class: center, middle, inverse, title-slide .title[ # R软件使用介绍 ] .author[ ### 巩超 ] .institute[ ### 北京大学公共卫生学院 ] .date[ ### 2023-11-09 ] --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 软件下载 .pull-left[ R软件下载地址:CRAN https://www.r-project.org/ https://cloud.r-project.org/ (自动寻找最近的服务器) <img src = 'https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/R_logo.svg/724px-R_logo.svg.png' height=200 /> ] .pull-right[ RStudio下载地址:https://posit.co/products/open-source/rstudio/ <img src = 'https://upload.wikimedia.org/wikipedia/commons/d/d0/RStudio_logo_flat.svg' height=200 /> ] --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 软件包安装 - 软件包安装方法 ```r #1.R官网的软件包,可以直接安装 install.packages("tidyverse")#软件包名称 #2.从github安装 install.packages("devtools") library(devtools)#软件包加载 install_github("yihui/xaringan")#软件包库地址 #3.从Bioconductor网站安装(生信分析常用) install.packages('BiocManager') library(BiocManager) install('edgeR')#软件包名称 ``` -- - 软件包或函数功能帮助 - help - ?软件包名称 - ??软件包名称 --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 数据读取和保存 .pull-left[ - 工作路径设置 ```r #1.查看当前路径 getwd() #2.设置工作路径 setwd("Path") ``` ] .pull-right[ <img src = 'https://pic4.zhimg.com/v2-0d0285e7b9433eeedf7e705d6e082d13_r.jpg' height=160 /> ] - 数据读取 ```r #1.txt文件 read.table(file,header=FALSE,sep=",",encoding="gbk") #2.csv文件 read.csv(file,header=FALSE,sep=",",encoding="gbk") #3.xlsx或xls文件 readxl::read_excel(file,sheet="",encoding="gbk") #4.dta文件 haven::read_dta(file,encoding="gbk") ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 数据保存 ```r #1.txt文件 write.table(x, file = ".txt",fileEncoding = "gbk") # 空格分隔 #2.csv文件 write.csv(x,file=".csv",fileEncoding = "gbk") #3.xlsx文件 xlsx::write.xlsx(x,file=".xlsx",sheetName="",fileEncoding = "gbk") #4.dta文件 haven::write_dta(x,".dta") #5.Rdata文件 save(x,file=".Rdata") load(".Rdata")#加载Rdata文件 ``` -- 1.数据读取和保存的软件包和函数并不唯一,可google了解 2.数据读取的各软件包速度不一,如data.table包的fread函数速度很快 --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 数据处理 - 常用软件包 - tidyverse ```r library(tidyverse) ``` ``` ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0 ## ✔ purrr 1.0.2 ## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ## ✖ dplyr::filter() masks stats::filter() ## ✖ dplyr::lag() masks stats::lag() ## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 导入NHANES示例数据 ```r #install.packages("NHANES") library(NHANES) ``` - 查看数据 ```r #names(NHANES)#查看变量名 #head(NHANES,n=3)#查看数据前3行 #tail(NHANES,n=3)#查看数据后3行 NHANES #查看数据框的大小,变量名,类型和取值 ``` ``` ## # A tibble: 10,000 × 76 ## ID SurveyYr Gender Age AgeDecade AgeMonths Race1 Race3 Education ## <int> <fct> <fct> <int> <fct> <int> <fct> <fct> <fct> ## 1 51624 2009_10 male 34 " 30-39" 409 White <NA> High School ## 2 51624 2009_10 male 34 " 30-39" 409 White <NA> High School ## 3 51624 2009_10 male 34 " 30-39" 409 White <NA> High School ## 4 51625 2009_10 male 4 " 0-9" 49 Other <NA> <NA> ## 5 51630 2009_10 female 49 " 40-49" 596 White <NA> Some College ## 6 51638 2009_10 male 9 " 0-9" 115 White <NA> <NA> ## 7 51646 2009_10 male 8 " 0-9" 101 White <NA> <NA> ## 8 51647 2009_10 female 45 " 40-49" 541 White <NA> College Grad ## 9 51647 2009_10 female 45 " 40-49" 541 White <NA> College Grad ## 10 51647 2009_10 female 45 " 40-49" 541 White <NA> College Grad ## # ℹ 9,990 more rows ## # ℹ 67 more variables: MaritalStatus <fct>, HHIncome <fct>, HHIncomeMid <int>, ## # Poverty <dbl>, HomeRooms <int>, HomeOwn <fct>, Work <fct>, Weight <dbl>, ## # Length <dbl>, HeadCirc <dbl>, Height <dbl>, BMI <dbl>, ## # BMICatUnder20yrs <fct>, BMI_WHO <fct>, Pulse <int>, BPSysAve <int>, ## # BPDiaAve <int>, BPSys1 <int>, BPDia1 <int>, BPSys2 <int>, BPDia2 <int>, ## # BPSys3 <int>, BPDia3 <int>, Testosterone <dbl>, DirectChol <dbl>, … ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 选择行子集(filter) ```r NHANES |> dplyr::filter(ID==54010,Age==79, Gender=="male") |> knitr::kable() ``` | ID|SurveyYr |Gender | Age|AgeDecade | AgeMonths|Race1 |Race3 |Education |MaritalStatus |HHIncome | HHIncomeMid| Poverty| HomeRooms|HomeOwn |Work | Weight| Length| HeadCirc| Height| BMI|BMICatUnder20yrs |BMI_WHO | Pulse| BPSysAve| BPDiaAve| BPSys1| BPDia1| BPSys2| BPDia2| BPSys3| BPDia3| Testosterone| DirectChol| TotChol| UrineVol1| UrineFlow1| UrineVol2| UrineFlow2|Diabetes | DiabetesAge|HealthGen | DaysPhysHlthBad| DaysMentHlthBad|LittleInterest |Depressed | nPregnancies| nBabies| Age1stBaby| SleepHrsNight|SleepTrouble |PhysActive | PhysActiveDays|TVHrsDay |CompHrsDay | TVHrsDayChild| CompHrsDayChild|Alcohol12PlusYr | AlcoholDay| AlcoholYear|SmokeNow |Smoke100 |Smoke100n | SmokeAge|Marijuana | AgeFirstMarij|RegularMarij | AgeRegMarij|HardDrugs |SexEver | SexAge| SexNumPartnLife| SexNumPartYear|SameSex |SexOrientation |PregnantNow | |-----:|:--------|:------|---:|:---------|---------:|:-------|:-----|:------------|:-------------|:-----------|-----------:|-------:|---------:|:-------|:----------|------:|------:|--------:|------:|-----:|:----------------|:------------|-----:|--------:|--------:|------:|------:|------:|------:|------:|------:|------------:|----------:|-------:|---------:|----------:|---------:|----------:|:--------|-----------:|:---------|---------------:|---------------:|:--------------|:---------|------------:|-------:|----------:|-------------:|:------------|:----------|--------------:|:--------|:----------|-------------:|---------------:|:---------------|----------:|-----------:|:--------|:--------|:---------|--------:|:---------|-------------:|:------------|-----------:|:---------|:-------|------:|---------------:|--------------:|:-------|:--------------|:-----------| | 54010|2009_10 |male | 79|70+ | 948|Mexican |NA |Some College |Married |75000-99999 | 87500| 5| 5|Own |NotWorking | 86.3| NA| NA| 170| 29.86|NA |25.0_to_29.9 | 50| 146| 64| 150| 60| 144| 62| 148| 66| NA| 1.01| 4.16| 74| 0.796| NA| NA|Yes | NA|Vgood | 30| 0|None |None | NA| NA| NA| 8|No |Yes | 3|NA |NA | NA| NA|No | 1| 3|No |Yes |Smoker | 18|NA | NA|NA | NA|NA |NA | NA| NA| NA|NA |NA |NA | -- .pull-left[ - 选择列子集(select) ```r NHANES |> dplyr::select(Age, Gender, Race1) |> knitr::kable() ``` | Age|Gender |Race1 | |---:|:------|:--------| | 34|male |White | | 34|male |White | | 34|male |White | | 4|male |Other | | 49|female |White | | 9|male |White | | 8|male |White | | 45|female |White | | 45|female |White | | 45|female |White | | 66|male |White | | 58|male |White | | 54|male |White | | 10|female |White | | 58|female |Mexican | | 50|male |White | | 9|female |Black | | 33|male |White | | 60|male |White | | 16|male |Other | | 56|female |White | | 56|female |White | | 57|female |White | | 54|male |Hispanic | | 54|male |Hispanic | | 38|male |White | | 8|male |White | | 36|male |White | | 44|male |White | | 44|male |White | | 64|female |Other | | 26|female |White | | 59|female |Other | | 59|female |Other | | 49|male |Mexican | | 51|female |Mexican | | 51|female |Mexican | | 17|male |White | | 12|male |Other | | 12|male |Other | | 16|male |White | | 16|male |White | | 16|male |White | | 16|male |White | | 28|male |Black | | 37|female |Hispanic | | 28|female |Black | | 32|male |White | | 25|male |White | | 21|female |Black | | 21|female |Black | | 8|male |Hispanic | | 8|male |Hispanic | | 44|male |White | | 56|male |Mexican | | 78|female |Mexican | | 3|male |White | | 58|female |White | | 58|female |White | | 58|female |White | | 1|male |White | | 13|female |White | | 27|female |Hispanic | | 31|female |Mexican | | 31|female |Mexican | | 29|female |White | | 29|female |White | | 29|female |White | | 11|female |White | | 6|female |White | | 6|female |White | | 56|female |White | | 56|female |White | | 16|female |White | | 30|male |Black | | 26|female |White | | 26|female |White | | 26|female |White | | 30|male |Mexican | | 54|female |White | | 54|female |White | | 54|female |White | | 29|male |White | | 29|male |White | | 29|male |White | | 29|male |White | | 17|male |Other | | 17|male |Other | | 9|female |Black | | 80|female |White | | 80|female |White | | 13|male |Hispanic | | 50|male |Mexican | | 26|male |Other | | 58|male |Black | | 80|male |White | | 35|female |White | | 7|male |White | | 8|female |White | | 12|female |Mexican | | 80|male |White | | 66|female |Black | | 66|female |Black | | 34|female |White | | 34|female |White | | 34|female |White | | 38|male |Other | | 38|male |Other | | 38|male |Other | | 2|male |Mexican | | 50|female |Other | | 43|male |White | | 43|male |White | | 41|female |Black | | 46|female |Mexican | | 61|female |Hispanic | | 44|female |Other | | 44|female |Other | | 44|female |Other | | 44|female |Other | | 80|male |White | | 15|female |White | | 76|female |Black | | 29|male |Other | | 63|female |Mexican | | 63|male |White | | 50|male |Other | | 70|female |White | | 16|female |Hispanic | | 31|female |White | | 30|female |White | | 30|female |White | | 30|female |White | | 52|female |Black | | 52|female |Black | | 46|male |White | | 22|female |Black | | 6|male |Mexican | | 48|male |White | | 48|male |White | | 27|female |White | | 55|male |Black | | 62|female |Other | | 54|male |White | | 39|female |White | | 39|female |White | | 42|male |Hispanic | | 42|male |Hispanic | | 46|female |Other | | 56|female |Other | | 56|female |Other | | 46|female |White | | 14|male |White | | 14|male |White | | 29|male |Other | | 29|male |Other | | 29|male |Other | | 29|male |Other | | 31|male |Mexican | | 36|female |White | | 0|female |Hispanic | | 49|male |White | | 49|male |White | | 60|female |White | | 28|female |White | | 8|male |White | | 32|male |Hispanic | | 46|male |White | | 46|male |White | | 55|female |Mexican | | 57|male |White | | 79|male |White | | 79|male |White | | 9|female |Mexican | | 46|male |Other | | 46|male |Other | | 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| | 55|male |White | | 16|male |White | | 16|male |White | | 39|male |White | | 39|male |White | | 65|female |White | | 20|male |White | | 48|female |White | | 48|female |White | | 38|male |Mexican | | 48|female |Other | | 48|female |Other | | 48|female |Other | | 12|male |White | | 65|male |White | | 65|male |White | | 65|male |White | | 39|female |White | | 39|female |White | | 21|male |Mexican | | 6|male |Mexican | | 63|male |White | | 54|male |White | | 56|female |White | | 56|female |White | | 56|female |White | | 56|female |White | | 21|female |Black | | 76|male |White | | 76|male |White | | 19|female |Black | | 43|female |Mexican | | 45|male |White | | 45|male |White | | 60|male |White | | 60|male |White | | 43|male |Hispanic | | 43|male |White | | 43|male |White | | 18|female |White | | 80|female |White | | 80|female |White | | 25|male |White | | 4|male |Mexican | | 25|male |White | | 54|male |White | | 29|male |White | | 29|male |White | | 7|female |White | | 46|male |White | | 33|female |White | | 50|female |Other | | 54|male |White | | 54|male |White | | 16|male |Black | | 16|male |Black | | 9|female |Other | | 39|female |White | | 80|male |White | | 32|male |White | | 56|female |White | | 56|female |White | | 56|female |White | | 17|male |White | | 18|male |White | | 0|male |Black | | 16|male |White | | 29|female |White | | 69|female |White | | 25|female |White | | 52|female |Black | | 52|female |Black | | 18|female |Other | | 28|male |White | | 40|female |White | | 51|female |Hispanic | | 20|female |White | | 72|male |White | | 10|male |White | | 65|female |Black | | 80|female |White | | 24|female |White | | 18|female |Mexican | | 57|male |Black | | 45|male |Black | | 45|male |Black | | 28|female |White | | 8|male |White | | 22|male |Hispanic | | 49|male |White | | 11|male |Black | | 34|female |White | | 69|female |White | | 33|male |White | | 31|female |White | | 62|male |White | | 62|male |White | | 0|male |Black | | 7|male |White | | 61|female |White | | 75|female |Black | | 47|female |Hispanic | | 35|male |White | | 78|male |White | | 8|male |White | | 54|female |White | | 56|male |White | | 56|male |White | | 56|male |White | | 30|male |White | | 73|female |Black | | 21|male |Black | | 33|female |White | | 33|female |White | | 45|female |Mexican | | 41|male |Black | | 80|male |White | | 80|male |White | | 0|female |White | | 0|female |White | | 0|female |White | | 0|female |White | | 71|male |Black | | 44|female |White | | 44|female |White | | 44|female |White | | 65|female |White | | 24|female |White | | 24|female |White | | 63|male |White | | 63|male |White | | 63|male |White | | 22|female |Mexican | | 80|female |White | | 80|female |White | | 64|male |Black | | 0|female |White | | 10|female |White | | 10|female |White | | 0|female |Mexican | | 58|male |White | | 58|male |White | | 46|female |Hispanic | | 46|female |Hispanic | | 46|female |Hispanic | | 2|male |Black | | 3|female |Black | | 19|male |Other | | 20|female |White | | 20|female |White | | 31|female |White | | 15|male |White | | 16|male |White | | 42|male |Mexican | | 78|female |White | | 78|female |White | | 78|female |White | | 60|female |White | | 60|female |White | | 38|male |White | | 11|female |White | | 54|male |White | | 54|male |White | | 54|male |White | | 54|male |White | | 77|female |White | | 50|female |White | | 16|female |Black | | 1|female |Mexican | | 52|female |White | | 54|female |White | | 54|female |White | | 19|female |White | | 14|female |White | | 9|male |Mexican | | 43|male |White | | 43|male |White | | 52|female |White | | 52|female |White | | 37|male |White | | 59|female |White | | 13|male |White | | 19|male |Mexican | | 59|female |White | | 59|female |White | | 58|male |White | | 24|male |Black | | 68|female |White | | 68|female |White | | 68|female |White | | 15|male |Mexican | | 29|female |White | | 39|female |White | | 30|male |Other | | 35|female |White | | 25|female |Mexican | | 32|male |Black | | 32|male |Black | | 49|male |Hispanic | | 78|male |White | | 78|male |White | | 15|male |White | | 64|female |White | | 39|male |White | | 39|male |White | | 36|female |White | | 36|female |White | | 64|female |White | | 64|female |White | | 72|female |Black | | 63|male |Black | | 24|female |White | | 75|male |Black | | 60|female |Hispanic | | 50|female |White | | 50|female |White | | 25|female |White | | 25|female |White | | 6|female |Other | | 35|male |Hispanic | | 35|male |Hispanic | | 18|female |White | | 64|female |Black | | 67|female |White | | 44|female |White | | 48|female |White | | 48|female |White | | 48|female |White | | 48|female |White | | 76|female |White | | 7|female |White | | 7|female |White | | 18|male |White | | 18|male |White | | 4|female |White | | 4|female |White | | 43|male |Mexican | | 39|male |White | | 80|male |White | | 71|male |White | | 66|female |Black | | 25|female |White | | 14|female |White | | 2|male |White | | 80|female |White | | 8|female |White | | 13|male |Mexican | | 77|female |Black | | 44|female |White | | 80|male |White | | 44|male |Mexican | | 31|female |White | | 56|male |White | | 28|female |White | | 67|male |White | | 10|female |Mexican | | 38|female |Black | | 38|female |Black | | 14|female |Black | | 51|male |Black | | 15|female |Hispanic | | 6|male |White | | 6|male |White | | 30|female |White | | 63|female |Black | | 63|female |Black | | 52|male |White | | 73|female |White | | 73|female |White | | 58|male |Black | | 68|female |White | | 68|female |White | | 35|female |Black | | 12|female |White | | 53|female |White | | 47|female |White | | 47|female |White | | 28|female |White | | 34|female |White | | 14|male |White | | 19|male |Other | | 73|male |White | | 26|male |Mexican | | 31|female |White | | 31|female |White | | 37|female |Black | | 36|female |Black | | 13|female |Mexican | | 52|male |White | | 3|female |Black | | 64|female |White | | 64|female |White | | 64|female |White | | 80|female |White | | 80|female |White | | 80|female |White | | 11|male |Other | | 25|male |Black | | 25|male |Black | | 34|male |Black | | 0|male |Other | | 61|male |Other | | 1|male |White | | 19|female |White | | 15|male |Black | | 33|female |White | | 29|female |White | | 76|female |White | | 18|male |White | | 0|male |Mexican | | 44|female |Mexican | | 44|female |Mexican | | 39|female |White | | 7|male |White | | 23|female |White | | 32|male |White | | 17|male |White | | 17|male |White | | 36|female |Black | | 67|female |White | | 67|female |White | | 67|female |White | | 80|female |White | | 80|female |White | | 77|female |White | | 31|male |White | | 31|male |White | | 53|female |Black | | 53|female |Black | | 73|male |Mexican | | 50|female |White | | 50|female |White | | 19|female |White | | 80|female |White | | 55|male |White | | 68|male |White | | 1|male |White | | 26|female |White | | 17|male |Mexican | | 51|female |White | | 44|female |Black | | 48|female |Other | | 48|female |Other | | 16|male |White | | 6|male |Mexican | | 43|female |Black | | 16|female |Black | | 9|male |White | | 22|male |Other | | 3|female |Mexican | | 51|male |Black | | 64|male |Mexican | | 12|female |Mexican | | 27|female |Other | | 27|female |Other | | 12|male |White | | 9|male |Hispanic | | 35|female |Hispanic | | 35|female |Hispanic | | 70|male |White | | 12|female |Mexican | | 2|female |Mexican | | 72|female |White | | 72|female |White | | 20|male |White | | 68|male |Black | | 36|male |White | | 36|male |White | | 36|male |White | | 12|female |Hispanic | | 11|female |Black | | 28|male |White | | 28|male |White | | 28|male |White | | 6|male |White | | 23|male |White | | 6|male |Mexican | | 28|female |Other | | 44|female |White | | 44|female |White | | 44|female |White | | 73|female |White | | 80|female |White | | 80|female |White | | 36|female |White | | 36|female |White | | 36|female |White | | 36|female |White | | 80|male |White | | 6|male |White | | 37|male |Other | | 38|male |White | | 0|male |White | | 0|male |White | | 16|male |White | | 16|male |White | | 9|male |Other | | 52|female |White | | 14|female |White | | 20|male |White | | 26|male |White | | 26|male |White | | 53|male |Mexican | | 2|male |Other | | 1|female |White | | 1|female |White | | 51|male |Black | | 65|female |White | | 65|female |White | | 5|male |White | | 44|female |Mexican | | 44|female |Mexican | | 14|male |White | | 14|male |White | | 2|male |White | | 15|female |Mexican | | 23|female |Mexican | | 1|female |White | | 16|female |Black | | 1|female |White | | 66|male |White | | 46|male |White | | 53|female |White | | 53|female |White | | 59|female |White | | 59|female |White | | 69|male |White | | 69|male |White | | 69|male |White | | 10|female |Black | | 2|female |Other | | 28|female |White | | 55|female |Other | | 13|male |White | | 22|female |White | | 72|female |White | | 2|female |White | | 44|female |White | | 44|female |White | | 25|male |Black | | 52|female |White | | 56|female |Mexican | | 73|female |White | | 2|female 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|White | | 4|female |Other | | 74|male |White | | 46|female |White | | 46|female |White | | 15|female |Hispanic | | 17|female |White | | 17|female |White | | 70|female |Black | | 77|male |White | | 67|male |Mexican | | 7|female |Black | | 11|female |White | | 11|female |White | | 24|male |Other | | 33|male |Black | | 40|female |Hispanic | | 63|male |Mexican | | 80|male |White | | 74|male |White | | 36|female |Mexican | | 20|female |Black | | 40|female |White | | 40|female |White | | 21|female |White | | 0|male |White | | 22|male |Hispanic | | 63|male |White | | 63|male |White | | 16|male |Black | | 12|male |White | | 40|male |White | | 40|male |White | | 40|male |White | | 33|male |Other | | 33|male |Other | | 38|female |Hispanic | | 38|male |Mexican | | 38|male |Mexican | | 7|female |White | | 24|male |Hispanic | | 24|male |Hispanic | | 53|male |Black | | 62|male |White | | 62|male |White | | 5|male |Other | | 24|female |White | | 64|male |Black | | 42|female |Other | | 50|female |Black | | 6|male |Mexican | | 34|male |White | | 57|female |White | | 57|female |White | | 4|male |Black | | 45|female |White | | 45|female |White | | 80|male |White | | 80|male |White | | 59|male |White | | 59|male |White | | 53|male |White | | 12|male |Mexican | | 5|male |Black | | 33|male |Hispanic | | 3|male |Hispanic | | 2|male |Mexican | | 47|male |White | | 19|female |Black | | 4|female |White | | 4|female |White | | 4|female |White | | 4|female |White | | 53|female |White | | 77|female |White | | 77|female |White | | 49|female |Mexican | | 30|female |Other | | 17|female |Black | | 57|female |White | | 37|male |White | | 37|male |White | | 56|female |Hispanic | | 19|male |Other | | 62|female |White | | 62|female |White | | 39|male |White | | 39|male |White | | 28|male |Black | | 43|male |Mexican | | 43|male |Mexican | | 43|male |Mexican | | 43|male |Mexican | | 69|male |White | | 69|male |White | | 69|male |White | | 20|female |White | | 42|male |Black | | 24|male |Black | | 46|male |Black | | 60|female |Black | | 65|female |Mexican | | 80|male |White | | 80|male |White | | 80|male |White | | 66|female |White | | 66|female |White | | 28|male |Mexican | | 28|male |Mexican | | 28|male |Mexican | | 0|female |White | | 27|male |Mexican | | 60|male |White | | 60|male |White | ] -- .pull-right[ - 去除缺失值的行(drop_na) ```r NHANES |> tidyr::drop_na() |> knitr::kable() ``` | ID|SurveyYr |Gender | Age|AgeDecade | AgeMonths|Race1 |Race3 |Education |MaritalStatus |HHIncome | HHIncomeMid| Poverty| HomeRooms|HomeOwn |Work | Weight| Length| HeadCirc| Height| BMI|BMICatUnder20yrs |BMI_WHO | Pulse| BPSysAve| BPDiaAve| BPSys1| BPDia1| BPSys2| BPDia2| BPSys3| BPDia3| Testosterone| DirectChol| TotChol| UrineVol1| UrineFlow1| UrineVol2| UrineFlow2|Diabetes | DiabetesAge|HealthGen | DaysPhysHlthBad| DaysMentHlthBad|LittleInterest |Depressed | nPregnancies| nBabies| Age1stBaby| SleepHrsNight|SleepTrouble |PhysActive | PhysActiveDays|TVHrsDay |CompHrsDay | TVHrsDayChild| CompHrsDayChild|Alcohol12PlusYr | AlcoholDay| AlcoholYear|SmokeNow |Smoke100 |Smoke100n | SmokeAge|Marijuana | AgeFirstMarij|RegularMarij | AgeRegMarij|HardDrugs |SexEver | SexAge| SexNumPartnLife| SexNumPartYear|SameSex |SexOrientation |PregnantNow | |--:|:--------|:------|---:|:---------|---------:|:-----|:-----|:---------|:-------------|:--------|-----------:|-------:|---------:|:-------|:----|------:|------:|--------:|------:|---:|:----------------|:-------|-----:|--------:|--------:|------:|------:|------:|------:|------:|------:|------------:|----------:|-------:|---------:|----------:|---------:|----------:|:--------|-----------:|:---------|---------------:|---------------:|:--------------|:---------|------------:|-------:|----------:|-------------:|:------------|:----------|--------------:|:--------|:----------|-------------:|---------------:|:---------------|----------:|-----------:|:--------|:--------|:---------|--------:|:---------|-------------:|:------------|-----------:|:---------|:-------|------:|---------------:|--------------:|:-------|:--------------|:-----------| "|>"为管道符,代表对前面的对象执行后面的操作,"%>%"是更早版本的管道符 ] --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 纳入新变量(mutate) ```r NHANES |> dplyr::slice(1:5) |> dplyr::select(ID,Weight,Height) |> dplyr::mutate(BMI=Weight/Height^2) |> knitr::kable() ``` | ID| Weight| Height| BMI| |-----:|------:|------:|---------:| | 51624| 87.4| 164.7| 0.0032220| | 51624| 87.4| 164.7| 0.0032220| | 51624| 87.4| 164.7| 0.0032220| | 51625| 17.0| 105.4| 0.0015303| | 51630| 86.7| 168.4| 0.0030573| --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 根据变量排序(arrange) ```r NHANES |> dplyr::slice(1:5) |> dplyr::select(ID,Weight,Height) |> dplyr::arrange(Weight,Height) |> knitr::kable() ``` | ID| Weight| Height| |-----:|------:|------:| | 51625| 17.0| 105.4| | 51630| 86.7| 168.4| | 51624| 87.4| 164.7| | 51624| 87.4| 164.7| | 51624| 87.4| 164.7| -- 1.arrange()是按照升序排列,若想按照降序排列,可以使用arrange(desc()) -- 2.arrange()可以执行更为复杂的操作,如arrange(desc(a+b))是将数据按照a与b的和降序排列,arrange((a**2))是按照a的绝对值升序排列 --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 调整变量次序(relocate) ```r NHANES |> dplyr::slice(1:5) |> dplyr::select(ID,Gender,Age,Weight,Height) |> dplyr::relocate(Weight,Height,.after=ID) |> knitr::kable() ``` | ID| Weight| Height|Gender | Age| |-----:|------:|------:|:------|---:| | 51624| 87.4| 164.7|male | 34| | 51624| 87.4| 164.7|male | 34| | 51624| 87.4| 164.7|male | 34| | 51625| 17.0| 105.4|male | 4| | 51630| 86.7| 168.4|female | 49| --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 数据集合并 -- .pull-left[ - 纵向合并(bind_rows) ```r d1 <- NHANES |> dplyr::slice(3:5) |> select(ID,Gender,Weight,Height) |> filter(Gender=="Male") d2 <- NHANES |> dplyr::slice(3:5) |> select(ID,Gender,Weight,Height) |> filter(Gender!="Male") d1 |> bind_rows(d2) |> knitr::kable() ``` | ID|Gender | Weight| Height| |-----:|:------|------:|------:| | 51624|male | 87.4| 164.7| | 51625|male | 17.0| 105.4| | 51630|female | 86.7| 168.4| ] -- .pull-right[ - 横向合并(inner_join) ```r d1 <- NHANES |> dplyr::slice(3:5) |> select(ID,Gender) d2 <- NHANES |> dplyr::slice(3:5) |> select(ID,Weight,Height) d1 |> inner_join(d2) |> knitr::kable() ``` ``` ## Joining with `by = join_by(ID)` ``` | ID|Gender | Weight| Height| |-----:|:------|------:|------:| | 51624|male | 87.4| 164.7| | 51625|male | 17.0| 105.4| | 51630|female | 86.7| 168.4| ] -- 1.rbind()、cbind()函数分别可以用于纵向合并和横向合并 2.在横向合并中,inner_join()、full_join()、left_join()、right_join()、cross_join()、semi_join()、anti_join()的区别留给大家思考 --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 数据信息汇总(CreateTableOne) ```r NHANES1 <- NHANES |> dplyr::slice(1:1000) |> mutate(Gender=factor(Gender),Race1=factor(Race1)) vars <- c('Gender','Race1','Age','Weight','Height') factorVars <- c('Gender','Race1') tableone::CreateTableOne(vars = vars, data = NHANES1, factorVars = factorVars) ``` ``` ## ## Overall ## n 1000 ## Gender = male (%) 481 (48.1) ## Race1 (%) ## Black 113 (11.3) ## Hispanic 40 ( 4.0) ## Mexican 103 (10.3) ## White 667 (66.7) ## Other 77 ( 7.7) ## Age (mean (SD)) 38.17 (22.22) ## Weight (mean (SD)) 73.55 (29.66) ## Height (mean (SD)) 162.83 (18.62) ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 交互式表格(datatable) ```r DT::datatable( head(NHANES, 10), fillContainer = FALSE, options = list(pageLength = 4) ) ```
--- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 线性回归 - 模型构建 ```r #线性回归模型 fit = lm(Height ~ 1+Weight+Gender, data = NHANES) summary(fit) ``` ``` ## ## Call: ## lm(formula = Height ~ 1 + Weight + Gender, data = NHANES) ## ## Residuals: ## Min 1Q Median 3Q Max ## -74.618 -6.579 2.134 9.189 32.189 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.206e+02 3.839e-01 314.22 <2e-16 *** ## Weight 5.310e-01 4.931e-03 107.68 <2e-16 *** ## Gendermale 5.238e+00 2.719e-01 19.26 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 13.12 on 9631 degrees of freedom ## (366 observations deleted due to missingness) ## Multiple R-squared: 0.5772, Adjusted R-squared: 0.5771 ## F-statistic: 6575 on 2 and 9631 DF, p-value: < 2.2e-16 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 回归诊断 - LINE条件:线性、独立、正态、等方差 - 离群值、高杠杆点、强影响点 ```r fit = lm(Pulse ~ Weight, data = NHANES) par(mfrow=c(2,2)); plot(fit) ``` ![](Rslides1_files/figure-html/unnamed-chunk-19-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% #### car包推荐 - 线性 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) car::crPlots(fit) ``` ![](Rslides1_files/figure-html/unnamed-chunk-20-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 独立性 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) car::durbinWatsonTest(fit) ``` ``` ## lag Autocorrelation D-W Statistic p-value ## 1 0.3377876 1.324402 0 ## Alternative hypothesis: rho != 0 ``` - 正态性 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) car::qqPlot(fit,main="Q-Q Plot") ``` ![](Rslides1_files/figure-html/unnamed-chunk-22-1.png)<!-- --> ``` ## [1] 5664 9874 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 等方差性 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) car::ncvTest(fit) ``` ``` ## Non-constant Variance Score Test ## Variance formula: ~ fitted.values ## Chisquare = 0.420029, Df = 1, p = 0.51692 ``` ```r car::spreadLevelPlot(fit) ``` ![](Rslides1_files/figure-html/unnamed-chunk-23-1.png)<!-- --> ``` ## ## Suggested power transformation: 1.060243 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 多重共线性 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) car::vif(fit) ``` ``` ## Weight Height ## 1.517514 1.517514 ``` - 离群点 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) car::outlierTest(fit) ``` ``` ## rstudent unadjusted p-value Bonferroni p ## 5664 5.434178 5.6574e-08 0.00048077 ## 9874 4.650271 3.3651e-06 0.02859600 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 高杠杆点 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) hat.plot <- function(fit){ p <- length(coefficients(fit)) n <- length(fitted(fit)) plot(hatvalues(fit),main="Index Plot of Hat Values") abline(h=c(2,3)*p/n,col="red",lty=2) identify(1:n,hatvalues(fit),names(hatvalues(fit))) } hat.plot(fit) ``` ![](Rslides1_files/figure-html/unnamed-chunk-26-1.png)<!-- --> ``` ## integer(0) ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 强影响点 ```r fit = lm(Pulse ~ Weight+Height, data = NHANES) cutoff <- 4/(nrow(NHANES)-length(fit$coefficients)-2) plot(fit,which=4,cook.levels=cutoff) abline(h=cutoff,col="red",lty=2) ``` ![](Rslides1_files/figure-html/unnamed-chunk-27-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 逐步回归 ```r fit = lm(Pulse ~ Height+Gender+Age, data = na.omit(NHANES[,c('Pulse','Weight','Height','Gender','Age')])) MASS::stepAIC(fit,direction = "backward") ``` ``` ## Start: AIC=41648.5 ## Pulse ~ Height + Gender + Age ## ## Df Sum of Sq RSS AIC ## <none> 1141235 41649 ## - Height 1 6993 1148228 41698 ## - Gender 1 12468 1153703 41739 ## - Age 1 58786 1200021 42073 ``` ``` ## ## Call: ## lm(formula = Pulse ~ Height + Gender + Age, data = na.omit(NHANES[, ## c("Pulse", "Weight", "Height", "Gender", "Age")])) ## ## Coefficients: ## (Intercept) Height Gendermale Age ## 95.59148 -0.09081 -2.85588 -0.13365 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### Logistic回归 - 模型构建 ```r #Logistic回归模型 lfit.1 = glm(Diabetes ~ Age+SmokeNow+BMI_WHO, family=binomial, data=NHANES) summary(lfit.1) ``` ``` ## ## Call: ## glm(formula = Diabetes ~ Age + SmokeNow + BMI_WHO, family = binomial, ## data = NHANES) ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -5.822667 0.673317 -8.648 < 2e-16 *** ## Age 0.052158 0.004209 12.392 < 2e-16 *** ## SmokeNowYes -0.147500 0.136909 -1.077 0.28132 ## BMI_WHO18.5_to_24.9 0.281698 0.622767 0.452 0.65103 ## BMI_WHO25.0_to_29.9 0.680183 0.616569 1.103 0.26995 ## BMI_WHO30.0_plus 1.632200 0.612352 2.665 0.00769 ** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 2267.8 on 3167 degrees of freedom ## Residual deviance: 1942.6 on 3162 degrees of freedom ## (6832 observations deleted due to missingness) ## AIC: 1954.6 ## ## Number of Fisher Scoring iterations: 6 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 模型解释 ```r #Logistic回归模型 lfit.1 = glm(Diabetes ~ Age+SmokeNow+BMI_WHO, family=binomial, data=NHANES) exp(coef(lfit.1))#回归系数指数化 ``` ``` ## (Intercept) Age SmokeNowYes BMI_WHO18.5_to_24.9 ## 0.002959702 1.053542196 0.862862564 1.325377761 ## BMI_WHO25.0_to_29.9 BMI_WHO30.0_plus ## 1.974239587 5.115115128 ``` ```r exp(confint(lfit.1))#回归系数置信区间 ``` ``` ## Waiting for profiling to be done... ``` ``` ## 2.5 % 97.5 % ## (Intercept) 0.0006430753 0.009709255 ## Age 1.0450012675 1.062395207 ## SmokeNowYes 0.6584370346 1.126682679 ## BMI_WHO18.5_to_24.9 0.4530984640 5.662702901 ## BMI_WHO25.0_to_29.9 0.6858432927 8.364582910 ## BMI_WHO30.0_plus 1.7970283984 21.550711505 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### R制图 .pull-left[ - 条形图 ```r barplot(table(NHANES$Gender), width=0.25, xlim=c(0, 1), main="gender", col=c("brown","green")) ``` ![](Rslides1_files/figure-html/unnamed-chunk-31-1.png)<!-- --> ] -- .pull-right[ - 直方图 ```r hist(NHANES$Height, main="Height") ``` ![](Rslides1_files/figure-html/unnamed-chunk-32-1.png)<!-- --> ] --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% .pull-left[ - 箱线图 ```r boxplot(NHANES$Height~ NHANES$Gender, main="Height") ``` ![](Rslides1_files/figure-html/unnamed-chunk-33-1.png)<!-- --> - 小提琴图 ```r vioplot::vioplot(NHANES$Height) ``` ![](Rslides1_files/figure-html/unnamed-chunk-34-1.png)<!-- --> ] -- .pull-right[ - 散点图 ```r plot(NHANES$Height, NHANES$Weight, main="Height~Weight") ``` ![](Rslides1_files/figure-html/unnamed-chunk-35-1.png)<!-- --> - 饼图 ```r pie(table(NHANES$Race1)) ``` ![](Rslides1_files/figure-html/unnamed-chunk-36-1.png)<!-- --> ] --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### ggplot2 ```r ggplot2::ggplot(data=NHANES,aes(x=Height,y=Weight))+ geom_point(pch=17,color="brown",size=2)+ geom_smooth(method="lm",color="green",linetype=2)+ labs(x="Weight",y="Height")+ ggtitle("Height~Weight")+ theme(plot.title=element_text(hjust=0.5)) ``` ![](Rslides1_files/figure-html/unnamed-chunk-37-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 交互式地图 ```r library(leaflet) leaflet() %>% addTiles() %>% setView(116.353,39.983, zoom = 17) ```
--- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 流行病学数据分析包 #### epicalc(CRAN中被epiDisplay取代) .pull-left[ - 变量分布 ```r epiDisplay::dotplot(NHANES$Age) ``` ![](Rslides1_files/figure-html/unnamed-chunk-39-1.png)<!-- --> ] -- .pull-right[ - 变量累计分布 ```r epiDisplay::summ(NHANES$Age) ``` ![](Rslides1_files/figure-html/unnamed-chunk-40-1.png)<!-- --> ``` ## obs. mean median s.d. min. max. ## 10000 36.742 36 22.398 0 80 ``` ] --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 变量交叉分布 ```r epiDisplay::tabpct(NHANES$Education,NHANES$HHIncomeMid) ``` ![](Rslides1_files/figure-html/unnamed-chunk-41-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - RR值 ```r epiDisplay::cs(NHANES$Diabetes,NHANES$Gender) ``` ``` ## ## Exposure ## Outcome Non-exposed Exposed Total ## Negative 4592 4506 9098 ## Positive 357 403 760 ## Total 4949 4909 9858 ## ## Rne Re Rt ## Risk 0.07 0.08 0.08 ## ## Estimate Lower95ci Upper95ci ## Risk difference (attributable risk) 0.01 0 0.02 ## Risk ratio 1.14 0.99 1.31 ## Attr. frac. exp. -- (Re-Rne)/Re 0.12 ## Attr. frac. pop. -- (Rt-Rne)/Rt*100 % 6.43 ## Number needed to harm (NNH) 100.42 -1717.9 48.76 ## or 1/(risk difference) ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 剂量反应关系 ```r NHANES2 <- NHANES[NHANES$BMI_WHO!="12.0_18.5",] |> droplevels(NHANES$BMI_WHO) epiDisplay::cs(NHANES2$Diabetes,NHANES2$BMI_WHO) ``` ``` ## ## NHANES2$BMI_WHO ## NHANES2$Diabetes 18.5_to_24.9 25.0_to_29.9 30.0_plus ## No 2797 2461 2321 ## Yes 111 203 428 ## ## Absolute risk 0.04 0.08 0.16 ## Risk ratio 1 2 4.08 ## lower 95% CI 1.6 3.39 ## upper 95% CI 2.49 4.9 ## ## Chi-squared = 248.42 , 2 d.f., P value = 0 ``` ![](Rslides1_files/figure-html/unnamed-chunk-43-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - OR值 ```r epiDisplay::cc(NHANES2$Diabetes,NHANES2$BMI_WHO) ``` ``` ## ## NHANES2$BMI_WHO ## NHANES2$Diabetes 18.5_to_24.9 25.0_to_29.9 30.0_plus ## No 2797 2461 2321 ## Yes 111 203 428 ## ## Odds ratio 1 2.08 4.65 ## lower 95% CI 1.63 3.73 ## upper 95% CI 2.66 5.82 ## ## Chi-squared = 248.42 , 2 d.f., P value = 0 ## Fisher's exact test (2-sided) P value = 0 ``` ![](Rslides1_files/figure-html/unnamed-chunk-44-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - 分层OR值(混杂、交互) ```r NHANES3 <- NHANES2 |> select(Diabetes,BMI_WHO,Gender) |> na.omit() epiDisplay::mhor(NHANES3$Diabetes,NHANES3$Gender,NHANES3$BMI_WHO) ``` ``` ## ## Stratified analysis by Var3 ## OR lower lim. upper lim. P value ## Var3 18.5_to_24.9 1.612 1.082 2.41 0.0148 ## Var3 25.0_to_29.9 1.420 1.047 1.94 0.0225 ## Var3 30.0_plus 0.895 0.724 1.11 0.2933 ## M-H combined 1.120 0.961 1.31 0.1474 ## ## M-H Chi2(1) = 2.1 , P value = 0.147 ## Homogeneity test, chi-squared 2 d.f. = 10.49 , P value = 0.005 ``` ![](Rslides1_files/figure-html/unnamed-chunk-45-1.png)<!-- --> --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - logistic回归 ```r #Logistic回归模型 lfit.1 = glm(Diabetes ~ Age+SmokeNow+BMI_WHO, family=binomial, data=NHANES) epiDisplay::logistic.display(lfit.1,crude=FALSE) ``` ``` ## ## Logistic regression predicting Diabetes : Yes vs No ## ## adj. OR(95%CI) P(Wald's test) P(LR-test) ## Age (cont. var.) 1.05 (1.04,1.06) < 0.001 < 0.001 ## ## SmokeNow: Yes vs No 0.86 (0.66,1.13) 0.281 0.28 ## ## BMI_WHO: ref.=12.0_18.5 < 0.001 ## 18.5_to_24.9 1.33 (0.39,4.49) 0.651 ## 25.0_to_29.9 1.97 (0.59,6.61) 0.27 ## 30.0_plus 5.12 (1.54,16.99) 0.008 ## ## Log-likelihood = -971.2769 ## No. of observations = 3168 ## AIC value = 1954.5538 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% - logisitc逐步回归 ```r #Logistic回归模型 NHANES4 <- NHANES |> select(Diabetes,Age,SmokeNow,BMI_WHO) |> na.omit() lfit.1 = glm(Diabetes ~ Age+SmokeNow+BMI_WHO, family=binomial, data=NHANES4) lfit.2 = step(lfit.1,direction="backward") ``` ``` ## Start: AIC=1954.55 ## Diabetes ~ Age + SmokeNow + BMI_WHO ## ## Df Deviance AIC ## - SmokeNow 1 1943.7 1953.7 ## <none> 1942.5 1954.5 ## - BMI_WHO 3 2038.9 2044.9 ## - Age 1 2119.7 2129.7 ## ## Step: AIC=1953.72 ## Diabetes ~ Age + BMI_WHO ## ## Df Deviance AIC ## <none> 1943.7 1953.7 ## - BMI_WHO 3 2044.1 2048.1 ## - Age 1 2161.0 2169.0 ``` --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### R软件的特点 - 免费、开放源代码,支持各主要计算机系统; - 具有丰富的数据类型,包括向量、矩阵、因子和数据集等; - 强调交互式数据分析,支持复杂算法描述,图形功能强; - 实现了假设检验、回归、判别、聚类等多种统计学方法; - 扩展包众多,功能完善(截止2023年11月在R扩展包主要分发网站CRAN上有近2万个)。 --- background-image: url(https://vim.pku.edu.cn/images/content/2017-10/20171027165035344238_1.jpg) background-size: 200px background-position: 115% 3% ### 参考资料推荐 - R语言实战 - 包含了R入门的数据管理、统计分析、图形绘制等内容,其中统计分析包括描述性统计分析、假设检验、回归、方差分析、主成分和因子分析、时间序列等,非常全面 - R for data science(中、英文版) - 对于R如何处理数据,将raw data整合为tidy data并输出结果讲解的很好 - https://yun.weicheng.men/Book/R%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6.pdf - https://r4ds.hadley.nz/ - R语言教程(北京大学李东风《统计软件》等课程讲义) - 这是我在做slides的时候发现的,内容和其他书籍类似,可作为额外参考 - https://www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/_Rbook/index.html - 谢益辉主页 - https://yihui.org/ - 统计之都 - https://cosx.org/ - stack overflow - https://stackoverflow.com/ - Google - https://www.google.com/ --- class: inverse, middle, center background-image: url(https://upload.wikimedia.org/wikipedia/commons/3/39/Naruto_Shiki_Fujin.svg) background-size: contain ### 祝愿大家在使用R的过程中 ### 实现更多功能 ### 发现更多乐趣 ### 谢谢! ### 本幻灯片由谢益辉开发的R包 [**xaringan**](https://github.com/yihui/xaringan) 生成