adash333/200726_-002.ipynb Secret

## 200726_-002.ipynb
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  "metadata": {
    "colab": {
      "name": "200726_oresen_002.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/adash333/fd7e673debefa7547895f05d3b50c04f/200726_-002.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ocso8i49BDz1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "346c1aca-e19e-4836-a2e0-37c7227dccd9"
      },
      "source": [
        "library(tidyverse)\n",
        " \n",
        "head(ToothGrowth)\n",
        " \n",
        "d <- as_tibble(ToothGrowth)\n",
        "head(d)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.0 ──\n",
            "\n",
            "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
            "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.0.2     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.0\n",
            "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.0     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
            "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
            "\n",
            "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
            "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
            "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  len  supp dose\n",
              "1  4.2 VC   0.5 \n",
              "2 11.5 VC   0.5 \n",
              "3  7.3 VC   0.5 \n",
              "4  5.8 VC   0.5 \n",
              "5  6.4 VC   0.5 \n",
              "6 10.0 VC   0.5 "
            ],
            "text/latex": "A data.frame: 6 × 3\n\\begin{tabular}{r|lll}\n  & len & supp & dose\\\\\n  & <dbl> & <fct> & <dbl>\\\\\n\\hline\n\t1 &  4.2 & VC & 0.5\\\\\n\t2 & 11.5 & VC & 0.5\\\\\n\t3 &  7.3 & VC & 0.5\\\\\n\t4 &  5.8 & VC & 0.5\\\\\n\t5 &  6.4 & VC & 0.5\\\\\n\t6 & 10.0 & VC & 0.5\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 3\n\n| <!--/--> | len &lt;dbl&gt; | supp &lt;fct&gt; | dose &lt;dbl&gt; |\n|---|---|---|---|\n| 1 |  4.2 | VC | 0.5 |\n| 2 | 11.5 | VC | 0.5 |\n| 3 |  7.3 | VC | 0.5 |\n| 4 |  5.8 | VC | 0.5 |\n| 5 |  6.4 | VC | 0.5 |\n| 6 | 10.0 | VC | 0.5 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>len</th><th scope=col>supp</th><th scope=col>dose</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td> 4.2</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>11.5</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><th scope=row>3</th><td> 7.3</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><th scope=row>4</th><td> 5.8</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><th scope=row>5</th><td> 6.4</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>10.0</td><td>VC</td><td>0.5</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  len  supp dose\n",
              "1  4.2 VC   0.5 \n",
              "2 11.5 VC   0.5 \n",
              "3  7.3 VC   0.5 \n",
              "4  5.8 VC   0.5 \n",
              "5  6.4 VC   0.5 \n",
              "6 10.0 VC   0.5 "
            ],
            "text/latex": "A tibble: 6 × 3\n\\begin{tabular}{lll}\n len & supp & dose\\\\\n <dbl> & <fct> & <dbl>\\\\\n\\hline\n\t  4.2 & VC & 0.5\\\\\n\t 11.5 & VC & 0.5\\\\\n\t  7.3 & VC & 0.5\\\\\n\t  5.8 & VC & 0.5\\\\\n\t  6.4 & VC & 0.5\\\\\n\t 10.0 & VC & 0.5\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 3\n\n| len &lt;dbl&gt; | supp &lt;fct&gt; | dose &lt;dbl&gt; |\n|---|---|---|\n|  4.2 | VC | 0.5 |\n| 11.5 | VC | 0.5 |\n|  7.3 | VC | 0.5 |\n|  5.8 | VC | 0.5 |\n|  6.4 | VC | 0.5 |\n| 10.0 | VC | 0.5 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 6 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>len</th><th scope=col>supp</th><th scope=col>dose</th></tr>\n",
              "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td> 4.2</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><td>11.5</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><td> 7.3</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><td> 5.8</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><td> 6.4</td><td>VC</td><td>0.5</td></tr>\n",
              "\t<tr><td>10.0</td><td>VC</td><td>0.5</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJCd0qDIBwrd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "53740fa6-b162-4cda-9eef-b464b2c315c1"
      },
      "source": [
        "# https://heavywatal.github.io/rstats/dplyr.html\n",
        "# tidyverse内のdplyrのsummarize()関数を利用\n",
        "summary_d <- d %>%\n",
        "  group_by(supp, dose) %>%\n",
        "  summarize(\n",
        "    length = mean(len), \n",
        "    sd = sd(len)\n",
        "  )\n",
        "summary_d"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "`summarise()` regrouping output by 'supp' (override with `.groups` argument)\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  supp dose length sd      \n",
              "1 OJ   0.5  13.23  4.459709\n",
              "2 OJ   1.0  22.70  3.910953\n",
              "3 OJ   2.0  26.06  2.655058\n",
              "4 VC   0.5   7.98  2.746634\n",
              "5 VC   1.0  16.77  2.515309\n",
              "6 VC   2.0  26.14  4.797731"
            ],
            "text/latex": "A grouped\\_df: 6 × 4\n\\begin{tabular}{llll}\n supp & dose & length & sd\\\\\n <fct> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t OJ & 0.5 & 13.23 & 4.459709\\\\\n\t OJ & 1.0 & 22.70 & 3.910953\\\\\n\t OJ & 2.0 & 26.06 & 2.655058\\\\\n\t VC & 0.5 &  7.98 & 2.746634\\\\\n\t VC & 1.0 & 16.77 & 2.515309\\\\\n\t VC & 2.0 & 26.14 & 4.797731\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA grouped_df: 6 × 4\n\n| supp &lt;fct&gt; | dose &lt;dbl&gt; | length &lt;dbl&gt; | sd &lt;dbl&gt; |\n|---|---|---|---|\n| OJ | 0.5 | 13.23 | 4.459709 |\n| OJ | 1.0 | 22.70 | 3.910953 |\n| OJ | 2.0 | 26.06 | 2.655058 |\n| VC | 0.5 |  7.98 | 2.746634 |\n| VC | 1.0 | 16.77 | 2.515309 |\n| VC | 2.0 | 26.14 | 4.797731 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A grouped_df: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>supp</th><th scope=col>dose</th><th scope=col>length</th><th scope=col>sd</th></tr>\n",
              "\t<tr><th scope=col>&lt;fct&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>OJ</td><td>0.5</td><td>13.23</td><td>4.459709</td></tr>\n",
              "\t<tr><td>OJ</td><td>1.0</td><td>22.70</td><td>3.910953</td></tr>\n",
              "\t<tr><td>OJ</td><td>2.0</td><td>26.06</td><td>2.655058</td></tr>\n",
              "\t<tr><td>VC</td><td>0.5</td><td> 7.98</td><td>2.746634</td></tr>\n",
              "\t<tr><td>VC</td><td>1.0</td><td>16.77</td><td>2.515309</td></tr>\n",
              "\t<tr><td>VC</td><td>2.0</td><td>26.14</td><td>4.797731</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGmxvx61DimN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "6c623f55-3eaf-4b8f-8095-7576c1c30ece"
      },
      "source": [
        "ggplot(\n",
        "  summary_d, \n",
        "  aes(x = dose, y = length, linetype = supp )) +\n",
        "  geom_line() +\n",
        "  geom_point() +\n",
        "  geom_errorbar(aes(ymax = length + sd, ymin = length - sd), width = 0.1) +\n",
        "  theme_classic()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IyQbFpBmMw3p51t46+AEZIL8A/JxhWE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\njQL/1OR5q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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    }
  ]
}