Summary(R_notes)

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+    "sources": [
+        {
+            "DOI": "10.1093/comjnl/27.2.97",
+            "ISSN": "0010-4620",
+            "URL": "https://doi.org/10.1093/comjnl/27.2.97",
+            "author": [
+                {
+                    "family": "Knuth",
+                    "given": "Donald E."
+                }
+            ],
+            "container-title": "Comput. J.",
+            "id": "knuth84",
+            "issue": "2",
+            "issued": {
+                "date-parts": [
+                    [
+                        1984,
+                        5
+                    ]
+                ]
+            },
+            "page": "97-111",
+            "publisher": "Oxford University Press, Inc.",
+            "publisher-place": "USA",
+            "title": "Literate programming",
+            "type": "article-journal",
+            "volume": "27"
+        }
+    ],
+    "project_biblios": [
+        "references.bib"
+    ]
+}

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+project:
+  type: book
+  output-dir: _book
+
+book:
+  title: "R for Data Science (2e)"
+  reader-mode: true
+
+  page-footer:
+    left: |
+      R for Data Science (2e) was written by Hadley Wickham, Mine
+      Çetinkaya-Rundel, and Garrett Grolemund.
+    right: |
+      This book was built with <a href="https://quarto.org/">Quarto</a>.
+  cover-image: cover.jpg
+  favicon: cover.jpg
+  site-url: https://r4ds.hadley.nz/
+  repo-url: https://github.com/hadley/r4ds/
+  repo-branch: main
+  repo-actions: [edit, issue]
+  chapters:
+    - index.qmd
+    - preface-2e.qmd
+    - intro.qmd
+
+    - part: whole-game.qmd
+      chapters:
+        - data-visualize.qmd
+        - workflow-basics.qmd
+        - data-transform.qmd
+        - workflow-style.qmd
+        - data-tidy.qmd
+        - workflow-scripts.qmd
+        - data-import.qmd
+        - workflow-help.qmd
+
+    - part: visualize.qmd
+      chapters:
+        - layers.qmd
+        - EDA.qmd
+        - communication.qmd
+
+    - part: transform.qmd
+      chapters:
+        - logicals.qmd
+        - numbers.qmd
+        - strings.qmd
+        - regexps.qmd
+        - factors.qmd
+        - datetimes.qmd
+        - missing-values.qmd
+        - joins.qmd
+
+    - part: import.qmd
+      chapters:
+        - spreadsheets.qmd
+        - databases.qmd
+        - arrow.qmd
+        - rectangling.qmd
+        - webscraping.qmd
+
+    - part: program.qmd
+      chapters:
+        - functions.qmd
+        - iteration.qmd
+        - base-R.qmd
+
+    - part: communicate.qmd
+      chapters:
+        - quarto.qmd
+        - quarto-formats.qmd
+
+format:
+  html:
+    theme:
+      - cosmo
+      - r4ds.scss
+    code-link: true
+
+    author-meta: "Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund"
+    include-in-header: "plausible.html"
+    callout-appearance: simple
+
+editor: visual
+

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+project:
+  type: book
+
+book:
+  title: "R_notes"
+  author: "Norah Jones"
+  date: "2023/12/19"
+  chapters:
+    - part: index.qmd
+      chapters: 
+    - intro.qmd
+    - basics.qmd
+    - summary.qmd
+    - references.qmd
+
+bibliography: references.bib
+
+format:
+  html:
+    theme: cosmo
+  pdf:
+    documentclass: scrreprt
+
+editor: visual
+

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+project:
+  type: book
+
+book:
+  title: "R_notes"
+  author: "Wang Anlin"
+  date: "2023/12/19"
+  chapters:
+    - index.qmd
+    - intro.qmd
+    - basics.qmd
+    - summary.qmd
+    - references.qmd
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R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D28DC2AE-contents

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+# Preface {.unnumbered}
+
+This is a Quarto book.
+
+To learn more about Quarto books visit <https://quarto.org/docs/books>.
+
+```{r}
+1 + 1
+```

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+F:/R/tidyverse/r4ds-main/_quarto.yml="DFFCB474"

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+    "markdown": "---\n  knitr:\n    opts_chunk:\n      comment: \"#>\"\n      collapse: true\n  format: \n    html: \n      code-fold: true\n      code-tools: true\n      code-link: true\n---\n\n\n# base R {.unnumbered}\n\n# 最重要的是帮助文档\n\nThe Comprehensive R Archive Network [CRAN](https://cran.r-project.org/)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nhelp.start()\nhelp()\n?c\n```\n:::\n\n\n# 包的安装和使用\n\n\n::: {.cell}\n\n```{.r .cell-code}\ninstalled.packages()\ninstall.packages(\"ggplot2\") \nlibrary(ggplot2) # 载入包\nrequire(ggplot2) # 另一种载入方式\nhelp(package = \"ggplot2\") # R包ggplot2的帮助文档\n?ggplot2\ndata(package=\"ggplot2\")   #查看R包ggplot2中的数据集\n\ndata(mpg,package=\"ggplot2\")  #加载数据集\nhelp(mpg) # 数据集mpg的帮助文档\n?mpg\nmpg\n```\n:::\n\n\n# 变量类型\n\n**数据类型（mode）**：表示对象在计算机内存中的存储类型\n\n-   **numeric -- 数值型（Integer/double）integer -- 整型 L**\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(4.3)\n#> [1] \"numeric\"\nclass(4.3)\n#> [1] \"numeric\"\nmode(1L)\n#> [1] \"numeric\"\nclass(1L)\n#> [1] \"integer\"\n```\n:::\n\n\n-   **character -- 字符型**\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(c(\"car\"))\n#> [1] \"character\"\nclass(\"car\")\n#> [1] \"character\"\n```\n:::\n\n\n-   **logical --逻辑型**\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(c(TRUE,FALSE))\n#> [1] \"logical\"\nclass(c(TRUE,FALSE))\n#> [1] \"logical\"\n```\n:::\n\n\n-   **factor--因子**\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(factor((c(1,2,3)))) \n#> [1] \"numeric\"\nclass(factor((c(1,2,3)))) \n#> [1] \"factor\"\n```\n:::\n\n\n-   **date/datetime--日期/日期时间**\n\n默认格式：`\"%Y-%m-%d\" xxxx-xx-xx,例如：2023-03-15`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(as.Date(\"2023-12-11\"))\n#> [1] \"numeric\"\nclass(as.Date(\"2023-12-11\")) \n#> [1] \"Date\"\n\nSys.Date()\n#> [1] \"2023-12-19\"\nas.Date(c(\"02 14-2002\",\"01 04-2013\"),\"%m %d-%Y\") #以\"%m %d-%Y\"格式读入\n#> [1] \"2002-02-14\" \"2013-01-04\"\nformat(Sys.Date(),\"%Y/%m/%d\") #以\"%Y/%m/%d\"格式输出\n#> [1] \"2023/12/19\"\n```\n:::\n\n\n-   **function--函数**\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(c)\n#> [1] \"function\"\nclass(c)\n#> [1] \"function\"\n```\n:::\n\n\n-   **list--列表**\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(mpg)\n#> [1] \"list\"\nclass(mpg)\n#> [1] \"tbl_df\"     \"tbl\"        \"data.frame\"\n```\n:::\n\n\n-   **complex -- 复数型**\n\n\n::: {.cell}\n\n```{.r .cell-code}\n\nmode(c(1+2i,3-4i)) \n#> [1] \"complex\"\nclass(c(1+2i,3-4i))\n#> [1] \"complex\"\n```\n:::\n\n\n-   **raw--原始型**\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncharToRaw(\"abcde12345\") #字符串中每个字符的原始存储格式（十六进制？）\n#>  [1] 61 62 63 64 65 31 32 33 34 35\nmode(charToRaw(\"abcde12345\")) \n#> [1] \"raw\"\nclass(charToRaw(\"abcde12345\"))\n#> [1] \"raw\"\n```\n:::\n\n\n# 数据结构\n\n**数据结构(class)**：是一种基于*面向对象*的R的抽象类型划分,或者理解为一种数据结构。\n\n-   **vector --向量**，同一类元素的集合。\n\n`?c      #Combine Values into a Vector or List`\n\n\n::: {.cell}\n\n```{.r .cell-code}\n#单元素向量\n1\n#> [1] 1\n\nis.vector(1)\n#> [1] TRUE\n\n\"a\"\n#> [1] \"a\"\nis.vector(\"a\")\n#> [1] TRUE\n\n#使用函数c()创建向量\n\nc(1)\n#> [1] 1\nc(1,2,3,4,5)\n#> [1] 1 2 3 4 5\nc(\"a\",\"b\",\"c\")\n#> [1] \"a\" \"b\" \"c\"\n```\n:::\n\n\n-   **factor --因子**，分类变量，其中每个级别都是一类。\n\n`?factor`\n\n`factor(vector,order=FALSE,levels=c(v1,v2,…),labels= ,...)` ，在内存中以整数`c(1,2,3,...,k)`形式存储。\n\n\n::: {.cell}\n\n```{.r .cell-code}\n#类别（名义型）变量\ndiabetes<-c(\"t1\",\"t2\",\"t1\",\"t1\") \nstr(diabetes)\n#>  chr [1:4] \"t1\" \"t2\" \"t1\" \"t1\"\ndiabetes<-factor(diabetes)\nstr(diabetes)\n#>  Factor w/ 2 levels \"t1\",\"t2\": 1 2 1 1\n\n\n#有序型变量     默认水平根据字母顺序而定\nstatus<-c(\"poor\",\"better\",\"best\",\"poor\")\nstatus<-factor(status,order=TRUE) \nstr(status) \n#>  Ord.factor w/ 3 levels \"best\"<\"better\"<..: 3 2 1 3\nstatus<-factor(status,order=TRUE,levels = c(\"poor\",\"better\",\"best\")) \nstr(status) \n#>  Ord.factor w/ 3 levels \"poor\"<\"better\"<..: 1 2 3 1\n\n#改变外在标签\nsex<-c(1,2,2,1)\nsex\n#> [1] 1 2 2 1\nsex<-factor(sex,levels=c(1,2),labels = c(\"男\",\"女\")) \nstr(sex) \n#>  Factor w/ 2 levels \"男\",\"女\": 1 2 2 1\nsex\n#> [1] 男 女 女 男\n#> Levels: 男 女\n```\n:::\n\n\n-   **matrix --矩阵**，所有元素必须是同一类型。\n\n`?matrix`\n\n`matrix(data= ,nrow=1 ,ncol=1 ,byrow=FALSE ,dimnames=list(rnames,cnames) ,...)`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnum<-c(16,22,24,28)\nrnames<-c(\"R1\",\"R2\")\ncnames<-c(\"C1\",\"C2\")\nmymatrix<-matrix(num,nrow=2,ncol=2,byrow=TRUE,dimnames=list(rnames,cnames))\nmymatrix   \n#>    C1 C2\n#> R1 16 22\n#> R2 24 28\n```\n:::\n\n\n-   **array--数组**，所有元素必须是同一类型。\n\n`?array`\n\n`array(data,dim_numeric_vector,dimnames = list(dim1,dim2,...),...)`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nv<-1:24 \ndim1<-c(\"A1\",\"A2\",\"A3\") \ndim2<-c(\"B1\",\"B2\",\"B3\",\"B4\")\ndim3<-c(\"C1\",\"C2\") \nmyarray<-array(v,c(3,4,2),dimnames = list(dim1,dim2,dim3)) \nmyarray\n#> , , C1\n#> \n#>    B1 B2 B3 B4\n#> A1  1  4  7 10\n#> A2  2  5  8 11\n#> A3  3  6  9 12\n#> \n#> , , C2\n#> \n#>    B1 B2 B3 B4\n#> A1 13 16 19 22\n#> A2 14 17 20 23\n#> A3 15 18 21 24\n```\n:::\n\n\n-   **data.frame/tibble-- 数据框**， 由列向量组成，每一列元素必须是同一类型，列与列之间长度必须相同，但类型可以不同。\n\n`?data.frame`\n\n`data.frame(name1=col1,name2=col2,...,row.names = ,...)`\n\n`?tibble`，`tibble()`是tidyverse风格的数据框，用法类似。\n\n\n::: {.cell}\n\n```{.r .cell-code}\nid<-c(1,2,3,4) \nage<-c(21,14,52,15) \ndiabetes<-c(\"t1\",\"t2\",\"t1\",\"t1\") \nstatus<-c(\"poor\",\"better\",\"best\",\"poor\") \npatient<-data.frame(patientID=id,age,diabetes,status,row.names = id) # 4个列向量组成数据框 \npatient\n#>   patientID age diabetes status\n#> 1         1  21       t1   poor\n#> 2         2  14       t2 better\n#> 3         3  52       t1   best\n#> 4         4  15       t1   poor\n```\n:::\n\n\nlist --列表，由以上各种数据结构组成\n\n`?list`\n\n`list(name1=object1,name2=object2,...)`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmylist<-list(title=\"My list\",\n             matr=matrix(c(\"a1\",\"b1\",\"a2\",\"b2\"),nrow=2,ncol=2,byrow=TRUE,\n                         dimnames = list(c(\"X1\",\"X2\"),c(\"Y1\",\"Y2\"))\n             ),\n             df=data.frame(id=matrix(c(\"Lisa\",\"BOb\",\"John\",\"Jule\"),\n                                     nrow=4,ncol=1,byrow=TRUE\n             ),\n             int=c(3,5,7,9),\n             TF=c(T,T,T,F)\n             ),\n             list=list(a=c(1,2,3),b=c(\"A\",\"B\"))\n)\nmylist\n#> $title\n#> [1] \"My list\"\n#> \n#> $matr\n#>    Y1   Y2  \n#> X1 \"a1\" \"b1\"\n#> X2 \"a2\" \"b2\"\n#> \n#> $df\n#>     id int    TF\n#> 1 Lisa   3  TRUE\n#> 2  BOb   5  TRUE\n#> 3 John   7  TRUE\n#> 4 Jule   9 FALSE\n#> \n#> $list\n#> $list$a\n#> [1] 1 2 3\n#> \n#> $list$b\n#> [1] \"A\" \"B\"\n```\n:::\n\n\n# qmd_format\n\n|                                                            |                                                         |\n|------------------------------------------------------------|---------------------------------------------------------|\n| 输出                                                       | 语法                                                    |\n| `code`                                                     | \\``code`\\`                                              |\n| **粗体**                                                   | `**粗体**`                                              |\n| *斜体*                                                     | `*斜体*`                                                |\n| [下横线]{.underline}                                       | `[下横线]{.underline}`                                  |\n| ~~删除线~~                                                 | `~~删除线~~`                                            |\n| 上标X^2^                                                   | `X^2^`                                                  |\n| 下标 X~1~                                                  | `X~1~`                                                  |\n| [SAMALL small caps]{.smallcaps}                            | 与小写字母同等高度的大写字母 `[small caps]{.smallcaps}` |\n| <https://r4ds.hadley.nz/>                                  | `<https://r4ds.hadley.nz/>`                             |\n| [quarto](https://quarto.org)                               | `[quarto](https://quarto.org)`                          |\n| ![data science](images/whole-game.png){alt=\"data science\"} | `![data science](images/whole-game.png)`                |\n",
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