000 &  6 & 196666.67 & N & A4.2029\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6 &  342900 &  7 &  48985.71 & N & A4.2029\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58 &  27672.41 & N & A4.2029\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5 &  325000 & 15 &  21666.67 & N & A4.2029\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16 & 1070000 & 33 &  32424.24 & N & A3.3039\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30 & 1150000 & 55 &  20909.09 & N & A2.4049\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17 &  57058.82 & N & A2.4049\\\\\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90 &  15555.56 & Y & A1.50++\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3 &  525000 & 11 &  47727.27 & Y & A1.50++\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2 &  280000 &  4 &  70000.00 & Y & A1.50++\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT AVG_AMT   AGE50_YN\n",
              "1   9 M   20  인천  930000  4    250000  2    1180000  6  196666.67 N       \n",
              "2   6 F   23  서울   42900  1    300000  6     342900  7   48985.71 N       \n",
              "3   5 M   27  서울  845000 30    760000 28    1605000 58   27672.41 N       \n",
              "4   3 F   28  제주  275000 10     50000  5     325000 15   21666.67 N       \n",
              "5  10 F   38  경기  520000 17    550000 16    1070000 33   32424.24 N       \n",
              "6   2 M   40  경기  450000 25    700000 30    1150000 55   20909.09 N       \n",
              "7   8 F   47  서울  570000 10    400000  7     970000 17   57058.82 N       \n",
              "8   1 F   50  서울 1300000 50    100000 40    1400000 90   15555.56 Y       \n",
              "9   4 M   50  서울  400000  8    125000  3     525000 11   47727.27 Y       \n",
              "10  7 F   56  경기  150000  2    130000  2     280000  4   70000.00 Y       \n",
              "   AGE_GR10\n",
              "1  A4.2029 \n",
              "2  A4.2029 \n",
              "3  A4.2029 \n",
              "4  A4.2029 \n",
              "5  A3.3039 \n",
              "6  A2.4049 \n",
              "7  A2.4049 \n",
              "8  A1.50++ \n",
              "9  A1.50++ \n",
              "10 A1.50++ "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df %>% arrange(desc(AGE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "PEQ3wYMprjdC",
        "outputId": "41cfd81e-97a9-4ea1-d796-0200681270fc"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 13</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>CNT17</th><th scope=col>AMT16</th><th scope=col>CNT16</th><th scope=col>AMT</th><th scope=col>CNT</th><th scope=col>AVG_AMT</th><th scope=col>AGE50_YN</th><th scope=col>AGE_GR10</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td><td> 280000</td><td> 4</td><td> 70000.00</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td><td>1400000</td><td>90</td><td> 15555.56</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td><td> 525000</td><td>11</td><td> 47727.27</td><td>Y</td><td>A1.50++</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td><td> 970000</td><td>17</td><td> 57058.82</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td><td>1150000</td><td>55</td><td> 20909.09</td><td>N</td><td>A2.4049</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td><td>1070000</td><td>33</td><td> 32424.24</td><td>N</td><td>A3.3039</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td><td> 325000</td><td>15</td><td> 21666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td><td>1605000</td><td>58</td><td> 27672.41</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td><td> 342900</td><td> 7</td><td> 48985.71</td><td>N</td><td>A4.2029</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td><td>1180000</td><td> 6</td><td>196666.67</td><td>N</td><td>A4.2029</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 13\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | CNT17 &lt;dbl&gt; | AMT16 &lt;dbl&gt; | CNT16 &lt;dbl&gt; | AMT &lt;dbl&gt; | CNT &lt;dbl&gt; | AVG_AMT &lt;dbl&gt; | AGE50_YN &lt;chr&gt; | AGE_GR10 &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |  280000 |  4 |  70000.00 | Y | A1.50++ |\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 | 1400000 | 90 |  15555.56 | Y | A1.50++ |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |  525000 | 11 |  47727.27 | Y | A1.50++ |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |  970000 | 17 |  57058.82 | N | A2.4049 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 | 1150000 | 55 |  20909.09 | N | A2.4049 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 | 1070000 | 33 |  32424.24 | N | A3.3039 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |  325000 | 15 |  21666.67 | N | A4.2029 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 | 1605000 | 58 |  27672.41 | N | A4.2029 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |  342900 |  7 |  48985.71 | N | A4.2029 |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 | 1180000 |  6 | 196666.67 | N | A4.2029 |\n\n",
            "text/latex": "A tibble: 10 × 13\n\\begin{tabular}{lllllllllllll}\n ID & SEX & AGE & AREA & AMT17 & CNT17 & AMT16 & CNT16 & AMT & CNT & AVG\\_AMT & AGE50\\_YN & AGE\\_GR10\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n\\hline\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2 &  280000 &  4 &  70000.00 & Y & A1.50++\\\\\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40 & 1400000 & 90 &  15555.56 & Y & A1.50++\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3 &  525000 & 11 &  47727.27 & Y & A1.50++\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7 &  970000 & 17 &  57058.82 & N & A2.4049\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30 & 1150000 & 55 &  20909.09 & N & A2.4049\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16 & 1070000 & 33 &  32424.24 & N & A3.3039\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5 &  325000 & 15 &  21666.67 & N & A4.2029\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28 & 1605000 & 58 &  27672.41 & N & A4.2029\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6 &  342900 &  7 &  48985.71 & N & A4.2029\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2 & 1180000 &  6 & 196666.67 & N & A4.2029\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   CNT17 AMT16  CNT16 AMT     CNT AVG_AMT   AGE50_YN\n",
              "1   7 F   56  경기  150000  2    130000  2     280000  4   70000.00 Y       \n",
              "2   1 F   50  서울 1300000 50    100000 40    1400000 90   15555.56 Y       \n",
              "3   4 M   50  서울  400000  8    125000  3     525000 11   47727.27 Y       \n",
              "4   8 F   47  서울  570000 10    400000  7     970000 17   57058.82 N       \n",
              "5   2 M   40  경기  450000 25    700000 30    1150000 55   20909.09 N       \n",
              "6  10 F   38  경기  520000 17    550000 16    1070000 33   32424.24 N       \n",
              "7   3 F   28  제주  275000 10     50000  5     325000 15   21666.67 N       \n",
              "8   5 M   27  서울  845000 30    760000 28    1605000 58   27672.41 N       \n",
              "9   6 F   23  서울   42900  1    300000  6     342900  7   48985.71 N       \n",
              "10  9 M   20  인천  930000  4    250000  2    1180000  6  196666.67 N       \n",
              "   AGE_GR10\n",
              "1  A1.50++ \n",
              "2  A1.50++ \n",
              "3  A1.50++ \n",
              "4  A2.4049 \n",
              "5  A2.4049 \n",
              "6  A3.3039 \n",
              "7  A4.2029 \n",
              "8  A4.2029 \n",
              "9  A4.2029 \n",
              "10 A4.2029 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Aggregating"
      ],
      "metadata": {
        "id": "G6z4lNnkCgPC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistic Functions"
      ],
      "metadata": {
        "id": "kLplPD8UBN12"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"psych\")\n",
        "library(psych)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rrACLouP_wzN",
        "outputId": "fc42dc57-85fb-4318-83a2-e833a5494c9f"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘mnormt’\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 합계\n",
        "sum(df$AGE)\n",
        "\n",
        "# 평균\n",
        "mean(df$AGE)\n",
        "\n",
        "# 중앙값\n",
        "median(df$AGE)\n",
        "\n",
        "# 최댓값\n",
        "max(df$AGE)\n",
        "\n",
        "# 최솟값\n",
        "min(df$AGE)\n",
        "\n",
        "# 범위\n",
        "range(df$AGE)\n",
        "\n",
        "# 분위수\n",
        "quantile(df$AGE, probs = seq(0, 1, 0.25))\n",
        "\n",
        "# 분산\n",
        "var(df$AGE)\n",
        "\n",
        "# 표준편차\n",
        "sd(df$AGE)\n",
        "\n",
        "# 첨도\n",
        "kurtosi(df$AGE)\n",
        "\n",
        "# 왜도\n",
        "skew(df$AGE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "id": "FPucM3p3-zPc",
        "outputId": "6c65aaa3-1d26-400c-bdf5-f5882e680755"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "379"
            ],
            "text/markdown": "379",
            "text/latex": "379",
            "text/plain": [
              "[1] 379"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "37.9"
            ],
            "text/markdown": "37.9",
            "text/latex": "37.9",
            "text/plain": [
              "[1] 37.9"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "39"
            ],
            "text/markdown": "39",
            "text/latex": "39",
            "text/plain": [
              "[1] 39"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "56"
            ],
            "text/markdown": "56",
            "text/latex": "56",
            "text/plain": [
              "[1] 56"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "20"
            ],
            "text/markdown": "20",
            "text/latex": "20",
            "text/plain": [
              "[1] 20"
            ]
          },
          "metadata": {}
        },
        {
          "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>20</li><li>56</li></ol>\n"
            ],
            "text/markdown": "1. 20\n2. 56\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 20\n\\item 56\n\\end{enumerate*}\n",
            "text/plain": [
              "[1] 20 56"
            ]
          },
          "metadata": {}
        },
        {
          "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>0%</dt><dd>20</dd><dt>25%</dt><dd>27.25</dd><dt>50%</dt><dd>39</dd><dt>75%</dt><dd>49.25</dd><dt>100%</dt><dd>56</dd></dl>\n"
            ],
            "text/markdown": "0%\n:   2025%\n:   27.2550%\n:   3975%\n:   49.25100%\n:   56\n\n",
            "text/latex": "\\begin{description*}\n\\item[0\\textbackslash{}\\%] 20\n\\item[25\\textbackslash{}\\%] 27.25\n\\item[50\\textbackslash{}\\%] 39\n\\item[75\\textbackslash{}\\%] 49.25\n\\item[100\\textbackslash{}\\%] 56\n\\end{description*}\n",
            "text/plain": [
              "   0%   25%   50%   75%  100% \n",
              "20.00 27.25 39.00 49.25 56.00 "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "162.988888888889"
            ],
            "text/markdown": "162.988888888889",
            "text/latex": "162.988888888889",
            "text/plain": [
              "[1] 162.9889"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "12.7667101826935"
            ],
            "text/markdown": "12.7667101826935",
            "text/latex": "12.7667101826935",
            "text/plain": [
              "[1] 12.76671"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "-1.74743051562411"
            ],
            "text/markdown": "-1.74743051562411",
            "text/latex": "-1.74743051562411",
            "text/plain": [
              "[1] -1.747431"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "-0.0515620697742034"
            ],
            "text/markdown": "-0.0515620697742034",
            "text/latex": "-0.0515620697742034",
            "text/plain": [
              "[1] -0.05156207"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "WXMUZZFM_nv4",
        "outputId": "94aeeaee-69ae-4681-920c-4fff2c4b81a9"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       ID            SEX                 AGE            AREA          \n",
              " Min.   : 1.00   Length:10          Min.   :20.00   Length:10         \n",
              " 1st Qu.: 3.25   Class :character   1st Qu.:27.25   Class :character  \n",
              " Median : 5.50   Mode  :character   Median :39.00   Mode  :character  \n",
              " Mean   : 5.50                      Mean   :37.90                     \n",
              " 3rd Qu.: 7.75                      3rd Qu.:49.25                     \n",
              " Max.   :10.00                      Max.   :56.00                     \n",
              "     AMT17             CNT17          AMT16            CNT16     \n",
              " Min.   :  42900   Min.   : 1.0   Min.   : 50000   Min.   : 2.0  \n",
              " 1st Qu.: 306250   1st Qu.: 5.0   1st Qu.:126250   1st Qu.: 3.5  \n",
              " Median : 485000   Median :10.0   Median :275000   Median : 6.5  \n",
              " Mean   : 548290   Mean   :15.7   Mean   :336500   Mean   :13.9  \n",
              " 3rd Qu.: 776250   3rd Qu.:23.0   3rd Qu.:512500   3rd Qu.:25.0  \n",
              " Max.   :1300000   Max.   :50.0   Max.   :760000   Max.   :40.0  \n",
              "      AMT               CNT          AVG_AMT         AGE50_YN        \n",
              " Min.   : 280000   Min.   : 4.0   Min.   : 15556   Length:10         \n",
              " 1st Qu.: 388425   1st Qu.: 8.0   1st Qu.: 23168   Class :character  \n",
              " Median :1020000   Median :16.0   Median : 40076   Mode  :character  \n",
              " Mean   : 884790   Mean   :29.6   Mean   : 53867                     \n",
              " 3rd Qu.:1172500   3rd Qu.:49.5   3rd Qu.: 55041                     \n",
              " Max.   :1605000   Max.   :90.0   Max.   :196667                     \n",
              "   AGE_GR10        \n",
              " Length:10         \n",
              " Class :character  \n",
              " Mode  :character  \n",
              "                   \n",
              "                   \n",
              "                   "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "describe(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "iaKKpW-s_l8z",
        "outputId": "74ff8b58-53ea-4b1c-8a44-cd95d992dfa2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A psych: 13 × 13</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>vars</th><th scope=col>n</th><th scope=col>mean</th><th scope=col>sd</th><th scope=col>median</th><th scope=col>trimmed</th><th scope=col>mad</th><th scope=col>min</th><th scope=col>max</th><th scope=col>range</th><th scope=col>skew</th><th scope=col>kurtosis</th><th scope=col>se</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>ID</th><td> 1</td><td>10</td><td>     5.50</td><td>3.027650e+00</td><td>      5.50</td><td>     5.500</td><td>     3.7065</td><td>     1.00</td><td>     10.0</td><td>      9.0</td><td> 0.00000000</td><td>-1.5616364</td><td>9.574271e-01</td></tr>\n",
              "\t<tr><th scope=row>SEX*</th><td> 2</td><td>10</td><td>     1.40</td><td>5.163978e-01</td><td>      1.00</td><td>     1.375</td><td>     0.0000</td><td>     1.00</td><td>      2.0</td><td>      1.0</td><td> 0.34856850</td><td>-2.0550000</td><td>1.632993e-01</td></tr>\n",
              "\t<tr><th scope=row>AGE</th><td> 3</td><td>10</td><td>    37.90</td><td>1.276671e+01</td><td>     39.00</td><td>    37.875</td><td>    16.3086</td><td>    20.00</td><td>     56.0</td><td>     36.0</td><td>-0.05156207</td><td>-1.7474305</td><td>4.037188e+00</td></tr>\n",
              "\t<tr><th scope=row>AREA*</th><td> 4</td><td>10</td><td>     2.00</td><td>9.428090e-01</td><td>      2.00</td><td>     1.875</td><td>     0.7413</td><td>     1.00</td><td>      4.0</td><td>      3.0</td><td> 0.71594562</td><td>-0.4687500</td><td>2.981424e-01</td></tr>\n",
              "\t<tr><th scope=row>AMT17</th><td> 5</td><td>10</td><td>548290.00</td><td>3.830392e+05</td><td> 485000.00</td><td>517500.000</td><td>404008.5000</td><td> 42900.00</td><td>1300000.0</td><td>1257100.0</td><td> 0.51531327</td><td>-0.9335146</td><td>1.211276e+05</td></tr>\n",
              "\t<tr><th scope=row>CNT17</th><td> 6</td><td>10</td><td>    15.70</td><td>1.539877e+01</td><td>     10.00</td><td>    13.250</td><td>    11.1195</td><td>     1.00</td><td>     50.0</td><td>     49.0</td><td> 0.98340486</td><td>-0.2616048</td><td>4.869520e+00</td></tr>\n",
              "\t<tr><th scope=row>AMT16</th><td> 7</td><td>10</td><td>336500.00</td><td>2.571862e+05</td><td> 275000.00</td><td>319375.000</td><td>240922.5000</td><td> 50000.00</td><td> 760000.0</td><td> 710000.0</td><td> 0.46008110</td><td>-1.5037415</td><td>8.132941e+04</td></tr>\n",
              "\t<tr><th scope=row>CNT16</th><td> 8</td><td>10</td><td>    13.90</td><td>1.388404e+01</td><td>      6.50</td><td>    12.125</td><td>     6.6717</td><td>     2.00</td><td>     40.0</td><td>     38.0</td><td> 0.69397748</td><td>-1.2845596</td><td>4.390520e+00</td></tr>\n",
              "\t<tr><th scope=row>AMT</th><td> 9</td><td>10</td><td>884790.00</td><td>4.813663e+05</td><td>1020000.00</td><td>870362.500</td><td>648637.5000</td><td>280000.00</td><td>1605000.0</td><td>1325000.0</td><td>-0.03644006</td><td>-1.7181262</td><td>1.522214e+05</td></tr>\n",
              "\t<tr><th scope=row>CNT</th><td>10</td><td>10</td><td>    29.60</td><td>2.896818e+01</td><td>     16.00</td><td>    25.250</td><td>    16.3086</td><td>     4.00</td><td>     90.0</td><td>     86.0</td><td> 0.85018359</td><td>-0.7893667</td><td>9.160543e+00</td></tr>\n",
              "\t<tr><th scope=row>AVG_AMT</th><td>11</td><td>10</td><td> 53866.64</td><td>5.318785e+04</td><td>  40075.76</td><td> 40805.528</td><td> 26236.2059</td><td> 15555.56</td><td> 196666.7</td><td> 181111.1</td><td> 1.83602911</td><td> 2.2604201</td><td>1.681948e+04</td></tr>\n",
              "\t<tr><th scope=row>AGE50_YN*</th><td>12</td><td>10</td><td>     1.30</td><td>4.830459e-01</td><td>      1.00</td><td>     1.250</td><td>     0.0000</td><td>     1.00</td><td>      2.0</td><td>      1.0</td><td> 0.74527080</td><td>-1.5728571</td><td>1.527525e-01</td></tr>\n",
              "\t<tr><th scope=row>AGE_GR10*</th><td>13</td><td>10</td><td>     2.60</td><td>1.349897e+00</td><td>      2.50</td><td>     2.625</td><td>     2.2239</td><td>     1.00</td><td>      4.0</td><td>      3.0</td><td>-0.06829789</td><td>-1.9365437</td><td>4.268749e-01</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA psych: 13 × 13\n\n| <!--/--> | vars &lt;int&gt; | n &lt;dbl&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; | median &lt;dbl&gt; | trimmed &lt;dbl&gt; | mad &lt;dbl&gt; | min &lt;dbl&gt; | max &lt;dbl&gt; | range &lt;dbl&gt; | skew &lt;dbl&gt; | kurtosis &lt;dbl&gt; | se &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| ID |  1 | 10 |      5.50 | 3.027650e+00 |       5.50 |      5.500 |      3.7065 |      1.00 |      10.0 |       9.0 |  0.00000000 | -1.5616364 | 9.574271e-01 |\n| SEX* |  2 | 10 |      1.40 | 5.163978e-01 |       1.00 |      1.375 |      0.0000 |      1.00 |       2.0 |       1.0 |  0.34856850 | -2.0550000 | 1.632993e-01 |\n| AGE |  3 | 10 |     37.90 | 1.276671e+01 |      39.00 |     37.875 |     16.3086 |     20.00 |      56.0 |      36.0 | -0.05156207 | -1.7474305 | 4.037188e+00 |\n| AREA* |  4 | 10 |      2.00 | 9.428090e-01 |       2.00 |      1.875 |      0.7413 |      1.00 |       4.0 |       3.0 |  0.71594562 | -0.4687500 | 2.981424e-01 |\n| AMT17 |  5 | 10 | 548290.00 | 3.830392e+05 |  485000.00 | 517500.000 | 404008.5000 |  42900.00 | 1300000.0 | 1257100.0 |  0.51531327 | -0.9335146 | 1.211276e+05 |\n| CNT17 |  6 | 10 |     15.70 | 1.539877e+01 |      10.00 |     13.250 |     11.1195 |      1.00 |      50.0 |      49.0 |  0.98340486 | -0.2616048 | 4.869520e+00 |\n| AMT16 |  7 | 10 | 336500.00 | 2.571862e+05 |  275000.00 | 319375.000 | 240922.5000 |  50000.00 |  760000.0 |  710000.0 |  0.46008110 | -1.5037415 | 8.132941e+04 |\n| CNT16 |  8 | 10 |     13.90 | 1.388404e+01 |       6.50 |     12.125 |      6.6717 |      2.00 |      40.0 |      38.0 |  0.69397748 | -1.2845596 | 4.390520e+00 |\n| AMT |  9 | 10 | 884790.00 | 4.813663e+05 | 1020000.00 | 870362.500 | 648637.5000 | 280000.00 | 1605000.0 | 1325000.0 | -0.03644006 | -1.7181262 | 1.522214e+05 |\n| CNT | 10 | 10 |     29.60 | 2.896818e+01 |      16.00 |     25.250 |     16.3086 |      4.00 |      90.0 |      86.0 |  0.85018359 | -0.7893667 | 9.160543e+00 |\n| AVG_AMT | 11 | 10 |  53866.64 | 5.318785e+04 |   40075.76 |  40805.528 |  26236.2059 |  15555.56 |  196666.7 |  181111.1 |  1.83602911 |  2.2604201 | 1.681948e+04 |\n| AGE50_YN* | 12 | 10 |      1.30 | 4.830459e-01 |       1.00 |      1.250 |      0.0000 |      1.00 |       2.0 |       1.0 |  0.74527080 | -1.5728571 | 1.527525e-01 |\n| AGE_GR10* | 13 | 10 |      2.60 | 1.349897e+00 |       2.50 |      2.625 |      2.2239 |      1.00 |       4.0 |       3.0 | -0.06829789 | -1.9365437 | 4.268749e-01 |\n\n",
            "text/latex": "A psych: 13 × 13\n\\begin{tabular}{r|lllllllllllll}\n  & vars & n & mean & sd & median & trimmed & mad & min & max & range & skew & kurtosis & se\\\\\n  & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\tID &  1 & 10 &      5.50 & 3.027650e+00 &       5.50 &      5.500 &      3.7065 &      1.00 &      10.0 &       9.0 &  0.00000000 & -1.5616364 & 9.574271e-01\\\\\n\tSEX* &  2 & 10 &      1.40 & 5.163978e-01 &       1.00 &      1.375 &      0.0000 &      1.00 &       2.0 &       1.0 &  0.34856850 & -2.0550000 & 1.632993e-01\\\\\n\tAGE &  3 & 10 &     37.90 & 1.276671e+01 &      39.00 &     37.875 &     16.3086 &     20.00 &      56.0 &      36.0 & -0.05156207 & -1.7474305 & 4.037188e+00\\\\\n\tAREA* &  4 & 10 &      2.00 & 9.428090e-01 &       2.00 &      1.875 &      0.7413 &      1.00 &       4.0 &       3.0 &  0.71594562 & -0.4687500 & 2.981424e-01\\\\\n\tAMT17 &  5 & 10 & 548290.00 & 3.830392e+05 &  485000.00 & 517500.000 & 404008.5000 &  42900.00 & 1300000.0 & 1257100.0 &  0.51531327 & -0.9335146 & 1.211276e+05\\\\\n\tCNT17 &  6 & 10 &     15.70 & 1.539877e+01 &      10.00 &     13.250 &     11.1195 &      1.00 &      50.0 &      49.0 &  0.98340486 & -0.2616048 & 4.869520e+00\\\\\n\tAMT16 &  7 & 10 & 336500.00 & 2.571862e+05 &  275000.00 & 319375.000 & 240922.5000 &  50000.00 &  760000.0 &  710000.0 &  0.46008110 & -1.5037415 & 8.132941e+04\\\\\n\tCNT16 &  8 & 10 &     13.90 & 1.388404e+01 &       6.50 &     12.125 &      6.6717 &      2.00 &      40.0 &      38.0 &  0.69397748 & -1.2845596 & 4.390520e+00\\\\\n\tAMT &  9 & 10 & 884790.00 & 4.813663e+05 & 1020000.00 & 870362.500 & 648637.5000 & 280000.00 & 1605000.0 & 1325000.0 & -0.03644006 & -1.7181262 & 1.522214e+05\\\\\n\tCNT & 10 & 10 &     29.60 & 2.896818e+01 &      16.00 &     25.250 &     16.3086 &      4.00 &      90.0 &      86.0 &  0.85018359 & -0.7893667 & 9.160543e+00\\\\\n\tAVG\\_AMT & 11 & 10 &  53866.64 & 5.318785e+04 &   40075.76 &  40805.528 &  26236.2059 &  15555.56 &  196666.7 &  181111.1 &  1.83602911 &  2.2604201 & 1.681948e+04\\\\\n\tAGE50\\_YN* & 12 & 10 &      1.30 & 4.830459e-01 &       1.00 &      1.250 &      0.0000 &      1.00 &       2.0 &       1.0 &  0.74527080 & -1.5728571 & 1.527525e-01\\\\\n\tAGE\\_GR10* & 13 & 10 &      2.60 & 1.349897e+00 &       2.50 &      2.625 &      2.2239 &      1.00 &       4.0 &       3.0 & -0.06829789 & -1.9365437 & 4.268749e-01\\\\\n\\end{tabular}\n",
            "text/plain": [
              "          vars n  mean      sd           median     trimmed    mad        \n",
              "ID         1   10      5.50 3.027650e+00       5.50      5.500      3.7065\n",
              "SEX*       2   10      1.40 5.163978e-01       1.00      1.375      0.0000\n",
              "AGE        3   10     37.90 1.276671e+01      39.00     37.875     16.3086\n",
              "AREA*      4   10      2.00 9.428090e-01       2.00      1.875      0.7413\n",
              "AMT17      5   10 548290.00 3.830392e+05  485000.00 517500.000 404008.5000\n",
              "CNT17      6   10     15.70 1.539877e+01      10.00     13.250     11.1195\n",
              "AMT16      7   10 336500.00 2.571862e+05  275000.00 319375.000 240922.5000\n",
              "CNT16      8   10     13.90 1.388404e+01       6.50     12.125      6.6717\n",
              "AMT        9   10 884790.00 4.813663e+05 1020000.00 870362.500 648637.5000\n",
              "CNT       10   10     29.60 2.896818e+01      16.00     25.250     16.3086\n",
              "AVG_AMT   11   10  53866.64 5.318785e+04   40075.76  40805.528  26236.2059\n",
              "AGE50_YN* 12   10      1.30 4.830459e-01       1.00      1.250      0.0000\n",
              "AGE_GR10* 13   10      2.60 1.349897e+00       2.50      2.625      2.2239\n",
              "          min       max       range     skew        kurtosis   se          \n",
              "ID             1.00      10.0       9.0  0.00000000 -1.5616364 9.574271e-01\n",
              "SEX*           1.00       2.0       1.0  0.34856850 -2.0550000 1.632993e-01\n",
              "AGE           20.00      56.0      36.0 -0.05156207 -1.7474305 4.037188e+00\n",
              "AREA*          1.00       4.0       3.0  0.71594562 -0.4687500 2.981424e-01\n",
              "AMT17      42900.00 1300000.0 1257100.0  0.51531327 -0.9335146 1.211276e+05\n",
              "CNT17          1.00      50.0      49.0  0.98340486 -0.2616048 4.869520e+00\n",
              "AMT16      50000.00  760000.0  710000.0  0.46008110 -1.5037415 8.132941e+04\n",
              "CNT16          2.00      40.0      38.0  0.69397748 -1.2845596 4.390520e+00\n",
              "AMT       280000.00 1605000.0 1325000.0 -0.03644006 -1.7181262 1.522214e+05\n",
              "CNT            4.00      90.0      86.0  0.85018359 -0.7893667 9.160543e+00\n",
              "AVG_AMT    15555.56  196666.7  181111.1  1.83602911  2.2604201 1.681948e+04\n",
              "AGE50_YN*      1.00       2.0       1.0  0.74527080 -1.5728571 1.527525e-01\n",
              "AGE_GR10*      1.00       4.0       3.0 -0.06829789 -1.9365437 4.268749e-01"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Group by Cat Variable"
      ],
      "metadata": {
        "id": "QqwwXOpUBRA-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df %>% group_by(AREA) %>% summarize(합계 = sum(AGE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "syfczKg6-a-S",
        "outputId": "8f9252b6-2208-4ce9-92b7-465e256ef9f8"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 4 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>AREA</th><th scope=col>합계</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>경기</td><td>134</td></tr>\n",
              "\t<tr><td>서울</td><td>197</td></tr>\n",
              "\t<tr><td>인천</td><td> 20</td></tr>\n",
              "\t<tr><td>제주</td><td> 28</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 4 × 2\n\n| AREA &lt;chr&gt; | 합계 &lt;dbl&gt; |\n|---|---|\n| 경기 | 134 |\n| 서울 | 197 |\n| 인천 |  20 |\n| 제주 |  28 |\n\n",
            "text/latex": "A tibble: 4 × 2\n\\begin{tabular}{ll}\n AREA & 합계\\\\\n <chr> & <dbl>\\\\\n\\hline\n\t 경기 & 134\\\\\n\t 서울 & 197\\\\\n\t 인천 &  20\\\\\n\t 제주 &  28\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  AREA 합계\n",
              "1 경기 134 \n",
              "2 서울 197 \n",
              "3 인천  20 \n",
              "4 제주  28 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df %>% group_by(AREA) %>% reframe(\n",
        "    평균 = mean(AGE),\n",
        "    중앙값 = median(AGE),\n",
        "    최댓값 = max(AGE),\n",
        "    최솟값 = min(AGE)\n",
        "    )"
      ],
      "metadata": {
        "id": "XUHPDFOXCp26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "36778c3e-dbdb-42f6-c2f6-7dccdb1311a0"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 4 × 5</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>AREA</th><th scope=col>평균</th><th scope=col>중앙값</th><th scope=col>최댓값</th><th scope=col>최솟값</th></tr>\n",
              "\t<tr><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><td>경기</td><td>44.66667</td><td>40</td><td>56</td><td>38</td></tr>\n",
              "\t<tr><td>서울</td><td>39.40000</td><td>47</td><td>50</td><td>23</td></tr>\n",
              "\t<tr><td>인천</td><td>20.00000</td><td>20</td><td>20</td><td>20</td></tr>\n",
              "\t<tr><td>제주</td><td>28.00000</td><td>28</td><td>28</td><td>28</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 4 × 5\n\n| AREA &lt;chr&gt; | 평균 &lt;dbl&gt; | 중앙값 &lt;dbl&gt; | 최댓값 &lt;dbl&gt; | 최솟값 &lt;dbl&gt; |\n|---|---|---|---|---|\n| 경기 | 44.66667 | 40 | 56 | 38 |\n| 서울 | 39.40000 | 47 | 50 | 23 |\n| 인천 | 20.00000 | 20 | 20 | 20 |\n| 제주 | 28.00000 | 28 | 28 | 28 |\n\n",
            "text/latex": "A tibble: 4 × 5\n\\begin{tabular}{lllll}\n AREA & 평균 & 중앙값 & 최댓값 & 최솟값\\\\\n <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t 경기 & 44.66667 & 40 & 56 & 38\\\\\n\t 서울 & 39.40000 & 47 & 50 & 23\\\\\n\t 인천 & 20.00000 & 20 & 20 & 20\\\\\n\t 제주 & 28.00000 & 28 & 28 & 28\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  AREA 평균     중앙값 최댓값 최솟값\n",
              "1 경기 44.66667 40     56     38    \n",
              "2 서울 39.40000 47     50     23    \n",
              "3 인천 20.00000 20     20     20    \n",
              "4 제주 28.00000 28     28     28    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing"
      ],
      "metadata": {
        "id": "IY82nljdCpGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"descr\")\n",
        "library(descr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucB1P1HpBu63",
        "outputId": "f77c9241-091a-4644-fe21-b54b86bd2235"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘xtable’\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Frequency"
      ],
      "metadata": {
        "id": "-iq8ClmTE33p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "table(df$AREA)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "acTAy8QaDreu",
        "outputId": "2c50231d-be00-49c1-a31a-8ec65376e6f7"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "경기 서울 인천 제주 \n",
              "   3    5    1    1 "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "freq(\n",
        "    df$AREA,\n",
        "    plot = TRUE,\n",
        "    main = \"by Area\"\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "1uQnwIe1BeKI",
        "outputId": "b9dc6856-3c2e-4e29-8bf3-b342d162d33b"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A freqtable: 5 × 2 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Frequency</th><th scope=col>Percent</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>경기</th><td> 3</td><td> 30</td></tr>\n",
              "\t<tr><th scope=row>서울</th><td> 5</td><td> 50</td></tr>\n",
              "\t<tr><th scope=row>인천</th><td> 1</td><td> 10</td></tr>\n",
              "\t<tr><th scope=row>제주</th><td> 1</td><td> 10</td></tr>\n",
              "\t<tr><th scope=row>Total</th><td>10</td><td>100</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA freqtable: 5 × 2 of type dbl\n\n| <!--/--> | Frequency | Percent |\n|---|---|---|\n| 경기 |  3 |  30 |\n| 서울 |  5 |  50 |\n| 인천 |  1 |  10 |\n| 제주 |  1 |  10 |\n| Total | 10 | 100 |\n\n",
            "text/latex": "A freqtable: 5 × 2 of type dbl\n\\begin{tabular}{r|ll}\n  & Frequency & Percent\\\\\n\\hline\n\t경기 &  3 &  30\\\\\n\t서울 &  5 &  50\\\\\n\t인천 &  1 &  10\\\\\n\t제주 &  1 &  10\\\\\n\tTotal & 10 & 100\\\\\n\\end{tabular}\n",
            "text/plain": [
              "      Frequency Percent\n",
              "경기   3         30    \n",
              "서울   5         50    \n",
              "인천   1         10    \n",
              "제주   1         10    \n",
              "Total 10        100    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “by Area”"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAC5VBMVEUAAAABAQECAgIDAwME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\nAAAAAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### hist"
      ],
      "metadata": {
        "id": "iCP2RozjE5_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hist(\n",
        "    df$AGE,\n",
        "    xlim = c(0, 60),\n",
        "    ylim = c(0, 5),\n",
        "    main = \"by Age\"\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "qNHIUF4xC7P_",
        "outputId": "d86d8b80-1808-4986-a90f-a19d32a299ba"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “by Age”"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAC+lBMVEUAAAABAQECAgIDAwME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"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### bar"
      ],
      "metadata": {
        "id": "wtdjIST_E8Dd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "barplot(\n",
        "    table(df$SEX),\n",
        "    main = \"by Sex\",\n",
        "    xlab = \"Sex\",\n",
        "    ylab = \"Frequency\",\n",
        "    names = c(\"female\", \"male\"),\n",
        "    col = c(\"pink\", \"navy\")\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "qKxleW6kEKVb",
        "outputId": "fa0448bc-3475-41f3-d37f-5926002c8364"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “by Sex”"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### box"
      ],
      "metadata": {
        "id": "mxbn2EXaFMLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "boxplot(\n",
        "    df$CNT17, df$CNT16,\n",
        "    main = \"Count by Year\",\n",
        "    xlab = \"Year\",\n",
        "    ylab = \"Count\",\n",
        "    names = c(\"2017\", \"2016\"),\n",
        "    col = c(\"yellow\", \"green\")\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "l0ie-S9jFOW-",
        "outputId": "6ad5d08a-d60f-442a-b104-4c58c78bbb54"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “Count by Year”"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nsAMACISwAwAIhLADAAiEsAMACMQ/Ad4viYL4QdtjAAAAAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Join"
      ],
      "metadata": {
        "id": "HELM99kBFn3y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 세로 결합(행 결합)"
      ],
      "metadata": {
        "id": "Kh3-XYGmJQ_L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m_history <- read_excel(\"data/Sample2.xlsx\")\n",
        "f_history <- read_excel(\"data/Sample3.xlsx\")"
      ],
      "metadata": {
        "id": "7xN6hevIKHQ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history <- bind_rows(m_history, f_history)\n",
        "history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "s0d5K9z5Fuhv",
        "outputId": "40e2f3b7-eaed-4731-ce20-41d24d95e335"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>Y17_CNT</th><th scope=col>AMT16</th><th scope=col>Y16_CNT</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>250000</td><td> 2</td></tr>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td></tr>\n",
              "\t<tr><td> 3</td><td>F</td><td>28</td><td>제주</td><td> 275000</td><td>10</td><td> 50000</td><td> 5</td></tr>\n",
              "\t<tr><td> 6</td><td>F</td><td>23</td><td>서울</td><td>  42900</td><td> 1</td><td>300000</td><td> 6</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 8\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | Y17_CNT &lt;dbl&gt; | AMT16 &lt;dbl&gt; | Y16_CNT &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 |\n|  9 | M | 20 | 인천 |  930000 |  4 | 250000 |  2 |\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 |\n|  3 | F | 28 | 제주 |  275000 | 10 |  50000 |  5 |\n|  6 | F | 23 | 서울 |   42900 |  1 | 300000 |  6 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 |\n\n",
            "text/latex": "A tibble: 10 × 8\n\\begin{tabular}{llllllll}\n ID & SEX & AGE & AREA & AMT17 & Y17\\_CNT & AMT16 & Y16\\_CNT\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 & 250000 &  2\\\\\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40\\\\\n\t  3 & F & 28 & 제주 &  275000 & 10 &  50000 &  5\\\\\n\t  6 & F & 23 & 서울 &   42900 &  1 & 300000 &  6\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   Y17_CNT AMT16  Y16_CNT\n",
              "1   2 M   40  경기  450000 25      700000 30     \n",
              "2   4 M   50  서울  400000  8      125000  3     \n",
              "3   5 M   27  서울  845000 30      760000 28     \n",
              "4   9 M   20  인천  930000  4      250000  2     \n",
              "5   1 F   50  서울 1300000 50      100000 40     \n",
              "6   3 F   28  제주  275000 10       50000  5     \n",
              "7   6 F   23  서울   42900  1      300000  6     \n",
              "8   7 F   56  경기  150000  2      130000  2     \n",
              "9   8 F   47  서울  570000 10      400000  7     \n",
              "10 10 F   38  경기  520000 17      550000 16     "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 가로 결합(열 결합)"
      ],
      "metadata": {
        "id": "o0rl-S4jJwGS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y17_history <- read_excel(\"data/Sample4.xlsx\")\n",
        "y16_history <- read_excel(\"data/Sample5.xlsx\")"
      ],
      "metadata": {
        "id": "9FkklIF4KMX6"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### left_join"
      ],
      "metadata": {
        "id": "ZX77zU9zJzQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 결합 기준 변수 ID 에 대하여 좌측 데이터 세트(y17_history) 중심으로 결합\n",
        "# ID == 3, 6 인 데이터는 좌측 데이터 세트에 존재하지 않으므로 누락되었음\n",
        "\n",
        "left_join(y17_history, y16_history, by = \"ID\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "P2h3mIYLJ8We",
        "outputId": "6a4d138d-17f9-4470-b878-ac31a9725133"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 8 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>Y17_CNT</th><th scope=col>AMT16</th><th scope=col>Y16_CNT</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td></tr>\n",
              "\t<tr><td> 9</td><td>M</td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>    NA</td><td>NA</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 8 × 8\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | Y17_CNT &lt;dbl&gt; | AMT16 &lt;dbl&gt; | Y16_CNT &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |\n|  9 | M | 20 | 인천 |  930000 |  4 |     NA | NA |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 |\n\n",
            "text/latex": "A tibble: 8 × 8\n\\begin{tabular}{llllllll}\n ID & SEX & AGE & AREA & AMT17 & Y17\\_CNT & AMT16 & Y16\\_CNT\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7\\\\\n\t  9 & M & 20 & 인천 &  930000 &  4 &     NA & NA\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  ID SEX AGE AREA AMT17   Y17_CNT AMT16  Y16_CNT\n",
              "1  1 F   50  서울 1300000 50      100000 40     \n",
              "2  2 M   40  경기  450000 25      700000 30     \n",
              "3  4 M   50  서울  400000  8      125000  3     \n",
              "4  5 M   27  서울  845000 30      760000 28     \n",
              "5  7 F   56  경기  150000  2      130000  2     \n",
              "6  8 F   47  서울  570000 10      400000  7     \n",
              "7  9 M   20  인천  930000  4          NA NA     \n",
              "8 10 F   38  경기  520000 17      550000 16     "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### inner_join"
      ],
      "metadata": {
        "id": "HABhgEINJ8z-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 결합 기준 변수 ID 에 대하여 두 데이터 세트의 교집합만 결합\n",
        "# ID == 3, 6 은 좌측 데이터 세트에, ID == 9 는 우측 데이터 세트에 존재하지 않으므로 누락되었음\n",
        "\n",
        "inner_join(y17_history, y16_history, by = \"ID\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "vZWLmQOmKCaW",
        "outputId": "2415f053-feb1-4a99-8089-6c39e894728f"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 7 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>Y17_CNT</th><th scope=col>AMT16</th><th scope=col>Y16_CNT</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F</td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td></tr>\n",
              "\t<tr><td> 2</td><td>M</td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td></tr>\n",
              "\t<tr><td> 4</td><td>M</td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td></tr>\n",
              "\t<tr><td> 5</td><td>M</td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td></tr>\n",
              "\t<tr><td> 7</td><td>F</td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td></tr>\n",
              "\t<tr><td> 8</td><td>F</td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td></tr>\n",
              "\t<tr><td>10</td><td>F</td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 7 × 8\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | Y17_CNT &lt;dbl&gt; | AMT16 &lt;dbl&gt; | Y16_CNT &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n|  1 | F | 50 | 서울 | 1300000 | 50 | 100000 | 40 |\n|  2 | M | 40 | 경기 |  450000 | 25 | 700000 | 30 |\n|  4 | M | 50 | 서울 |  400000 |  8 | 125000 |  3 |\n|  5 | M | 27 | 서울 |  845000 | 30 | 760000 | 28 |\n|  7 | F | 56 | 경기 |  150000 |  2 | 130000 |  2 |\n|  8 | F | 47 | 서울 |  570000 | 10 | 400000 |  7 |\n| 10 | F | 38 | 경기 |  520000 | 17 | 550000 | 16 |\n\n",
            "text/latex": "A tibble: 7 × 8\n\\begin{tabular}{llllllll}\n ID & SEX & AGE & AREA & AMT17 & Y17\\_CNT & AMT16 & Y16\\_CNT\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  1 & F & 50 & 서울 & 1300000 & 50 & 100000 & 40\\\\\n\t  2 & M & 40 & 경기 &  450000 & 25 & 700000 & 30\\\\\n\t  4 & M & 50 & 서울 &  400000 &  8 & 125000 &  3\\\\\n\t  5 & M & 27 & 서울 &  845000 & 30 & 760000 & 28\\\\\n\t  7 & F & 56 & 경기 &  150000 &  2 & 130000 &  2\\\\\n\t  8 & F & 47 & 서울 &  570000 & 10 & 400000 &  7\\\\\n\t 10 & F & 38 & 경기 &  520000 & 17 & 550000 & 16\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  ID SEX AGE AREA AMT17   Y17_CNT AMT16  Y16_CNT\n",
              "1  1 F   50  서울 1300000 50      100000 40     \n",
              "2  2 M   40  경기  450000 25      700000 30     \n",
              "3  4 M   50  서울  400000  8      125000  3     \n",
              "4  5 M   27  서울  845000 30      760000 28     \n",
              "5  7 F   56  경기  150000  2      130000  2     \n",
              "6  8 F   47  서울  570000 10      400000  7     \n",
              "7 10 F   38  경기  520000 17      550000 16     "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### full_join"
      ],
      "metadata": {
        "id": "E3mek9EDJ_wf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 결합 기준 변수 ID 에 대하여 두 데이터 세트의 합집합을 결합\n",
        "\n",
        "full_join(y17_history, y16_history, by = \"ID\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "Jq2QvTq7KDTM",
        "outputId": "c29d1613-ef48-4c20-c839-3247a7306c9e"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 10 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>AGE</th><th scope=col>AREA</th><th scope=col>AMT17</th><th scope=col>Y17_CNT</th><th scope=col>AMT16</th><th scope=col>Y16_CNT</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&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></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 1</td><td>F </td><td>50</td><td>서울</td><td>1300000</td><td>50</td><td>100000</td><td>40</td></tr>\n",
              "\t<tr><td> 2</td><td>M </td><td>40</td><td>경기</td><td> 450000</td><td>25</td><td>700000</td><td>30</td></tr>\n",
              "\t<tr><td> 4</td><td>M </td><td>50</td><td>서울</td><td> 400000</td><td> 8</td><td>125000</td><td> 3</td></tr>\n",
              "\t<tr><td> 5</td><td>M </td><td>27</td><td>서울</td><td> 845000</td><td>30</td><td>760000</td><td>28</td></tr>\n",
              "\t<tr><td> 7</td><td>F </td><td>56</td><td>경기</td><td> 150000</td><td> 2</td><td>130000</td><td> 2</td></tr>\n",
              "\t<tr><td> 8</td><td>F </td><td>47</td><td>서울</td><td> 570000</td><td>10</td><td>400000</td><td> 7</td></tr>\n",
              "\t<tr><td> 9</td><td>M </td><td>20</td><td>인천</td><td> 930000</td><td> 4</td><td>    NA</td><td>NA</td></tr>\n",
              "\t<tr><td>10</td><td>F </td><td>38</td><td>경기</td><td> 520000</td><td>17</td><td>550000</td><td>16</td></tr>\n",
              "\t<tr><td> 3</td><td>NA</td><td>NA</td><td>NA  </td><td>     NA</td><td>NA</td><td> 50000</td><td> 5</td></tr>\n",
              "\t<tr><td> 6</td><td>NA</td><td>NA</td><td>NA  </td><td>     NA</td><td>NA</td><td>300000</td><td> 6</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 10 × 8\n\n| ID &lt;dbl&gt; | SEX &lt;chr&gt; | AGE &lt;dbl&gt; | AREA &lt;chr&gt; | AMT17 &lt;dbl&gt; | Y17_CNT &lt;dbl&gt; | AMT16 &lt;dbl&gt; | Y16_CNT &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n|  1 | F  | 50 | 서울 | 1300000 | 50 | 100000 | 40 |\n|  2 | M  | 40 | 경기 |  450000 | 25 | 700000 | 30 |\n|  4 | M  | 50 | 서울 |  400000 |  8 | 125000 |  3 |\n|  5 | M  | 27 | 서울 |  845000 | 30 | 760000 | 28 |\n|  7 | F  | 56 | 경기 |  150000 |  2 | 130000 |  2 |\n|  8 | F  | 47 | 서울 |  570000 | 10 | 400000 |  7 |\n|  9 | M  | 20 | 인천 |  930000 |  4 |     NA | NA |\n| 10 | F  | 38 | 경기 |  520000 | 17 | 550000 | 16 |\n|  3 | NA | NA | NA   |      NA | NA |  50000 |  5 |\n|  6 | NA | NA | NA   |      NA | NA | 300000 |  6 |\n\n",
            "text/latex": "A tibble: 10 × 8\n\\begin{tabular}{llllllll}\n ID & SEX & AGE & AREA & AMT17 & Y17\\_CNT & AMT16 & Y16\\_CNT\\\\\n <dbl> & <chr> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t  1 & F  & 50 & 서울 & 1300000 & 50 & 100000 & 40\\\\\n\t  2 & M  & 40 & 경기 &  450000 & 25 & 700000 & 30\\\\\n\t  4 & M  & 50 & 서울 &  400000 &  8 & 125000 &  3\\\\\n\t  5 & M  & 27 & 서울 &  845000 & 30 & 760000 & 28\\\\\n\t  7 & F  & 56 & 경기 &  150000 &  2 & 130000 &  2\\\\\n\t  8 & F  & 47 & 서울 &  570000 & 10 & 400000 &  7\\\\\n\t  9 & M  & 20 & 인천 &  930000 &  4 &     NA & NA\\\\\n\t 10 & F  & 38 & 경기 &  520000 & 17 & 550000 & 16\\\\\n\t  3 & NA & NA & NA   &      NA & NA &  50000 &  5\\\\\n\t  6 & NA & NA & NA   &      NA & NA & 300000 &  6\\\\\n\\end{tabular}\n",
            "text/plain": [
              "   ID SEX AGE AREA AMT17   Y17_CNT AMT16  Y16_CNT\n",
              "1   1 F   50  서울 1300000 50      100000 40     \n",
              "2   2 M   40  경기  450000 25      700000 30     \n",
              "3   4 M   50  서울  400000  8      125000  3     \n",
              "4   5 M   27  서울  845000 30      760000 28     \n",
              "5   7 F   56  경기  150000  2      130000  2     \n",
              "6   8 F   47  서울  570000 10      400000  7     \n",
              "7   9 M   20  인천  930000  4          NA NA     \n",
              "8  10 F   38  경기  520000 17      550000 16     \n",
              "9   3 NA  NA  NA        NA NA       50000  5     \n",
              "10  6 NA  NA  NA        NA NA      300000  6     "
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}