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200726_ggplot-line-chart_001.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "200726_ggplot-line-chart_001.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "ir",
"display_name": "R"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/adash333/af401dc1aca27c9dad9a37ccfd076beb/200726_ggplot-line-chart_001.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": 367
},
"outputId": "264bdf13-43ef-4106-f076-0a57d90c6de9"
},
"source": [
"# 後で使用するので、とりあえずtidyverseを使用するための準備\n",
"library(tidyverse)\n",
"\n",
"# read_excel()関数を使用するための準備\n",
"library(readxl)\n",
"\n",
"# colaboratoryでは content フォルダにbook2.xlsxなどをアップロードする\n",
"# Rでのデフォルトの場所は content　フォルダとなっている\n",
"# d に、book2.xlsxの1番目のsheetをtibbleデータとして代入する\n",
"# d <- read_excel(\"book2.xlsx\", sheet=1)\n",
"# d\n",
"\n",
"# Google Colaboratoryでは、book2.xlsxはすぐに消えてしまうので、\n",
"# 今回は、以下にtibbleでbook2.xlsxの中身を定義しておきます。\n",
"\n",
"d <- tribble(\n",
" ~id, ~age, ~sex, ~DM,\t~HbA1c00M, ~HbA1c01M,\t~HbA1c02M, ~HbA1c03M,\n",
" 1,\t56,\t\"M\",\t1,\t7.3,\t7.4,\t7.3,\t7.4,\n",
" 2,\t77,\t\"F\",\t1,\t8.3,\t8.4,\t8.3,\t8.2,\n",
" 3,\t68,\t\"M\",\t1,\t7.1,\t7,\t7,\t7,\n",
" 4,\t81,\t\"F\",\t1,\t7.6,\t7.4,\t7.3,\t7.4,\n",
" 5,\t42,\t\"M\",\t1,\t10.1,\t8.4,\t7.3,\t6.5,\n",
" 6,\t65,\t\"M\",\t1,\t6.1,\t6.2,\t6.,\t6.3,\n",
" 7,\t68,\t\"F\",\t1,\t7.8,\t7.4,\t7.9,\t8.4,\n",
")\n",
"d"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.2.1 ──\n",
"\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.2.1 \u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.2\n",
"\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 2.1.3 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 0.8.3\n",
"\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 0.8.3 \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 0.4.0\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"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" id age sex DM HbA1c00M HbA1c01M HbA1c02M HbA1c03M\n",
"1 1 56 M 1 7.3 7.4 7.3 7.4 \n",
"2 2 77 F 1 8.3 8.4 8.3 8.2 \n",
"3 3 68 M 1 7.1 7.0 7.0 7.0 \n",
"4 4 81 F 1 7.6 7.4 7.3 7.4 \n",
"5 5 42 M 1 10.1 8.4 7.3 6.5 \n",
"6 6 65 M 1 6.1 6.2 6.0 6.3 \n",
"7 7 68 F 1 7.8 7.4 7.9 8.4 "
],
"text/latex": "A tibble: 7 × 8\n\\begin{tabular}{r|llllllll}\n id & age & sex & DM & HbA1c00M & HbA1c01M & HbA1c02M & HbA1c03M\\\\\n <dbl> & <dbl> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t 1 & 56 & M & 1 & 7.3 & 7.4 & 7.3 & 7.4\\\\\n\t 2 & 77 & F & 1 & 8.3 & 8.4 & 8.3 & 8.2\\\\\n\t 3 & 68 & M & 1 & 7.1 & 7.0 & 7.0 & 7.0\\\\\n\t 4 & 81 & F & 1 & 7.6 & 7.4 & 7.3 & 7.4\\\\\n\t 5 & 42 & M & 1 & 10.1 & 8.4 & 7.3 & 6.5\\\\\n\t 6 & 65 & M & 1 & 6.1 & 6.2 & 6.0 & 6.3\\\\\n\t 7 & 68 & F & 1 & 7.8 & 7.4 & 7.9 & 8.4\\\\\n\\end{tabular}\n",
"text/markdown": "\nA tibble: 7 × 8\n\n| id &lt;dbl&gt; | age &lt;dbl&gt; | sex &lt;chr&gt; | DM &lt;dbl&gt; | HbA1c00M &lt;dbl&gt; | HbA1c01M &lt;dbl&gt; | HbA1c02M &lt;dbl&gt; | HbA1c03M &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n| 1 | 56 | M | 1 | 7.3 | 7.4 | 7.3 | 7.4 |\n| 2 | 77 | F | 1 | 8.3 | 8.4 | 8.3 | 8.2 |\n| 3 | 68 | M | 1 | 7.1 | 7.0 | 7.0 | 7.0 |\n| 4 | 81 | F | 1 | 7.6 | 7.4 | 7.3 | 7.4 |\n| 5 | 42 | M | 1 | 10.1 | 8.4 | 7.3 | 6.5 |\n| 6 | 65 | M | 1 | 6.1 | 6.2 | 6.0 | 6.3 |\n| 7 | 68 | F | 1 | 7.8 | 7.4 | 7.9 | 8.4 |\n\n",
"text/html": [
"<table>\n",
"<caption>A tibble: 7 × 8</caption>\n",
"<thead>\n",
"\t<tr><th scope=col>id</th><th scope=col>age</th><th scope=col>sex</th><th scope=col>DM</th><th scope=col>HbA1c00M</th><th scope=col>HbA1c01M</th><th scope=col>HbA1c02M</th><th scope=col>HbA1c03M</th></tr>\n",
"\t<tr><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;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>56</td><td>M</td><td>1</td><td> 7.3</td><td>7.4</td><td>7.3</td><td>7.4</td></tr>\n",
"\t<tr><td>2</td><td>77</td><td>F</td><td>1</td><td> 8.3</td><td>8.4</td><td>8.3</td><td>8.2</td></tr>\n",
"\t<tr><td>3</td><td>68</td><td>M</td><td>1</td><td> 7.1</td><td>7.0</td><td>7.0</td><td>7.0</td></tr>\n",
"\t<tr><td>4</td><td>81</td><td>F</td><td>1</td><td> 7.6</td><td>7.4</td><td>7.3</td><td>7.4</td></tr>\n",
"\t<tr><td>5</td><td>42</td><td>M</td><td>1</td><td>10.1</td><td>8.4</td><td>7.3</td><td>6.5</td></tr>\n",
"\t<tr><td>6</td><td>65</td><td>M</td><td>1</td><td> 6.1</td><td>6.2</td><td>6.0</td><td>6.3</td></tr>\n",
"\t<tr><td>7</td><td>68</td><td>F</td><td>1</td><td> 7.8</td><td>7.4</td><td>7.9</td><td>8.4</td></tr>\n",
"</tbody>\n",
"</table>\n"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XIA194jNF5JZ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 657
},
"outputId": "1fe2064f-dbf9-40ab-de8b-1c9cfc09521a"
},
"source": [
"# 横長のデータを縦長(tidy data)にする\n",
"tidy_d <- gather(d, \n",
" key=\"time\", value=\"HbA1c\", # 新しくできる列の名前を指定\n",
" HbA1c00M, HbA1c01M, HbA1c02M,\tHbA1c03M) # 変形する対象の列を指定\n",
"tidy_d"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" id age sex DM time HbA1c\n",
"1 1 56 M 1 HbA1c00M 7.3 \n",
"2 2 77 F 1 HbA1c00M 8.3 \n",
"3 3 68 M 1 HbA1c00M 7.1 \n",
"4 4 81 F 1 HbA1c00M 7.6 \n",
"5 5 42 M 1 HbA1c00M 10.1 \n",
"6 6 65 M 1 HbA1c00M 6.1 \n",
"7 7 68 F 1 HbA1c00M 7.8 \n",
"8 1 56 M 1 HbA1c01M 7.4 \n",
"9 2 77 F 1 HbA1c01M 8.4 \n",
"10 3 68 M 1 HbA1c01M 7.0 \n",
"11 4 81 F 1 HbA1c01M 7.4 \n",
"12 5 42 M 1 HbA1c01M 8.4 \n",
"13 6 65 M 1 HbA1c01M 6.2 \n",
"14 7 68 F 1 HbA1c01M 7.4 \n",
"15 1 56 M 1 HbA1c02M 7.3 \n",
"16 2 77 F 1 HbA1c02M 8.3 \n",
"17 3 68 M 1 HbA1c02M 7.0 \n",
"18 4 81 F 1 HbA1c02M 7.3 \n",
"19 5 42 M 1 HbA1c02M 7.3 \n",
"20 6 65 M 1 HbA1c02M 6.0 \n",
"21 7 68 F 1 HbA1c02M 7.9 \n",
"22 1 56 M 1 HbA1c03M 7.4 \n",
"23 2 77 F 1 HbA1c03M 8.2 \n",
"24 3 68 M 1 HbA1c03M 7.0 \n",
"25 4 81 F 1 HbA1c03M 7.4 \n",
"26 5 42 M 1 HbA1c03M 6.5 \n",
"27 6 65 M 1 HbA1c03M 6.3 \n",
"28 7 68 F 1 HbA1c03M 8.4 "
],
"text/latex": "A tibble: 28 × 6\n\\begin{tabular}{r|llllll}\n id & age & sex & DM & time & HbA1c\\\\\n <dbl> & <dbl> & <chr> & <dbl> & <chr> & <dbl>\\\\\n\\hline\n\t 1 & 56 & M & 1 & HbA1c00M & 7.3\\\\\n\t 2 & 77 & F & 1 & HbA1c00M & 8.3\\\\\n\t 3 & 68 & M & 1 & HbA1c00M & 7.1\\\\\n\t 4 & 81 & F & 1 & HbA1c00M & 7.6\\\\\n\t 5 & 42 & M & 1 & HbA1c00M & 10.1\\\\\n\t 6 & 65 & M & 1 & HbA1c00M & 6.1\\\\\n\t 7 & 68 & F & 1 & HbA1c00M & 7.8\\\\\n\t 1 & 56 & M & 1 & HbA1c01M & 7.4\\\\\n\t 2 & 77 & F & 1 & HbA1c01M & 8.4\\\\\n\t 3 & 68 & M & 1 & HbA1c01M & 7.0\\\\\n\t 4 & 81 & F & 1 & HbA1c01M & 7.4\\\\\n\t 5 & 42 & M & 1 & HbA1c01M & 8.4\\\\\n\t 6 & 65 & M & 1 & HbA1c01M & 6.2\\\\\n\t 7 & 68 & F & 1 & HbA1c01M & 7.4\\\\\n\t 1 & 56 & M & 1 & HbA1c02M & 7.3\\\\\n\t 2 & 77 & F & 1 & HbA1c02M & 8.3\\\\\n\t 3 & 68 & M & 1 & HbA1c02M & 7.0\\\\\n\t 4 & 81 & F & 1 & HbA1c02M & 7.3\\\\\n\t 5 & 42 & M & 1 & HbA1c02M & 7.3\\\\\n\t 6 & 65 & M & 1 & HbA1c02M & 6.0\\\\\n\t 7 & 68 & F & 1 & HbA1c02M & 7.9\\\\\n\t 1 & 56 & M & 1 & HbA1c03M & 7.4\\\\\n\t 2 & 77 & F & 1 & HbA1c03M & 8.2\\\\\n\t 3 & 68 & M & 1 & HbA1c03M & 7.0\\\\\n\t 4 & 81 & F & 1 & HbA1c03M & 7.4\\\\\n\t 5 & 42 & M & 1 & HbA1c03M & 6.5\\\\\n\t 6 & 65 & M & 1 & HbA1c03M & 6.3\\\\\n\t 7 & 68 & F & 1 & HbA1c03M & 8.4\\\\\n\\end{tabular}\n",
"text/markdown": "\nA tibble: 28 × 6\n\n| id &lt;dbl&gt; | age &lt;dbl&gt; | sex &lt;chr&gt; | DM &lt;dbl&gt; | time &lt;chr&gt; | HbA1c &lt;dbl&gt; |\n|---|---|---|---|---|---|\n| 1 | 56 | M | 1 | HbA1c00M | 7.3 |\n| 2 | 77 | F | 1 | HbA1c00M | 8.3 |\n| 3 | 68 | M | 1 | HbA1c00M | 7.1 |\n| 4 | 81 | F | 1 | HbA1c00M | 7.6 |\n| 5 | 42 | M | 1 | HbA1c00M | 10.1 |\n| 6 | 65 | M | 1 | HbA1c00M | 6.1 |\n| 7 | 68 | F | 1 | HbA1c00M | 7.8 |\n| 1 | 56 | M | 1 | HbA1c01M | 7.4 |\n| 2 | 77 | F | 1 | HbA1c01M | 8.4 |\n| 3 | 68 | M | 1 | HbA1c01M | 7.0 |\n| 4 | 81 | F | 1 | HbA1c01M | 7.4 |\n| 5 | 42 | M | 1 | HbA1c01M | 8.4 |\n| 6 | 65 | M | 1 | HbA1c01M | 6.2 |\n| 7 | 68 | F | 1 | HbA1c01M | 7.4 |\n| 1 | 56 | M | 1 | HbA1c02M | 7.3 |\n| 2 | 77 | F | 1 | HbA1c02M | 8.3 |\n| 3 | 68 | M | 1 | HbA1c02M | 7.0 |\n| 4 | 81 | F | 1 | HbA1c02M | 7.3 |\n| 5 | 42 | M | 1 | HbA1c02M | 7.3 |\n| 6 | 65 | M | 1 | HbA1c02M | 6.0 |\n| 7 | 68 | F | 1 | HbA1c02M | 7.9 |\n| 1 | 56 | M | 1 | HbA1c03M | 7.4 |\n| 2 | 77 | F | 1 | HbA1c03M | 8.2 |\n| 3 | 68 | M | 1 | HbA1c03M | 7.0 |\n| 4 | 81 | F | 1 | HbA1c03M | 7.4 |\n| 5 | 42 | M | 1 | HbA1c03M | 6.5 |\n| 6 | 65 | M | 1 | HbA1c03M | 6.3 |\n| 7 | 68 | F | 1 | HbA1c03M | 8.4 |\n\n",
"text/html": [
"<table>\n",
"<caption>A tibble: 28 × 6</caption>\n",
"<thead>\n",
"\t<tr><th scope=col>id</th><th scope=col>age</th><th scope=col>sex</th><th scope=col>DM</th><th scope=col>time</th><th scope=col>HbA1c</th></tr>\n",
"\t<tr><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;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><td>1</td><td>56</td><td>M</td><td>1</td><td>HbA1c00M</td><td> 7.3</td></tr>\n",
"\t<tr><td>2</td><td>77</td><td>F</td><td>1</td><td>HbA1c00M</td><td> 8.3</td></tr>\n",
"\t<tr><td>3</td><td>68</td><td>M</td><td>1</td><td>HbA1c00M</td><td> 7.1</td></tr>\n",
"\t<tr><td>4</td><td>81</td><td>F</td><td>1</td><td>HbA1c00M</td><td> 7.6</td></tr>\n",
"\t<tr><td>5</td><td>42</td><td>M</td><td>1</td><td>HbA1c00M</td><td>10.1</td></tr>\n",
"\t<tr><td>6</td><td>65</td><td>M</td><td>1</td><td>HbA1c00M</td><td> 6.1</td></tr>\n",
"\t<tr><td>7</td><td>68</td><td>F</td><td>1</td><td>HbA1c00M</td><td> 7.8</td></tr>\n",
"\t<tr><td>1</td><td>56</td><td>M</td><td>1</td><td>HbA1c01M</td><td> 7.4</td></tr>\n",
"\t<tr><td>2</td><td>77</td><td>F</td><td>1</td><td>HbA1c01M</td><td> 8.4</td></tr>\n",
"\t<tr><td>3</td><td>68</td><td>M</td><td>1</td><td>HbA1c01M</td><td> 7.0</td></tr>\n",
"\t<tr><td>4</td><td>81</td><td>F</td><td>1</td><td>HbA1c01M</td><td> 7.4</td></tr>\n",
"\t<tr><td>5</td><td>42</td><td>M</td><td>1</td><td>HbA1c01M</td><td> 8.4</td></tr>\n",
"\t<tr><td>6</td><td>65</td><td>M</td><td>1</td><td>HbA1c01M</td><td> 6.2</td></tr>\n",
"\t<tr><td>7</td><td>68</td><td>F</td><td>1</td><td>HbA1c01M</td><td> 7.4</td></tr>\n",
"\t<tr><td>1</td><td>56</td><td>M</td><td>1</td><td>HbA1c02M</td><td> 7.3</td></tr>\n",
"\t<tr><td>2</td><td>77</td><td>F</td><td>1</td><td>HbA1c02M</td><td> 8.3</td></tr>\n",
"\t<tr><td>3</td><td>68</td><td>M</td><td>1</td><td>HbA1c02M</td><td> 7.0</td></tr>\n",
"\t<tr><td>4</td><td>81</td><td>F</td><td>1</td><td>HbA1c02M</td><td> 7.3</td></tr>\n",
"\t<tr><td>5</td><td>42</td><td>M</td><td>1</td><td>HbA1c02M</td><td> 7.3</td></tr>\n",
"\t<tr><td>6</td><td>65</td><td>M</td><td>1</td><td>HbA1c02M</td><td> 6.0</td></tr>\n",
"\t<tr><td>7</td><td>68</td><td>F</td><td>1</td><td>HbA1c02M</td><td> 7.9</td></tr>\n",
"\t<tr><td>1</td><td>56</td><td>M</td><td>1</td><td>HbA1c03M</td><td> 7.4</td></tr>\n",
"\t<tr><td>2</td><td>77</td><td>F</td><td>1</td><td>HbA1c03M</td><td> 8.2</td></tr>\n",
"\t<tr><td>3</td><td>68</td><td>M</td><td>1</td><td>HbA1c03M</td><td> 7.0</td></tr>\n",
"\t<tr><td>4</td><td>81</td><td>F</td><td>1</td><td>HbA1c03M</td><td> 7.4</td></tr>\n",
"\t<tr><td>5</td><td>42</td><td>M</td><td>1</td><td>HbA1c03M</td><td> 6.5</td></tr>\n",
"\t<tr><td>6</td><td>65</td><td>M</td><td>1</td><td>HbA1c03M</td><td> 6.3</td></tr>\n",
"\t<tr><td>7</td><td>68</td><td>F</td><td>1</td><td>HbA1c03M</td><td> 8.4</td></tr>\n",
"</tbody>\n",
"</table>\n"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "L002PYUBLASt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 161
},
"outputId": "58774d6c-7d25-48f9-d460-511eb5d62770"
},
"source": [
"d2 <- tidy_d %>% \n",
" group_by(time) %>%\n",
" summarise(HbA1c_avg = mean(HbA1c), HbA1c_sd = sd(HbA1c))\n",
"d2"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" time HbA1c_avg HbA1c_sd \n",
"1 HbA1c00M 7.757143 1.2380861\n",
"2 HbA1c01M 7.457143 0.7721337\n",
"3 HbA1c02M 7.300000 0.7234178\n",
"4 HbA1c03M 7.314286 0.7925246"
],
"text/latex": "A tibble: 4 × 3\n\\begin{tabular}{r|lll}\n time & HbA1c\\_avg & HbA1c\\_sd\\\\\n <chr> & <dbl> & <dbl>\\\\\n\\hline\n\t HbA1c00M & 7.757143 & 1.2380861\\\\\n\t HbA1c01M & 7.457143 & 0.7721337\\\\\n\t HbA1c02M & 7.300000 & 0.7234178\\\\\n\t HbA1c03M & 7.314286 & 0.7925246\\\\\n\\end{tabular}\n",
"text/markdown": "\nA tibble: 4 × 3\n\n| time &lt;chr&gt; | HbA1c_avg &lt;dbl&gt; | HbA1c_sd &lt;dbl&gt; |\n|---|---|---|\n| HbA1c00M | 7.757143 | 1.2380861 |\n| HbA1c01M | 7.457143 | 0.7721337 |\n| HbA1c02M | 7.300000 | 0.7234178 |\n| HbA1c03M | 7.314286 | 0.7925246 |\n\n",
"text/html": [
"<table>\n",
"<caption>A tibble: 4 × 3</caption>\n",
"<thead>\n",
"\t<tr><th scope=col>time</th><th scope=col>HbA1c_avg</th><th scope=col>HbA1c_sd</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></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><td>HbA1c00M</td><td>7.757143</td><td>1.2380861</td></tr>\n",
"\t<tr><td>HbA1c01M</td><td>7.457143</td><td>0.7721337</td></tr>\n",
"\t<tr><td>HbA1c02M</td><td>7.300000</td><td>0.7234178</td></tr>\n",
"\t<tr><td>HbA1c03M</td><td>7.314286</td><td>0.7925246</td></tr>\n",
"</tbody>\n",
"</table>\n"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WJQ1VKasOp_p",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 929
},
"outputId": "13e1db75-73ab-4378-832a-4232492c9312"
},
"source": [
"# Google Colaboratoryでは以下のコードはうまくいかない\n",
"# install.packages(\"ggplotgui\")\n",
"# library(ggplotgui)\n",
"# ggplot_shiny(dataset=d2)\n",
"\n",
"# ggplot()関数とstat_summary()関数で折れ線グラフを描く\n",
"g <- ggplot(data = tidy_d, aes(x = time, y=HbA1c)) +\n",
" theme_set(theme_classic(base_size = 12)) +\n",
" stat_summary(\n",
" aes(group=sex), # sex性別ごとに\n",
" fun.y=mean, # 平均値を\n",
" geom=\"line\", # 線でつなげて\n",
" colour=\"black\", # 色は黒で\n",
" size=0.5 # 先の太さは0.5mm\n",
" ) \n",
"\n",
"# エラーバーの追加\n",
"# Google Colaboratoryでは、fun.data = mean_sdl (標準偏差)はうまくいかない\n",
"# R Studioではうまくいくよう\n",
"g <- g +\n",
" stat_summary(aes(group=sex), # sex性別ごとに\n",
" fun.data=mean_se, # mean_seで標準誤差、#mean_cl_normalで95%信頼区間(正規分布)\n",
" geom=\"errorbar\",\n",
" size=0.5, # 線の太さ\n",
" width=0.1\n",
" )\n",
"\n",
"# エラーバーの上から重ねて、点(マーク)を描き入れる\n",
"g <-g +\n",
" stat_summary(\n",
" aes(shape=sex), # 種類ごとに点の形を変えて\n",
" fun.y=mean, # 種類ごとの平均値のところに\n",
" geom=\"point\", # 点で\n",
" colour=\"black\",\n",
" size=4)\n",
"\n",
"# 形の指定\n",
"# 点の形を１番◯、１５番■(、２３番◇)にマニュアル変更\n",
"g <- g +\n",
" scale_shape_manual(values=c(1,15))\n",
"\n",
"# 原点を左下にする\n",
"g <- g +\n",
" scale_y_continuous(limits = c(0,10))\n",
"\n",
"g"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Warning message:\n",
"“Removed 1 rows containing non-finite values (stat_summary).”Warning message:\n",
"“Removed 1 rows containing non-finite values (stat_summary).”Warning message:\n",
"“Removed 1 rows containing non-finite values (stat_summary).”"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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7fq+ZC2JRoSlIHmzFRHbqWr6nqfmKxT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",
"text/plain": [
"plot without title"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xofa_PmxB5MF",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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