diff --git a/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio-files b/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio-files new file mode 100644 index 0000000..103c44b --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio-files @@ -0,0 +1 @@ +1687795504:F:/R/GitHub/Wanganlin00.github.io/R_notes/references.bib diff --git a/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio-refblock b/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio-refblock new file mode 100644 index 0000000..e69de29 diff --git a/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio.json b/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio.json new file mode 100644 index 0000000..446e8a0 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/bibliography-index/biblio.json @@ -0,0 +1,35 @@ +{ + "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" + ] +} \ No newline at end of file diff --git a/R_notes/.Rproj.user/54283FDB/pcs/files-pane.pper b/R_notes/.Rproj.user/54283FDB/pcs/files-pane.pper new file mode 100644 index 0000000..3938c4e --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/pcs/files-pane.pper @@ -0,0 +1,9 @@ +{ + "sortOrder": [ + { + "columnIndex": 2, + "ascending": true + } + ], + "path": "F:/R/GitHub/Wanganlin00.github.io/R_notes" +} \ No newline at end of file diff --git a/R_notes/.Rproj.user/54283FDB/pcs/source-pane.pper b/R_notes/.Rproj.user/54283FDB/pcs/source-pane.pper new file mode 100644 index 0000000..902cc6f --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/pcs/source-pane.pper @@ -0,0 +1,3 @@ +{ + "activeTab": 0 +} \ No newline at end of file diff --git a/R_notes/.Rproj.user/54283FDB/pcs/windowlayoutstate.pper b/R_notes/.Rproj.user/54283FDB/pcs/windowlayoutstate.pper new file mode 100644 index 0000000..460a354 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/pcs/windowlayoutstate.pper @@ -0,0 +1,14 @@ +{ + "left": { + "splitterpos": 328, + "topwindowstate": "NORMAL", + "panelheight": 784, + "windowheight": 822 + }, + "right": { + "splitterpos": 454, + "topwindowstate": "NORMAL", + "panelheight": 784, + "windowheight": 822 + } +} \ No newline at end of file diff --git a/R_notes/.Rproj.user/54283FDB/pcs/workbench-pane.pper b/R_notes/.Rproj.user/54283FDB/pcs/workbench-pane.pper new file mode 100644 index 0000000..cca65c4 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/pcs/workbench-pane.pper @@ -0,0 +1,5 @@ +{ + "TabSet1": 1, + "TabSet2": 1, + "TabZoom": {} +} \ No newline at end of file diff --git a/R_notes/.Rproj.user/54283FDB/sources/prop/2107E1BD 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b/R_notes/.Rproj.user/54283FDB/sources/prop/F12B354C @@ -0,0 +1,6 @@ +{ + "source_window_id": "", + "Source": "Source", + "cursorPosition": "20,0", + "scrollLine": "3" +} \ No newline at end of file diff --git a/R_notes/.Rproj.user/54283FDB/sources/prop/INDEX b/R_notes/.Rproj.user/54283FDB/sources/prop/INDEX new file mode 100644 index 0000000..b30c618 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/sources/prop/INDEX @@ -0,0 +1,3 @@ +F%3A%2FR%2FGitHub%2FWanganlin00.github.io%2FR_notes%2F_quarto.yml="F12B354C" +F%3A%2FR%2FGitHub%2FWanganlin00.github.io%2FR_notes%2Findex.qmd="2107E1BD" +F%3A%2FR%2Ftidyverse%2Fr4ds-main%2F_quarto.yml="BEDFCC1D" diff --git a/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/08E44B16-contents b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/08E44B16-contents new file mode 100644 index 0000000..6204636 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/08E44B16-contents @@ -0,0 +1,85 @@ +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 Quarto. + 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 + diff --git a/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/5F1CEF4E-contents b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/5F1CEF4E-contents new file mode 100644 index 0000000..a8f20ad --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/5F1CEF4E-contents @@ -0,0 +1,25 @@ +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 + diff --git a/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D1B599F4 b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D1B599F4 new file mode 100644 index 0000000..b1dbb17 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D1B599F4 @@ -0,0 +1,26 @@ +{ + "id": "D1B599F4", + "path": "F:/R/GitHub/Wanganlin00.github.io/R_notes/_quarto.yml", + "project_path": "_quarto.yml", + "type": "yaml", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1702964481355.0, + "source_on_save": false, + "relative_order": 2, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "20,0", + "scrollLine": "3" + }, + "folds": "", + "lastKnownWriteTime": 1702964636, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1702964635066, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D1B599F4-contents b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D1B599F4-contents new file mode 100644 index 0000000..f6cf717 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D1B599F4-contents @@ -0,0 +1,23 @@ +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 +bibliography: references.bib + +format: + html: + theme: cosmo + pdf: + documentclass: scrreprt + +editor: visual + diff --git a/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D28DC2AE-contents b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D28DC2AE-contents new file mode 100644 index 0000000..7e9edb0 --- /dev/null +++ b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/D28DC2AE-contents @@ -0,0 +1,9 @@ +# Preface {.unnumbered} + +This is a Quarto book. + +To learn more about Quarto books visit . + +```{r} +1 + 1 +``` diff --git a/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/lock_file b/R_notes/.Rproj.user/54283FDB/sources/session-ce08ca9e/lock_file new file mode 100644 index 0000000..e69de29 diff --git a/R_notes/.Rproj.user/shared/notebooks/patch-chunk-names b/R_notes/.Rproj.user/shared/notebooks/patch-chunk-names new file mode 100644 index 0000000..e69de29 diff --git a/R_notes/.Rproj.user/shared/notebooks/paths b/R_notes/.Rproj.user/shared/notebooks/paths new file mode 100644 index 0000000..e392b2f --- /dev/null +++ b/R_notes/.Rproj.user/shared/notebooks/paths @@ -0,0 +1,3 @@ +F:/R/GitHub/Wanganlin00.github.io/R_notes/_quarto.yml="9C734D88" +F:/R/GitHub/Wanganlin00.github.io/R_notes/index.qmd="D580FCA1" +F:/R/tidyverse/r4ds-main/_quarto.yml="DFFCB474" diff --git a/R_notes/.quarto/_freeze/basics/execute-results/html.json b/R_notes/.quarto/_freeze/basics/execute-results/html.json new file mode 100644 index 0000000..0e2da18 --- /dev/null +++ b/R_notes/.quarto/_freeze/basics/execute-results/html.json @@ -0,0 +1,16 @@ +{ + "hash": "c02a8815f5653d69be8398468425f56c", + "result": { + "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| | `` |\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", + "supporting": [ + "basics_files" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/R_notes/.quarto/_freeze/basics/execute-results/tex.json b/R_notes/.quarto/_freeze/basics/execute-results/tex.json new file mode 100644 index 0000000..f29ca58 --- /dev/null +++ b/R_notes/.quarto/_freeze/basics/execute-results/tex.json @@ -0,0 +1,20 @@ +{ + "hash": "c02a8815f5653d69be8398468425f56c", + "result": { + "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\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\n::: {.cell}\n\n```{.r .cell-code}\nhelp.start()\nhelp()\n?c\n```\n:::\n\n\n\n# 包的安装和使用\n\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\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\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\n- **character -- 字符型**\n\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\n- **logical --逻辑型**\n\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\n- **factor--因子**\n\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\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\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\n- **function--函数**\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmode(c)\n#> [1] \"function\"\nclass(c)\n#> [1] \"function\"\n```\n:::\n\n\n\n- **list--列表**\n\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\n- **complex -- 复数型**\n\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\n- **raw--原始型**\n\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\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\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\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\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\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\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\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\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\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\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\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\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\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| | `` |\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", + "supporting": [ + "basics_files" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": { + "knitr": [ + "{\"type\":\"list\",\"attributes\":{},\"value\":[]}" + ] + }, + "preserve": null, + "postProcess": false + } +} \ No newline at end of file diff --git a/basics_files/libs/bootstrap/bootstrap-icons.css 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R_notes/.quarto/_freeze/basics/libs/quarto-html/tippy.umd.min.js diff --git a/R_notes/.quarto/_freeze/index/execute-results/html.json b/R_notes/.quarto/_freeze/index/execute-results/html.json new file mode 100644 index 0000000..cd41221 --- /dev/null +++ b/R_notes/.quarto/_freeze/index/execute-results/html.json @@ -0,0 +1,14 @@ +{ + "hash": "f89d2cae73031aafc1d0dc4ae48f295e", + "result": { + "markdown": "# Preface {.unnumbered}\n\nThis is a Quarto book.\n\nTo learn more about Quarto books visit .\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/R_notes/.quarto/_freeze/index/execute-results/tex.json b/R_notes/.quarto/_freeze/index/execute-results/tex.json new file mode 100644 index 0000000..a636a27 --- /dev/null +++ b/R_notes/.quarto/_freeze/index/execute-results/tex.json @@ -0,0 +1,20 @@ +{ + "hash": "f89d2cae73031aafc1d0dc4ae48f295e", + "result": { + "markdown": "# Preface {.unnumbered}\n\nThis is a Quarto book.\n\nTo learn more about Quarto books visit .\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n", + "supporting": [ + "index_files" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": { + "knitr": [ + "{\"type\":\"list\",\"attributes\":{},\"value\":[]}" + ] + }, + "preserve": null, + "postProcess": false + } +} \ No newline at end of file diff --git a/R_notes/.quarto/_freeze/intro/execute-results/html.json b/R_notes/.quarto/_freeze/intro/execute-results/html.json new file mode 100644 index 0000000..39e2f60 --- /dev/null +++ b/R_notes/.quarto/_freeze/intro/execute-results/html.json @@ -0,0 +1,14 @@ +{ + "hash": "e514ad799b2b10ced9d02b36ca26bbd2", + "result": { + "markdown": "# Introduction\n\nThis is a book created from markdown and executable code.\n\nSee @knuth84 for additional discussion of literate programming.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/R_notes/.quarto/_freeze/intro/execute-results/tex.json b/R_notes/.quarto/_freeze/intro/execute-results/tex.json new file mode 100644 index 0000000..6cf418e --- /dev/null +++ b/R_notes/.quarto/_freeze/intro/execute-results/tex.json @@ -0,0 +1,20 @@ +{ + "hash": "e514ad799b2b10ced9d02b36ca26bbd2", + "result": { + "markdown": "# Introduction\n\nThis is a book created from markdown and executable code.\n\nSee @knuth84 for additional discussion of literate programming.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n1 + 1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2\n```\n:::\n:::\n", + "supporting": [ + "intro_files" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": { + "knitr": [ + "{\"type\":\"list\",\"attributes\":{},\"value\":[]}" + ] + }, + "preserve": null, + "postProcess": false + } +} \ No newline at end of file diff --git a/R_notes/.quarto/_freeze/site_libs/clipboard/clipboard.min.js b/R_notes/.quarto/_freeze/site_libs/clipboard/clipboard.min.js new file mode 100644 index 0000000..1103f81 --- /dev/null +++ b/R_notes/.quarto/_freeze/site_libs/clipboard/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.11 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use 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[CRAN](https://cran.r-project.org/)\n\n```{r eval=FALSE}\nhelp.start()\nhelp()\n?c\n```\n\n# 包的安装和使用\n\n```{r eval=FALSE}\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# 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```{r}\nmode(4.3)\nclass(4.3)\nmode(1L)\nclass(1L)\n```\n\n- **character -- 字符型**\n\n```{r}\nmode(c(\"car\"))\nclass(\"car\")\n```\n\n- **logical --逻辑型**\n\n```{r}\nmode(c(TRUE,FALSE))\nclass(c(TRUE,FALSE))\n```\n\n- **factor--因子**\n\n```{r}\nmode(factor((c(1,2,3)))) \nclass(factor((c(1,2,3)))) \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```{r}\nmode(as.Date(\"2023-12-11\"))\nclass(as.Date(\"2023-12-11\")) \n\nSys.Date()\nas.Date(c(\"02 14-2002\",\"01 04-2013\"),\"%m %d-%Y\") #以\"%m %d-%Y\"格式读入\nformat(Sys.Date(),\"%Y/%m/%d\") #以\"%Y/%m/%d\"格式输出\n```\n\n- **function--函数**\n\n```{r}\nmode(c)\nclass(c)\n```\n\n- **list--列表**\n\n```{r}\nmode(mpg)\nclass(mpg)\n```\n\n- **complex -- 复数型**\n\n```{r}\n\nmode(c(1+2i,3-4i)) \nclass(c(1+2i,3-4i))\n```\n\n- **raw--原始型**\n\n```{r}\ncharToRaw(\"abcde12345\") #字符串中每个字符的原始存储格式（十六进制？）\nmode(charToRaw(\"abcde12345\")) \nclass(charToRaw(\"abcde12345\"))\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```{r}\n#单元素向量\n1\n\nis.vector(1)\n\n\"a\"\nis.vector(\"a\")\n\n#使用函数c()创建向量\n\nc(1)\nc(1,2,3,4,5)\nc(\"a\",\"b\",\"c\")\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```{r}\n#类别（名义型）变量\ndiabetes<-c(\"t1\",\"t2\",\"t1\",\"t1\") \nstr(diabetes)\ndiabetes<-factor(diabetes)\nstr(diabetes)\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) \nstatus<-factor(status,order=TRUE,levels = c(\"poor\",\"better\",\"best\")) \nstr(status) \n\n#改变外在标签\nsex<-c(1,2,2,1)\nsex\nsex<-factor(sex,levels=c(1,2),labels = c(\"男\",\"女\")) \nstr(sex) \nsex\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```{r}\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```\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```{r}\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```\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```{r}\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```\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```{r}\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```\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| | `` |\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","srcMarkdownNoYaml":"\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```{r eval=FALSE}\nhelp.start()\nhelp()\n?c\n```\n\n# 包的安装和使用\n\n```{r eval=FALSE}\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# 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```{r}\nmode(4.3)\nclass(4.3)\nmode(1L)\nclass(1L)\n```\n\n- **character -- 字符型**\n\n```{r}\nmode(c(\"car\"))\nclass(\"car\")\n```\n\n- **logical --逻辑型**\n\n```{r}\nmode(c(TRUE,FALSE))\nclass(c(TRUE,FALSE))\n```\n\n- **factor--因子**\n\n```{r}\nmode(factor((c(1,2,3)))) \nclass(factor((c(1,2,3)))) \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```{r}\nmode(as.Date(\"2023-12-11\"))\nclass(as.Date(\"2023-12-11\")) \n\nSys.Date()\nas.Date(c(\"02 14-2002\",\"01 04-2013\"),\"%m %d-%Y\") #以\"%m %d-%Y\"格式读入\nformat(Sys.Date(),\"%Y/%m/%d\") #以\"%Y/%m/%d\"格式输出\n```\n\n- **function--函数**\n\n```{r}\nmode(c)\nclass(c)\n```\n\n- **list--列表**\n\n```{r}\nmode(mpg)\nclass(mpg)\n```\n\n- **complex -- 复数型**\n\n```{r}\n\nmode(c(1+2i,3-4i)) \nclass(c(1+2i,3-4i))\n```\n\n- **raw--原始型**\n\n```{r}\ncharToRaw(\"abcde12345\") #字符串中每个字符的原始存储格式（十六进制？）\nmode(charToRaw(\"abcde12345\")) \nclass(charToRaw(\"abcde12345\"))\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```{r}\n#单元素向量\n1\n\nis.vector(1)\n\n\"a\"\nis.vector(\"a\")\n\n#使用函数c()创建向量\n\nc(1)\nc(1,2,3,4,5)\nc(\"a\",\"b\",\"c\")\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```{r}\n#类别（名义型）变量\ndiabetes<-c(\"t1\",\"t2\",\"t1\",\"t1\") \nstr(diabetes)\ndiabetes<-factor(diabetes)\nstr(diabetes)\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) \nstatus<-factor(status,order=TRUE,levels = c(\"poor\",\"better\",\"best\")) \nstr(status) \n\n#改变外在标签\nsex<-c(1,2,2,1)\nsex\nsex<-factor(sex,levels=c(1,2),labels = c(\"男\",\"女\")) \nstr(sex) \nsex\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```{r}\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```\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```{r}\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```\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```{r}\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```\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```{r}\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```\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| | `` |\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)` 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