{ "cells": [ { "cell_type": "markdown", "id": "5ea350a0", "metadata": {}, "source": [ "# \"데분방01) Basics and R environment\"\n", "> \"Basics and R environment\"\n", "\n", "- toc:true\n", "- branch: master\n", "- badges: true\n", "- comments: true\n", "- author: tingstyle1\n", "- categories: [R, 통계, 대학원, 데분방1, R환경]\n", "- image: \"images/posts/data.png\"" ] }, { "cell_type": "markdown", "id": "b8c55b25", "metadata": {}, "source": [ "- 강의주소:https://lms.knou.ac.kr/dks/user/home/initUSTHomeIndex_GRSC.do?stLeftMenuId=0\n", "- 선배블로그: https://insb.tistory.com/9?category=967351\n", " - 작년 김성수 교수 강의로 진행" ] }, { "cell_type": "markdown", "id": "3085f0a0", "metadata": {}, "source": [ "## 기본" ] }, { "cell_type": "markdown", "id": "02e947a3", "metadata": {}, "source": [ "### R 소개\n", "- 무료\n", "- `대화형` 프로그램 언어\n", "- `객체지향` 시스템\n", " - 데이터, 변수, 행렬 등은 모두 객체\n", " - 생성은 `=` or `<-`로 생성" ] }, { "cell_type": "markdown", "id": "251d6e50", "metadata": {}, "source": [ "![image-20220301225507466](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220301225507466.png)" ] }, { "cell_type": "code", "execution_count": 2, "id": "19dc5ee8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 2
    2. \n", "\t
  2. 3
    3. \n", "\t
  3. 4
    4. \n", "\t
  4. 5
    5. \n", "\t
  5. 6
    6. \n", "\t
  6. 7
    7. \n", "\t
  7. 8
    8. \n", "\t
  8. 9
    9. \n", "\t
  9. 10
    10. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\item 5\n", "\\item 6\n", "\\item 7\n", "\\item 8\n", "\\item 9\n", "\\item 10\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 2\n", "2. 3\n", "3. 4\n", "4. 5\n", "5. 6\n", "6. 7\n", "7. 8\n", "8. 9\n", "9. 10\n", "\n", "\n" ], "text/plain": [ "[1] 2 3 4 5 6 7 8 9 10" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 인덱싱이 다 inclusive네\n", "x = 2:10 \n", "x" ] }, { "cell_type": "code", "execution_count": 3, "id": "b49416e5", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 11
    2. \n", "\t
  2. 14
    3. \n", "\t
  3. 17
    4. \n", "\t
  4. 20
    5. \n", "\t
  5. 23
    6. \n", "\t
  6. 26
    7. \n", "\t
  7. 29
    8. \n", "\t
  8. 32
    9. \n", "\t
  9. 35
    10. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 11\n", "\\item 14\n", "\\item 17\n", "\\item 20\n", "\\item 23\n", "\\item 26\n", "\\item 29\n", "\\item 32\n", "\\item 35\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 11\n", "2. 14\n", "3. 17\n", "4. 20\n", "5. 23\n", "6. 26\n", "7. 29\n", "8. 32\n", "9. 35\n", "\n", "\n" ], "text/plain": [ "[1] 11 14 17 20 23 26 29 32 35" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- 3*x + 5\n", "y" ] }, { "cell_type": "code", "execution_count": 4, "id": "fab8ed6f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a <- 3:9\n", "b <- 3*a + 5\n", "plot(a, b)" ] }, { "cell_type": "code", "execution_count": 5, "id": "661db328", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pch는 point character -> 18번재의 pch(다이아먼드)로 그려라\n", "plot(a, b, pch=18)" ] }, { "cell_type": "markdown", "id": "9743813d", "metadata": {}, "source": [ "### R의 역사" ] }, { "cell_type": "markdown", "id": "febdb5d2", "metadata": {}, "source": [ "\n", "\n", "![image-20220301225937235](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220301225937235.png)\n", "\n" ] }, { "cell_type": "markdown", "id": "528b1c01", "metadata": {}, "source": [ "1. 1980년대 `S` 언어 탄생 by AT&T Bell Lab(c언어 개발한 연구소)에서 통계언어 S를 구현함\n", " - 1998 ACM S/W 상을 John Chambers가 탐 \"Forever altered how people analyze, visualize and manipulate data\n", " - 사람들이 어떻게 데이터 분석, 시각화, 다루는지를 영원히 바꾼 언어다\n", " \n", "2. 오클란드 대학의 Ross and Robert 교수 2명이 S 축소버전인 `R & R`을 만듦\n", "\n", "3. 1995년 Martin Maechler(마틴 매흘러)가 R&R(이하카, 젠틀만)을 설득하여, linux처럼 오픈소스(GPL)로 쓰게 한 것인 `R source code` 발표\n", "\n", "4. 1997년 8월 `R core team` 결성. 2000년 02월 29일 `R version 1.0.0` 발표 \n", " - 2015. 12월 R version 3.2.3\n", " \n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "4a3dc212", "metadata": {}, "source": [ "### R 다운받기\n" ] }, { "cell_type": "markdown", "id": "e0c12cbc", "metadata": {}, "source": [ "![image-20220301230703197](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220301230703197.png)\n", "\n" ] }, { "cell_type": "markdown", "id": "1af1615a", "metadata": {}, "source": [ "- base버전으로 다운 받기" ] }, { "cell_type": "markdown", "id": "d114a740", "metadata": {}, "source": [ "### 작업영역 지정" ] }, { "cell_type": "markdown", "id": "15ec158f", "metadata": {}, "source": [ "![image-20220301230749977](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220301230749977.png)\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "7ce72b0b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "'C:/Users/cho_desktop/2022_RProjects/대학원/1-01 데이터분석방법론 1'" ], "text/latex": [ "'C:/Users/cho\\_desktop/2022\\_RProjects/대학원/1-01 데이터분석방법론 1'" ], "text/markdown": [ "'C:/Users/cho_desktop/2022_RProjects/대학원/1-01 데이터분석방법론 1'" ], "text/plain": [ "[1] \"C:/Users/cho_desktop/2022_RProjects/대학원/1-01 데이터분석방법론 1\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "getwd()" ] }, { "cell_type": "code", "execution_count": 8, "id": "381d38cf", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# / 맥용 슬러쉬를 이용해서 절대경로 지정 c:/grad/data\n", "setwd(\".\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "75f937e0", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in setwd(choose.dir()): missing value is invalid\n", "output_type": "error", "traceback": [ "Error in setwd(choose.dir()): missing value is invalid\nTraceback:\n", "1. setwd(choose.dir())" ] } ], "source": [ "# 탐색기를 통해, 인터렉티브하게 직접 지정\n", "setwd( choose.dir() )" ] }, { "cell_type": "markdown", "id": "a6bd560b", "metadata": {}, "source": [ "### R studio 소개\n", "- rsutdio.com" ] }, { "cell_type": "markdown", "id": "6990d3fd", "metadata": {}, "source": [ "### R Commander 소개\n", "- 메뉴 방식으로 처리된 R 패키지/ John fox가 개발\n", "![image-20220301231131248](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220301231131248.png)\n", "\n", "![image-20220301231228214](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220301231228214.png)\n" ] }, { "cell_type": "markdown", "id": "6727ea3d", "metadata": {}, "source": [ "## 교재" ] }, { "cell_type": "markdown", "id": "2d04ac64", "metadata": {}, "source": [ "### First step" ] }, { "cell_type": "code", "execution_count": 11, "id": "58932fe8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "package 'ISwR' successfully unpacked and MD5 sums checked\n", "\n", "The downloaded binary packages are in\n", "\tC:\\Users\\cho_desktop\\AppData\\Local\\Temp\\Rtmpo5uUeT\\downloaded_packages\n" ] } ], "source": [ "# ISwR 패키지 설치\n", "install.packages(\"ISwR\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "e0f18b22", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "\"package 'ISwR' was built under R version 3.6.3\"" ] } ], "source": [ "# 가동\n", "library(ISwR)" ] }, { "cell_type": "code", "execution_count": 13, "id": "ef854574", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 정규분포를 따르는 r랜덤넘버 1000개 \n", "plot( rnorm(1000), pch = 19)" ] }, { "cell_type": "markdown", "id": "4323bc58", "metadata": {}, "source": [ "#### 1.1.3 벡터연산" ] }, { "cell_type": "code", "execution_count": 14, "id": "9b92af20", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# 원소들끼리 순서대로 짝지어서 연산 가능한 장점 : 벡터연산c(,)의 장점\n", "weight <- c(60, 72, 57, 90, 95, 72)\n", "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)\n", "bmi <- weight/height^2" ] }, { "cell_type": "code", "execution_count": 15, "id": "d4dcf793", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 19.5918367346939
    2. \n", "\t
  2. 22.2222222222222
    3. \n", "\t
  3. 20.9366391184573
    4. \n", "\t
  4. 24.9307479224377
    5. \n", "\t
  5. 31.3779891663364
    6. \n", "\t
  6. 19.7363010882377
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "markdown", "id": "8ec6d163", "metadata": {}, "source": [ "![image-20220301231917539](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220301231917539.png)" ] }, { "cell_type": "code", "execution_count": 16, "id": "eafb1255", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "74.3333333333333" ], "text/latex": [ "74.3333333333333" ], "text/markdown": [ "74.3333333333333" ], "text/plain": [ "[1] 74.33333" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 평균(xbar)과 SD 계산\n", "# 벡터c의 갯수 -> length( )\n", "xbar <- sum(weight) / length(weight)\n", "xbar" ] }, { "cell_type": "code", "execution_count": 17, "id": "89876262", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "74.3333333333333" ], "text/latex": [ "74.3333333333333" ], "text/markdown": [ "74.3333333333333" ], "text/plain": [ "[1] 74.33333" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean(weight)" ] }, { "cell_type": "code", "execution_count": 20, "id": "c8ef770e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "15.4229266569827" ], "text/latex": [ "15.4229266569827" ], "text/markdown": [ "15.4229266569827" ], "text/plain": [ "[1] 15.42293" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# sd: 편차 제곱의 n-1으로 나눈 평균 \n", "sqrt(sum((weight-xbar)^2) / (length(weight) - 1))" ] }, { "cell_type": "code", "execution_count": 19, "id": "0be6210d", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "15.4229266569827" ], "text/latex": [ "15.4229266569827" ], "text/markdown": [ "15.4229266569827" ], "text/plain": [ "[1] 15.42293" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sd(weight)" ] }, { "cell_type": "markdown", "id": "cbe3d128", "metadata": {}, "source": [ "#### 1.1.4 standard procedures" ] }, { "cell_type": "code", "execution_count": 21, "id": "e09855c9", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 19.5918367346939
    2. \n", "\t
  2. 22.2222222222222
    3. \n", "\t
  3. 20.9366391184573
    4. \n", "\t
  4. 24.9307479224377
    5. \n", "\t
  5. 31.3779891663364
    6. \n", "\t
  6. 19.7363010882377
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "code", "execution_count": 22, "id": "ec40e73a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "\n", "\tOne Sample t-test\n", "\n", "data: bmi\n", "t = 0.34488, df = 5, p-value = 0.7442\n", "alternative hypothesis: true mean is not equal to 22.5\n", "95 percent confidence interval:\n", " 18.41734 27.84791\n", "sample estimates:\n", "mean of x \n", " 23.13262 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1 표본 t검정(one sample t.test)\n", "t.test(bmi, mu=22.5) # H0: mu=22.5 / H1: mu!=22.5\n", "\n", "# t값 , 자유도, p값" ] }, { "cell_type": "markdown", "id": "f933fd67", "metadata": {}, "source": [ "##### 1.1.5 Graph" ] }, { "cell_type": "markdown", "id": "9c128f3b", "metadata": {}, "source": [ "![image-20220304085422931](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220304085422931.png)\n", "\n" ] }, { "cell_type": "code", "execution_count": 25, "id": "87eebbde", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 산점도\n", "\n", "# color=\"대or소문자\" 대신 col=2번호를 줘도 된다. like pch=숫자\n", "\n", "plot(height, weight, pch=2, col=\"RED\")" ] }, { "cell_type": "code", "execution_count": 28, "id": "3ecd69c0", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in plot.xy(xy.coords(x, y), type = type, ...): plot.new has not been called yet\n", "output_type": "error", "traceback": [ "Error in plot.xy(xy.coords(x, y), type = type, ...): plot.new has not been called yet\nTraceback:\n", "1. lines(hh, 22.5 * hh^2, col = \"BLUE\", lty = 2)", "2. lines.default(hh, 22.5 * hh^2, col = \"BLUE\", lty = 2)", "3. plot.xy(xy.coords(x, y), type = type, ...)" ] } ], "source": [ "# R의 장점 -> 그림을 그리고 위에 덧그릴 수 있다. -> superimpose of curve\n", "\n", "\n", "# c벡터객체 1개 만들고\n", "hh <- c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90) \n", "# lines로 선 그리기\n", "lines(hh, 22.5*hh^2, col=\"BLUE\", lty=2) # lty = line type -> 2번은 점점점 타입" ] }, { "cell_type": "code", "execution_count": 29, "id": "1254fb08", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAANlBMVEUAAAAAAP9NTU1oaGh8fHyMjIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enw8PD/AAD////xw1/KAAAACXBIWXMAABJ0AAASdAHeZh94AAAWcklEQVR4nO3d6ULiSBiG0UwAQRGB+7/ZYXEBt1Z5U6kk5/xQ7O6ZjyCPqYSozR64WdP3HYAxEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIKBBSAwPzh2d5PpweRkCSkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKqyK7vO8CfCakeOyUNl5DqIaQBE1I1dnuLu+ESUjWENGRCqsXu4i2DI6RK7N69Z1iEVAkhDZuQ6rD75BYDIqQ6CGnghFSF3aW+7wx/ICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCgoCiIT2uFs3RYvnY1QjoRcGQtrPmzbyTEdCTgiEtm/Zhc7r1tG6bZRcjoCcFQ2qbzevtTdN2MQIidr/+LwqG1DRffRAbAQm735dkjwTv1R3S4Rhp/XS65RiJmu32v1/clTz9Pb84azfbdjICbld7SPvH5el1pHax8joS1dpdvP0xVzbAtYGH1FzqZgT82+7d+58pGtJmeT5Mmi0euhoBN6o/pNXFLmfRzQi40e6TWz9QMKR1c/e03z/OF/vN/axZdzECblV/SPPmdMp706wOOX2/SxISPdld+sV/18MlQqeLGlwixKgUvUTotEfanhoSEqNS9BKh+eN+/7Ro7vbbu8ObDkZAT3q4RKjdHvZH7VMnI6AfRV9Huj+kNFsdbrTLby+1ExJDU8+VDYVHQJKQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAk+OC/X/8XQoJ3/vt9R0KCd/6QkZAgQkjw6i+LujMhwbO/ZyQkeHFDRkKCiKIhPa4WzdFi+djVCPiDWxZ1ZwVD2s6aN/NORsAf3J5R0ZCWTfuwOd16WrfNsosR8HuBjIqG1Dab19ubpu1iBPSkYEhN89UHsRHwK4lF3Zk9EpOVy6j0MdL66XTLMRIVCGZU9vT3/OKs3WzbyQjoR9nXkZan15HaxcrrSPQpuag7c2UDk5PPqKaQmkvdjIB9+NjoRcmQnu6adrXf38+a9ttTDfZIDE7JS4Ta477mfuUSIfrSxaLurOjp78N+aNk2d9v9dun0N8V1l1HhF2RP/3VzOvHtBVlK6zCjHi4Rej6R4BIhRqWHPdLx7dYeiYK6XNSd9XCMtNw+386PgE90n5GzdoxfgYy8jgQR9VzZUHgEU1BiUXcmJEarXEZCYrwKZiQkiBASI1RyUXcmJEanfEZCYnx6yEhIECEkRqSPRd2ZkBiN/jISEuPRY0ZCggghMQa97o2OhMTw9Xlw9ExIDF7/GQmJ4auhIyFBgpAYsAoOjp4JicGqJyMhMVwVZSQkiBASA1TTou5MSAxOfRndHNLrj/Buv/0RxLeMgGsVZhQL6Sn7S/aExMDcENL66pdVznq+V0xBjYu6s1v2SLPLjv7xe8o7v1eMX70Z5Y6RsoTERxVn5KwdRAiJAah5UXd2a0j3rwdKqXv0YQRTV39GN4e0ejvdELtLeyFxaQAZ3RxS29zH7soXI2AAnLWjYkNY1J3dGNKy2cbuyhcjmKzhZHT7yYbFPPpK7GcjmKgBZXRTSM21nu8V9ElI1GhIq7oTL8hSn8FlJCQqNLyMAqe/X82XsTslJIYmF1LT5L5JVkhTNcBF3dmtS7u7dn14u26bx/2iie2ThDRNg80o8ILs5vR+08z329x3yQppkoabUe4SoeON3ClwITEwN1+0+rJHaoXE3w14UXd289Lu5RhpuX84LO/6u1cM2OAzuv1kw/zl5PdxhxT7lgohTcrwMwq8ILteHDJaHHdLzSpzlz6MgOq5soE+jWBRdyYk+jOajG68+vvqyoae7xXDM56MhAQRlnb0YUSLujMhUd7oMgqd/t7vF0+h+/PZCEZmfBmFXpA9/FkbLUlIDMyNId038+0xpPvmLnaX9kIarxEu6s5uvmh1e75W1Vk7/m20GUW+jUJI/Mx4M7o5pNnzHmnjV18yaZljpHX4h+kLaWxGvKg7u/lHFr99G0WQkMZl9BndHNLj87dRPMTu0IcRDN74M7r9ZEO7yr4U+3EEDMCNId0dV3UP8V/tIqSxmMCi7uzmS4Qejtc23K1Dd+fTEQzVZDKKXLT6tJo1TRv8gcVCGonpZBS6+nt75/uRmLbbQ9ocd0jNPPeTTz6OYHAmtKg7uzGk9bJtmtkyfIgkpIGbXEaJ30ax2MTuzKcjGJzpZXT7Hul4dHTYI4VPgAuJgbn9GOnxuLo7xJS5P5+OYDAmuKg7i5y1e3TWjqPJZhQJaXs8bTdz1o7pZpS6smH5GLo7n46A6iWutUuf/BbS8Ex4UXfm6m9uN/mMAt+P1AkhDYqM/KRViBASt7CoeyYk/k5Gr4TEn8nojZAgQEj8hUXdO0Li92T0gZD4NRl9JCQIEBK/YVH3BSHxczL6kpD4MRl9TUgQICR+wqLuH4TEv8non4TEP8no34QEAULiOxZ1PyQkviajHxMSBAiJT9kZ/Y6Q+ISMfktIfCSjXxMSBAiJK+8Xdbt+7kalvn40hMSFD8dGOyVd+ObREBJvPh4bCemSkPib3d7i7s13j4aQOPn8hLeQLu1e33wkJPZfvm60u3jLt4+GkPjydaPdu/fT9v2jISS+JKRLuw83Lglp4r65GGj3ya3p2n1685WQJu3ba+qEdElIfOnba+p2l0rdo2rt/vFwCAkChDRRvlEiS0iTJKM0IU2RjOKEBAFCmhiLum4IaVJk1BUhTYmMOiMkCBDSRFjUdUtIkyCjrglpCmTUOSFBgJBGzqKuDCGNmoxKEdKYyaiYoiE9rhbN0WL52NUI6EXBkLaz5s28kxG8sqgrq2BIy6Z92JxuPa3bZtnFCJ7JqLSCIbXN5vX2pmm7GMGZjIorGFLTfPVBbAT0xB5pZCzq+lH2GGn9dLrlGKkrMupLydPf84uzdrNtJyMmTka9Kfs60vL0OlK7WHkdiXFxZcNIWNT1q56QmkvdjBgvGfWt5Onv9h8LuttHTJWMelf0daRm8e0phttHQE+KhnQ86/2jlIT0YxZ1dSh7ZcN20TR36+5GTI6MalH6EqHN8QT44n7z/Y5JSD8jo2qUv9Zus2z/eWJOSAxMLxetbu4XMyHdxqKuLn1d/d3NiMmQUW2ENEQyqk49VzYUHgFJQhoYi7o6CWlQZFQrIQ2JjKolJAgQ0kBY1NVNSIMgo9oJaQhkVD0h1c7OaBCEVDcZDYSQqiajoRASBAipVhZ1gyKkOsloYIRUJRkNjZAgQEi1sagbJCHVRUYDJaSqyGiohAQBQqqFRd2gCakOMho4IVVBRkMnJAgQUs/+s6gbBSH1SkVjISQIEFJf7IxGRUj9kNHICKkXMhobIUGAkEqzqBslIZUlo5ESUlEyGishQYCQSrGoGzUhlSGjkRNSETIaOyFBgJA65tskpkFInVLRVAgJAoTUFTujSRFSN2Q0MULqhIymRkgQIKQ0i7pJElKWjCZKSFEymiohQYCQUizqJk1IGTKaOCFFyGjqhAQBQrqVRR17Id1KRpwI6SYy4kxIf2VfxAUh/Y2MuCKkP5ER14QEAUL6LYs6PiGk35ERnxLSr8iIzwkJAoT0UxZ1fENIPyMjviWkH5ER3xMSBAjpXyzq+AEhfU9G/IiQviUjfkZIX7Ev4heE9DkZ8StC+pSM+B0hQYCQ3rOo4w+EdE1G/ImQrsiIvxESBAjphUUdNxDSmYy4iZBOZMRthGRfRMDUQ5IRERMPSUZkTDwkyJhuSBZ1BE01JBkRNdGQZETWREOCrOmFZFFHB6YWkozoxMRCkhHdmE5I9kV0aCohyYhOTSQkGdGtiYQE3Rp/SBZ1FDD2kGREESMPSUaUMfKQoIzxhmRRR0FjDUlGFDXSkGREWeMLyb6IHowtJBnRi5GFJCP6MbKQoB/jCcmijh6NJSQZ0auRhCQj+jWSkKBfww/Joo4KDD0kGVGFgYckI+ow3JDsi6jIUEOSEVUZaEgyoi4DDQnqMryQLOqo0NBCkhFVGlhIMqJOAwsJ6iQkCBASBAgJAoQEAUKCACFBgJAgoGhIj6tFc7RYPnY1AnpRMKTtrHkz72QE9KRgSMumfdicbj2t22bZxQjoScGQ2mbzenvTtF2MGKtd33eAfykYUtN89UFsxEjtlFQ9e6QBEFL9yh4jrZ9Otxwj/cpub3FXvZKnv+cXZ+1m205GjJKQBqDs60jL0+tI7WLldaSf2128pVaubKjd7t17qlRPSM2lbkYMkpAGoWRI2+XxVN1q1jTzh45GjM/uk1vUp2BIT+1hT7NtXSL0K0IahoIh3TWL7eHN3dOhqTunv39md6nvO8PXil7ZsH1+c1jleUGWUSl9iVDbXHwQHwE9Kbq02+z3q/N1QtvvD5KExMAUDGnTtMvNftEeSlrPmnUXI6AnJU9/r9u3F4pW3YyAfpR9Qfbh7vRdsovVU2cjoA/1XNlQeAQkCQkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoKAgYXktz9Sp2GFtFMSdRISBAwqpN3e4o46DS4kJVGjIYW0u3gLVRleSEqiQgMKaffuPdRjgCEpifoMJ6TdpzehCkKCgMGEtLvS/V2A3xhMSFAzIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBQKUhwcD84VmeD8dso6c3eqJP5tF/Xo0uPHqiT+bRf16NLjx6ok/m0X9ejS48eqJP5tF/Xo0uPHqiT+bRf16NLjx6ok/m0X9ejS48eqJP5tF/Xo0uPHqiT+bRf16NLjx6ok/m0X9ejS48eqJP5tF/Xo0uPHqiT+bRf16NLjzapdkQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIKBoSPfX0zZ3TXP3dLoXf/3R5X+cffXD0pdt0y63fYzufrOvH/HtxaaW3erL0aW3+rip8/XrzY62umRIm+tHb316PNvt6S+6fmyvZ788mdvD7fnp1qyH0d1v9vXop/Y8+fi1q/BWX4wuvdXPm7p6u9nJVhcMadNeb2DbbvbbRbM8bvmi8OyTdfO43z82h7tx+OvH8qM73+x3o++Oj/V+2dyV3+qL0aW3+r6Zb/fbu2bT7VaXC+mwQVcb+HB6bLfHL833568X5WafbNvjZ3TZrE93pqt78M3orjf7/ejnD47vSm/1xejSWz0/dfN0fLZ1udXlQjpsybsvUpuXm/fNfdnZJ4tme3p7XOl092Xym9Fdb/b70e3zs7ktv9UXo0tv9UvD8263ulxIm/31Bs6a/apt7s5P5vXd4SCw3OzTn532iJdfK0uP7nqz349ePa+vVuW3+mJ06a2+2NQut7roWbt3G7h4Od5fnA8/58Vm7/cve4XOn1LfjC6w2dej74+H/O39voetfhtdeqtnp93Q45hDOp5suDt/fXw4niDtdJ///tHbHA98972E9Da6882+Hr16PX9VfKsvR5fd6lWz2O438zGHdDxGeno7Gbnt8mzsh0fvfODZS0gvo8863ex3568OC6rDl6778lt9Mfqs3FbvT2feF2MO6f2fdfpC0vv/+fPh7+thcMGQ2ubbv+5s9Oy0ojw9hUtv9cXoz/66y9HHftvV6c+63Or+Qlr0GtLrqZvzmZynLl/c+Gr053/d3eiLL12lt7rsV81P/t+bY8NdbnV/Ia1OC5yn41Fne/p61emn9f2D+3oS9nw31k2H5wy/Gl1gsz85B3165a70Vl+MLr/V55caFt1udX8hHY6OTq84PxwPGpan48/1l/9lePbxi9Pzq1idv8b/9egCm301+jBv+zy19FZfjC6/1ccrOWbH59k4rmzYv23g+f3q9TTo9nwlVodfHt/Pfl60n2+VOhv7fnSBzb4ePX/b1NJbPS/5yb4a/TzvtAPscKt7DGm/nr+8MHe8NnjW8Qve17Pfvmidr0vubXS3m/1u9NumFt/q69Elt/rp7pDR+nV0R1tdNCQYKyFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIVXv09///e4PLz7s8hfx8iUhVe93Ic18RnvhYa/epyF9+W9+8q/J87BXT0hD4GGv3iGNZdOuTrfvZ017//yH+9OvCl+ebr7+m+aoxzs7WR706jXN4ljHsZ/TjWa+fw5pfvzo7hzS878RUk886NU7lLPd3zez/X59vLWdN+tzSOum3ew37Tmkl38jo3542KvXNI/7cyCLZnu4tW0WLx8eT3WvzyG9/Bsh9cPDXr1zGudInl0183ZTSP3xsFdPSEPgYa/ep5EIqTIe9uq9RXI+KNpff7gWUg087NV7i+TheJpuf/9ysuHqrN3Lv2map37v7kQJqXpvkZxfOGrap6sPr0OaHf6+z3s7VUKq3kVIxysbmrun1w+XbTN/vA7pcSakPghp8E5XOtAzIQ1X0zzs99tFs+z7jiCkIVudj5Cs5GogpAG7nzfNzP6oCkKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAg4H8AQrZms9X8ggAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# lines는 반드시 plot()그린 것 위에다 덧그리는 superimpose다.\n", "plot(height, weight, pch=2, col=\"RED\")\n", "\n", "hh <- c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90) \n", "lines(hh, 22.5*hh^2, col=\"BLUE\", lty=2) " ] }, { "cell_type": "markdown", "id": "8b084d5a", "metadata": {}, "source": [ "### 1.2 R languange essentials" ] }, { "cell_type": "code", "execution_count": 30, "id": "5af3ec46", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'Huey'
    2. \n", "\t
  2. 'Dewey'
    3. \n", "\t
  3. 'Louie'
    4. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Huey'\n", "\\item 'Dewey'\n", "\\item 'Louie'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Huey'\n", "2. 'Dewey'\n", "3. 'Louie'\n", "\n", "\n" ], "text/plain": [ "[1] \"Huey\" \"Dewey\" \"Louie\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 문자열 벡터는 큰/작은따옴표 다 쓸 수 있다. \n", "# - 작따->큰따로 변환되서 표기된다.\n", "# - 여기선 큰따-> 작따로 표기되네..like python\n", "c(\"Huey\", \"Dewey\", \"Louie\")" ] }, { "cell_type": "code", "execution_count": 31, "id": "6012b09a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'Huey'
    2. \n", "\t
  2. 'Dewey'
    3. \n", "\t
  3. 'Louie'
    4. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Huey'\n", "\\item 'Dewey'\n", "\\item 'Louie'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Huey'\n", "2. 'Dewey'\n", "3. 'Louie'\n", "\n", "\n" ], "text/plain": [ "[1] \"Huey\" \"Dewey\" \"Louie\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c('Huey', 'Dewey', 'Louie')" ] }, { "cell_type": "code", "execution_count": 32, "id": "dded4830", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. TRUE
    2. \n", "\t
  2. TRUE
    3. \n", "\t
  3. FALSE
    4. \n", "\t
  4. TRUE
    5. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item TRUE\n", "\\item TRUE\n", "\\item FALSE\n", "\\item TRUE\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. TRUE\n", "2. TRUE\n", "3. FALSE\n", "4. TRUE\n", "\n", "\n" ], "text/plain": [ "[1] TRUE TRUE FALSE TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# True/False는 1글자로 정의-> 올 대문자로 표기된다.\n", "c(T, T, F, T)" ] }, { "cell_type": "code", "execution_count": 33, "id": "1272cf02", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 19.5918367346939
    2. \n", "\t
  2. 22.2222222222222
    3. \n", "\t
  3. 20.9366391184573
    4. \n", "\t
  4. 24.9307479224377
    5. \n", "\t
  5. 31.3779891663364
    6. \n", "\t
  6. 19.7363010882377
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "code", "execution_count": 36, "id": "94c7bf39", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. FALSE
    2. \n", "\t
  2. FALSE
    3. \n", "\t
  3. FALSE
    4. \n", "\t
  4. FALSE
    5. \n", "\t
  5. TRUE
    6. \n", "\t
  6. FALSE
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item TRUE\n", "\\item FALSE\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. FALSE\n", "2. FALSE\n", "3. FALSE\n", "4. FALSE\n", "5. TRUE\n", "6. FALSE\n", "\n", "\n" ], "text/plain": [ "[1] FALSE FALSE FALSE FALSE TRUE FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# logical vector\n", "# 벡터 + 조건식 -> 마스크가 된다.\n", "bmi > 25" ] }, { "cell_type": "code", "execution_count": 37, "id": "9e0077eb", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Huey Dewey Louie" ] } ], "source": [ "# 문자열 벡터를 1문자열로 연결 cat\n", "cat( c(\"Huey\", \"Dewey\", \"Louie\") )" ] }, { "cell_type": "code", "execution_count": 38, "id": "1b7cf409", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Huey Dewey Louie \n" ] } ], "source": [ "# 프롬프트에서 cat은 맨 뒤에 백슬래쉬n을 붙여 쓴다.\n", "cat( c(\"Huey\", \"Dewey\", \"Louie\", \"\\n\") )" ] }, { "cell_type": "markdown", "id": "63ce291a", "metadata": {}, "source": [ "![image-20220304090642019](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220304090642019.png)\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "id": "2c3f85f2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "What is \"R\"?\n" ] } ], "source": [ "# 특수문자ex>따옴표 도 사용하고 싶다면 backslash를 활요한다.\n", "cat(\"What is \\\"R\\\"?\\n\")" ] }, { "cell_type": "markdown", "id": "245251d8", "metadata": {}, "source": [ "### 예제 참고자료 사이트\n", "- 예제 파일을 들고 있는 블로그: https://booolean.tistory.com/913?category=807312" ] }, { "cell_type": "markdown", "id": "3a5396c0", "metadata": {}, "source": [ "#### 1.2.5 Missing Value\n", "- R에서는 결측치를 `NA`로 표시한다." ] }, { "cell_type": "markdown", "id": "cb9ce1a2", "metadata": {}, "source": [ "![image-20220304090903439](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220304090903439.png)\n", "\n" ] }, { "cell_type": "code", "execution_count": 45, "id": "653317f4", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
M1M2M3M4M5Y
0.573 0.10590.465 0.538 0.841 0.534
0.651 0.13560.527 0.545 0.887 0.535
0.606 0.12730.494 0.522 0.920 0.570
0.437 0.15910.446 0.423 0.992 0.450
0.547 0.11350.531 0.519 0.915 0.548
0.444 0.16280.429 0.411 0.984 0.431
0.489 0.12310.562 0.455 0.824 0.401
0.536 0.14730.410 0.430 0.978 0.423
0.413 0.11820.592 0.464 0.854 0.475
0.685 0.15640.631 0.564 0.914 0.486
0.664 0.15880.506 0.481 0.867 0.554
0.703 0.13350.519 0.484 0.812 0.519
0.653 0.13950.625 0.519 0.892 0.492
0.586 0.11140.505 0.565 0.889 0.517
0.534 0.11430.521 0.571 0.889 0.502
0.523 0.13200.508 0.412 0.919 0.508
0.580 0.12490.546 0.608 0.954 0.520
0.448 0.10280.522 0.534 0.918 0.506
0.417 0.16840.405 0.415 0.981 0.401
0.528 0.10570.424 0.566 0.909 0.568
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " M1 & M2 & M3 & M4 & M5 & Y\\\\\n", "\\hline\n", "\t 0.573 & 0.1059 & 0.465 & 0.538 & 0.841 & 0.534 \\\\\n", "\t 0.651 & 0.1356 & 0.527 & 0.545 & 0.887 & 0.535 \\\\\n", "\t 0.606 & 0.1273 & 0.494 & 0.522 & 0.920 & 0.570 \\\\\n", "\t 0.437 & 0.1591 & 0.446 & 0.423 & 0.992 & 0.450 \\\\\n", "\t 0.547 & 0.1135 & 0.531 & 0.519 & 0.915 & 0.548 \\\\\n", "\t 0.444 & 0.1628 & 0.429 & 0.411 & 0.984 & 0.431 \\\\\n", "\t 0.489 & 0.1231 & 0.562 & 0.455 & 0.824 & 0.401 \\\\\n", "\t 0.536 & 0.1473 & 0.410 & 0.430 & 0.978 & 0.423 \\\\\n", "\t 0.413 & 0.1182 & 0.592 & 0.464 & 0.854 & 0.475 \\\\\n", "\t 0.685 & 0.1564 & 0.631 & 0.564 & 0.914 & 0.486 \\\\\n", "\t 0.664 & 0.1588 & 0.506 & 0.481 & 0.867 & 0.554 \\\\\n", "\t 0.703 & 0.1335 & 0.519 & 0.484 & 0.812 & 0.519 \\\\\n", "\t 0.653 & 0.1395 & 0.625 & 0.519 & 0.892 & 0.492 \\\\\n", "\t 0.586 & 0.1114 & 0.505 & 0.565 & 0.889 & 0.517 \\\\\n", "\t 0.534 & 0.1143 & 0.521 & 0.571 & 0.889 & 0.502 \\\\\n", "\t 0.523 & 0.1320 & 0.508 & 0.412 & 0.919 & 0.508 \\\\\n", "\t 0.580 & 0.1249 & 0.546 & 0.608 & 0.954 & 0.520 \\\\\n", "\t 0.448 & 0.1028 & 0.522 & 0.534 & 0.918 & 0.506 \\\\\n", "\t 0.417 & 0.1684 & 0.405 & 0.415 & 0.981 & 0.401 \\\\\n", "\t 0.528 & 0.1057 & 0.424 & 0.566 & 0.909 & 0.568 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| M1 | M2 | M3 | M4 | M5 | Y |\n", "|---|---|---|---|---|---|\n", "| 0.573 | 0.1059 | 0.465 | 0.538 | 0.841 | 0.534 |\n", "| 0.651 | 0.1356 | 0.527 | 0.545 | 0.887 | 0.535 |\n", "| 0.606 | 0.1273 | 0.494 | 0.522 | 0.920 | 0.570 |\n", "| 0.437 | 0.1591 | 0.446 | 0.423 | 0.992 | 0.450 |\n", "| 0.547 | 0.1135 | 0.531 | 0.519 | 0.915 | 0.548 |\n", "| 0.444 | 0.1628 | 0.429 | 0.411 | 0.984 | 0.431 |\n", "| 0.489 | 0.1231 | 0.562 | 0.455 | 0.824 | 0.401 |\n", "| 0.536 | 0.1473 | 0.410 | 0.430 | 0.978 | 0.423 |\n", "| 0.413 | 0.1182 | 0.592 | 0.464 | 0.854 | 0.475 |\n", "| 0.685 | 0.1564 | 0.631 | 0.564 | 0.914 | 0.486 |\n", "| 0.664 | 0.1588 | 0.506 | 0.481 | 0.867 | 0.554 |\n", "| 0.703 | 0.1335 | 0.519 | 0.484 | 0.812 | 0.519 |\n", "| 0.653 | 0.1395 | 0.625 | 0.519 | 0.892 | 0.492 |\n", "| 0.586 | 0.1114 | 0.505 | 0.565 | 0.889 | 0.517 |\n", "| 0.534 | 0.1143 | 0.521 | 0.571 | 0.889 | 0.502 |\n", "| 0.523 | 0.1320 | 0.508 | 0.412 | 0.919 | 0.508 |\n", "| 0.580 | 0.1249 | 0.546 | 0.608 | 0.954 | 0.520 |\n", "| 0.448 | 0.1028 | 0.522 | 0.534 | 0.918 | 0.506 |\n", "| 0.417 | 0.1684 | 0.405 | 0.415 | 0.981 | 0.401 |\n", "| 0.528 | 0.1057 | 0.424 | 0.566 | 0.909 | 0.568 |\n", "\n" ], "text/plain": [ " M1 M2 M3 M4 M5 Y \n", "1 0.573 0.1059 0.465 0.538 0.841 0.534\n", "2 0.651 0.1356 0.527 0.545 0.887 0.535\n", "3 0.606 0.1273 0.494 0.522 0.920 0.570\n", "4 0.437 0.1591 0.446 0.423 0.992 0.450\n", "5 0.547 0.1135 0.531 0.519 0.915 0.548\n", "6 0.444 0.1628 0.429 0.411 0.984 0.431\n", "7 0.489 0.1231 0.562 0.455 0.824 0.401\n", "8 0.536 0.1473 0.410 0.430 0.978 0.423\n", "9 0.413 0.1182 0.592 0.464 0.854 0.475\n", "10 0.685 0.1564 0.631 0.564 0.914 0.486\n", "11 0.664 0.1588 0.506 0.481 0.867 0.554\n", "12 0.703 0.1335 0.519 0.484 0.812 0.519\n", "13 0.653 0.1395 0.625 0.519 0.892 0.492\n", "14 0.586 0.1114 0.505 0.565 0.889 0.517\n", "15 0.534 0.1143 0.521 0.571 0.889 0.502\n", "16 0.523 0.1320 0.508 0.412 0.919 0.508\n", "17 0.580 0.1249 0.546 0.608 0.954 0.520\n", "18 0.448 0.1028 0.522 0.534 0.918 0.506\n", "19 0.417 0.1684 0.405 0.415 0.981 0.401\n", "20 0.528 0.1057 0.424 0.566 0.909 0.568" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# wd라는 데이터객체가 있다고 가정한다.\n", "wd <- read.table(\"./data//01/wd.txt\", sep = \"\\t\", header=T) # \"./따옴표경로\" , sep=\"구분자\" , header=T (첫줄이 타이틀)\n", "wd" ] }, { "cell_type": "code", "execution_count": 46, "id": "d72a78bc", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
M1M2M3M4M5Y
0.573 0.10590.465 0.538 0.841 0.534
0.651 0.13560.527 0.545 0.887 0.535
0.606 0.12730.494 0.522 0.920 0.570
0.437 0.15910.446 0.423 0.992 0.450
0.547 0.11350.531 0.519 0.915 0.548
0.444 0.16280.429 0.411 0.984 0.431
0.489 0.12310.562 0.455 0.824 0.401
0.536 0.14730.410 0.430 0.978 0.423
0.413 0.11820.592 0.464 0.854 0.475
0.685 0.15640.631 0.564 0.914 0.486
0.664 0.15880.506 0.481 0.867 0.554
0.703 0.13350.519 0.484 0.812 0.519
0.653 0.13950.625 0.519 0.892 0.492
0.586 0.11140.505 0.565 0.889 0.517
0.534 0.11430.521 0.571 0.889 0.502
0.523 0.13200.508 0.412 0.919 0.508
0.580 0.12490.546 0.608 0.954 0.520
0.448 0.10280.522 0.534 0.918 0.506
0.417 0.16840.405 0.415 0.981 0.401
0.528 0.10570.424 0.566 0.909 0.568
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " M1 & M2 & M3 & M4 & M5 & Y\\\\\n", "\\hline\n", "\t 0.573 & 0.1059 & 0.465 & 0.538 & 0.841 & 0.534 \\\\\n", "\t 0.651 & 0.1356 & 0.527 & 0.545 & 0.887 & 0.535 \\\\\n", "\t 0.606 & 0.1273 & 0.494 & 0.522 & 0.920 & 0.570 \\\\\n", "\t 0.437 & 0.1591 & 0.446 & 0.423 & 0.992 & 0.450 \\\\\n", "\t 0.547 & 0.1135 & 0.531 & 0.519 & 0.915 & 0.548 \\\\\n", "\t 0.444 & 0.1628 & 0.429 & 0.411 & 0.984 & 0.431 \\\\\n", "\t 0.489 & 0.1231 & 0.562 & 0.455 & 0.824 & 0.401 \\\\\n", "\t 0.536 & 0.1473 & 0.410 & 0.430 & 0.978 & 0.423 \\\\\n", "\t 0.413 & 0.1182 & 0.592 & 0.464 & 0.854 & 0.475 \\\\\n", "\t 0.685 & 0.1564 & 0.631 & 0.564 & 0.914 & 0.486 \\\\\n", "\t 0.664 & 0.1588 & 0.506 & 0.481 & 0.867 & 0.554 \\\\\n", "\t 0.703 & 0.1335 & 0.519 & 0.484 & 0.812 & 0.519 \\\\\n", "\t 0.653 & 0.1395 & 0.625 & 0.519 & 0.892 & 0.492 \\\\\n", "\t 0.586 & 0.1114 & 0.505 & 0.565 & 0.889 & 0.517 \\\\\n", "\t 0.534 & 0.1143 & 0.521 & 0.571 & 0.889 & 0.502 \\\\\n", "\t 0.523 & 0.1320 & 0.508 & 0.412 & 0.919 & 0.508 \\\\\n", "\t 0.580 & 0.1249 & 0.546 & 0.608 & 0.954 & 0.520 \\\\\n", "\t 0.448 & 0.1028 & 0.522 & 0.534 & 0.918 & 0.506 \\\\\n", "\t 0.417 & 0.1684 & 0.405 & 0.415 & 0.981 & 0.401 \\\\\n", "\t 0.528 & 0.1057 & 0.424 & 0.566 & 0.909 & 0.568 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| M1 | M2 | M3 | M4 | M5 | Y |\n", "|---|---|---|---|---|---|\n", "| 0.573 | 0.1059 | 0.465 | 0.538 | 0.841 | 0.534 |\n", "| 0.651 | 0.1356 | 0.527 | 0.545 | 0.887 | 0.535 |\n", "| 0.606 | 0.1273 | 0.494 | 0.522 | 0.920 | 0.570 |\n", "| 0.437 | 0.1591 | 0.446 | 0.423 | 0.992 | 0.450 |\n", "| 0.547 | 0.1135 | 0.531 | 0.519 | 0.915 | 0.548 |\n", "| 0.444 | 0.1628 | 0.429 | 0.411 | 0.984 | 0.431 |\n", "| 0.489 | 0.1231 | 0.562 | 0.455 | 0.824 | 0.401 |\n", "| 0.536 | 0.1473 | 0.410 | 0.430 | 0.978 | 0.423 |\n", "| 0.413 | 0.1182 | 0.592 | 0.464 | 0.854 | 0.475 |\n", "| 0.685 | 0.1564 | 0.631 | 0.564 | 0.914 | 0.486 |\n", "| 0.664 | 0.1588 | 0.506 | 0.481 | 0.867 | 0.554 |\n", "| 0.703 | 0.1335 | 0.519 | 0.484 | 0.812 | 0.519 |\n", "| 0.653 | 0.1395 | 0.625 | 0.519 | 0.892 | 0.492 |\n", "| 0.586 | 0.1114 | 0.505 | 0.565 | 0.889 | 0.517 |\n", "| 0.534 | 0.1143 | 0.521 | 0.571 | 0.889 | 0.502 |\n", "| 0.523 | 0.1320 | 0.508 | 0.412 | 0.919 | 0.508 |\n", "| 0.580 | 0.1249 | 0.546 | 0.608 | 0.954 | 0.520 |\n", "| 0.448 | 0.1028 | 0.522 | 0.534 | 0.918 | 0.506 |\n", "| 0.417 | 0.1684 | 0.405 | 0.415 | 0.981 | 0.401 |\n", "| 0.528 | 0.1057 | 0.424 | 0.566 | 0.909 | 0.568 |\n", "\n" ], "text/plain": [ " M1 M2 M3 M4 M5 Y \n", "1 0.573 0.1059 0.465 0.538 0.841 0.534\n", "2 0.651 0.1356 0.527 0.545 0.887 0.535\n", "3 0.606 0.1273 0.494 0.522 0.920 0.570\n", "4 0.437 0.1591 0.446 0.423 0.992 0.450\n", "5 0.547 0.1135 0.531 0.519 0.915 0.548\n", "6 0.444 0.1628 0.429 0.411 0.984 0.431\n", "7 0.489 0.1231 0.562 0.455 0.824 0.401\n", "8 0.536 0.1473 0.410 0.430 0.978 0.423\n", "9 0.413 0.1182 0.592 0.464 0.854 0.475\n", "10 0.685 0.1564 0.631 0.564 0.914 0.486\n", "11 0.664 0.1588 0.506 0.481 0.867 0.554\n", "12 0.703 0.1335 0.519 0.484 0.812 0.519\n", "13 0.653 0.1395 0.625 0.519 0.892 0.492\n", "14 0.586 0.1114 0.505 0.565 0.889 0.517\n", "15 0.534 0.1143 0.521 0.571 0.889 0.502\n", "16 0.523 0.1320 0.508 0.412 0.919 0.508\n", "17 0.580 0.1249 0.546 0.608 0.954 0.520\n", "18 0.448 0.1028 0.522 0.534 0.918 0.506\n", "19 0.417 0.1684 0.405 0.415 0.981 0.401\n", "20 0.528 0.1057 0.424 0.566 0.909 0.568" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 객체를 = 할당으로 복사한다. (다른 언어에선 메모리주소 공유되서 안될 듯?)\n", "nwd = wd\n", "\n", "nwd" ] }, { "cell_type": "code", "execution_count": 47, "id": "398e38e2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# 0.9보다 큰 것만 골라서 99로 바꿔보자.\n", "nwd[ nwd > 0.9 ] = 99 " ] }, { "cell_type": "code", "execution_count": 51, "id": "26424819", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
M1M2M3M4M5Y
0.573 0.10590.465 0.538 0.8410.534
0.651 0.13560.527 0.545 0.8870.535
0.606 0.12730.494 0.522 99.0000.570
0.437 0.15910.446 0.423 99.0000.450
0.547 0.11350.531 0.519 99.0000.548
0.444 0.16280.429 0.411 99.0000.431
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " M1 & M2 & M3 & M4 & M5 & Y\\\\\n", "\\hline\n", "\t 0.573 & 0.1059 & 0.465 & 0.538 & 0.841 & 0.534 \\\\\n", "\t 0.651 & 0.1356 & 0.527 & 0.545 & 0.887 & 0.535 \\\\\n", "\t 0.606 & 0.1273 & 0.494 & 0.522 & 99.000 & 0.570 \\\\\n", "\t 0.437 & 0.1591 & 0.446 & 0.423 & 99.000 & 0.450 \\\\\n", "\t 0.547 & 0.1135 & 0.531 & 0.519 & 99.000 & 0.548 \\\\\n", "\t 0.444 & 0.1628 & 0.429 & 0.411 & 99.000 & 0.431 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| M1 | M2 | M3 | M4 | M5 | Y |\n", "|---|---|---|---|---|---|\n", "| 0.573 | 0.1059 | 0.465 | 0.538 | 0.841 | 0.534 |\n", "| 0.651 | 0.1356 | 0.527 | 0.545 | 0.887 | 0.535 |\n", "| 0.606 | 0.1273 | 0.494 | 0.522 | 99.000 | 0.570 |\n", "| 0.437 | 0.1591 | 0.446 | 0.423 | 99.000 | 0.450 |\n", "| 0.547 | 0.1135 | 0.531 | 0.519 | 99.000 | 0.548 |\n", "| 0.444 | 0.1628 | 0.429 | 0.411 | 99.000 | 0.431 |\n", "\n" ], "text/plain": [ " M1 M2 M3 M4 M5 Y \n", "1 0.573 0.1059 0.465 0.538 0.841 0.534\n", "2 0.651 0.1356 0.527 0.545 0.887 0.535\n", "3 0.606 0.1273 0.494 0.522 99.000 0.570\n", "4 0.437 0.1591 0.446 0.423 99.000 0.450\n", "5 0.547 0.1135 0.531 0.519 99.000 0.548\n", "6 0.444 0.1628 0.429 0.411 99.000 0.431" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(nwd)" ] }, { "cell_type": "code", "execution_count": 52, "id": "809037e2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
M1M2M3M4M5Y
0.573 0.10590.465 0.538 0.841 0.534
0.651 0.13560.527 0.545 0.887 0.535
0.606 0.12730.494 0.522 NA 0.570
0.437 0.15910.446 0.423 NA 0.450
0.547 0.11350.531 0.519 NA 0.548
0.444 0.16280.429 0.411 NA 0.431
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " M1 & M2 & M3 & M4 & M5 & Y\\\\\n", "\\hline\n", "\t 0.573 & 0.1059 & 0.465 & 0.538 & 0.841 & 0.534 \\\\\n", "\t 0.651 & 0.1356 & 0.527 & 0.545 & 0.887 & 0.535 \\\\\n", "\t 0.606 & 0.1273 & 0.494 & 0.522 & NA & 0.570 \\\\\n", "\t 0.437 & 0.1591 & 0.446 & 0.423 & NA & 0.450 \\\\\n", "\t 0.547 & 0.1135 & 0.531 & 0.519 & NA & 0.548 \\\\\n", "\t 0.444 & 0.1628 & 0.429 & 0.411 & NA & 0.431 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| M1 | M2 | M3 | M4 | M5 | Y |\n", "|---|---|---|---|---|---|\n", "| 0.573 | 0.1059 | 0.465 | 0.538 | 0.841 | 0.534 |\n", "| 0.651 | 0.1356 | 0.527 | 0.545 | 0.887 | 0.535 |\n", "| 0.606 | 0.1273 | 0.494 | 0.522 | NA | 0.570 |\n", "| 0.437 | 0.1591 | 0.446 | 0.423 | NA | 0.450 |\n", "| 0.547 | 0.1135 | 0.531 | 0.519 | NA | 0.548 |\n", "| 0.444 | 0.1628 | 0.429 | 0.411 | NA | 0.431 |\n", "\n" ], "text/plain": [ " M1 M2 M3 M4 M5 Y \n", "1 0.573 0.1059 0.465 0.538 0.841 0.534\n", "2 0.651 0.1356 0.527 0.545 0.887 0.535\n", "3 0.606 0.1273 0.494 0.522 NA 0.570\n", "4 0.437 0.1591 0.446 0.423 NA 0.450\n", "5 0.547 0.1135 0.531 0.519 NA 0.548\n", "6 0.444 0.1628 0.429 0.411 NA 0.431" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 0.9보다 큰 99를 다시 NA로 바꿔보자.\n", "nwd[nwd == 99] = NA\n", "\n", "head(nwd)" ] }, { "cell_type": "code", "execution_count": 53, "id": "82750a63", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
M1M2M3M4M5Y
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSEFALSEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
FALSEFALSEFALSEFALSE TRUEFALSE
\n" ], "text/latex": [ "\\begin{tabular}{llllll}\n", " M1 & M2 & M3 & M4 & M5 & Y\\\\\n", "\\hline\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\t FALSE & FALSE & FALSE & FALSE & TRUE & FALSE\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| M1 | M2 | M3 | M4 | M5 | Y |\n", "|---|---|---|---|---|---|\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "| FALSE | FALSE | FALSE | FALSE | TRUE | FALSE |\n", "\n" ], "text/plain": [ " M1 M2 M3 M4 M5 Y \n", " [1,] FALSE FALSE FALSE FALSE FALSE FALSE\n", " [2,] FALSE FALSE FALSE FALSE FALSE FALSE\n", " [3,] FALSE FALSE FALSE FALSE TRUE FALSE\n", " [4,] FALSE FALSE FALSE FALSE TRUE FALSE\n", " [5,] FALSE FALSE FALSE FALSE TRUE FALSE\n", " [6,] FALSE FALSE FALSE FALSE TRUE FALSE\n", " [7,] FALSE FALSE FALSE FALSE FALSE FALSE\n", " [8,] FALSE FALSE FALSE FALSE TRUE FALSE\n", " [9,] FALSE FALSE FALSE FALSE FALSE FALSE\n", "[10,] FALSE FALSE FALSE FALSE TRUE FALSE\n", "[11,] FALSE FALSE FALSE FALSE FALSE FALSE\n", "[12,] FALSE FALSE FALSE FALSE FALSE FALSE\n", "[13,] FALSE FALSE FALSE FALSE FALSE FALSE\n", "[14,] FALSE FALSE FALSE FALSE FALSE FALSE\n", "[15,] FALSE FALSE FALSE FALSE FALSE FALSE\n", "[16,] FALSE FALSE FALSE FALSE TRUE FALSE\n", "[17,] FALSE FALSE FALSE FALSE TRUE FALSE\n", "[18,] FALSE FALSE FALSE FALSE TRUE FALSE\n", "[19,] FALSE FALSE FALSE FALSE TRUE FALSE\n", "[20,] FALSE FALSE FALSE FALSE TRUE FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 행별 결측치 : 결측치마스크 + 행별 합\n", "\n", "# is.na()는 데이터객체 전체에 대한 boolean mask(logical character)를 만든다. -> 행렬mask -> 행 or 열별로 접근해줘야한다.\n", "is.na(nwd)" ] }, { "cell_type": "code", "execution_count": 54, "id": "0cc881d5", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 0
    2. \n", "\t
  2. 0
    3. \n", "\t
  3. 1
    4. \n", "\t
  4. 1
    5. \n", "\t
  5. 1
    6. \n", "\t
  6. 1
    7. \n", "\t
  7. 0
    8. \n", "\t
  8. 1
    9. \n", "\t
  9. 0
    10. \n", "\t
  10. 1
    11. \n", "\t
  11. 0
    12. \n", "\t
  12. 0
    13. \n", "\t
  13. 0
    14. \n", "\t
  14. 0
    15. \n", "\t
  15. 0
    16. \n", "\t
  16. 1
    17. \n", "\t
  17. 1
    18. \n", "\t
  18. 1
    19. \n", "\t
  19. 1
    20. \n", "\t
  20. 1
    21. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0\n", "\\item 0\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 0\n", "\\item 1\n", "\\item 0\n", "\\item 1\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0\n", "2. 0\n", "3. 1\n", "4. 1\n", "5. 1\n", "6. 1\n", "7. 0\n", "8. 1\n", "9. 0\n", "10. 1\n", "11. 0\n", "12. 0\n", "13. 0\n", "14. 0\n", "15. 0\n", "16. 1\n", "17. 1\n", "18. 1\n", "19. 1\n", "20. 1\n", "\n", "\n" ], "text/plain": [ " [1] 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rowSums( is.na(nwd) )" ] }, { "cell_type": "code", "execution_count": 55, "id": "9438ad58", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\t
M1
\n", "\t\t
0
\n", "\t
M2
\n", "\t\t
0
\n", "\t
M3
\n", "\t\t
0
\n", "\t
M4
\n", "\t\t
0
\n", "\t
M5
\n", "\t\t
11
\n", "\t
Y
\n", "\t\t
0
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[M1] 0\n", "\\item[M2] 0\n", "\\item[M3] 0\n", "\\item[M4] 0\n", "\\item[M5] 11\n", "\\item[Y] 0\n", "\\end{description*}\n" ], "text/markdown": [ "M1\n", ": 0M2\n", ": 0M3\n", ": 0M4\n", ": 0M5\n", ": 11Y\n", ": 0\n", "\n" ], "text/plain": [ "M1 M2 M3 M4 M5 Y \n", " 0 0 0 0 11 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colSums( is.na(nwd) )" ] }, { "cell_type": "code", "execution_count": 56, "id": "96d7716a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
M1M2M3M4M5Y
10.573 0.10590.465 0.538 0.841 0.534
20.651 0.13560.527 0.545 0.887 0.535
70.489 0.12310.562 0.455 0.824 0.401
90.413 0.11820.592 0.464 0.854 0.475
110.664 0.15880.506 0.481 0.867 0.554
120.703 0.13350.519 0.484 0.812 0.519
130.653 0.13950.625 0.519 0.892 0.492
140.586 0.11140.505 0.565 0.889 0.517
150.534 0.11430.521 0.571 0.889 0.502
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & M1 & M2 & M3 & M4 & M5 & Y\\\\\n", "\\hline\n", "\t1 & 0.573 & 0.1059 & 0.465 & 0.538 & 0.841 & 0.534 \\\\\n", "\t2 & 0.651 & 0.1356 & 0.527 & 0.545 & 0.887 & 0.535 \\\\\n", "\t7 & 0.489 & 0.1231 & 0.562 & 0.455 & 0.824 & 0.401 \\\\\n", "\t9 & 0.413 & 0.1182 & 0.592 & 0.464 & 0.854 & 0.475 \\\\\n", "\t11 & 0.664 & 0.1588 & 0.506 & 0.481 & 0.867 & 0.554 \\\\\n", "\t12 & 0.703 & 0.1335 & 0.519 & 0.484 & 0.812 & 0.519 \\\\\n", "\t13 & 0.653 & 0.1395 & 0.625 & 0.519 & 0.892 & 0.492 \\\\\n", "\t14 & 0.586 & 0.1114 & 0.505 & 0.565 & 0.889 & 0.517 \\\\\n", "\t15 & 0.534 & 0.1143 & 0.521 & 0.571 & 0.889 & 0.502 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | M1 | M2 | M3 | M4 | M5 | Y |\n", "|---|---|---|---|---|---|---|\n", "| 1 | 0.573 | 0.1059 | 0.465 | 0.538 | 0.841 | 0.534 |\n", "| 2 | 0.651 | 0.1356 | 0.527 | 0.545 | 0.887 | 0.535 |\n", "| 7 | 0.489 | 0.1231 | 0.562 | 0.455 | 0.824 | 0.401 |\n", "| 9 | 0.413 | 0.1182 | 0.592 | 0.464 | 0.854 | 0.475 |\n", "| 11 | 0.664 | 0.1588 | 0.506 | 0.481 | 0.867 | 0.554 |\n", "| 12 | 0.703 | 0.1335 | 0.519 | 0.484 | 0.812 | 0.519 |\n", "| 13 | 0.653 | 0.1395 | 0.625 | 0.519 | 0.892 | 0.492 |\n", "| 14 | 0.586 | 0.1114 | 0.505 | 0.565 | 0.889 | 0.517 |\n", "| 15 | 0.534 | 0.1143 | 0.521 | 0.571 | 0.889 | 0.502 |\n", "\n" ], "text/plain": [ " M1 M2 M3 M4 M5 Y \n", "1 0.573 0.1059 0.465 0.538 0.841 0.534\n", "2 0.651 0.1356 0.527 0.545 0.887 0.535\n", "7 0.489 0.1231 0.562 0.455 0.824 0.401\n", "9 0.413 0.1182 0.592 0.464 0.854 0.475\n", "11 0.664 0.1588 0.506 0.481 0.867 0.554\n", "12 0.703 0.1335 0.519 0.484 0.812 0.519\n", "13 0.653 0.1395 0.625 0.519 0.892 0.492\n", "14 0.586 0.1114 0.505 0.565 0.889 0.517\n", "15 0.534 0.1143 0.521 0.571 0.889 0.502" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# NA를 제거한 데이터를 복사할 수 있다.\n", "# -> 행별로 제거하는 듯 싶다. \n", "# -> na.omit()의 결과 <기존 행번호>가 같이 찍히니 확인하면 된다.\n", "na.omit(nwd)" ] }, { "cell_type": "code", "execution_count": 57, "id": "c27c23a1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
M1M2M3M4M5Y
0.573 0.10590.465 0.538 0.841 0.534
0.651 0.13560.527 0.545 0.887 0.535
0.606 0.12730.494 0.522 NA 0.570
0.437 0.15910.446 0.423 NA 0.450
0.547 0.11350.531 0.519 NA 0.548
0.444 0.16280.429 0.411 NA 0.431
0.489 0.12310.562 0.455 0.824 0.401
0.536 0.14730.410 0.430 NA 0.423
0.413 0.11820.592 0.464 0.854 0.475
0.685 0.15640.631 0.564 NA 0.486
0.664 0.15880.506 0.481 0.867 0.554
0.703 0.13350.519 0.484 0.812 0.519
0.653 0.13950.625 0.519 0.892 0.492
0.586 0.11140.505 0.565 0.889 0.517
0.534 0.11430.521 0.571 0.889 0.502
0.523 0.13200.508 0.412 NA 0.508
0.580 0.12490.546 0.608 NA 0.520
0.448 0.10280.522 0.534 NA 0.506
0.417 0.16840.405 0.415 NA 0.401
0.528 0.10570.424 0.566 NA 0.568
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " M1 & M2 & M3 & M4 & M5 & Y\\\\\n", "\\hline\n", "\t 0.573 & 0.1059 & 0.465 & 0.538 & 0.841 & 0.534 \\\\\n", "\t 0.651 & 0.1356 & 0.527 & 0.545 & 0.887 & 0.535 \\\\\n", "\t 0.606 & 0.1273 & 0.494 & 0.522 & NA & 0.570 \\\\\n", "\t 0.437 & 0.1591 & 0.446 & 0.423 & NA & 0.450 \\\\\n", "\t 0.547 & 0.1135 & 0.531 & 0.519 & NA & 0.548 \\\\\n", "\t 0.444 & 0.1628 & 0.429 & 0.411 & NA & 0.431 \\\\\n", "\t 0.489 & 0.1231 & 0.562 & 0.455 & 0.824 & 0.401 \\\\\n", "\t 0.536 & 0.1473 & 0.410 & 0.430 & NA & 0.423 \\\\\n", "\t 0.413 & 0.1182 & 0.592 & 0.464 & 0.854 & 0.475 \\\\\n", "\t 0.685 & 0.1564 & 0.631 & 0.564 & NA & 0.486 \\\\\n", "\t 0.664 & 0.1588 & 0.506 & 0.481 & 0.867 & 0.554 \\\\\n", "\t 0.703 & 0.1335 & 0.519 & 0.484 & 0.812 & 0.519 \\\\\n", "\t 0.653 & 0.1395 & 0.625 & 0.519 & 0.892 & 0.492 \\\\\n", "\t 0.586 & 0.1114 & 0.505 & 0.565 & 0.889 & 0.517 \\\\\n", "\t 0.534 & 0.1143 & 0.521 & 0.571 & 0.889 & 0.502 \\\\\n", "\t 0.523 & 0.1320 & 0.508 & 0.412 & NA & 0.508 \\\\\n", "\t 0.580 & 0.1249 & 0.546 & 0.608 & NA & 0.520 \\\\\n", "\t 0.448 & 0.1028 & 0.522 & 0.534 & NA & 0.506 \\\\\n", "\t 0.417 & 0.1684 & 0.405 & 0.415 & NA & 0.401 \\\\\n", "\t 0.528 & 0.1057 & 0.424 & 0.566 & NA & 0.568 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| M1 | M2 | M3 | M4 | M5 | Y |\n", "|---|---|---|---|---|---|\n", "| 0.573 | 0.1059 | 0.465 | 0.538 | 0.841 | 0.534 |\n", "| 0.651 | 0.1356 | 0.527 | 0.545 | 0.887 | 0.535 |\n", "| 0.606 | 0.1273 | 0.494 | 0.522 | NA | 0.570 |\n", "| 0.437 | 0.1591 | 0.446 | 0.423 | NA | 0.450 |\n", "| 0.547 | 0.1135 | 0.531 | 0.519 | NA | 0.548 |\n", "| 0.444 | 0.1628 | 0.429 | 0.411 | NA | 0.431 |\n", "| 0.489 | 0.1231 | 0.562 | 0.455 | 0.824 | 0.401 |\n", "| 0.536 | 0.1473 | 0.410 | 0.430 | NA | 0.423 |\n", "| 0.413 | 0.1182 | 0.592 | 0.464 | 0.854 | 0.475 |\n", "| 0.685 | 0.1564 | 0.631 | 0.564 | NA | 0.486 |\n", "| 0.664 | 0.1588 | 0.506 | 0.481 | 0.867 | 0.554 |\n", "| 0.703 | 0.1335 | 0.519 | 0.484 | 0.812 | 0.519 |\n", "| 0.653 | 0.1395 | 0.625 | 0.519 | 0.892 | 0.492 |\n", "| 0.586 | 0.1114 | 0.505 | 0.565 | 0.889 | 0.517 |\n", "| 0.534 | 0.1143 | 0.521 | 0.571 | 0.889 | 0.502 |\n", "| 0.523 | 0.1320 | 0.508 | 0.412 | NA | 0.508 |\n", "| 0.580 | 0.1249 | 0.546 | 0.608 | NA | 0.520 |\n", "| 0.448 | 0.1028 | 0.522 | 0.534 | NA | 0.506 |\n", "| 0.417 | 0.1684 | 0.405 | 0.415 | NA | 0.401 |\n", "| 0.528 | 0.1057 | 0.424 | 0.566 | NA | 0.568 |\n", "\n" ], "text/plain": [ " M1 M2 M3 M4 M5 Y \n", "1 0.573 0.1059 0.465 0.538 0.841 0.534\n", "2 0.651 0.1356 0.527 0.545 0.887 0.535\n", "3 0.606 0.1273 0.494 0.522 NA 0.570\n", "4 0.437 0.1591 0.446 0.423 NA 0.450\n", "5 0.547 0.1135 0.531 0.519 NA 0.548\n", "6 0.444 0.1628 0.429 0.411 NA 0.431\n", "7 0.489 0.1231 0.562 0.455 0.824 0.401\n", "8 0.536 0.1473 0.410 0.430 NA 0.423\n", "9 0.413 0.1182 0.592 0.464 0.854 0.475\n", "10 0.685 0.1564 0.631 0.564 NA 0.486\n", "11 0.664 0.1588 0.506 0.481 0.867 0.554\n", "12 0.703 0.1335 0.519 0.484 0.812 0.519\n", "13 0.653 0.1395 0.625 0.519 0.892 0.492\n", "14 0.586 0.1114 0.505 0.565 0.889 0.517\n", "15 0.534 0.1143 0.521 0.571 0.889 0.502\n", "16 0.523 0.1320 0.508 0.412 NA 0.508\n", "17 0.580 0.1249 0.546 0.608 NA 0.520\n", "18 0.448 0.1028 0.522 0.534 NA 0.506\n", "19 0.417 0.1684 0.405 0.415 NA 0.401\n", "20 0.528 0.1057 0.424 0.566 NA 0.568" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nwd" ] }, { "cell_type": "markdown", "id": "13d97c19", "metadata": {}, "source": [ "#### 1.2.6 Functions that create vectors" ] }, { "cell_type": "code", "execution_count": 59, "id": "1efcad39", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 1
    2. \n", "\t
  2. 2
    3. \n", "\t
  3. 3
    4. \n", "\t
  4. 10
    5. \n", "\t
  5. 20
    6. \n", "\t
  6. 5
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 10\n", "\\item 20\n", "\\item 5\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 10\n", "5. 20\n", "6. 5\n", "\n", "\n" ], "text/plain": [ "[1] 1 2 3 10 20 5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 벡터생성은 create의 c()로 각 객체들을 연결해서 벡터를 만든다.\n", "x <- c(1,2,3)\n", "y <- c(10, 20)\n", "\n", "c(x, y, 5) # 벡터객체와 값이 동등하게 1차원으로 연결된다." ] }, { "cell_type": "code", "execution_count": 62, "id": "ed0692b8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 0
    2. \n", "\t
  2. 3
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0\n", "\\item 3\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0\n", "2. 3\n", "\n", "\n" ], "text/plain": [ "[1] 0 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 다양한 타입이 벡터안의 요소로 들어갈 경우, 제한적인 쪽으로 자동 convert된다.\n", "\n", "# boolean + 숫자 -> 숫자로 type 0/1로 바뀌어서 type통일\n", "c(FALSE, 3)" ] }, { "cell_type": "code", "execution_count": 63, "id": "6eb6bd8d", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'FALSE'
    2. \n", "\t
  2. 'abc'
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'FALSE'\n", "\\item 'abc'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'FALSE'\n", "2. 'abc'\n", "\n", "\n" ], "text/plain": [ "[1] \"FALSE\" \"abc\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# boolean + 문자열 -> 문자열로 바뀐다.\n", "c(FALSE, \"abc\")" ] }, { "cell_type": "code", "execution_count": 64, "id": "f5debbe1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. '3.14159265358979'
    2. \n", "\t
  2. 'abc'
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item '3.14159265358979'\n", "\\item 'abc'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. '3.14159265358979'\n", "2. 'abc'\n", "\n", "\n" ], "text/plain": [ "[1] \"3.14159265358979\" \"abc\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 상수+ 문자열 -> 문자열\n", "c(pi, \"abc\") " ] }, { "cell_type": "code", "execution_count": 65, "id": "b21ca76a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 4
    2. \n", "\t
  2. 5
    3. \n", "\t
  3. 6
    4. \n", "\t
  4. 7
    5. \n", "\t
  5. 8
    6. \n", "\t
  6. 9
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 4\n", "\\item 5\n", "\\item 6\n", "\\item 7\n", "\\item 8\n", "\\item 9\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 4\n", "2. 5\n", "3. 6\n", "4. 7\n", "5. 8\n", "6. 9\n", "\n", "\n" ], "text/plain": [ "[1] 4 5 6 7 8 9" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 연속적인 값은 한번에 벡터로 생성할 수 있다.\n", "seq(4, 9)" ] }, { "cell_type": "code", "execution_count": 66, "id": "a3c576d6", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "oops <- c(7, 9, 13)" ] }, { "cell_type": "code", "execution_count": 68, "id": "3c693b68", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 7
    2. \n", "\t
  2. 9
    3. \n", "\t
  3. 13
    4. \n", "\t
  4. 7
    5. \n", "\t
  5. 9
    6. \n", "\t
  6. 13
    7. \n", "\t
  7. 7
    8. \n", "\t
  8. 9
    9. \n", "\t
  9. 13
    10. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 7\n", "\\item 9\n", "\\item 13\n", "\\item 7\n", "\\item 9\n", "\\item 13\n", "\\item 7\n", "\\item 9\n", "\\item 13\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 7\n", "2. 9\n", "3. 13\n", "4. 7\n", "5. 9\n", "6. 13\n", "7. 7\n", "8. 9\n", "9. 13\n", "\n", "\n" ], "text/plain": [ "[1] 7 9 13 7 9 13 7 9 13" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 반복문대신 단순 [원소복사] 반복 해주는 rep\n", "rep(oops, 3)" ] }, { "cell_type": "code", "execution_count": 78, "id": "232b0b4a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 7
    2. \n", "\t
  2. 9
    3. \n", "\t
  3. 9
    4. \n", "\t
  4. 13
    5. \n", "\t
  5. 13
    6. \n", "\t
  6. 13
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 7\n", "\\item 9\n", "\\item 9\n", "\\item 13\n", "\\item 13\n", "\\item 13\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 7\n", "2. 9\n", "3. 9\n", "4. 13\n", "5. 13\n", "6. 13\n", "\n", "\n" ], "text/plain": [ "[1] 7 9 9 13 13 13" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 반복을 원소위치에 따라 지정해줄 수 도 있다.\n", "# - index순서대로 1번, 2번, 3번 반복복사\n", "# - index별 반복회수도 벡터로 제공하면 된다.\n", "rep(oops, c(1,2,3))" ] }, { "cell_type": "code", "execution_count": 79, "id": "cb82c014", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 7
    2. \n", "\t
  2. 9
    3. \n", "\t
  3. 9
    4. \n", "\t
  4. 13
    5. \n", "\t
  5. 13
    6. \n", "\t
  6. 13
    7. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 7\n", "\\item 9\n", "\\item 9\n", "\\item 13\n", "\\item 13\n", "\\item 13\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 7\n", "2. 9\n", "3. 9\n", "4. 13\n", "5. 13\n", "6. 13\n", "\n", "\n" ], "text/plain": [ "[1] 7 9 9 13 13 13" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rep(oops, 1:3)" ] }, { "cell_type": "code", "execution_count": 80, "id": "cee728d1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 1
    2. \n", "\t
  2. 1
    3. \n", "\t
  3. 1
    4. \n", "\t
  4. 1
    5. \n", "\t
  5. 1
    6. \n", "\t
  6. 1
    7. \n", "\t
  7. 1
    8. \n", "\t
  8. 1
    9. \n", "\t
  9. 1
    10. \n", "\t
  10. 1
    11. \n", "\t
  11. 2
    12. \n", "\t
  12. 2
    13. \n", "\t
  13. 2
    14. \n", "\t
  14. 2
    15. \n", "\t
  15. 2
    16. \n", "\t
  16. 2
    17. \n", "\t
  17. 2
    18. \n", "\t
  18. 2
    19. \n", "\t
  19. 2
    20. \n", "\t
  20. 2
    21. \n", "\t
  21. 2
    22. \n", "\t
  22. 2
    23. \n", "\t
  23. 2
    24. \n", "\t
  24. 2
    25. \n", "\t
  25. 2
    26. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 1\n", "3. 1\n", "4. 1\n", "5. 1\n", "6. 1\n", "7. 1\n", "8. 1\n", "9. 1\n", "10. 1\n", "11. 2\n", "12. 2\n", "13. 2\n", "14. 2\n", "15. 2\n", "16. 2\n", "17. 2\n", "18. 2\n", "19. 2\n", "20. 2\n", "21. 2\n", "22. 2\n", "23. 2\n", "24. 2\n", "25. 2\n", "\n", "\n" ], "text/plain": [ " [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 벡터1과 2가 있고, 1을 10번, 2을 15번\n", "rep(1:2, c(10, 15)) " ] }, { "cell_type": "markdown", "id": "e6aaf566", "metadata": {}, "source": [ "#### 1.2.7 Matrices and arrays" ] }, { "cell_type": "code", "execution_count": 81, "id": "3f6f785c", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 1
    2. \n", "\t
  2. 2
    3. \n", "\t
  3. 3
    4. \n", "\t
  4. 4
    5. \n", "\t
  5. 5
    6. \n", "\t
  6. 6
    7. \n", "\t
  7. 7
    8. \n", "\t
  8. 8
    9. \n", "\t
  9. 9
    10. \n", "\t
  10. 10
    11. \n", "\t
  11. 11
    12. \n", "\t
  12. 12
    13. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\item 5\n", "\\item 6\n", "\\item 7\n", "\\item 8\n", "\\item 9\n", "\\item 10\n", "\\item 11\n", "\\item 12\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 4\n", "5. 5\n", "6. 6\n", "7. 7\n", "8. 8\n", "9. 9\n", "10. 10\n", "11. 11\n", "12. 12\n", "\n", "\n" ], "text/plain": [ " [1] 1 2 3 4 5 6 7 8 9 10 11 12" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- 1:12\n", "x" ] }, { "cell_type": "code", "execution_count": 84, "id": "5afad931", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
1 4 7 10
2 5 8 11
3 6 9 12
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", "\t 1 & 4 & 7 & 10\\\\\n", "\t 2 & 5 & 8 & 11\\\\\n", "\t 3 & 6 & 9 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 4 | 7 | 10 |\n", "| 2 | 5 | 8 | 11 |\n", "| 3 | 6 | 9 | 12 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4]\n", "[1,] 1 4 7 10 \n", "[2,] 2 5 8 11 \n", "[3,] 3 6 9 12 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1차원 벡터에 대해, 차원을 c(3, 4) 벡터로 만들어서 dim()에 넣어주면\n", "# 행렬이 된다.\n", "dim(x) = c(3, 4)\n", "\n", "x # 보이진 않지만, 1, 2, 3, 행 ,1 ,2 ,3 ,4 렬이 된다." ] }, { "cell_type": "code", "execution_count": 85, "id": "6d702883", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
1 2 3 4
5 6 7 8
9 101112
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", "\t 1 & 2 & 3 & 4\\\\\n", "\t 5 & 6 & 7 & 8\\\\\n", "\t 9 & 10 & 11 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 2 | 3 | 4 |\n", "| 5 | 6 | 7 | 8 |\n", "| 9 | 10 | 11 | 12 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4]\n", "[1,] 1 2 3 4 \n", "[2,] 5 6 7 8 \n", "[3,] 9 10 11 12 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 애초에 생성할 떄부터 m,n차원 matrix를 만들 수 도 있다.\n", "# - 특히 byrow=를 T루로 주면, ncol없이 알아서 된다.\n", "matrix(1:12, nrow=3, byrow=T)" ] }, { "cell_type": "code", "execution_count": null, "id": "7246cbbe", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 86, "id": "27ce3d4c", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'A'
    2. \n", "\t
  2. 'B'
    3. \n", "\t
  3. 'C'
    4. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'A'\n", "\\item 'B'\n", "\\item 'C'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'A'\n", "2. 'B'\n", "3. 'C'\n", "\n", "\n" ], "text/plain": [ "[1] \"A\" \"B\" \"C\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "LETTERS[1:3]" ] }, { "cell_type": "code", "execution_count": 87, "id": "00ab2fed", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# 행렬의 row에 rownames를 걸어줄 수 있다.\n", "rownames(x) <- LETTERS[1:3]" ] }, { "cell_type": "code", "execution_count": 88, "id": "3ed90f72", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A1 4 7 10
B2 5 8 11
C3 6 9 12
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", "\tA & 1 & 4 & 7 & 10\\\\\n", "\tB & 2 & 5 & 8 & 11\\\\\n", "\tC & 3 & 6 & 9 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| A | 1 | 4 | 7 | 10 |\n", "| B | 2 | 5 | 8 | 11 |\n", "| C | 3 | 6 | 9 | 12 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4]\n", "A 1 4 7 10 \n", "B 2 5 8 11 \n", "C 3 6 9 12 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 89, "id": "ec1f7407", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
ABC
1 2 3
4 5 6
7 8 9
101112
\n" ], "text/latex": [ "\\begin{tabular}{lll}\n", " A & B & C\\\\\n", "\\hline\n", "\t 1 & 2 & 3\\\\\n", "\t 4 & 5 & 6\\\\\n", "\t 7 & 8 & 9\\\\\n", "\t 10 & 11 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| A | B | C |\n", "|---|---|---|\n", "| 1 | 2 | 3 |\n", "| 4 | 5 | 6 |\n", "| 7 | 8 | 9 |\n", "| 10 | 11 | 12 |\n", "\n" ], "text/plain": [ " A B C \n", "[1,] 1 2 3\n", "[2,] 4 5 6\n", "[3,] 7 8 9\n", "[4,] 10 11 12" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# transverse는 t()로 하면 된다.\n", "t(x)" ] }, { "cell_type": "code", "execution_count": 91, "id": "e7c110f4", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A
1
2
3
4
\n" ], "text/latex": [ "\\begin{tabular}{l}\n", " A\\\\\n", "\\hline\n", "\t 1\\\\\n", "\t 2\\\\\n", "\t 3\\\\\n", "\t 4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| A |\n", "|---|\n", "| 1 |\n", "| 2 |\n", "| 3 |\n", "| 4 |\n", "\n" ], "text/plain": [ " A\n", "[1,] 1\n", "[2,] 2\n", "[3,] 3\n", "[4,] 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1차원들을 -> 세워서 열로 합하는 cbind\n", "# 1차원들을 -> 눕혀서 행으로 더하는 rbind\n", "\n", "cbind(A = 1:4)" ] }, { "cell_type": "code", "execution_count": 92, "id": "72bf4292", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
ABC
1 5 9
2 6 10
3 5 11
4 6 12
\n" ], "text/latex": [ "\\begin{tabular}{lll}\n", " A & B & C\\\\\n", "\\hline\n", "\t 1 & 5 & 9\\\\\n", "\t 2 & 6 & 10\\\\\n", "\t 3 & 5 & 11\\\\\n", "\t 4 & 6 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| A | B | C |\n", "|---|---|---|\n", "| 1 | 5 | 9 |\n", "| 2 | 6 | 10 |\n", "| 3 | 5 | 11 |\n", "| 4 | 6 | 12 |\n", "\n" ], "text/plain": [ " A B C \n", "[1,] 1 5 9\n", "[2,] 2 6 10\n", "[3,] 3 5 11\n", "[4,] 4 6 12" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cbind(A = 1:4, B = 5:6, C = 9:12)" ] }, { "cell_type": "code", "execution_count": 93, "id": "fe4f46da", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A1 2 3 4
B5 6 7 8
C9 101112
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", "\tA & 1 & 2 & 3 & 4\\\\\n", "\tB & 5 & 6 & 7 & 8\\\\\n", "\tC & 9 & 10 & 11 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| A | 1 | 2 | 3 | 4 |\n", "| B | 5 | 6 | 7 | 8 |\n", "| C | 9 | 10 | 11 | 12 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4]\n", "A 1 2 3 4 \n", "B 5 6 7 8 \n", "C 9 10 11 12 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rbind(A=1:4,B=5:8,C=9:12)" ] }, { "cell_type": "code", "execution_count": 95, "id": "8b7a6f3e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
ABC
1 5 9
2 6 10
3 7 11
4 8 12
\n" ], "text/latex": [ "\\begin{tabular}{lll}\n", " A & B & C\\\\\n", "\\hline\n", "\t 1 & 5 & 9\\\\\n", "\t 2 & 6 & 10\\\\\n", "\t 3 & 7 & 11\\\\\n", "\t 4 & 8 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| A | B | C |\n", "|---|---|---|\n", "| 1 | 5 | 9 |\n", "| 2 | 6 | 10 |\n", "| 3 | 7 | 11 |\n", "| 4 | 8 | 12 |\n", "\n" ], "text/plain": [ " A B C \n", "[1,] 1 5 9\n", "[2,] 2 6 10\n", "[3,] 3 7 11\n", "[4,] 4 8 12" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aa <- cbind(A=1:4,B=5:8,C=9:12)\n", "aa" ] }, { "cell_type": "code", "execution_count": 97, "id": "95de0f53", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
ABC
1 1 5 9
1 2 6 10
1 3 7 11
1 4 8 12
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", " & A & B & C\\\\\n", "\\hline\n", "\t 1 & 1 & 5 & 9\\\\\n", "\t 1 & 2 & 6 & 10\\\\\n", "\t 1 & 3 & 7 & 11\\\\\n", "\t 1 & 4 & 8 & 12\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | A | B | C |\n", "|---|---|---|---|\n", "| 1 | 1 | 5 | 9 |\n", "| 1 | 2 | 6 | 10 |\n", "| 1 | 3 | 7 | 11 |\n", "| 1 | 4 | 8 | 12 |\n", "\n" ], "text/plain": [ " A B C \n", "[1,] 1 1 5 9\n", "[2,] 1 2 6 10\n", "[3,] 1 3 7 11\n", "[4,] 1 4 8 12" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# bind에서 상수를 넣으면, 방향 전체가 1로 채워진다.\n", "# - 좌or우 똑같은 크기의 상수를 붙이려면 상수로 bind하면 된다.\n", "\n", "cbind(1, aa)" ] }, { "cell_type": "markdown", "id": "5462ca01", "metadata": {}, "source": [ "#### factors(범주형 자료)\n", " - `카테고리칼 변수`는 `인자`로 만들어주는게 중요하다. 그 때 쓰는 것이 `factor()`" ] }, { "cell_type": "code", "execution_count": 98, "id": "1f0aee0b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "pain <- c(0, 3, 2, 2, 1)" ] }, { "cell_type": "code", "execution_count": 99, "id": "c40f7e63", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 0
    2. \n", "\t
  2. 3
    3. \n", "\t
  3. 2
    4. \n", "\t
  4. 2
    5. \n", "\t
  5. 1
    6. \n", "
\n", "\n", "
\n", "\t\n", "\t\tLevels:\n", "\t\n", "\t
    \n", "\t\t
  1. '0'
    2. \n", "\t\t
  2. '1'
    3. \n", "\t\t
  3. '2'
    4. \n", "\t\t
  4. '3'
    5. \n", "\t
\n", "
" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0\n", "\\item 3\n", "\\item 2\n", "\\item 2\n", "\\item 1\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item '0'\n", "\\item '1'\n", "\\item '2'\n", "\\item '3'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0\n", "2. 3\n", "3. 2\n", "4. 2\n", "5. 1\n", "\n", "\n", "\n", "**Levels**: 1. '0'\n", "2. '1'\n", "3. '2'\n", "4. '3'\n", "\n", "\n" ], "text/plain": [ "[1] 0 3 2 2 1\n", "Levels: 0 1 2 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 앞에 f를 붙여 factor임을 명시해주자.\n", "# -> levels=에는 [요인들과 동일한 값]으로 레벨을 정해서 매칭해놓는다.\n", "# -> 연속적인 숫자벡터를 줬어도 '0'~'3'의 문자열로 정해진다.\n", "# -> 매칭된 요인-레벨에 대해서, 레벨이름levels을 바꿔주면 요인들도 따라 바뀐다.\n", "\n", "fpain <- factor(pain, levels = 0:3)\n", "\n", "fpain" ] }, { "cell_type": "code", "execution_count": 102, "id": "e50266c4", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. none
    2. \n", "\t
  2. severe
    3. \n", "\t
  3. medium
    4. \n", "\t
  4. medium
    5. \n", "\t
  5. mild
    6. \n", "
\n", "\n", "
\n", "\t\n", "\t\tLevels:\n", "\t\n", "\t
    \n", "\t\t
  1. 'none'
    2. \n", "\t\t
  2. 'mild'
    3. \n", "\t\t
  3. 'medium'
    4. \n", "\t\t
  4. 'severe'
    5. \n", "\t
\n", "
" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item none\n", "\\item severe\n", "\\item medium\n", "\\item medium\n", "\\item mild\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'none'\n", "\\item 'mild'\n", "\\item 'medium'\n", "\\item 'severe'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. none\n", "2. severe\n", "3. medium\n", "4. medium\n", "5. mild\n", "\n", "\n", "\n", "**Levels**: 1. 'none'\n", "2. 'mild'\n", "3. 'medium'\n", "4. 'severe'\n", "\n", "\n" ], "text/plain": [ "[1] none severe medium medium mild \n", "Levels: none mild medium severe" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# factor의 [levels의 이름]을 문자열 벡터로 level에 대한 [라벨]적용이 가능하다.\n", "# -> 원본 값들이 레벨에 따른 해당 [라벨]로 바껴버린다.\n", "levels(fpain) <- c(\"none\", \"mild\", \"medium\", \"severe\")\n", "\n", "fpain" ] }, { "cell_type": "markdown", "id": "fb94abf7", "metadata": {}, "source": [ "#### 숫자 -> 라벨 by요인덮 바꾸기\n", "\n", "![image-20220304153734000](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220304153734000.png)" ] }, { "cell_type": "markdown", "id": "0d71dd12", "metadata": {}, "source": [ "변수 job, edu를 숫자 -> 라벨(요인)으로 바꿔보자." ] }, { "cell_type": "code", "execution_count": 130, "id": "48f79aac", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "insurance = read.table(\"./data/01//insurance.txt\", sep = \"\\t\", header=T)" ] }, { "cell_type": "code", "execution_count": 131, "id": "90c86351", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
idsexjobreligioneduamountsalary
1 m 1 1 3 7.0110
2 m 2 1 4 12.0135
3 f 2 3 5 8.5127
4 f 3 3 5 5.0150
5 m 1 3 3 4.5113
6 m 2 1 2 3.5 95
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllll}\n", " id & sex & job & religion & edu & amount & salary\\\\\n", "\\hline\n", "\t 1 & m & 1 & 1 & 3 & 7.0 & 110 \\\\\n", "\t 2 & m & 2 & 1 & 4 & 12.0 & 135 \\\\\n", "\t 3 & f & 2 & 3 & 5 & 8.5 & 127 \\\\\n", "\t 4 & f & 3 & 3 & 5 & 5.0 & 150 \\\\\n", "\t 5 & m & 1 & 3 & 3 & 4.5 & 113 \\\\\n", "\t 6 & m & 2 & 1 & 2 & 3.5 & 95 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| id | sex | job | religion | edu | amount | salary |\n", "|---|---|---|---|---|---|---|\n", "| 1 | m | 1 | 1 | 3 | 7.0 | 110 |\n", "| 2 | m | 2 | 1 | 4 | 12.0 | 135 |\n", "| 3 | f | 2 | 3 | 5 | 8.5 | 127 |\n", "| 4 | f | 3 | 3 | 5 | 5.0 | 150 |\n", "| 5 | m | 1 | 3 | 3 | 4.5 | 113 |\n", "| 6 | m | 2 | 1 | 2 | 3.5 | 95 |\n", "\n" ], "text/plain": [ " id sex job religion edu amount salary\n", "1 1 m 1 1 3 7.0 110 \n", "2 2 m 2 1 4 12.0 135 \n", "3 3 f 2 3 5 8.5 127 \n", "4 4 f 3 3 5 5.0 150 \n", "5 5 m 1 3 3 4.5 113 \n", "6 6 m 2 1 2 3.5 95 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(insurance)" ] }, { "cell_type": "code", "execution_count": 132, "id": "f8e618e9", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 1
    2. \n", "\t
  2. 2
    3. \n", "\t
  3. 2
    4. \n", "\t
  4. 3
    5. \n", "\t
  5. 1
    6. \n", "\t
  6. 2
    7. \n", "\t
  7. 3
    8. \n", "\t
  8. 3
    9. \n", "\t
  9. 2
    10. \n", "\t
  10. 1
    11. \n", "\t
  11. 1
    12. \n", "\t
  12. 2
    13. \n", "\t
  13. 3
    14. \n", "\t
  14. 3
    15. \n", "\t
  15. -9
    16. \n", "\t
  16. 1
    17. \n", "\t
  17. 1
    18. \n", "\t
  18. 2
    19. \n", "\t
  19. 2
    20. \n", "\t
  20. 3
    21. \n", "\t
  21. 3
    22. \n", "\t
  22. 3
    23. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 2\n", "\\item 3\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 3\n", "\\item 2\n", "\\item 1\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 3\n", "\\item -9\n", "\\item 1\n", "\\item 1\n", "\\item 2\n", "\\item 2\n", "\\item 3\n", "\\item 3\n", "\\item 3\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 2\n", "4. 3\n", "5. 1\n", "6. 2\n", "7. 3\n", "8. 3\n", "9. 2\n", "10. 1\n", "11. 1\n", "12. 2\n", "13. 3\n", "14. 3\n", "15. -9\n", "16. 1\n", "17. 1\n", "18. 2\n", "19. 2\n", "20. 3\n", "21. 3\n", "22. 3\n", "\n", "\n" ], "text/plain": [ " [1] 1 2 2 3 1 2 3 3 2 1 1 2 3 3 -9 1 1 2 2 3 3 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "insurance$job" ] }, { "cell_type": "code", "execution_count": 133, "id": "6fe86992", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# (1) -9를 na로 취급하도록 na.string= 을 지정해준다.\n", "insurance = read.table(\"./data/01//insurance.txt\", sep = \"\\t\", header=T, \n", " na.strings = \"-9\")" ] }, { "cell_type": "code", "execution_count": 134, "id": "2f38f09c", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 1
    2. \n", "\t
  2. 2
    3. \n", "\t
  3. 2
    4. \n", "\t
  4. 3
    5. \n", "\t
  5. 1
    6. \n", "\t
  6. 2
    7. \n", "\t
  7. 3
    8. \n", "\t
  8. 3
    9. \n", "\t
  9. 2
    10. \n", "\t
  10. 1
    11. \n", "\t
  11. 1
    12. \n", "\t
  12. 2
    13. \n", "\t
  13. 3
    14. \n", "\t
  14. 3
    15. \n", "\t
  15. <NA>
    16. \n", "\t
  16. 1
    17. \n", "\t
  17. 1
    18. \n", "\t
  18. 2
    19. \n", "\t
  19. 2
    20. \n", "\t
  20. 3
    21. \n", "\t
  21. 3
    22. \n", "\t
  22. 3
    23. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 2\n", "\\item 3\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 3\n", "\\item 2\n", "\\item 1\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 3\n", "\\item \n", "\\item 1\n", "\\item 1\n", "\\item 2\n", "\\item 2\n", "\\item 3\n", "\\item 3\n", "\\item 3\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 2\n", "4. 3\n", "5. 1\n", "6. 2\n", "7. 3\n", "8. 3\n", "9. 2\n", "10. 1\n", "11. 1\n", "12. 2\n", "13. 3\n", "14. 3\n", "15. <NA>\n", "16. 1\n", "17. 1\n", "18. 2\n", "19. 2\n", "20. 3\n", "21. 3\n", "22. 3\n", "\n", "\n" ], "text/plain": [ " [1] 1 2 2 3 1 2 3 3 2 1 1 2 3 3 NA 1 1 2 2 3 3 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "insurance$job" ] }, { "cell_type": "code", "execution_count": 135, "id": "5f4e3d3e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# (2) 카테고리 종류(값들)을 묶어서 levels로 매핑시키고 & \n", "# (3) 덮어쓸 labels도 순서대로 동시에 준다.\n", "insurance$job <- factor(insurance$job, \n", " levels = 1:3,\n", " labels = c(\"근로자\", \"사무직\", \"전문가\")\n", " )" ] }, { "cell_type": "code", "execution_count": 136, "id": "c4e7c475", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 근로자
    2. \n", "\t
  2. 사무직
    3. \n", "\t
  3. 사무직
    4. \n", "\t
  4. 전문가
    5. \n", "\t
  5. 근로자
    6. \n", "\t
  6. 사무직
    7. \n", "\t
  7. 전문가
    8. \n", "\t
  8. 전문가
    9. \n", "\t
  9. 사무직
    10. \n", "\t
  10. 근로자
    11. \n", "\t
  11. 근로자
    12. \n", "\t
  12. 사무직
    13. \n", "\t
  13. 전문가
    14. \n", "\t
  14. 전문가
    15. \n", "\t
  15. <NA>
    16. \n", "\t
  16. 근로자
    17. \n", "\t
  17. 근로자
    18. \n", "\t
  18. 사무직
    19. \n", "\t
  19. 사무직
    20. \n", "\t
  20. 전문가
    21. \n", "\t
  21. 전문가
    22. \n", "\t
  22. 전문가
    23. \n", "
\n", "\n", "
\n", "\t\n", "\t\tLevels:\n", "\t\n", "\t
    \n", "\t\t
  1. '근로자'
    2. \n", "\t\t
  2. '사무직'
    3. \n", "\t\t
  3. '전문가'
    4. \n", "\t
\n", "
" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 근로자\n", "\\item 사무직\n", "\\item 사무직\n", "\\item 전문가\n", "\\item 근로자\n", "\\item 사무직\n", "\\item 전문가\n", "\\item 전문가\n", "\\item 사무직\n", "\\item 근로자\n", "\\item 근로자\n", "\\item 사무직\n", "\\item 전문가\n", "\\item 전문가\n", "\\item \n", "\\item 근로자\n", "\\item 근로자\n", "\\item 사무직\n", "\\item 사무직\n", "\\item 전문가\n", "\\item 전문가\n", "\\item 전문가\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item '근로자'\n", "\\item '사무직'\n", "\\item '전문가'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 근로자\n", "2. 사무직\n", "3. 사무직\n", "4. 전문가\n", "5. 근로자\n", "6. 사무직\n", "7. 전문가\n", "8. 전문가\n", "9. 사무직\n", "10. 근로자\n", "11. 근로자\n", "12. 사무직\n", "13. 전문가\n", "14. 전문가\n", "15. <NA>\n", "16. 근로자\n", "17. 근로자\n", "18. 사무직\n", "19. 사무직\n", "20. 전문가\n", "21. 전문가\n", "22. 전문가\n", "\n", "\n", "\n", "**Levels**: 1. '근로자'\n", "2. '사무직'\n", "3. '전문가'\n", "\n", "\n" ], "text/plain": [ " [1] 근로자 사무직 사무직 전문가 근로자 사무직 전문가 전문가 사무직 근로자\n", "[11] 근로자 사무직 전문가 전문가 근로자 근로자 사무직 사무직 전문가\n", "[21] 전문가 전문가\n", "Levels: 근로자 사무직 전문가" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "insurance$job" ] }, { "cell_type": "code", "execution_count": 137, "id": "6884f4cf", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
idsexjobreligioneduamountsalary
1 m 근로자1 3 7.0 110
2 m 사무직1 4 12.0 135
3 f 사무직3 5 8.5 127
4 f 전문가3 5 5.0 150
5 m 근로자3 3 4.5 113
6 m 사무직1 2 3.5 95
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllll}\n", " id & sex & job & religion & edu & amount & salary\\\\\n", "\\hline\n", "\t 1 & m & 근로자 & 1 & 3 & 7.0 & 110 \\\\\n", "\t 2 & m & 사무직 & 1 & 4 & 12.0 & 135 \\\\\n", "\t 3 & f & 사무직 & 3 & 5 & 8.5 & 127 \\\\\n", "\t 4 & f & 전문가 & 3 & 5 & 5.0 & 150 \\\\\n", "\t 5 & m & 근로자 & 3 & 3 & 4.5 & 113 \\\\\n", "\t 6 & m & 사무직 & 1 & 2 & 3.5 & 95 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| id | sex | job | religion | edu | amount | salary |\n", "|---|---|---|---|---|---|---|\n", "| 1 | m | 근로자 | 1 | 3 | 7.0 | 110 |\n", "| 2 | m | 사무직 | 1 | 4 | 12.0 | 135 |\n", "| 3 | f | 사무직 | 3 | 5 | 8.5 | 127 |\n", "| 4 | f | 전문가 | 3 | 5 | 5.0 | 150 |\n", "| 5 | m | 근로자 | 3 | 3 | 4.5 | 113 |\n", "| 6 | m | 사무직 | 1 | 2 | 3.5 | 95 |\n", "\n" ], "text/plain": [ " id sex job religion edu amount salary\n", "1 1 m 근로자 1 3 7.0 110 \n", "2 2 m 사무직 1 4 12.0 135 \n", "3 3 f 사무직 3 5 8.5 127 \n", "4 4 f 전문가 3 5 5.0 150 \n", "5 5 m 근로자 3 3 4.5 113 \n", "6 6 m 사무직 1 2 3.5 95 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(insurance)" ] }, { "cell_type": "code", "execution_count": 138, "id": "81f965d3", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# edu1에 덮어쓰는게 아니라, 매핑후 새로운 칼럼으로 할당해주기\n", "insurance$edu2 <-\n", "factor(insurance$edu, \n", " levels = 1:5,\n", " labels = c(\"무학\", \"국졸\", \"중졸\", \"고졸\", \"대졸\")\n", " )" ] }, { "cell_type": "code", "execution_count": 139, "id": "968579b1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
idsexjobreligioneduamountsalaryedu2
1 m 근로자1 3 7.0 110 중졸
2 m 사무직1 4 12.0 135 고졸
3 f 사무직3 5 8.5 127 대졸
4 f 전문가3 5 5.0 150 대졸
5 m 근로자3 3 4.5 113 중졸
6 m 사무직1 2 3.5 95 국졸
7 m 전문가2 4 4.0 102 고졸
8 f 전문가2 4 4.0 122 고졸
9 f 사무직3 4 4.5 140 고졸
10 m 근로자3 5 17.0 100 대졸
11 f 근로자1 3 22.0 NA 중졸
12 m 사무직1 2 5.5 106 국졸
13 m 전문가2 1 4.5 130 무학
14 m 전문가2 5 7.0 150 대졸
15 m NA 3 4 6.0 110 고졸
16 f 근로자3 NA 7.0 88 NA
17 m 근로자1 4 6.0 138 고졸
18 f 사무직1 5 5.0 110 대졸
19 m 사무직3 3 7.0 85 중졸
20 m 전문가3 4 9.5 110 고졸
21 m 전문가1 4 10.0 95 고졸
22 m 전문가2 3 12.0 88 중졸
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllll}\n", " id & sex & job & religion & edu & amount & salary & edu2\\\\\n", "\\hline\n", "\t 1 & m & 근로자 & 1 & 3 & 7.0 & 110 & 중졸 \\\\\n", "\t 2 & m & 사무직 & 1 & 4 & 12.0 & 135 & 고졸 \\\\\n", "\t 3 & f & 사무직 & 3 & 5 & 8.5 & 127 & 대졸 \\\\\n", "\t 4 & f & 전문가 & 3 & 5 & 5.0 & 150 & 대졸 \\\\\n", "\t 5 & m & 근로자 & 3 & 3 & 4.5 & 113 & 중졸 \\\\\n", "\t 6 & m & 사무직 & 1 & 2 & 3.5 & 95 & 국졸 \\\\\n", "\t 7 & m & 전문가 & 2 & 4 & 4.0 & 102 & 고졸 \\\\\n", "\t 8 & f & 전문가 & 2 & 4 & 4.0 & 122 & 고졸 \\\\\n", "\t 9 & f & 사무직 & 3 & 4 & 4.5 & 140 & 고졸 \\\\\n", "\t 10 & m & 근로자 & 3 & 5 & 17.0 & 100 & 대졸 \\\\\n", "\t 11 & f & 근로자 & 1 & 3 & 22.0 & NA & 중졸 \\\\\n", "\t 12 & m & 사무직 & 1 & 2 & 5.5 & 106 & 국졸 \\\\\n", "\t 13 & m & 전문가 & 2 & 1 & 4.5 & 130 & 무학 \\\\\n", "\t 14 & m & 전문가 & 2 & 5 & 7.0 & 150 & 대졸 \\\\\n", "\t 15 & m & NA & 3 & 4 & 6.0 & 110 & 고졸 \\\\\n", "\t 16 & f & 근로자 & 3 & NA & 7.0 & 88 & NA \\\\\n", "\t 17 & m & 근로자 & 1 & 4 & 6.0 & 138 & 고졸 \\\\\n", "\t 18 & f & 사무직 & 1 & 5 & 5.0 & 110 & 대졸 \\\\\n", "\t 19 & m & 사무직 & 3 & 3 & 7.0 & 85 & 중졸 \\\\\n", "\t 20 & m & 전문가 & 3 & 4 & 9.5 & 110 & 고졸 \\\\\n", "\t 21 & m & 전문가 & 1 & 4 & 10.0 & 95 & 고졸 \\\\\n", "\t 22 & m & 전문가 & 2 & 3 & 12.0 & 88 & 중졸 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| id | sex | job | religion | edu | amount | salary | edu2 |\n", "|---|---|---|---|---|---|---|---|\n", "| 1 | m | 근로자 | 1 | 3 | 7.0 | 110 | 중졸 |\n", "| 2 | m | 사무직 | 1 | 4 | 12.0 | 135 | 고졸 |\n", "| 3 | f | 사무직 | 3 | 5 | 8.5 | 127 | 대졸 |\n", "| 4 | f | 전문가 | 3 | 5 | 5.0 | 150 | 대졸 |\n", "| 5 | m | 근로자 | 3 | 3 | 4.5 | 113 | 중졸 |\n", "| 6 | m | 사무직 | 1 | 2 | 3.5 | 95 | 국졸 |\n", "| 7 | m | 전문가 | 2 | 4 | 4.0 | 102 | 고졸 |\n", "| 8 | f | 전문가 | 2 | 4 | 4.0 | 122 | 고졸 |\n", "| 9 | f | 사무직 | 3 | 4 | 4.5 | 140 | 고졸 |\n", "| 10 | m | 근로자 | 3 | 5 | 17.0 | 100 | 대졸 |\n", "| 11 | f | 근로자 | 1 | 3 | 22.0 | NA | 중졸 |\n", "| 12 | m | 사무직 | 1 | 2 | 5.5 | 106 | 국졸 |\n", "| 13 | m | 전문가 | 2 | 1 | 4.5 | 130 | 무학 |\n", "| 14 | m | 전문가 | 2 | 5 | 7.0 | 150 | 대졸 |\n", "| 15 | m | NA | 3 | 4 | 6.0 | 110 | 고졸 |\n", "| 16 | f | 근로자 | 3 | NA | 7.0 | 88 | NA |\n", "| 17 | m | 근로자 | 1 | 4 | 6.0 | 138 | 고졸 |\n", "| 18 | f | 사무직 | 1 | 5 | 5.0 | 110 | 대졸 |\n", "| 19 | m | 사무직 | 3 | 3 | 7.0 | 85 | 중졸 |\n", "| 20 | m | 전문가 | 3 | 4 | 9.5 | 110 | 고졸 |\n", "| 21 | m | 전문가 | 1 | 4 | 10.0 | 95 | 고졸 |\n", "| 22 | m | 전문가 | 2 | 3 | 12.0 | 88 | 중졸 |\n", "\n" ], "text/plain": [ " id sex job religion edu amount salary edu2\n", "1 1 m 근로자 1 3 7.0 110 중졸\n", "2 2 m 사무직 1 4 12.0 135 고졸\n", "3 3 f 사무직 3 5 8.5 127 대졸\n", "4 4 f 전문가 3 5 5.0 150 대졸\n", "5 5 m 근로자 3 3 4.5 113 중졸\n", "6 6 m 사무직 1 2 3.5 95 국졸\n", "7 7 m 전문가 2 4 4.0 102 고졸\n", "8 8 f 전문가 2 4 4.0 122 고졸\n", "9 9 f 사무직 3 4 4.5 140 고졸\n", "10 10 m 근로자 3 5 17.0 100 대졸\n", "11 11 f 근로자 1 3 22.0 NA 중졸\n", "12 12 m 사무직 1 2 5.5 106 국졸\n", "13 13 m 전문가 2 1 4.5 130 무학\n", "14 14 m 전문가 2 5 7.0 150 대졸\n", "15 15 m NA 3 4 6.0 110 고졸\n", "16 16 f 근로자 3 NA 7.0 88 NA \n", "17 17 m 근로자 1 4 6.0 138 고졸\n", "18 18 f 사무직 1 5 5.0 110 대졸\n", "19 19 m 사무직 3 3 7.0 85 중졸\n", "20 20 m 전문가 3 4 9.5 110 고졸\n", "21 21 m 전문가 1 4 10.0 95 고졸\n", "22 22 m 전문가 2 3 12.0 88 중졸" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "insurance" ] }, { "cell_type": "markdown", "id": "1c137376", "metadata": {}, "source": [ "#### 1.2.9 list\n", "- 객체들을 복합 오브젝트로 만들어준다." ] }, { "cell_type": "code", "execution_count": 142, "id": "ea5baf39", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "intake.pre <- c(5260, 5470, 5640, 6180, 6390)\n", "intake.post <- c(3910, 4220, 3885, 5160, 5645)" ] }, { "cell_type": "code", "execution_count": 143, "id": "f90d2901", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\t
$before
\n", "\t\t
    \n", "\t
  1. 5260
    2. \n", "\t
  2. 5470
    3. \n", "\t
  3. 5640
    4. \n", "\t
  4. 6180
    5. \n", "\t
  5. 6390
    6. \n", "
\n", "
\n", "\t
$after
\n", "\t\t
    \n", "\t
  1. 3910
    2. \n", "\t
  2. 4220
    3. \n", "\t
  3. 3885
    4. \n", "\t
  4. 5160
    5. \n", "\t
  5. 5645
    6. \n", "
\n", "
\n", "
\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$before] \\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\end{enumerate*}\n", "\n", "\\item[\\$after] \\begin{enumerate*}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5160\n", "\\item 5645\n", "\\end{enumerate*}\n", "\n", "\\end{description}\n" ], "text/markdown": [ "$before\n", ": 1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "\n", "\n", "\n", "$after\n", ": 1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5160\n", "5. 5645\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "$before\n", "[1] 5260 5470 5640 6180 6390\n", "\n", "$after\n", "[1] 3910 4220 3885 5160 5645\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# list( before= , after=)\n", "mylist <- list(before=intake.pre, after=intake.post)\n", "\n", "\n", "mylist # df의 칼럼처럼 뽑아쓸 수 있게 모아준다." ] }, { "cell_type": "code", "execution_count": 144, "id": "2f7f66c2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5260
    2. \n", "\t
  2. 5470
    3. \n", "\t
  3. 5640
    4. \n", "\t
  4. 6180
    5. \n", "\t
  5. 6390
    6. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "\n", "\n" ], "text/plain": [ "[1] 5260 5470 5640 6180 6390" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mylist$before" ] }, { "cell_type": "markdown", "id": "6c35a6ee", "metadata": {}, "source": [ "#### dataframe\n", "- 복합 오브젝트 중에 행렬을 만든다.\n", "- text파일을 읽을 땐, 거의 df의 행렬로 만든다고 생각하면 된다." ] }, { "cell_type": "code", "execution_count": 145, "id": "43ec7155", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
intake.preintake.post
52603910
54704220
56403885
61805160
63905645
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " intake.pre & intake.post\\\\\n", "\\hline\n", "\t 5260 & 3910\\\\\n", "\t 5470 & 4220\\\\\n", "\t 5640 & 3885\\\\\n", "\t 6180 & 5160\\\\\n", "\t 6390 & 5645\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| intake.pre | intake.post |\n", "|---|---|\n", "| 5260 | 3910 |\n", "| 5470 | 4220 |\n", "| 5640 | 3885 |\n", "| 6180 | 5160 |\n", "| 6390 | 5645 |\n", "\n" ], "text/plain": [ " intake.pre intake.post\n", "1 5260 3910 \n", "2 5470 4220 \n", "3 5640 3885 \n", "4 6180 5160 \n", "5 6390 5645 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d <- data.frame(intake.pre, intake.post)\n", "d" ] }, { "cell_type": "code", "execution_count": 146, "id": "162dd315", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5260
    2. \n", "\t
  2. 5470
    3. \n", "\t
  3. 5640
    4. \n", "\t
  4. 6180
    5. \n", "\t
  5. 6390
    6. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "\n", "\n" ], "text/plain": [ "[1] 5260 5470 5640 6180 6390" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d$intake.pre" ] }, { "cell_type": "markdown", "id": "c71153cb", "metadata": {}, "source": [ "#### 1.2.11 indexing 기능\n", "- 순서대로, 혹은 일부만 벡터/list/df의 `특정index(또는 열)을 여러개` 뽑아쓰는 기능" ] }, { "cell_type": "code", "execution_count": 147, "id": "be5753e1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5260
    2. \n", "\t
  2. 6390
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5260\n", "\\item 6390\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5260\n", "2. 6390\n", "\n", "\n" ], "text/plain": [ "[1] 5260 6390" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intake.pre[c(1,5)]" ] }, { "cell_type": "code", "execution_count": 149, "id": "d1ea5634", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5470
    2. \n", "\t
  2. 5640
    3. \n", "\t
  3. 6180
    4. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5470\n", "2. 5640\n", "3. 6180\n", "\n", "\n" ], "text/plain": [ "[1] 5470 5640 6180" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 특정 index를 제외하고 다 뽑기\n", "intake.pre[- c(1,5)]" ] }, { "cell_type": "markdown", "id": "5b79cf01", "metadata": {}, "source": [ "#### 1.2.12 Conditional selection\n", "- 인덱싱 처럼 대괄호를 쓰며, 인덱싱 명시 자리에 `조건식`을 넣는다.\n", " - `데이터 순서를 (환자)id로 공유하는 list 데이터`라면, 서로 다른 list && 상관없어 보이는 데이터를 통해 만들기 가능한` boolean 벡터를 만들어서 대괄호에 넣는다." ] }, { "cell_type": "code", "execution_count": 150, "id": "3332289f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 3910
    2. \n", "\t
  2. 4220
    3. \n", "\t
  3. 3885
    4. \n", "\t
  4. 5160
    5. \n", "\t
  5. 5645
    6. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5160\n", "\\item 5645\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5160\n", "5. 5645\n", "\n", "\n" ], "text/plain": [ "[1] 3910 4220 3885 5160 5645" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intake.post" ] }, { "cell_type": "code", "execution_count": 151, "id": "16292fb3", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5260
    2. \n", "\t
  2. 5470
    3. \n", "\t
  3. 5640
    4. \n", "\t
  4. 6180
    5. \n", "\t
  5. 6390
    6. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "\n", "\n" ], "text/plain": [ "[1] 5260 5470 5640 6180 6390" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# intake.post와는 전혀 다른 것 같지만, 데이터 순서 = id를 서로 공유되고 있다.\n", "intake.pre " ] }, { "cell_type": "code", "execution_count": 153, "id": "03a5a72e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5160
    2. \n", "\t
  2. 5645
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5160\n", "\\item 5645\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5160\n", "2. 5645\n", "\n", "\n" ], "text/plain": [ "[1] 5160 5645" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 순서로 id가 공유되는 list에 대해\n", "# 1) [pre가 6000보다 컸]던 id의 환자들에 대해\n", "# 2) post 수치는?\n", "intake.post[ intake.pre > 6000 ]" ] }, { "cell_type": "code", "execution_count": 154, "id": "f588b970", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 3885
    2. \n", "\t
  2. 5160
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 3885\n", "\\item 5160\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 3885\n", "2. 5160\n", "\n", "\n" ], "text/plain": [ "[1] 3885 5160" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intake.post[ intake.pre > 5500 & intake.pre <= 6200 ]" ] }, { "cell_type": "code", "execution_count": 157, "id": "8d1e534f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
intake.preintake.post
461805160
563905645
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & intake.pre & intake.post\\\\\n", "\\hline\n", "\t4 & 6180 & 5160\\\\\n", "\t5 & 6390 & 5645\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | intake.pre | intake.post |\n", "|---|---|---|\n", "| 4 | 6180 | 5160 |\n", "| 5 | 6390 | 5645 |\n", "\n" ], "text/plain": [ " intake.pre intake.post\n", "4 6180 5160 \n", "5 6390 5645 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# df행렬에 대해서는 [ , ]로 불린 인덱싱 해주면 된다.\n", "# - 비운 행or렬 = 전체\n", "d [ d$intake.pre > 6000, ]" ] }, { "cell_type": "markdown", "id": "63b20838", "metadata": {}, "source": [ "#### 1.2.15 implicit loops\n", " - 내장된 반복을 가진 기능들" ] }, { "cell_type": "code", "execution_count": 158, "id": "b5fcb323", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "library(\"ISwR\")" ] }, { "cell_type": "code", "execution_count": 159, "id": "26775a87", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "data(thuesen)" ] }, { "cell_type": "code", "execution_count": 160, "id": "44b9bcd3", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
blood.glucoseshort.velocity
15.31.76
10.81.34
8.11.27
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " blood.glucose & short.velocity\\\\\n", "\\hline\n", "\t 15.3 & 1.76\\\\\n", "\t 10.8 & 1.34\\\\\n", "\t 8.1 & 1.27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| blood.glucose | short.velocity |\n", "|---|---|\n", "| 15.3 | 1.76 |\n", "| 10.8 | 1.34 |\n", "| 8.1 | 1.27 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(thuesen, 3) # 2개의 변수가 있는 데이터 투센" ] }, { "cell_type": "code", "execution_count": 163, "id": "0ca658e0", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\t
$blood.glucose
\n", "\t\t
10.3
\n", "\t
$short.velocity
\n", "\t\t
<NA>
\n", "
\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$blood.glucose] 10.3\n", "\\item[\\$short.velocity] \n", "\\end{description}\n" ], "text/markdown": [ "$blood.glucose\n", ": 10.3\n", "$short.velocity\n", ": <NA>\n", "\n", "\n" ], "text/plain": [ "$blood.glucose\n", "[1] 10.3\n", "\n", "$short.velocity\n", "[1] NA\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# df의 집계를 간단하게 적용하는 l(ist) or s or t + apply\n", "# - l을 붙이면, 각 변수들이 변수명을 가진 list()로 합쳐진다. like before=,after=\n", "lapply( thuesen, mean)" ] }, { "cell_type": "code", "execution_count": 164, "id": "348b9e70", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\t
$blood.glucose
\n", "\t\t
10.3
\n", "\t
$short.velocity
\n", "\t\t
1.32565217391304
\n", "
\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$blood.glucose] 10.3\n", "\\item[\\$short.velocity] 1.32565217391304\n", "\\end{description}\n" ], "text/markdown": [ "$blood.glucose\n", ": 10.3\n", "$short.velocity\n", ": 1.32565217391304\n", "\n", "\n" ], "text/plain": [ "$blood.glucose\n", "[1] 10.3\n", "\n", "$short.velocity\n", "[1] 1.325652\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# apply 집계하는데, 가 뜬다? -> na.rm = T 옵션 주자\n", "# - na.rm = T : 미싱밸류를.제거한 \n", "lapply( thuesen, mean, na.rm = T)" ] }, { "cell_type": "code", "execution_count": 165, "id": "9c055bc5", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\t
blood.glucose
\n", "\t\t
10.3
\n", "\t
short.velocity
\n", "\t\t
1.32565217391304
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[blood.glucose] 10.3\n", "\\item[short.velocity] 1.32565217391304\n", "\\end{description*}\n" ], "text/markdown": [ "blood.glucose\n", ": 10.3short.velocity\n", ": 1.32565217391304\n", "\n" ], "text/plain": [ " blood.glucose short.velocity \n", " 10.300000 1.325652 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 간단한 simple 벡터로 -> sapply\n", "# - 그냥 벡터형식으로 풀어진다.\n", "sapply(thuesen, mean, na.rm = T)" ] }, { "cell_type": "code", "execution_count": 174, "id": "36480cf9", "metadata": { "scrolled": true, "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in unique.default(x, nmax = nmax): unique()는 오로지 벡터들에만 적용됩니다\n", "output_type": "error", "traceback": [ "Error in unique.default(x, nmax = nmax): unique()는 오로지 벡터들에만 적용됩니다\nTraceback:\n", "1. tapply(thuesen, mean, na.rm = T)", "2. lapply(INDEX, as.factor)", "3. FUN(X[[i]], ...)", "4. factor(x)", "5. unique(x, nmax = nmax)", "6. unique.default(x, nmax = nmax)" ] } ], "source": [ "# (연속변수의, 범주종류별 집계) -> tapply\n", "# - 데이터를 바로 넣으면 안해준다.\n", "# - (데이터변수 , 그룹변수, 집계)순으로 지정해줘야한다.\n", "tapply(thuesen, mean, na.rm = T)" ] }, { "cell_type": "code", "execution_count": 176, "id": "3ad174c8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\t
1.03
\n", "\t\t
4.9
\n", "\t
1.05
\n", "\t\t
16.1
\n", "\t
1.09
\n", "\t\t
11.1
\n", "\t
1.12
\n", "\t\t
6.5
\n", "\t
1.18
\n", "\t\t
7.5
\n", "\t
1.19
\n", "\t\t
8.85
\n", "\t
1.22
\n", "\t\t
12.2
\n", "\t
1.25
\n", "\t\t
6.7
\n", "\t
1.27
\n", "\t\t
7.65
\n", "\t
1.28
\n", "\t\t
15.1
\n", "\t
1.31
\n", "\t\t
9.3
\n", "\t
1.32
\n", "\t\t
13.3
\n", "\t
1.34
\n", "\t\t
10.8
\n", "\t
1.37
\n", "\t\t
10.3
\n", "\t
1.47
\n", "\t\t
19.5
\n", "\t
1.49
\n", "\t\t
5.3
\n", "\t
1.52
\n", "\t\t
6.7
\n", "\t
1.7
\n", "\t\t
9.5
\n", "\t
1.76
\n", "\t\t
15.3
\n", "\t
1.95
\n", "\t\t
19
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[1.03] 4.9\n", "\\item[1.05] 16.1\n", "\\item[1.09] 11.1\n", "\\item[1.12] 6.5\n", "\\item[1.18] 7.5\n", "\\item[1.19] 8.85\n", "\\item[1.22] 12.2\n", "\\item[1.25] 6.7\n", "\\item[1.27] 7.65\n", "\\item[1.28] 15.1\n", "\\item[1.31] 9.3\n", "\\item[1.32] 13.3\n", "\\item[1.34] 10.8\n", "\\item[1.37] 10.3\n", "\\item[1.47] 19.5\n", "\\item[1.49] 5.3\n", "\\item[1.52] 6.7\n", "\\item[1.7] 9.5\n", "\\item[1.76] 15.3\n", "\\item[1.95] 19\n", "\\end{description*}\n" ], "text/markdown": [ "1.03\n", ": 4.91.05\n", ": 16.11.09\n", ": 11.11.12\n", ": 6.51.18\n", ": 7.51.19\n", ": 8.851.22\n", ": 12.21.25\n", ": 6.71.27\n", ": 7.651.28\n", ": 15.11.31\n", ": 9.31.32\n", ": 13.31.34\n", ": 10.81.37\n", ": 10.31.47\n", ": 19.51.49\n", ": 5.31.52\n", ": 6.71.7\n", ": 9.51.76\n", ": 15.31.95\n", ": 19\n", "\n" ], "text/plain": [ " 1.03 1.05 1.09 1.12 1.18 1.19 1.22 1.25 1.27 1.28 1.31 1.32 1.34 \n", " 4.90 16.10 11.10 6.50 7.50 8.85 12.20 6.70 7.65 15.10 9.30 13.30 10.80 \n", " 1.37 1.47 1.49 1.52 1.7 1.76 1.95 \n", "10.30 19.50 5.30 6.70 9.50 15.30 19.00 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# thuesen은 다 연속변수라 범주형 변수가 없기 때문에 tapply를 제대로 활용할 수 없다.\n", "tapply(thuesen$blood.glucose, thuesen$short.velocity, mean, na.rm = T)" ] }, { "cell_type": "code", "execution_count": 167, "id": "9eb8e6bc", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "data(energy)" ] }, { "cell_type": "code", "execution_count": 171, "id": "0b6ffcac", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
expendstature
9.21obese
7.53lean
7.48lean
8.08lean
8.09lean
10.15lean
8.40lean
10.88lean
6.13lean
7.90lean
11.51obese
12.79obese
7.05lean
11.85obese
9.97obese
7.48lean
8.79obese
9.69obese
9.68obese
7.58lean
9.19obese
8.11lean
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " expend & stature\\\\\n", "\\hline\n", "\t 9.21 & obese\\\\\n", "\t 7.53 & lean \\\\\n", "\t 7.48 & lean \\\\\n", "\t 8.08 & lean \\\\\n", "\t 8.09 & lean \\\\\n", "\t 10.15 & lean \\\\\n", "\t 8.40 & lean \\\\\n", "\t 10.88 & lean \\\\\n", "\t 6.13 & lean \\\\\n", "\t 7.90 & lean \\\\\n", "\t 11.51 & obese\\\\\n", "\t 12.79 & obese\\\\\n", "\t 7.05 & lean \\\\\n", "\t 11.85 & obese\\\\\n", "\t 9.97 & obese\\\\\n", "\t 7.48 & lean \\\\\n", "\t 8.79 & obese\\\\\n", "\t 9.69 & obese\\\\\n", "\t 9.68 & obese\\\\\n", "\t 7.58 & lean \\\\\n", "\t 9.19 & obese\\\\\n", "\t 8.11 & lean \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| expend | stature |\n", "|---|---|\n", "| 9.21 | obese |\n", "| 7.53 | lean |\n", "| 7.48 | lean |\n", "| 8.08 | lean |\n", "| 8.09 | lean |\n", "| 10.15 | lean |\n", "| 8.40 | lean |\n", "| 10.88 | lean |\n", "| 6.13 | lean |\n", "| 7.90 | lean |\n", "| 11.51 | obese |\n", "| 12.79 | obese |\n", "| 7.05 | lean |\n", "| 11.85 | obese |\n", "| 9.97 | obese |\n", "| 7.48 | lean |\n", "| 8.79 | obese |\n", "| 9.69 | obese |\n", "| 9.68 | obese |\n", "| 7.58 | lean |\n", "| 9.19 | obese |\n", "| 8.11 | lean |\n", "\n" ], "text/plain": [ " expend stature\n", "1 9.21 obese \n", "2 7.53 lean \n", "3 7.48 lean \n", "4 8.08 lean \n", "5 8.09 lean \n", "6 10.15 lean \n", "7 8.40 lean \n", "8 10.88 lean \n", "9 6.13 lean \n", "10 7.90 lean \n", "11 11.51 obese \n", "12 12.79 obese \n", "13 7.05 lean \n", "14 11.85 obese \n", "15 9.97 obese \n", "16 7.48 lean \n", "17 8.79 obese \n", "18 9.69 obese \n", "19 9.68 obese \n", "20 7.58 lean \n", "21 9.19 obese \n", "22 8.11 lean " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "energy" ] }, { "cell_type": "code", "execution_count": 177, "id": "d6d834e2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\t
lean
\n", "\t\t
8.06615384615385
\n", "\t
obese
\n", "\t\t
10.2977777777778
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[lean] 8.06615384615385\n", "\\item[obese] 10.2977777777778\n", "\\end{description*}\n" ], "text/markdown": [ "lean\n", ": 8.06615384615385obese\n", ": 10.2977777777778\n", "\n" ], "text/plain": [ " lean obese \n", " 8.066154 10.297778 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# tapply(집계대상-연속형변수, 그룹-범주형변수, 집계)\n", "tapply(energy$expend, energy$stature, mean, na.rm = T)" ] }, { "cell_type": "code", "execution_count": 182, "id": "8a43b243", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
0.9182174-0.7425004 1.0621464
-1.0513366 0.8554100-0.2472357
-0.9116358-1.6340607-0.4725558
-0.1975745-0.1045159 1.0616053
\n" ], "text/latex": [ "\\begin{tabular}{lll}\n", "\t 0.9182174 & -0.7425004 & 1.0621464\\\\\n", "\t -1.0513366 & 0.8554100 & -0.2472357\\\\\n", "\t -0.9116358 & -1.6340607 & -0.4725558\\\\\n", "\t -0.1975745 & -0.1045159 & 1.0616053\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 0.9182174 | -0.7425004 | 1.0621464 |\n", "| -1.0513366 | 0.8554100 | -0.2472357 |\n", "| -0.9116358 | -1.6340607 | -0.4725558 |\n", "| -0.1975745 | -0.1045159 | 1.0616053 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] \n", "[1,] 0.9182174 -0.7425004 1.0621464\n", "[2,] -1.0513366 0.8554100 -0.2472357\n", "[3,] -0.9116358 -1.6340607 -0.4725558\n", "[4,] -0.1975745 -0.1045159 1.0616053" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 기본 apply(df, 1or2, 집계) \n", "# - 1=row별 가로집계 / 2=col별 세로집계\n", "m = matrix(rnorm(12), 4) # 2번째인자가 row수만 지정해주는 듯\n", "m" ] }, { "cell_type": "code", "execution_count": 184, "id": "629471bf", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. -1.05133658709921
    2. \n", "\t
  2. -1.63406066310988
    3. \n", "\t
  3. -0.472555766861294
    4. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item -1.05133658709921\n", "\\item -1.63406066310988\n", "\\item -0.472555766861294\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. -1.05133658709921\n", "2. -1.63406066310988\n", "3. -0.472555766861294\n", "\n", "\n" ], "text/plain": [ "[1] -1.0513366 -1.6340607 -0.4725558" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pandas- > 세0가1 / r -> (세0)가1세2\n", "# - 칼럼별 최소값\n", "apply(m, 2, min)" ] }, { "cell_type": "markdown", "id": "40673e2a", "metadata": {}, "source": [ "#### Sorting\n", "- 특정 변수에 대한 `order객체`를 만든 뒤 -> boolean mask자리에 `order mask`를 인덱싱 자리에 넣어줘야한다." ] }, { "cell_type": "code", "execution_count": 185, "id": "09c0fccc", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
prepost
52603910
54704220
56403885
61805160
63905645
65154680
68055265
75155975
75156790
82306900
87707335
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " pre & post\\\\\n", "\\hline\n", "\t 5260 & 3910\\\\\n", "\t 5470 & 4220\\\\\n", "\t 5640 & 3885\\\\\n", "\t 6180 & 5160\\\\\n", "\t 6390 & 5645\\\\\n", "\t 6515 & 4680\\\\\n", "\t 6805 & 5265\\\\\n", "\t 7515 & 5975\\\\\n", "\t 7515 & 6790\\\\\n", "\t 8230 & 6900\\\\\n", "\t 8770 & 7335\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| pre | post |\n", "|---|---|\n", "| 5260 | 3910 |\n", "| 5470 | 4220 |\n", "| 5640 | 3885 |\n", "| 6180 | 5160 |\n", "| 6390 | 5645 |\n", "| 6515 | 4680 |\n", "| 6805 | 5265 |\n", "| 7515 | 5975 |\n", "| 7515 | 6790 |\n", "| 8230 | 6900 |\n", "| 8770 | 7335 |\n", "\n" ], "text/plain": [ " pre post\n", "1 5260 3910\n", "2 5470 4220\n", "3 5640 3885\n", "4 6180 5160\n", "5 6390 5645\n", "6 6515 4680\n", "7 6805 5265\n", "8 7515 5975\n", "9 7515 6790\n", "10 8230 6900\n", "11 8770 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intake" ] }, { "cell_type": "code", "execution_count": 189, "id": "956c513d", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "'double'" ], "text/latex": [ "'double'" ], "text/markdown": [ "'double'" ], "text/plain": [ "[1] \"double\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "typeof(intake$post)" ] }, { "cell_type": "code", "execution_count": 190, "id": "4ea21d67", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 3
    2. \n", "\t
  2. 1
    3. \n", "\t
  3. 2
    4. \n", "\t
  4. 6
    5. \n", "\t
  5. 4
    6. \n", "\t
  6. 7
    7. \n", "\t
  7. 5
    8. \n", "\t
  8. 8
    9. \n", "\t
  9. 9
    10. \n", "\t
  10. 10
    11. \n", "\t
  11. 11
    12. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 3\n", "\\item 1\n", "\\item 2\n", "\\item 6\n", "\\item 4\n", "\\item 7\n", "\\item 5\n", "\\item 8\n", "\\item 9\n", "\\item 10\n", "\\item 11\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 3\n", "2. 1\n", "3. 2\n", "4. 6\n", "5. 4\n", "6. 7\n", "7. 5\n", "8. 8\n", "9. 9\n", "10. 10\n", "11. 11\n", "\n", "\n" ], "text/plain": [ " [1] 3 1 2 6 4 7 5 8 9 10 11" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# order( ) : order mask 객체 생성\n", "o1 <- order(intake$post)\n", "o1" ] }, { "cell_type": "code", "execution_count": 191, "id": "92bc1e85", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "'integer'" ], "text/latex": [ "'integer'" ], "text/markdown": [ "'integer'" ], "text/plain": [ "[1] \"integer\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "typeof(o1)" ] }, { "cell_type": "code", "execution_count": 194, "id": "cded93b8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 3910
    2. \n", "\t
  2. 4220
    3. \n", "\t
  3. 3885
    4. \n", "\t
  4. 5160
    5. \n", "\t
  5. 5645
    6. \n", "\t
  6. 4680
    7. \n", "\t
  7. 5265
    8. \n", "\t
  8. 5975
    9. \n", "\t
  9. 6790
    10. \n", "\t
  10. 6900
    11. \n", "\t
  11. 7335
    12. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5160\n", "\\item 5645\n", "\\item 4680\n", "\\item 5265\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5160\n", "5. 5645\n", "6. 4680\n", "7. 5265\n", "8. 5975\n", "9. 6790\n", "10. 6900\n", "11. 7335\n", "\n", "\n" ], "text/plain": [ " [1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intake$post" ] }, { "cell_type": "code", "execution_count": 193, "id": "aeabe065", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 3885
    2. \n", "\t
  2. 3910
    3. \n", "\t
  3. 4220
    4. \n", "\t
  4. 4680
    5. \n", "\t
  5. 5160
    6. \n", "\t
  6. 5265
    7. \n", "\t
  7. 5645
    8. \n", "\t
  8. 5975
    9. \n", "\t
  9. 6790
    10. \n", "\t
  10. 6900
    11. \n", "\t
  11. 7335
    12. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 3885\n", "\\item 3910\n", "\\item 4220\n", "\\item 4680\n", "\\item 5160\n", "\\item 5265\n", "\\item 5645\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 3885\n", "2. 3910\n", "3. 4220\n", "4. 4680\n", "5. 5160\n", "6. 5265\n", "7. 5645\n", "8. 5975\n", "9. 6790\n", "10. 6900\n", "11. 7335\n", "\n", "\n" ], "text/plain": [ " [1] 3885 3910 4220 4680 5160 5265 5645 5975 6790 6900 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 인덱싱 자리에 넣어줘야 정렬된다.\n", "# -> 해당칼럼에 대한 오름차순 order객체로 정렬\n", "intake$post[o1]" ] }, { "cell_type": "code", "execution_count": 196, "id": "13cd1894", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5260
    2. \n", "\t
  2. 5470
    3. \n", "\t
  3. 5640
    4. \n", "\t
  4. 6180
    5. \n", "\t
  5. 6390
    6. \n", "\t
  6. 6515
    7. \n", "\t
  7. 6805
    8. \n", "\t
  8. 7515
    9. \n", "\t
  9. 7515
    10. \n", "\t
  10. 8230
    11. \n", "\t
  11. 8770
    12. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n" ], "text/plain": [ " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intake$pre" ] }, { "cell_type": "code", "execution_count": 195, "id": "ad42a69b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 5640
    2. \n", "\t
  2. 5260
    3. \n", "\t
  3. 5470
    4. \n", "\t
  4. 6515
    5. \n", "\t
  5. 6180
    6. \n", "\t
  6. 6805
    7. \n", "\t
  7. 6390
    8. \n", "\t
  8. 7515
    9. \n", "\t
  9. 7515
    10. \n", "\t
  10. 8230
    11. \n", "\t
  11. 8770
    12. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5640\n", "\\item 5260\n", "\\item 5470\n", "\\item 6515\n", "\\item 6180\n", "\\item 6805\n", "\\item 6390\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5640\n", "2. 5260\n", "3. 5470\n", "4. 6515\n", "5. 6180\n", "6. 6805\n", "7. 6390\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n" ], "text/plain": [ " [1] 5640 5260 5470 6515 6180 6805 6390 7515 7515 8230 8770" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 타 칼럼이 [특정칼럼기준으로 만들어진 order객체]로 정렬\n", "intake$pre[o1]" ] }, { "cell_type": "markdown", "id": "da47bc59", "metadata": {}, "source": [ "## R environment" ] }, { "cell_type": "markdown", "id": "79cc40b1", "metadata": {}, "source": [ "### Session management\n", "- `ls()`: 내 `워크스페이스에 존재하는 모든 객체`\n", "- `rm()`: 데이터 객체 삭제" ] }, { "cell_type": "code", "execution_count": 197, "id": "00d65bef", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'a'
    2. \n", "\t
  2. 'aa'
    3. \n", "\t
  3. 'b'
    4. \n", "\t
  4. 'bmi'
    5. \n", "\t
  5. 'd'
    6. \n", "\t
  6. 'energy'
    7. \n", "\t
  7. 'fpain'
    8. \n", "\t
  8. 'height'
    9. \n", "\t
  9. 'hh'
    10. \n", "\t
  10. 'insurance'
    11. \n", "\t
  11. 'intake.post'
    12. \n", "\t
  12. 'intake.pre'
    13. \n", "\t
  13. 'm'
    14. \n", "\t
  14. 'mylist'
    15. \n", "\t
  15. 'nwd'
    16. \n", "\t
  16. 'o1'
    17. \n", "\t
  17. 'oops'
    18. \n", "\t
  18. 'pain'
    19. \n", "\t
  19. 'thuesen'
    20. \n", "\t
  20. 'wd'
    21. \n", "\t
  21. 'weight'
    22. \n", "\t
  22. 'x'
    23. \n", "\t
  23. 'xbar'
    24. \n", "\t
  24. 'y'
    25. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'a'\n", "\\item 'aa'\n", "\\item 'b'\n", "\\item 'bmi'\n", "\\item 'd'\n", "\\item 'energy'\n", "\\item 'fpain'\n", "\\item 'height'\n", "\\item 'hh'\n", "\\item 'insurance'\n", "\\item 'intake.post'\n", "\\item 'intake.pre'\n", "\\item 'm'\n", "\\item 'mylist'\n", "\\item 'nwd'\n", "\\item 'o1'\n", "\\item 'oops'\n", "\\item 'pain'\n", "\\item 'thuesen'\n", "\\item 'wd'\n", "\\item 'weight'\n", "\\item 'x'\n", "\\item 'xbar'\n", "\\item 'y'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'a'\n", "2. 'aa'\n", "3. 'b'\n", "4. 'bmi'\n", "5. 'd'\n", "6. 'energy'\n", "7. 'fpain'\n", "8. 'height'\n", "9. 'hh'\n", "10. 'insurance'\n", "11. 'intake.post'\n", "12. 'intake.pre'\n", "13. 'm'\n", "14. 'mylist'\n", "15. 'nwd'\n", "16. 'o1'\n", "17. 'oops'\n", "18. 'pain'\n", "19. 'thuesen'\n", "20. 'wd'\n", "21. 'weight'\n", "22. 'x'\n", "23. 'xbar'\n", "24. 'y'\n", "\n", "\n" ], "text/plain": [ " [1] \"a\" \"aa\" \"b\" \"bmi\" \"d\" \n", " [6] \"energy\" \"fpain\" \"height\" \"hh\" \"insurance\" \n", "[11] \"intake.post\" \"intake.pre\" \"m\" \"mylist\" \"nwd\" \n", "[16] \"o1\" \"oops\" \"pain\" \"thuesen\" \"wd\" \n", "[21] \"weight\" \"x\" \"xbar\" \"y\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ls()" ] }, { "cell_type": "code", "execution_count": 198, "id": "5bb2be74", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "rm(aa, b) # rm으로 데이터객체 삭제 -> 콤마로 한번에 여러개 가능" ] }, { "cell_type": "code", "execution_count": 199, "id": "877dcf18", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'a'
    2. \n", "\t
  2. 'bmi'
    3. \n", "\t
  3. 'd'
    4. \n", "\t
  4. 'energy'
    5. \n", "\t
  5. 'fpain'
    6. \n", "\t
  6. 'height'
    7. \n", "\t
  7. 'hh'
    8. \n", "\t
  8. 'insurance'
    9. \n", "\t
  9. 'intake.post'
    10. \n", "\t
  10. 'intake.pre'
    11. \n", "\t
  11. 'm'
    12. \n", "\t
  12. 'mylist'
    13. \n", "\t
  13. 'nwd'
    14. \n", "\t
  14. 'o1'
    15. \n", "\t
  15. 'oops'
    16. \n", "\t
  16. 'pain'
    17. \n", "\t
  17. 'thuesen'
    18. \n", "\t
  18. 'wd'
    19. \n", "\t
  19. 'weight'
    20. \n", "\t
  20. 'x'
    21. \n", "\t
  21. 'xbar'
    22. \n", "\t
  22. 'y'
    23. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'a'\n", "\\item 'bmi'\n", "\\item 'd'\n", "\\item 'energy'\n", "\\item 'fpain'\n", "\\item 'height'\n", "\\item 'hh'\n", "\\item 'insurance'\n", "\\item 'intake.post'\n", "\\item 'intake.pre'\n", "\\item 'm'\n", "\\item 'mylist'\n", "\\item 'nwd'\n", "\\item 'o1'\n", "\\item 'oops'\n", "\\item 'pain'\n", "\\item 'thuesen'\n", "\\item 'wd'\n", "\\item 'weight'\n", "\\item 'x'\n", "\\item 'xbar'\n", "\\item 'y'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'a'\n", "2. 'bmi'\n", "3. 'd'\n", "4. 'energy'\n", "5. 'fpain'\n", "6. 'height'\n", "7. 'hh'\n", "8. 'insurance'\n", "9. 'intake.post'\n", "10. 'intake.pre'\n", "11. 'm'\n", "12. 'mylist'\n", "13. 'nwd'\n", "14. 'o1'\n", "15. 'oops'\n", "16. 'pain'\n", "17. 'thuesen'\n", "18. 'wd'\n", "19. 'weight'\n", "20. 'x'\n", "21. 'xbar'\n", "22. 'y'\n", "\n", "\n" ], "text/plain": [ " [1] \"a\" \"bmi\" \"d\" \"energy\" \"fpain\" \n", " [6] \"height\" \"hh\" \"insurance\" \"intake.post\" \"intake.pre\" \n", "[11] \"m\" \"mylist\" \"nwd\" \"o1\" \"oops\" \n", "[16] \"pain\" \"thuesen\" \"wd\" \"weight\" \"x\" \n", "[21] \"xbar\" \"y\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ls()" ] }, { "cell_type": "code", "execution_count": null, "id": "0a34b7fd", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# df$칼럼 에서 df$ 없이 쓰는 방법은?\n", "# df객체 속 변수를 바로 쓰는 방법은?\n", "# -> session에 df자체를 attach해버리는 것 -> 가진 칼럼들이 다 변수로 붙어진다?\n", "# --> 함수들의 인자로 넣기 쉽게 하기 위함인듯?" ] }, { "cell_type": "code", "execution_count": 203, "id": "d285ca22", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
blood.glucoseshort.velocity
15.31.76
10.81.34
8.11.27
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " blood.glucose & short.velocity\\\\\n", "\\hline\n", "\t 15.3 & 1.76\\\\\n", "\t 10.8 & 1.34\\\\\n", "\t 8.1 & 1.27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| blood.glucose | short.velocity |\n", "|---|---|\n", "| 15.3 | 1.76 |\n", "| 10.8 | 1.34 |\n", "| 8.1 | 1.27 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(thuesen, 3)" ] }, { "cell_type": "code", "execution_count": 200, "id": "cc49bf3e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "attach(thuesen)" ] }, { "cell_type": "code", "execution_count": 201, "id": "5887527a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'a'
    2. \n", "\t
  2. 'bmi'
    3. \n", "\t
  3. 'd'
    4. \n", "\t
  4. 'energy'
    5. \n", "\t
  5. 'fpain'
    6. \n", "\t
  6. 'height'
    7. \n", "\t
  7. 'hh'
    8. \n", "\t
  8. 'insurance'
    9. \n", "\t
  9. 'intake.post'
    10. \n", "\t
  10. 'intake.pre'
    11. \n", "\t
  11. 'm'
    12. \n", "\t
  12. 'mylist'
    13. \n", "\t
  13. 'nwd'
    14. \n", "\t
  14. 'o1'
    15. \n", "\t
  15. 'oops'
    16. \n", "\t
  16. 'pain'
    17. \n", "\t
  17. 'thuesen'
    18. \n", "\t
  18. 'wd'
    19. \n", "\t
  19. 'weight'
    20. \n", "\t
  20. 'x'
    21. \n", "\t
  21. 'xbar'
    22. \n", "\t
  22. 'y'
    23. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'a'\n", "\\item 'bmi'\n", "\\item 'd'\n", "\\item 'energy'\n", "\\item 'fpain'\n", "\\item 'height'\n", "\\item 'hh'\n", "\\item 'insurance'\n", "\\item 'intake.post'\n", "\\item 'intake.pre'\n", "\\item 'm'\n", "\\item 'mylist'\n", "\\item 'nwd'\n", "\\item 'o1'\n", "\\item 'oops'\n", "\\item 'pain'\n", "\\item 'thuesen'\n", "\\item 'wd'\n", "\\item 'weight'\n", "\\item 'x'\n", "\\item 'xbar'\n", "\\item 'y'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'a'\n", "2. 'bmi'\n", "3. 'd'\n", "4. 'energy'\n", "5. 'fpain'\n", "6. 'height'\n", "7. 'hh'\n", "8. 'insurance'\n", "9. 'intake.post'\n", "10. 'intake.pre'\n", "11. 'm'\n", "12. 'mylist'\n", "13. 'nwd'\n", "14. 'o1'\n", "15. 'oops'\n", "16. 'pain'\n", "17. 'thuesen'\n", "18. 'wd'\n", "19. 'weight'\n", "20. 'x'\n", "21. 'xbar'\n", "22. 'y'\n", "\n", "\n" ], "text/plain": [ " [1] \"a\" \"bmi\" \"d\" \"energy\" \"fpain\" \n", " [6] \"height\" \"hh\" \"insurance\" \"intake.post\" \"intake.pre\" \n", "[11] \"m\" \"mylist\" \"nwd\" \"o1\" \"oops\" \n", "[16] \"pain\" \"thuesen\" \"wd\" \"weight\" \"x\" \n", "[21] \"xbar\" \"y\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# attach를 해도 session속 객체로 보이진 않는다. ******\n", "ls()" ] }, { "cell_type": "code", "execution_count": 205, "id": "2981892a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 15.3
    2. \n", "\t
  2. 10.8
    3. \n", "\t
  3. 8.1
    4. \n", "\t
  4. 19.5
    5. \n", "\t
  5. 7.2
    6. \n", "\t
  6. 5.3
    7. \n", "\t
  7. 9.3
    8. \n", "\t
  8. 11.1
    9. \n", "\t
  9. 7.5
    10. \n", "\t
  10. 12.2
    11. \n", "\t
  11. 6.7
    12. \n", "\t
  12. 5.2
    13. \n", "\t
  13. 19
    14. \n", "\t
  14. 15.1
    15. \n", "\t
  15. 6.7
    16. \n", "\t
  16. 8.6
    17. \n", "\t
  17. 4.2
    18. \n", "\t
  18. 10.3
    19. \n", "\t
  19. 12.5
    20. \n", "\t
  20. 16.1
    21. \n", "\t
  21. 13.3
    22. \n", "\t
  22. 4.9
    23. \n", "\t
  23. 8.8
    24. \n", "\t
  24. 9.5
    25. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 15.3\n", "\\item 10.8\n", "\\item 8.1\n", "\\item 19.5\n", "\\item 7.2\n", "\\item 5.3\n", "\\item 9.3\n", "\\item 11.1\n", "\\item 7.5\n", "\\item 12.2\n", "\\item 6.7\n", "\\item 5.2\n", "\\item 19\n", "\\item 15.1\n", "\\item 6.7\n", "\\item 8.6\n", "\\item 4.2\n", "\\item 10.3\n", "\\item 12.5\n", "\\item 16.1\n", "\\item 13.3\n", "\\item 4.9\n", "\\item 8.8\n", "\\item 9.5\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 15.3\n", "2. 10.8\n", "3. 8.1\n", "4. 19.5\n", "5. 7.2\n", "6. 5.3\n", "7. 9.3\n", "8. 11.1\n", "9. 7.5\n", "10. 12.2\n", "11. 6.7\n", "12. 5.2\n", "13. 19\n", "14. 15.1\n", "15. 6.7\n", "16. 8.6\n", "17. 4.2\n", "18. 10.3\n", "19. 12.5\n", "20. 16.1\n", "21. 13.3\n", "22. 4.9\n", "23. 8.8\n", "24. 9.5\n", "\n", "\n" ], "text/plain": [ " [1] 15.3 10.8 8.1 19.5 7.2 5.3 9.3 11.1 7.5 12.2 6.7 5.2 19.0 15.1 6.7\n", "[16] 8.6 4.2 10.3 12.5 16.1 13.3 4.9 8.8 9.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 근데 칼럼을 변수로 바로 쓸 수 있다.\n", "blood.glucose" ] }, { "cell_type": "code", "execution_count": 207, "id": "a7eaf2fd", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
blood.glucoseshort.velocity
65.3 1.49
116.7 1.25
125.2 1.19
156.7 1.52
174.2 1.12
224.9 1.03
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & blood.glucose & short.velocity\\\\\n", "\\hline\n", "\t6 & 5.3 & 1.49\\\\\n", "\t11 & 6.7 & 1.25\\\\\n", "\t12 & 5.2 & 1.19\\\\\n", "\t15 & 6.7 & 1.52\\\\\n", "\t17 & 4.2 & 1.12\\\\\n", "\t22 & 4.9 & 1.03\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | blood.glucose | short.velocity |\n", "|---|---|---|\n", "| 6 | 5.3 | 1.49 |\n", "| 11 | 6.7 | 1.25 |\n", "| 12 | 5.2 | 1.19 |\n", "| 15 | 6.7 | 1.52 |\n", "| 17 | 4.2 | 1.12 |\n", "| 22 | 4.9 | 1.03 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "6 5.3 1.49 \n", "11 6.7 1.25 \n", "12 5.2 1.19 \n", "15 6.7 1.52 \n", "17 4.2 1.12 \n", "22 4.9 1.03 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 데이터 객체의 일부 변수들을 뽑을 땐? subset()\n", "# - 인덱싱과 마찬가진데, 함수를 쓴다.?!\n", "\n", "thue2 <- subset(thuesen, blood.glucose < 7)\n", "thue2" ] }, { "cell_type": "code", "execution_count": 210, "id": "33bdf2b4", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
blood.glucoseshort.velocitylog.gluc
15.3 1.76 2.727853
10.8 1.34 2.379546
8.1 1.27 2.091864
19.5 1.47 2.970414
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " blood.glucose & short.velocity & log.gluc\\\\\n", "\\hline\n", "\t 15.3 & 1.76 & 2.727853\\\\\n", "\t 10.8 & 1.34 & 2.379546\\\\\n", "\t 8.1 & 1.27 & 2.091864\\\\\n", "\t 19.5 & 1.47 & 2.970414\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| blood.glucose | short.velocity | log.gluc |\n", "|---|---|---|\n", "| 15.3 | 1.76 | 2.727853 |\n", "| 10.8 | 1.34 | 2.379546 |\n", "| 8.1 | 1.27 | 2.091864 |\n", "| 19.5 | 1.47 | 2.970414 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity log.gluc\n", "1 15.3 1.76 2.727853\n", "2 10.8 1.34 2.379546\n", "3 8.1 1.27 2.091864\n", "4 19.5 1.47 2.970414" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 함수로 새로운 변수 생성1 : transform( df, 새변수=기존변수+조작)\n", "# - 로그값을 씌운 변수를 함수로 생성하여 반환\n", "thue3 <- transform(thuesen, log.gluc = log(blood.glucose))\n", "head(thue3, 4)" ] }, { "cell_type": "code", "execution_count": 212, "id": "7ac81bd5", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
blood.glucoseshort.velocitycentered.log.gluclog.gluc
15.3 1.76 0.48187982.727853
10.8 1.34 0.13357312.379546
8.1 1.27 -0.15410902.091864
19.5 1.47 0.72444142.970414
7.2 1.27 -0.27189201.974081
5.3 1.49 -0.57826621.667707
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " blood.glucose & short.velocity & centered.log.gluc & log.gluc\\\\\n", "\\hline\n", "\t 15.3 & 1.76 & 0.4818798 & 2.727853 \\\\\n", "\t 10.8 & 1.34 & 0.1335731 & 2.379546 \\\\\n", "\t 8.1 & 1.27 & -0.1541090 & 2.091864 \\\\\n", "\t 19.5 & 1.47 & 0.7244414 & 2.970414 \\\\\n", "\t 7.2 & 1.27 & -0.2718920 & 1.974081 \\\\\n", "\t 5.3 & 1.49 & -0.5782662 & 1.667707 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| blood.glucose | short.velocity | centered.log.gluc | log.gluc |\n", "|---|---|---|---|\n", "| 15.3 | 1.76 | 0.4818798 | 2.727853 |\n", "| 10.8 | 1.34 | 0.1335731 | 2.379546 |\n", "| 8.1 | 1.27 | -0.1541090 | 2.091864 |\n", "| 19.5 | 1.47 | 0.7244414 | 2.970414 |\n", "| 7.2 | 1.27 | -0.2718920 | 1.974081 |\n", "| 5.3 | 1.49 | -0.5782662 | 1.667707 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity centered.log.gluc log.gluc\n", "1 15.3 1.76 0.4818798 2.727853\n", "2 10.8 1.34 0.1335731 2.379546\n", "3 8.1 1.27 -0.1541090 2.091864\n", "4 19.5 1.47 0.7244414 2.970414\n", "5 7.2 1.27 -0.2718920 1.974081\n", "6 5.3 1.49 -0.5782662 1.667707" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 함수로 새로운 변수 생성2: within\n", "# -> 중괄호{}를 이용하여, 입력된 df내에서 변수생성/조작/중간변수 삭제가 가능하다.\n", "thue4 <- within(thuesen, {\n", " log.gluc <- log(blood.glucose) # + log씌운 칼럼 생성\n", " m <- mean(log.gluc) # 평균값 계산(중간변수)\n", " centered.log.gluc <- log.gluc - m # 중간변수를 이용해 새로운 칼럼 추가 생성\n", " rm(m)\n", " })\n", "\n", "\n", "head(thue4)" ] }, { "cell_type": "markdown", "id": "081ef68e", "metadata": {}, "source": [ "### The graphics system" ] }, { "cell_type": "markdown", "id": "4a5c55a8", "metadata": {}, "source": [ "#### 2.2.1 plot layout" ] }, { "cell_type": "code", "execution_count": 213, "id": "1d7b13bc", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# r unif (50,0,2)-> 유니폼 랜덤넘버를 50개를 0~2 범위로 만들기\n", "x <- runif(50,0,2)\n", "y <- runif(50, 0,2)" ] }, { "cell_type": "code", "execution_count": 214, "id": "a4f001c7", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Main title\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot( x, y, \n", " main=\"Main title\",\n", " sub=\"subtitle\",\n", " xlab=\"x-label\",\n", " ylab=\"y-label\",\n", " )" ] }, { "cell_type": "code", "execution_count": 215, "id": "7539a718", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in text.default(0.6, 0.6, \"text at (0.6, 0.6)\"): plot.new has not been called yet\n", "output_type": "error", "traceback": [ "Error in text.default(0.6, 0.6, \"text at (0.6, 0.6)\"): plot.new has not been called yet\nTraceback:\n", "1. text(0.6, 0.6, \"text at (0.6, 0.6)\")", "2. text.default(0.6, 0.6, \"text at (0.6, 0.6)\")" ] } ], "source": [ "# text는 plot()과 같은 섹션에서 사용되어야하며\n", "# -> x, y좌표에 텍스트를 넣어준다.\n", "text(0.6, 0.6,\n", " \"text at (0.6, 0.6)\")" ] }, { "cell_type": "code", "execution_count": 216, "id": "f95217f1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Main title\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot( x, y, \n", " main=\"Main title\",\n", " sub=\"subtitle\",\n", " xlab=\"x-label\",\n", " ylab=\"y-label\",\n", " )\n", "\n", "text(0.6, 0.6,\n", " \"text at (0.6, 0.6)\")" ] }, { "cell_type": "code", "execution_count": 217, "id": "3dd5c439", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in int_abline(a = a, b = b, h = h, v = v, untf = untf, ...): plot.new has not been called yet\n", "output_type": "error", "traceback": [ "Error in int_abline(a = a, b = b, h = h, v = v, untf = untf, ...): plot.new has not been called yet\nTraceback:\n", "1. abline(h = 0.6, v = 0.6, lty = 2)", "2. int_abline(a = a, b = b, h = h, v = v, untf = untf, ...)" ] } ], "source": [ "# abline도 text와 마찬가지로 plot() 과 같은 세션에서 실행되어야한다.\n", "abline(h=.6, v=.6, lty=2)" ] }, { "cell_type": "code", "execution_count": 218, "id": "a7113e59", "metadata": { "scrolled": false, "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Main title\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot( x, y, \n", " main=\"Main title\",\n", " sub=\"subtitle\",\n", " xlab=\"x-label\",\n", " ylab=\"y-label\",\n", " )\n", "\n", "text(0.6, 0.6,\n", " \"text at (0.6, 0.6)\")\n", "\n", "abline(h=.6, v=.6, lty=2)" ] }, { "cell_type": "code", "execution_count": 222, "id": "dfe3d2c1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in mtext(-1:4, side = side, at = 0.7, line = -1:4): plot.new has not been called yet\n", "output_type": "error", "traceback": [ "Error in mtext(-1:4, side = side, at = 0.7, line = -1:4): plot.new has not been called yet\nTraceback:\n", "1. mtext(-1:4, side = side, at = 0.7, line = -1:4)" ] } ], "source": [ "# for문 side 1,2,3,4를 돌면서 -> mtext( , side=side)\n", "# -> -1부터 4숫자를, 각 side에 , 0.7크기로, -1~4의 위치에 적기\n", "for (side in 1:4) \n", " mtext(-1:4, side=side, at=.7, line=-1:4)" ] }, { "cell_type": "code", "execution_count": 223, "id": "19fb633f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Main title\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot( x, y, \n", " main=\"Main title\",\n", " sub=\"subtitle\",\n", " xlab=\"x-label\",\n", " ylab=\"y-label\",\n", " )\n", "\n", "text(0.6, 0.6,\n", " \"text at (0.6, 0.6)\")\n", "\n", "abline(h=.6, v=.6, lty=2)\n", "\n", "for (side in 1:4) \n", " # -1부터 4숫자를, 각 side에 , 0.7크기로, -1~4의 위치에 적기\n", " mtext(-1:4, side=side, at=.7, line=-1:4)" ] }, { "cell_type": "code", "execution_count": 225, "id": "4371ae60", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 1
    2. \n", "\t
  2. 2
    3. \n", "\t
  3. 3
    4. \n", "\t
  4. 4
    5. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 4\n", "\n", "\n" ], "text/plain": [ "[1] 1 2 3 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c(1:4)" ] }, { "cell_type": "code", "execution_count": 226, "id": "d07d54fd", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 1
    2. \n", "\t
  2. 2
    3. \n", "\t
  3. 3
    4. \n", "\t
  4. 4
    5. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 4\n", "\n", "\n" ], "text/plain": [ "[1] 1 2 3 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "1:4" ] }, { "cell_type": "code", "execution_count": 224, "id": "dda25785", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in mtext(paste(\"side\", 1:4), side = c(1:4), line = -1, font = 2): plot.new has not been called yet\n", "output_type": "error", "traceback": [ "Error in mtext(paste(\"side\", 1:4), side = c(1:4), line = -1, font = 2): plot.new has not been called yet\nTraceback:\n", "1. mtext(paste(\"side\", 1:4), side = c(1:4), line = -1, font = 2)" ] } ], "source": [ "# 반복문없이 side= 연속벡터1:4를 줘서 돌리기\n", "\n", "mtext(paste(\"side\", 1:4), side=c(1:4), line=-1, font=2)" ] }, { "cell_type": "code", "execution_count": 229, "id": "e75aef16", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Main title\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot( x, y, \n", " main=\"Main title\",\n", " sub=\"subtitle\",\n", " xlab=\"x-label\",\n", " ylab=\"y-label\",\n", " )\n", "\n", "text(0.6, 0.6,\n", " \"text at (0.6, 0.6)\")\n", "\n", "abline(h=.6, v=.6, lty=2)\n", "\n", "for (side in 1:4) \n", " mtext(-1:4, side=side, at=.7, line=-1:4)\n", "\n", "mtext(paste(\"side\", 1:4), side=c(1:4), line=-1, font=2)" ] }, { "cell_type": "markdown", "id": "b75f4b07", "metadata": {}, "source": [ "#### 2.2.2 Building a plot from pieces\n", "- 각 축에 라벨 주기" ] }, { "cell_type": "code", "execution_count": 235, "id": "18ade383", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyMjIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enw8PD////QFLu4AAAACXBIWXMAABJ0AAASdAHeZh94AAAVNElEQVR4nO3da1viyBqG0SA00or4///tKD2eQJHDk+StZK0PvRmv3V3VGW6SqgDTPQM368aeAEyBkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCgOtDerxfda9W68fgfKBJ14a0u+s+LKNTgvZcG9K6W/zd7h89PSy6dW5C0KJrQ1p02/fH226RmQy06tqQuu6nf4AZckaCgBvWSA9P+0fWSHD99vfy067d3S45JWjPDfeR1vv7SIvVvftIzN4A2wQdNOaKZ3k+nBGGgCQhQcBYIZ0+FQqJxtQJ6cYLThiTSzsIEBIECAkChgxp96frlg///yE2G5iSAUPaLfb7CKt/f4iQmJIBQ1p3m5eaNov9h2OFxKQMGNLi3298Wtw9CYmJGTCkt3Z2y6WQmJgBQ7rr3j46cbcUEtMyYEib7s//j566pZCYlCG3v9fv9Tz88i4gIdGYQW/Ibldvj57+CIkp8c4GCBASBAipUT5rUouQmrSvSEqFCKlJ3adfqUBILeoO/pfRCalFQipHSC0SUjlCapI1UjVCapJdu2qE1Cj3kWoREgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASDStyteSCYmG1fmiTCHRsDpf3Swk2lXoPyYgJNolpPGHYAKENP4QTIE10uhDMAV27UYfgmlwH2nkIUZQ5d85PRDSUOpchdADIQ2lzrqYHghpIIV2aumBkAYipGkT0kCENG1CGoo10qQJaSh27SZNSMNxH2nChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASjar1qRQh0aRqn5MUEk2q9sl9IdGict8lI6SUWpfsUyekKkOEVbtknzohVRkirNol++RVO+BCiij3Ajl51S4BhBQhpOHVWpQKKUJIcyekjGqX7AxMSBnVLtkZmJBSal2yD2/mf38hkTD7M7KQSGhmjdjXiVNIBLSya9nfiVNIBDQT0qdf+/iT+/4tBYcgqZGQepymkEhoY40kJIprY9dOSJTXxH0kayQIsGsHEe4jQWFCggAhQYCQrtHEDhVDEtLl2rhnwqCEdLk27uIzKCFdrJH3lTEoIV1MSBwT0sWExDEhXc4aiSNCupxdO44I6RruI3FASBAgJAgQEgQICQKEBAFCOhzahhxXENLXgd0i4ipC+mZgIXEpIX03rpK4kJC+G1dIXEhI340rJC4kpG8G1hGXEtLXge3acRUhHQ4tI64gJAgQEgQICQKEBAFCggAhQUCzIdmmppJGQ3LjlFqGD2lz13WrhxuH8FYeahkwpH8nkGW3t75pCG8upZihQ1p3693z89O629wyhJAoZuiQFt3u9fGuu7tlCCFRzNAhvW0QnN4osEaiMUOH9OctpMVNQ9i1o5ZBQ1rdbx66vy8Pd+vTuw3uI9GYQUP6Z/9wsetjCBjJkPeRttvNZrXabzmsT3YkJFrT6DsboBYhQYCQIGCskG68jwS11Amp+ywxBAzHpR0ECAkChAQBQoIAIUGAkCBghDetnrHDLSQaM2BIGyGNyM25fg367u/Fsu8h+J4PQvZt0DXS9pcvDwoMwbd8NL9vw242bLpt30PwDV8W0zu7dnMgpN4JaQ6E1DshzYI1Ut+ENAt27fompJlwH6lfQoIAIUGAkOBcJ66PhQTnObljIyQ4z8l7CEKCs5y+qy0kOIuQIEBIkGCNBAF27SDCfSTol5AgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgYF4h+U9705M5hXTyS9DhFrMKqcc/m7EUucqYUUin/0NRNKnMVYaQaFmZqwwh0bA6/05nFFKdVy9ShDTGEGWup0kR0jhDFNnhIabMVca8QmJqylxlCIm2FbnKEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAiJlhX5fKyQaFmZb2wQEi0r8x1CQqJhdb7VTkg0TEgFhqB9QiowBBNgjTT+EEyAXbvxh2AS3EcaewhIEhIECAkChAQBQoIAIUGAkCBASBAgpIuUuf9HMUK6QKF3pPTPa8ZFhHSBQu+R7NusXjMShHS+Su/a79uMXjMyhHS+GYU0o79qiJDON6Nn14z+qiFCusB8rneEdCkhXWBGK/BCrxltbB8K6SJt/EsNKPOaUWYivxASx15fL4q8ZhQ6NZ4kJA5VOgk0s1gTEocqnQSEdJPyh23KSj13S03mFCFxoNZzt9Lp8RQhcaBYSIUWbKcIiUPFTgJFtg9/ISQOtXISKEVIHGvjJFCKkCBASBAgpOu5AOKdkK5lSc4nQrpWsU1ixiWkK9W6bcnYhHQlIfGZkK40WEgfWxo2NwoT0rWGWSN9bGnY3ChNSNca5on9kavNjdKEdL0BLrU+LiCtyWoTUmlCaoWQShNSK4RUmzVSIxoOaRa7wXbtGtFsSLN5XrmP1IR2Qzr3/wgDaDUka29KERIEDBrS4/2qe7VaP946hJAoZcCQdnfdh+WtQ1RYI1n982bAkNbd4u92/+jpYdGtbxxi/F278WdAHQOGtOi274+33eLmIcY+H1Q4J1LFgCF9ed6fjqCFZ6dVGp80fEYamZD4ZNg10sPT/lFijTS6s0Ia+/KToQy5/b38tGt3t+tliCH9vkayHTEfw95HWu/vIy1W9zffRyrg90xsR8xHq+9sKOGXCzerqBkRUn+ENCNC6o+QZmSskNq/j3QGa6T5qBNS91liiPHZtZsPl3a9msxrAr8QEgTcGNLd/VNsKj8MAQ24MaTX9yj00JKQaMyNIe3+/umjJSHRmMAa6fH+Lt2SkGhMZrNhu3g5L21++33d2TvcQqIxkZAelmd8D8PzRkhM1u0h7e5fTkd3D7uXmlanf+N28Utqt8wKxnRrSI+vmw3rfx99/fXm4/b0x/lumhWM6db7SC8no83bZ/ROf3z81ebTp83Ds4Ix3XofafUQm8oPQ0ADbr2PFJvIj0NAA7zXjrno9R3EQmIeev5Mi5CYh54/ZSkkZqHvz/0LiVkQEgQICRKskSDArh1EuI8E1QkJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQ0k38V8v5R0g36PnTyzRESDfo+fs0aIiQrtf3NzzRECFdT0i8E9L1hMQ7Id3gcI1kD2++hHSDr7t29vDmTEg3+XwOsoc3Z0JKsWLqX+FrZyGlCKlvpa+dhZQipL6VvnYWUkzpf88TUPuVSkgxpa88JkBIJYfoQ+G18AQIqeQQNKf0tbOQaEXpa2ch0Y7C185CggAhQYCQIEBIECAk+MElextCgm9dttsuJPjWZfd/hQTfufAdSUKC7wgJAoQECdZIEGDXDiLcR4KBCQkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFC4jyFv5yxAiFxjtJfF1yBkDhH6S+wr0BInKH2f1KlAiFxBiH9RkicQUi/ERLnsEb6hZA4h127XwiJ87iPdJKQIEBIaV65Z0lIWdYSMyWkLLtbMyWkKPdb5kpIUZMMyarvDEKKmmBIVn1nEVLW9NZI0/sb9UJIWZN7/Z7gObYXQkprc0Xx46yFdB4hcfI8OnxIjb4SDfJbCg7BJ6fWQQOvkVq9NhYSp886Az+zW93bEBK/Xb4Nea3V7JJMSFR69haaymWERKXrKSFFNXcYG1dohV+n6csIadLOXt6U2XMu1PRFhDRhbT4pyzR9ESFNWKuXSS0S0nQ1u3B/19DJSUjT1XpITV2ZCmm6mg/p06/lCWnCmnomHmnrdUBIE9bUtdERId2ujWPXgIZW60eEdLs2jh39aurKVEhcrefzXVNXpkLiSgM8zxu6MhUSV2rqyqt3QuI6be0F9E5IXEdIXwiJ6wjpCyFxJWukz4TElZrane6dkLhaQ7vTvRMSBAgJAoQEAUKCACFBgJAgQEgQICQIEBIEDB/S5q7rVg+9DgFDGzCkf+8nWXZ7616GgJEMHdK6W++en5/W3aaPIWAkQ4e06Havj3fdXR9DwEiGDunt/cKn3zcsJBozdEh/3kJa9DEEjGTQkFb3m4fu78vD3fr0boOQaMygIf2zf7jY9TEEjGTI+0jb7WazWu23HNYnOxISrfHOBggQEgQICQLGCsl9pCDf5jO+OiF1nyWGmAvfL1eBS7vm+cbTCoTUOt/BXYKQWiekEgYN6fF+tV8BrdaPfQ0xP0IqYcCQdnefdhOWvQwxS9ZIFQwY0rpb/N3uHz09LLxpNcauXQUDhrTotu+Ptz5GEeR+wfgG/86G7/4hNgSMxBkJAoZdIz087R9ZIzE1Q25/Lz/t2t35YB9TMux9pPX+PtJide8+EtPinQ0QICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAiYS0hdJ056NI+Q9hVJif7MJKRe/lR4N4uQuoP/hTQhQYCQIKCtkK7de7NGomcthXT93ptdO3rWVEg3DO8+Er1qKCQrHeoSEgQICQIaCsneG3U1FZK9N6pqKSR7b5TVVkhQlJAgQEjMUnqVICRmKL9vJSRmKH8nRUjMTw/39oXE/AgJAoQECdZIEGDXDiLcR4KChAQBQoIAIUGAkCBASL/xYULOIKTTfLydswjpNF+4wlmEdJKvAOM8QjpJSJxHSCcJifMI6TRrJM4ipNPs2nEWIf3GfSTOICQIEBIECAkChAQBUw7JNgGDmW5INq4Z0IRDiv1JpTnt1jDZkObx5h6n3SqE1LSZnHYbIKSWzeIv2YbJhjSLF2shlTHhkGawfBBS1g07N9MNaRYbWnM47Q7mppfeKYc0A3M47Q7mplclITWutdNu3fnedp0sJAZU+QwqJHqSP3tUXtMJiV70cPaovctojUQfejh7FA/Jrh15fTzpa4fkPhI96OVJX3mNdBsh8b1+Qiq8a3cbIfGDfs4ede8j3UZI/GC6Z48+CIkfTfXs0QchQYCQIEBIs+biLUVIM2Y7IUdIMzbd26PDE9J8VX/DTlOENF9CChLSfAkpSEgzZo2UI6QZs2uXI6RZcx8pRUgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAGDhvR4v+perdaPfQ0BoxgwpN1d92HZyxAwkgFDWneLv9v9o6eHRbfuYwgYyYAhLbrt++Ntt+hjCBjJgCF9+TDm6U9mConGOCNBwLBrpIen/SNrJKZmyO3v5addu7tdL0MwEN+acmDY+0jr/X2kxerefaSm+R6vI97ZwOV8s+QRIXEx33V8TEhcTEjHxgrJfaSGCelYnZC6zxJD0BtrpCMu7bicXbsjQuIarhoOCAkCfLAPAnywDwJ8sA8CfIwCAnywDwKckSDAB/sgwAf7IMAH+yDAOxsgQEgQICQIEBIECAkChAQBQoIAIUFA0ZCgMVc8y/PhFBvwcqaY0MAUk3MU0jFTTGhgikLqlykmNDBFIfXLFBMamKKQ+mWKCQ1MUUj9MsWEBqYopH6ZYkIDUxRSv0wxoYEpCqlfppjQwBSF1C9TTGhgikLqlykmNDBFIfXLFBMamGLTIcEUCQkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBgmpPWiW6x3p34wusMZXf1t6n3afJ1PvaN4OMWKR3Fz18eTcZC/43J/NO9O/GB0hzPaVnwKbL/Op95RPJxixaO43s9o8RFO5jAO8Xd87Bbb5+2ie/zxB6M7mtG2W405n2+9zO/zv656R/FoigWP4rb7s3s9b/55+0HoMA4R0rp7ePn1b3f/4w9GdzSjTaHJ/W/TLb88S+sdxaMpFjyKq3/z+5hm6DAOEdKqe3r+8up09IPRHc1o021GnM63uvXzl2dpvaN4NMWCR/F/H9MMHcYhQuoOXwSOfjC6oxmtuoc/L0vQ0Sb0je3BEat3FI+mWPAo/rPrlm8PQ4dRSHvfhLS3/PF3jKJ6SM9HIVU8is+vp8qHt4dCSvpmin9fXrfWxS5NWgup5lF8fn5afFzICSnphxntim0utxbSP9WO4vNu8ekc2VBIi8OpHv1gdD/NqNAUX32ZTr2j+Oq76RSb4vPyc9ihwzjcrt3T4a7dU6H9pp9mVOwp8M2uXaWj+Kp+SE93y6dP/xg6jEP8Fe/3K7uHbv3jD0Z3NKNF93rru/SztN5RfHVw0qx3FB8O9j5Ch9E7G/aOZrR+PbC79cfmTgnl39lwMMWCR/HpcA+xoXc2PN99bIL+O8535XZFD6e4W+x/UOvV/v1ZWvUoPh9MseBR/NN9vP8veRgHCWm3f3/tv/G6gx8U8e0U76pt234Nqd5RfP5uirWOYncUUuYwlloGQquEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEhNWnaPL78+dn/Gngj/E1KTnrrFy6+LxW7sifA/IbVp090/33d/x54Gb4TUqGW36VZjT4J3QmrUU9d1T2NPgndCatW6W489BT4IqVHOSLUIqVGrlzXScuxJ8E5Ibfr7cmF3323GngZvhNSk3WJ/H8nFXRlCatKf/9/Z4OKuCiFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQB/wEy81bJTr2RlwAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, y)" ] }, { "cell_type": "code", "execution_count": 237, "id": "124a8c12", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# type=\"n\" -> [안에 표시는 안찍을 테니] 따로 그림 그릴 준비하고 있어라\n", "# -> 흰색으로 표시만 지우고 도화지만 그려놓는다는 의미\n", "\n", "# 라벨도 \"\"로 다 지우고\n", "\n", "# axes= F -> 축도 그리지 말고 있어라.\n", "\n", "plot(x, y, \n", " type=\"n\", \n", " xlab=\"\", ylab=\"\",\n", " axes=F\n", " )" ] }, { "cell_type": "code", "execution_count": 240, "id": "1dc5bcbe", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1) 빈 도화지에 산점도를 찍어라\n", "plot(x, y, \n", " type=\"n\", xlab=\"\", ylab=\"\", axes=F)\n", "\n", "points(x, y)" ] }, { "cell_type": "code", "execution_count": 241, "id": "a647e69f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1) points(,)-> 빈 도화지에 산점도를 찍어라\n", "# 2) axis(1) -> 1번 축(x축)만 그려라\n", "plot(x, y, \n", " type=\"n\", xlab=\"\", ylab=\"\", axes=F)\n", "\n", "points(x, y)\n", "axis(1)" ] }, { "cell_type": "code", "execution_count": 242, "id": "218a91b6", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1) points(,)-> 빈 도화지에 산점도를 찍어라\n", "# 2) axis(1) -> 1번 축(x축)만 그려라\n", "# 3) axis(2, at= ) -> 2번 축(좌 y축)에는 점을 직접 찍어준다.\n", "\n", "\n", "plot(x, y, \n", " type=\"n\", xlab=\"\", ylab=\"\", axes=F)\n", "\n", "points(x, y)\n", "axis(1)\n", "axis(2, at=seq(0.2, 1.8, 0.2))" ] }, { "cell_type": "code", "execution_count": 244, "id": "08d408f1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1) points(,)-> 빈 도화지에 산점도를 찍어라\n", "# 2) axis(1) -> 1번 축(x축)만 그려라\n", "# 3) axis(2, at= ) -> 2번 축(좌 y축)에는 점을 직접 찍어준다.\n", "# 4) box() -> 4방향 축이 사각형으로 모두 그려진다.\n", "\n", "plot(x, y, \n", " type=\"n\", xlab=\"\", ylab=\"\", axes=F)\n", "\n", "points(x, y)\n", "axis(1)\n", "axis(2, at=seq(0.2, 1.8, 0.2))\n", "box()" ] }, { "cell_type": "code", "execution_count": 245, "id": "aa0942b2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyMjIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enw8PD////QFLu4AAAACXBIWXMAABJ0AAASdAHeZh94AAAc3ElEQVR4nO3d52KiQBhG4aGIBin3f7dLsWBZG+8M38B5fmQ1uwmE9QRmQHUtgNnc0isArAEhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIRnhevVwsx5uP/sXr77B7vYf3d2FZ2xnI4Z4/oabfz+EdEzc9B/d3YV3bGcjhniy4Wb2Q0jnvzz9eXcX3rGdjXDXetzzkN59+bO7hBQK29mI7iGfO1d2t8rh1vgf89ffSov69C/Of5TdTmtX3Xzxqb3h493dTlMkLhm/DbwgJCO6h/yhq6O7tRtuDf8x2SkJd2ynIRXjJ6vpF78MqU6u3wZeEJIRfUi5S7pbictPIXV/ZE3bdt3k7TSkk930i1+GdOpo+PbwgpCM6Pcwh36fceyKqsYA0tOE+E0UfQ/lsN9yt18+/fP27hhksztPC0KPkIzoQ6qd27f7rp7qWSXXRvqRVPNFSN1Aqxnv5d5/jq0iJCOGMU/i0m43lLTXkOq/InP3IbXTP9v2yWdv716PBjm284WQjBhC6g6+yn7scw7pLz0X0GpC4r/bF7asEUNIf86l/UDmFFJ/iUO6O4+Yfg8pISDv2MJGDCGNV9nV7XWyoTz9XTsnpNMJKnhESEYMAfXpdMOkIaj2ksOne6Tm9h9d73Z7tuQ4/JEF/Im2hZCMGB/y/anW4nIvG+6UyQchJTdf+OzugDOyvhCSEeNDvjxNbo/3jue5tqGAlyH1p5Wy62fv7panb1QE/IE2hpCMGB/y/dmhyRFa1QWR7PrzS/m7C7vz8UqH82fv7jZFd9CYM1Dyh5AAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJEAgQkgMi88OjXB/OAosAlAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgpEj99ORmeENIURoqIiVDCClKbvIRFhBSjNzdn1gcIcWIkMwhpBgRkjmEFCXGSNYQUpSYtbOGkCLFeSRbCAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJEAgZEj1ziX7tj2kLik8LQJbY+VlyQKG1CTdD+0O+/6jy7wsAhtj54UyA4ZUuG4/VCRu17TNcFu/CGyMnZduDhhSMnyhc83wR+JjEdgWQ28mEDAk564f3+yODWwYRGCbISWTkBr2SJhvmyGdx0hFc7qtXwQ2ZpNjJGbtoLbJWTvOI0Fvg+eRbC1iAVb+z+EBIYVi5ygEHhBSKHbGxfBgqZA2dx7J0EwtPLATkptSLMIWQlo3Du0CIaR1I6RQGCOtGiGFwqzdqhFSOKsc+2FESIAAIQECQZ+P9PEMNyEhMgFDOhASVivkoV2VvH7yhGARwDKCjpGq10/nUywCWETYyYaDq3wvAlgCs3aAACEBAoQECBASIEBIgAAhAQKEBAgQEiJl61kphIQoWXueJCEhStaeuU9IiJG515IhJBVbh+xrR0hWFiFm7ZB97QjJyiLErB2yr561DU5IEuZ+Qa6etUMAQpIgpPBsDUoJSYKQto6QNKwdsiMwQtKwdsiOwAhJxdYhe3gb//kJCQqb3yMTEhSiGSP62nESEgRimbX0t+MkJAhEE9Lko4/v7PtLDC4CSpGE5HE1CQkKcYyRCAnGxTFrR0gwL4rzSIyRAAFm7QAJziMBhhESIEBIgAAh/SKKGSqEFDKkpki6j/vUuezP0yKCiOOcCYIKGFKddA++pvvQe/3+5rYfpHGcxUdQAUPaubzpPuzqrqnd6/c3N/0gjeS6MgQVMCTnmtOH7ijPJT4WEQQh4VHQkLoPiZvckS8iCELCo6CHdlXb7vsP/R7p5SDJ9mOUMRIeBAypcklRtXnSlVSmrvSxiDCYtcODkNPf5WnGrrf3s4hAOI+EO2FPyP7t0r6ifF97WwSwBK5sAAQICRAgJEBgqZAiPo8EPLITkptSLOI3TMjhFxza3S6YU0T4CSE9WTAh4VuE9Gy5lIQvEdKz5RISvkRIz5ZLSPgSIT1ZMB3hW0Gfj/TxDDezdohMwJAOEYTEeST8JuShXZW8fskTwSKAZQQdI1WvX/JEsQhgEWEnGw7jE819LgJYArN2gAAhAQKEBAhEGxLT1LAk0pA4cQpbYg3ps38GBBJnSFxcCmMICRAgJEAgzpAYI8GYWENi1g6mRBoS55FgS7QhAZYQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASBvB0078IqRN4ImQvhHSJvDUfN8IaQt4sRjvCGkLCMk7QtoCQvKOkDaBMZJvhLQJzNr5RkgbwXkkvxYJ6e3/Kf/niAwhAQIBQ3K3fCwC8OnFwzZgSMeEkBCxlzM2IQ/tmtxl9WWNvCwC8OblOYSwY6Q/5/5aQkKMXp/VDjzZUGcubwgJETIVUtvuXVISEuJjLKS2St+fGyQk2GNojDTaERIiZGbWztQigK/ZOI9kaxGA0lIhcUIWq2InpI8vewDs4dAOECAkQICQAAFCAgQICRAgJECAZ8gCAgFDOhASVivkoV2VZL4XASwj6BipcoXvRQCLCDvZcHCV70UAS2DWDhAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgS2FRKvmAdPthTSyxdBB+bYVEgevzeWYuQoY0MhvX6jKETJzFEGISFmZo4yCAkRs/N/uqGQ7Pz2ggohLbEIM8fTUCGkZRZhZIYHMmaOMrYVEtbGzFEGISFuRo4yCAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkACBkCE1O+ey8vRNXn4XQkJkAobUJK6Xj9+EkLAmAUMq3KGr6ZBkwzchJKxJwJCS8QvrJK0JCSsTMKRzO02WERJWJmBIqWvOtzJCwroEDOngdqdbtcsICQpGnh8bdvq7uPzQ5Zuf38jGgXFmXrEh8AnZKj/fqneEhNnMvIYQVzYgYnZe1Y6QEDFCMrAIxI+QOI8EBcZIT0JyU4pFYPW2OmtnaRFYBTO/dAkJECAkQICQAAFCAgQICRAI+nykj2e4CQmRCfo0CkLCWoU8tKvGl2vwuQhgGWGfRuEK34vwzMz5PxgTdrLh4Crfi/DJ0BUp/vE74yvM2n3B0DWSvm3qd4YCIX3O0lX7vm3od4YGIX1uQyFt6EcVIaTPbejRtaEfVYSQvrCd4x1C+hYhfWFDI3BDvzPimD4kpK/E8Z8qYOZ3hpkVeYOQ8Kj/fWHkd4ahXeNLhIR7lnYC0QzWCAn3LO0ECGkW85ttzUw9dk2tzCuEhDu2HruWdo+vEBLuGAvJ0IDtFULCPWM7ASPTh28QEu7FshMwhZDwKI6dgCmEBAgQEiBASL/jAAgXhPQrhuSYIKRfGZskxrII6Ue2TltiaYT0I0LCFCH9KFhI1ykNJjcMI6RfhRkjXac0mNwwjZB+FeaBfc2VyQ3TCOl3AQ61rgeQjMlsIyTTCCkWM0L64o3DAqzVOhFSLAjJNsZIkYj40G4Ts8HM2kUi2pA287jiPFIUZodU5v1/b16L1ufZIl78Ex5ZsGFuSNk4PHKJtKT3a8XYG6bMDOngsqYP6eB2slVqCQnRmRlS4povRivHfT5M8OXFce5aERJMmRnScFj3YUhNOpksz+aulYUxEqN/nM0MKT3tkSqXvv26wiV/45ua12Xiiplrtfys3fJrADs0Y6QujMPbr0tcdblduWT2Wi29P7CwT4QVc2ft8o8O1cavuz8mlK5VcIzSMCE5j+Tyvw++Tr1HWhghYSLglQ3dGKkczzYpxkiL+yikpQ8/EUrIS4Syyaxd2nhZREjvx0hMR2zH7JD+hkO78qOvPBbDkCrJ97PPIxnwPhOmI7ZDcolQn5JqhR4XYdebAzdGURsyM6R+3NP90Y159qo1ul9EvAhpQ2ZfIjTOxL2ZhZuziHgR0oYILhG6vfHpN4n+PNIHGCNtx+xDu/Me6eV09pNv8rBgb89bXw6zdtsx+8qGYYx0TKTPoljPL/HV/E7AGzNC4sVPgDNCAgRCXtlgahGAEiEBAqqQjtJLGwgJkZkbUvH5GOmLMRUhITKzzyOdvb9s9UBIWK3Zlwj9tZmr68y9uZ67VyUfPI/217UCliS4RGjf7Y2qT55r/vn1D4SEyAhCKvsXPvnsPNJh8mxz8VoBS5oZUt4d2tUubY+ckMWmzQyp7AMantwX+CWLAVPmTn/v+3s79+3F398sApDwegUxVzZgGzw/p4WQsA2en2XJ1d/YBN/P+yckbILhkDwiJIgREqBgd4x0/YR87QgJahHM2hESYmD+PBIhYfMICRAgJECAkACBmSGl+1q2Kv9ZBBCB2U/scz5aIiREZmZIzd/OR0uEhMgIxkjHfapuiZAQGc0lQlXS7ZcO89fmxSIAyyQhleM7yX76Ylu/LAIwbX5Izb7bHaVl09Uke9liQkJk5oZ07CcbivFFtnTnkwgJkZl7HqnbGR2a81/I3pCZkBCZueeR8vev+f0DQkJk5p5Hkq3IfxcBRIBnyAIChDQL71qOESHN4PnZy4gIIc3g+fU0EBFC+p3vV3hCRAjpd4SEC0L6HSHhgpBmuB8jMYe3XSFDqncu2bftIXXJm7dTiuTxeDtrxxzelgUMqemftOQO+w+echHNo3G6D2IOb8sChlT0b+tXJG7XtE3x+i3+Ynw0MmLyz/Cxc8CQkuELnRuuz3t9pbjZzfUCIflm+tg5YEjOXT++2R5GN9ZLhOSb6WPnBfZI/cdmfXsk2//PK2D7N9UCY6SiOd3WL2JRpo88VoCQTtY4a3fL8Fh4BQjpbHXnkRCU6WNnrmxALEwfOxMS4mH42JmQAIGlQlrfeSRsmp2Q3JRiEUA4HNoBAoQE/Mc3h0aEBDz13Wx70JCO+3wYAeXF0dciAJHvzv+GvEQoncwmrPISIazIl1ckBb1oNfkb3/+lLpMVXrSKVbEbUuKqy+1qhU+jwKrYDelm3MYJWRhndozEHgkxMTtr142Rynq4xRgJMbB6HimbzNqlL9+ijJAQmbDnkYrhPFKS7zmPhHXhygZAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkPAZXtvpJULCJ0y/XLAFhIRPmH4BewsICR+w/ZYqFhASPkBI7xASPkBI7xASPsEY6Q1CwieYtXuDkPAZziO9REiAACGp8Zt7kwhJi7HERhGSFrNbG0VIUpxv2SpCklplSIz6PkBIUisMiVHfRwhJa31jpPX9RF4Qktbqfn+vcB/rBSGpxTmi+O9aE9JnCAkv96PhQ4r0N1GQLzG4CEy8GgcFHiPFemxMSHi91wn8yI51boOQ8O7wLeSxVrRDMkKCpUevoVX5TsiQmqJ/B+Z96lz252kR+Imd4ylCeq9OukOEJhnfQzbzsgj8xtAI307T3wkY0s7lTfdhV3dN7XhX8yA+Ht6YmXM21PRXAobkXHP60B3lucTHInAjzgelmaa/EjSk7kPiJnfki8CNWA+TYhT00K5q233/od8jvRwk8X+vEO3A/SKinVPAkCqXFFWbJ11JZepKH4vAVOwhRXVkGnL6uzzN2PX2fhaBiehDmnw0L+wJ2b9d2leU72tvi8BVVI/EB3H9HuDKhhWL6tjoASHNF8e2i0BEo/UHhDRfHNsOfkV1ZLpUSJxHWgHP+7uojkzthOSmFIuAXwEe5xE9Eji0w4+iOvLyjpDwm7jmArwjJPyGkG4EDem4z4cRUF4cfS0CoRDSjYAhNelkNoEn9kWPMdJUwJAKl/wNl363dZnwxL7oRTU77V3AkJLxGRSDiif2rUBEs9PehX5i39M7skUAC2GPBAiEHSOV49MnGCNhbUJOf2eTWbu08bIIYBlhzyMVw3mkJN9zHgnrwpUNgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASCvAq/ksj5Cix+vLWUBI0eMVTy0gpNjxGtwmEFLsCMkEQoodIZlASNFjjGQBIUWPWTsLCGkFOI+0PEICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIEFgnp7Yl4QkJkCAkQCBiSu+VjEcBCAoZ0TAgJaxXy0K7JXVYP34FDO6xM2DHSn3N/LSFhfQJPNtSZyxtCwuoEn7Xbu6QkJKxN+OnvKn3/hE5CQmSWOI+0IySsDZcIAQKEBAgsFRInZLEqdkL6+LIHwB4O7QABQgIECAkQCBrScZ8PI6C8OPpaBLCIgCE16WQ2IfOyCGAhAUMqXPJXDbfqMnGFj0UACwkYUuKqy+3KJT4WASwk6FPN/3dHtghgIeyRAIGwY6RyeKY5YySsTsjp72wya5c2XhYBLCPseaRiOI+U5HvOI2FduLIBECAkQICQAAFCAgS2EhJPFoRX2whpqIiU4M9GQvLyXYGLTYTk7v4E1AgJECAkQCCukH6de2OMBM9iCun3uTdm7eBZVCHNWDznkeBVRCEx0oFdhAQIEBIgEFFIzL3BrqhCYu4NVsUUEnNvMCuukACjCAkQICRsknqUQEjYIP28FSFhg/RnUggJ2+Ph3D4hYXsICRAgJECBMRIgwKwdIMF5JMAgQgIECAkQCBlSs3MuK0/f5OV3ISREJmBITTK8e2w+fhNCwpoEDKlwh66mQ5IN3ySakHgyIT4QMKRk/MI6Set4QuLp7fhIwJDOD8cmyyIKafIR+K+AIaWuOd/KYgmJlwDDZwKGdHC7063aZYSEVQk5/V1c6infjODNPG4JCZ8JekK2ys+36l0cITFGwme4suE1Zu3wEUJ6h/NI+AAhAQJLhRTJZAPwGTshuSnFIoBwOLQDBNYcEns2BLPekJi4RkBBQzru8/EpScXR1yLuv8fqQ2K3a0PIJ/alk9mEzMsinnyLdT/M2O1aEfSJfclfNdyqy8QVPhbx5Fus+0G2kd1uBII+sa+63K5c4mMRT77Fqh9km/gh47DAE/se78gW8fg91v0QIyQzVrtH2sTwgZC0ZszchB0jlfVwK8QYqd3EhNYWdrvBzPrVG3L6O5vM2qXNq3/JI+NDW9jtBjPrt1LY80jFcB4pyfcBziNtRGy7XbvrO+84eb1XNsAgy3tQQoIn+r2H5TEdIcELD3sP27OM8YyRLC0C73jYexgPKZZZO1OLwBs+HvS2Q4rlPJKtReANLw96y2OkeQgJz/kJyfCs3TyEhP/ws/ewex5pHkLCf6x37+EDIeG/1rr38IGQAAFCAgQIadM4eFMhpA1jOkGHkDZsvadHwyOk7bJ+wU5UCGm7CEmIkLaLkIQIacMYI+kQ0oYxa6dDSJvGeSQVQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBAgJECAkQICQAAFCwi941ZQ7hITv8TpeDwgJ3+OVJR8QEr7Gax0/IiR8jZAeERK+RkiPCAnfY4z0gJDwPWbtHhASfsF5pDuEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgYDQkIDI/PMr14Rhb4PdYRYUIVlG5joT0iFVUiGAVCckvVlEhglUkJL9YRYUIVpGQ/GIVFSJYRULyi1VUiGAVCckvVlEhglUkJL9YRYUIVpGQ/GIVFSJYRULyi1VUiGAVCckvVlEhglUkJL9YRYUIVjHqkIA1IiRAgJAAAUICBAgJECAkQICQAAFCAgQICRAgJECAkAABQgIECAkQICRAgJAAAUICBMKEVCQuKZpXn1jc/Rr9/GrqPh1u18feVrxfRYtb8ZD6eDAG+RmzYWumLz6xuPs1qiw+BKrb9bG3Fe9X0eJWLIY1Sq7haDZjiJ/x6JKqrRJ3/O8nFvewRpXLl1yfp7r1m/532duKD6tocCtWbtf0+83d+ROizRgipMKV3cc/t//vJxb3sEYHQyt3cnDZzaPU3lZ8WEWDWzEf1++6mqLNGCKk3NXtzW+nh08s7mGNDu6w4Oo85Yr25lFqbys+rKLBrXhyXU3RZgwRkrv/JfDwicU9rFHuyl03BF1shZ6o7raYva34sIoGt+Kocdn5pmgzEtLgSUiD7L9fsQjrIbUPIVncim2/qyzPNwlJ6ckq/nW/twpjhyaxhWRzK7ZtnVwP5AhJ6T9r1BibXI4tpJG1rdg2yWQfGVFIyf2qPnxicf9bI0Or2LtZHXtbsfdsdYytYptNwxZtxnCzdvX9rF1taL7pf2tk7CHwZNbO0lbs2Q+pTrN6cle0GUP8iPthZFe64r+fWNzDGiWuP/Vt+lFqbyv27naa9rZieTf3IdqMXNkweFijot+wTXGd3DHB/JUNd6tocCvW93OIEV3Z0KbXSdBxO6fmZkXvV7FJhk/Y+m1/eZRa3Yrt3Soa3Io7d73+T7kZg4TUDNfXjstzd58w4ukqptambW9DsrcV22eraGsruoeQNJvR1DAQiBUhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIgAAhAQKEBAgQEiBASIAAIQEChAQIEBIgQEiAACHF5e4Nwp+/X7itdxHfBjZ5XAjJKDZ5XAjJKDZ5XAjJKDa5aZk7dh+Pbnf+xNBImbvT+3B3d4vLW3IfUpccLv8IQbHJTatd0n1Mkub8ib6R/fj+9sVwN+9vZv1fDbeGm4QUHpvctoPbd+H8Xe73jbj+/t9QS7dnqtoq6T9Ruqxpm8yVhLQENrlxmTu4/Hr32sgppK6brqG83yH1u62mv0lI4bHJjau7w7X6endspC732Smky2fdGSEtgU1uXXEaDE0ayc73CMkMNrlxpz3StJGdSw9l/RjS5WsIKTw2uXF5N0bKrncvyZxD6qfHT2OkcvqPEBab3La/7sBu7w6X+2NIx7bKbmbtyv5fdjfbA5MNy2CTm9Ykw3mk63RD30hxGgwd+7u7/tYwrTeOnJKakJbAJjdtd7qy4XJwNzTSxZMdy9O+p3DJfvy7Q9p1VbeEtAQ2OSBASIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBIUZles1BeP46v5HD/DxAQ2z0qk05Sd/3Yf/p6Cwtgu0fl4UlHd089J6SlsN2jQkhWsd3tKjPnsn7kc23k+jp24zNmr8+bvXmW+eUV7hAKIZl1GJ91dLgN6fw6dq9Cur7CHUIhJLMSV/XPe01vQ7q8jt30UO66L+o/Tl7hDqEQklnuUsK0lsvr2P0/pMkr3CEUQjKr6I7jqqq/dV/LXTj39ycvzIVQ2Nh27ZOb12AgJMvY2JaVRXo/Rmpvbz4PaaHV3TK2uXGXMo7nV+L6YIzENENwhGRWOr7pRNrfOvSzcJNZu+EtJ8YXDDq/bND11uQV7hAKIZn1d3n1uuGMUj7Wcnkdu9T17500fhyvtTvfmrzCHUIhJLuGKxv6Q7l+2mE3ubJheB27Y9qHM37s/+p6a/IKdwiFkAABQgIECAkQICRAgJAAAUICBAgJEPgHkifaeFZlbJkAAAAASUVORK5CYII=", "text/plain": [ "Plot with title \"Main title\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1) points(,)-> 빈 도화지에 산점도를 찍어라\n", "# 2) axis(1) -> 1번 축(x축)만 그려라\n", "# 3) axis(2, at= ) -> 2번 축(좌 y축)에는 점을 직접 찍어준다.\n", "# 4) box() -> 4방향 축이 사각형으로 모두 그려진다.\n", "# 5) title() -> main(위) sub(아래) xlab, ylab -> 2개 축 라벨\n", "\n", "plot(x, y, \n", " type=\"n\", xlab=\"\", ylab=\"\", axes=F)\n", "\n", "points(x, y)\n", "axis(1)\n", "axis(2, at=seq(0.2, 1.8, 0.2))\n", "box()\n", "title(main=\"Main title\", sub = \"subttitle\",\n", " xlab=\"x-label\", ylab=\"y-label\")" ] }, { "cell_type": "markdown", "id": "b66c05ea", "metadata": {}, "source": [ "#### 2.2.3 Using par\n", "- 그래프 그릴 때 유용한 `par문`" ] }, { "cell_type": "code", "execution_count": 247, "id": "b6ccdd9b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "?par" ] }, { "cell_type": "code", "execution_count": 248, "id": "f4d6181a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "par(mfrow=c(2,2)) # 한 화면을 2x2 로 나눠서 그린다. " ] }, { "cell_type": "code", "execution_count": 249, "id": "5ffa0f6c", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# 다시 돌아오려면, 하나의 화면에 1x1 1개의 그림만 그려라고 돌려준다.\n", "par(mfrow=c(1,1)) " ] }, { "cell_type": "markdown", "id": "03a72440", "metadata": {}, "source": [ "#### 2.2.4 Combinin plots\n", "- 그래프를 합치는(겹쳐그리는) 방법: 2번째 그래프에 `add=T`옵션 주기\n", " - 정규분포 변수 -> rnorm(갯수) \n", " - 정규분포 함수 -> dnorm(변수)\n", "- **hist그램 그린 후 -> 정규분포를 겹쳐그려서 -> 정규분포를 따르는지 확인**" ] }, { "cell_type": "code", "execution_count": 255, "id": "78036074", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Histogram of x\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 2번째 그래프에는 add=T 옵션을 줘서 1그림에 겹처 그린다.\n", "# - 안주면 각각의 그림이 2개로 나눠서 각각 그려진다.\n", "\n", "\n", "x <- rnorm(100) # 정규분포(r norm)를 따르는 놈 100개\n", "\n", "hist(x, freq=F) # 히스토그램: freq=F 를 주면 빈도대신 비율(밀도)가 y축을 차지한다.\n", "curve(dnorm(x), add=T) # 정규분포 함수dnorm(x) 를 curve로 그리는데, 겹쳐서 그려라 add=T\n", "\n", "# my) 정규분포 변수 -> rnorm(갯수) \n", "# my) 정규분포 함수 -> dnorm(변수)" ] }, { "cell_type": "code", "execution_count": 257, "id": "0e906192", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "$breaks\n", "[1] -3 -2 -1 0 1 2 3 4\n", "\n", "$counts\n", "[1] 4 9 39 28 17 2 1\n", "\n", "$density\n", "[1] 0.04 0.09 0.39 0.28 0.17 0.02 0.01\n", "\n", "$mids\n", "[1] -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5\n", "\n", "$xname\n", "[1] \"x\"\n", "\n", "$equidist\n", "[1] TRUE\n", "\n", "attr(,\"class\")\n", "[1] \"histogram\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# , plot=F로 첫번째 그림을 안그리고 객체로 만든 뒤 옵션부여\n", "# 다시 그리고 -> add=T로 2번째 그림 그리고\n", "\n", "\n", "h <- hist(x, plot=F)\n", "h" ] }, { "cell_type": "code", "execution_count": 258, "id": "0ae2546b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Histogram of x\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h <- hist(x, plot=F)\n", "\n", "# 기존 hist plot 객체에서 y축 정보만 가져와 -> 조작후 객체로 만든다.\n", "ylim <- range(0, h$density, dnorm(0))\n", "\n", "# 조작된 ylim으로 새로운 hist를 그린다.\n", "hist(x, freq=F, ylim=ylim)\n", "\n", "curve(dnorm(x), add=T)" ] }, { "cell_type": "markdown", "id": "85456a3b", "metadata": {}, "source": [ "### 2.3 R programming\n", "- 함수를 정의하고 -> .r 파일로 저장후 -> source() 문으로 로딩한 뒤 -> 사용" ] }, { "cell_type": "code", "execution_count": 260, "id": "39c3fa35", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# 1. 함수명 <- function(x) { }로 정의 \n", "# -> 이후 파일로 저장하기 위해 스트링으로 바꿈\n", "func_string <- \"hist.with.normal <- function(x) \n", "{\n", " hist <- hist(x, plot=F)\n", " s <- sd(x)\n", " m <- mean(x)\n", " ylim <- range(0, h$dentisy, dnorm(0, sd=s)) \n", " \n", " hist(x, freq=F, ylim=ylim)\n", " curve(dnorm(x, m, s), add=T)\n", "}\"" ] }, { "cell_type": "code", "execution_count": 263, "id": "f4896680", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# 2. 작성한 함수를 파일로 저장\n", "filename <- \"./01_p44.r\"\n", "\n", "fileConn <- file(filename)\n", "\n", "writeLines(func_string, fileConn)\n", "close(fileConn)" ] }, { "cell_type": "code", "execution_count": 264, "id": "e272a222", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# 3. source()로 r파일을 session에 laod\n", "source(filename)" ] }, { "cell_type": "code", "execution_count": 265, "id": "6ae82199", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'a'
    2. \n", "\t
  2. 'bmi'
    3. \n", "\t
  3. 'd'
    4. \n", "\t
  4. 'energy'
    5. \n", "\t
  5. 'fileConn'
    6. \n", "\t
  6. 'filename'
    7. \n", "\t
  7. 'fpain'
    8. \n", "\t
  8. 'func_string'
    9. \n", "\t
  9. 'h'
    10. \n", "\t
  10. 'height'
    11. \n", "\t
  11. 'hh'
    12. \n", "\t
  12. 'hist.with.normal'
    13. \n", "\t
  13. 'insurance'
    14. \n", "\t
  14. 'intake.post'
    15. \n", "\t
  15. 'intake.pre'
    16. \n", "\t
  16. 'm'
    17. \n", "\t
  17. 'mylist'
    18. \n", "\t
  18. 'nwd'
    19. \n", "\t
  19. 'o1'
    20. \n", "\t
  20. 'oops'
    21. \n", "\t
  21. 'pain'
    22. \n", "\t
  22. 'side'
    23. \n", "\t
  23. 'thue2'
    24. \n", "\t
  24. 'thue3'
    25. \n", "\t
  25. 'thue4'
    26. \n", "\t
  26. 'thuesen'
    27. \n", "\t
  27. 'wd'
    28. \n", "\t
  28. 'weight'
    29. \n", "\t
  29. 'x'
    30. \n", "\t
  30. 'xbar'
    31. \n", "\t
  31. 'y'
    32. \n", "\t
  32. 'ylim'
    33. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'a'\n", "\\item 'bmi'\n", "\\item 'd'\n", "\\item 'energy'\n", "\\item 'fileConn'\n", "\\item 'filename'\n", "\\item 'fpain'\n", "\\item 'func\\_string'\n", "\\item 'h'\n", "\\item 'height'\n", "\\item 'hh'\n", "\\item 'hist.with.normal'\n", "\\item 'insurance'\n", "\\item 'intake.post'\n", "\\item 'intake.pre'\n", "\\item 'm'\n", "\\item 'mylist'\n", "\\item 'nwd'\n", "\\item 'o1'\n", "\\item 'oops'\n", "\\item 'pain'\n", "\\item 'side'\n", "\\item 'thue2'\n", "\\item 'thue3'\n", "\\item 'thue4'\n", "\\item 'thuesen'\n", "\\item 'wd'\n", "\\item 'weight'\n", "\\item 'x'\n", "\\item 'xbar'\n", "\\item 'y'\n", "\\item 'ylim'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'a'\n", "2. 'bmi'\n", "3. 'd'\n", "4. 'energy'\n", "5. 'fileConn'\n", "6. 'filename'\n", "7. 'fpain'\n", "8. 'func_string'\n", "9. 'h'\n", "10. 'height'\n", "11. 'hh'\n", "12. 'hist.with.normal'\n", "13. 'insurance'\n", "14. 'intake.post'\n", "15. 'intake.pre'\n", "16. 'm'\n", "17. 'mylist'\n", "18. 'nwd'\n", "19. 'o1'\n", "20. 'oops'\n", "21. 'pain'\n", "22. 'side'\n", "23. 'thue2'\n", "24. 'thue3'\n", "25. 'thue4'\n", "26. 'thuesen'\n", "27. 'wd'\n", "28. 'weight'\n", "29. 'x'\n", "30. 'xbar'\n", "31. 'y'\n", "32. 'ylim'\n", "\n", "\n" ], "text/plain": [ " [1] \"a\" \"bmi\" \"d\" \"energy\" \n", " [5] \"fileConn\" \"filename\" \"fpain\" \"func_string\" \n", " [9] \"h\" \"height\" \"hh\" \"hist.with.normal\"\n", "[13] \"insurance\" \"intake.post\" \"intake.pre\" \"m\" \n", "[17] \"mylist\" \"nwd\" \"o1\" \"oops\" \n", "[21] \"pain\" \"side\" \"thue2\" \"thue3\" \n", "[25] \"thue4\" \"thuesen\" \"wd\" \"weight\" \n", "[29] \"x\" \"xbar\" \"y\" \"ylim\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ls()" ] }, { "cell_type": "code", "execution_count": 266, "id": "f1e17e20", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "Plot with title \"Histogram of x\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 4. 함수 사용해보기\n", "x <- rnorm(100)\n", "\n", "hist.with.normal(x)" ] }, { "cell_type": "code", "execution_count": null, "id": "3cf4d6f5", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6be2e8e0", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "37061cdf", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "b7dcbd29", "metadata": {}, "source": [ "#### 2.3.1 Flow control\n", "- R의 반복문 중 3가지\n", " 1. while 문 -> `반복될 조건문`\n", " 2. repeat 문 -> `탈출할 조건문` -> while의 반대\n", " 3. for 문 -> 뿌려줄 벡터 -> 그걸로 그림그리기\n", " " ] }, { "cell_type": "code", "execution_count": 272, "id": "7b9d9afb", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "111.108055513541" ], "text/latex": [ "111.108055513541" ], "text/markdown": [ "111.108055513541" ], "text/plain": [ "[1] 111.1081" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1. using while\n", "y <- 12345\n", "\n", "# 1. while (조건) 식 으로 구하는 root\n", "x <- y/2\n", "while (abs(x*x-y) > 1e-10 ) x <- (x + y/x)/2\n", "\n", "x" ] }, { "cell_type": "code", "execution_count": 273, "id": "fd16f17b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "12345" ], "text/latex": [ "12345" ], "text/markdown": [ "12345" ], "text/plain": [ "[1] 12345" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x^2" ] }, { "cell_type": "code", "execution_count": 274, "id": "9c8c19c6", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "111.108055513541" ], "text/latex": [ "111.108055513541" ], "text/markdown": [ "111.108055513541" ], "text/plain": [ "[1] 111.1081" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 2. using repeat로 구한는 newton's method (root)\n", "\n", "\n", "# 1. while (조건) 식 으로 구하는 root\n", "# 2. repeat { 식 if 탈출조건 break}\n", "\n", "repeat{\n", " x <- (x + y/x)/2\n", " if (abs(x*x-y) < 1e-10) break\n", "}\n", "\n", "x" ] }, { "cell_type": "code", "execution_count": 275, "id": "d7b7bffc", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- seq(0, 1, 0.5)\n", "\n", "plot(x, x, ylab=\"y\", type=\"l\")\n", "\n", "for ( j in 2:8 ) lines(x, x^j)" ] }, { "cell_type": "code", "execution_count": null, "id": "b1d12d1b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "f768c408", "metadata": {}, "source": [ "### 2.4 Data entry\n", "- [김성수 2015 강의교안 저장된페이지](http://webcache.googleusercontent.com/search?q=cache:4IHnNbw_n8cJ:ep.knou.ac.kr/comm/downloadFile.do%3FfileId%3D20151212111956cUVBSt+&cd=20&hl=ko&ct=clnk&gl=kr)\n", "- `01 2015자료 데이터시각화.pdf`에 코드 검색됨" ] }, { "cell_type": "markdown", "id": "b9e60404", "metadata": {}, "source": [ "1. `text` file: read.`table`, read.`csv`\n", "2. `excel` file : read.`xlsx` ( package: xlsx)" ] }, { "cell_type": "markdown", "id": "4c6c47df", "metadata": {}, "source": [ "```r\n", "install.packages(\"xlsx“)\n", "library(xlsx)\n", "drug.data = read.xlsx(\"c:/Rfolder/data/drug.xlsx\", 1) # 1은 default인데, sheet번호다.\n", "edit(drug.data) # R전용 데이터에디터에 가져와서 조작하고 싶을 때\n", "```" ] }, { "cell_type": "markdown", "id": "0976bab0", "metadata": {}, "source": [ "#### 변수갑 변환(recode) Method1 : 인덱싱\n", "\n", "![image-20220311212816679](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220311212816679.png)" ] }, { "cell_type": "code", "execution_count": null, "id": "8ac2db31", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "72d1e1d9", "metadata": {}, "source": [ "- (범위별 매핑 -> ) `recode` = `변수 값 변환`이라고 한다.\n", "\n", "\n", "1. 새 변수를 만들고 (할당으로 복사)\n", "2. 인덱싱을 이용해서 범위별로 값 변환" ] }, { "cell_type": "markdown", "id": "095ddf0b", "metadata": {}, "source": [ "#### 변수갑 변환(recode) Method2 : car패키지의 recode()함수 \n", "\n", "![image-20220311213101638](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220311213101638.png)" ] }, { "cell_type": "markdown", "id": "3ae67932", "metadata": {}, "source": [ "1. 새 변수 만들어놓는 것은 똑같고\n", "2. `recode()`함수를 이용하여 `범위를 간단하게 표현`할 수 있다.\n", " - \" `lo:40=1`; `40:60=2`;`60:hi=3` \" \n", " \n", "3. 1,2,3 범주형 자료이면서 **순서형 변수** 순서를 ordered()를 이용해서 factor-라벨로 준다.\n", " - 라벨을 주면 그래프에서도 나온다." ] }, { "cell_type": "code", "execution_count": null, "id": "20ebfcc8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "96a97ecc", "metadata": {}, "source": [ "#### 값 라벨(Value labels): 숫자 -> 라벨로 바꾸기\n", "![image-20220311214651402](https://raw.githubusercontent.com/is3js/screenshots/main/image-20220311214651402.png)\n", "\n", "\n", "1. 명목형 변수 -> **순서 없는 일반 범주**`factor()`를 이용해서 바꾼다.\n", "1. 순서형 변수 -> **순서를 가진 범주**`ordered()`를 이용해서 바꾼다." ] }, { "cell_type": "code", "execution_count": null, "id": "98d3f049", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 5 }