R快速入门

李宗璋

2025-10-17

大纲

• 1.1 R主界面

• 1.2 安装包

• 1.3 项目的创建与管理

• 1.4 R代码学习方法建议

• 1.5 常用R代码

1.1 RStudio的界面

1.2 安装包

• tidyverse

  • 一系列用于数据导入、清洗、转换、可视化与建模的 R 包

R代码: 安装包

# 安装单个包
install.packages("tidyverse")

install.packages("psych")

# 安装多个包
install.packages(c("tidyverse", "psych"))

R代码: 查看包是否需要更新

options(repos = c(CRAN = "https://cloud.r-project.org")) #使用主镜像
available.packages()["survminer", "Version"]

[1] "0.5.1"

安装包的常见问题

• 安装包的速度太慢，切换到国内镜像

• 安装了包后，还需要加载包，方可调用包中的函数

• could not find function “XXX”

1.3 项目的创建与管理

• 创建项目的好处

  • 项目创建后，所有相对路径都以项目文件夹为根目录

  • 不再需要使用 setwd() 指定工作目录，减少路径错误

  • 便于协作与再现

  • 团队成员只需克隆项目文件夹，即可重现整个分析过程

• 项目文件的管理

  • 数据文件及其导入

  • 代码

  • 导出的文件：图片, Word, PDF等

1.4 R代码学习方法建议

学习代码: 参数项(需要修改的/不需要改动的)

录入代码

查看函数帮助

AI工具的使用

1.5 R常用代码

1.5.1 管道符

安装和加载包tidyverse

install.packages("tidyverse")
library(tidyverse)

# %>% 快捷键
# Windows: Ctrl + Shift + M
# Mac OS : Cmd + Shift + M

x <- runif(10,0,1)
x

 [1] 0.17427839 0.03479087 0.07140071 0.03805745 0.62612918 0.48453600
 [7] 0.51406349 0.73006162 0.08948937 0.66131663

round(sqrt(abs(log(x))),2)

 [1] 1.32 1.83 1.62 1.81 0.68 0.85 0.82 0.56 1.55 0.64

y <- log(x)
z <- abs(y)
w <- sqrt(z)
v <- round(w,2)
sort(v)

 [1] 0.56 0.64 0.68 0.82 0.85 1.32 1.55 1.62 1.81 1.83

x %>% 
  log() %>% 
  abs() %>% 
  sqrt() %>% 
  round(2) %>% 
  sort()

 [1] 0.56 0.64 0.68 0.82 0.85 1.32 1.55 1.62 1.81 1.83

pi %>% 
  round(2)

[1] 3.14

2 %>% 
  round(pi,.)

[1] 3.14

6 %>% 
  round(pi,.)

[1] 3.141593

6 %>% 
  round()

[1] 6

#占位符
paste(1:3, letters[1:3])

[1] "1 a" "2 b" "3 c"

1:3 %>% 
  paste(.,letters[.])

[1] "1 a" "2 b" "3 c"

1.5.2 数据管理

• 筛选个案：filter()
• 选择变量：select()
• 追加变量：mutate()
• 重命名：rename(新名 = 旧名)
• 条件转换：二值转换if_else()
• 条件转换：多值转换case_when()

filter(), select(), mutate()

data(mpg)

#1 miles per gallon = 0.425 kilometers per liter

mpg %>% 
  filter(year == 1999) %>% 
  select(displ,cty, hwy,trans) %>% 
  arrange(displ) %>% 
  mutate(cty.kpl = 0.425*cty, 
               hwy.kpl = 0.425*hwy) %>% 
  mutate(transmission = 
           if_else(substring(trans, 1,4) == "auto", 
                   "auto","manual"))

# A tibble: 117 × 7
   displ   cty   hwy trans      cty.kpl hwy.kpl transmission
   <dbl> <int> <int> <chr>        <dbl>   <dbl> <chr>       
 1   1.6    28    33 manual(m5)   11.9     14.0 manual      
 2   1.6    24    32 auto(l4)     10.2     13.6 auto        
 3   1.6    25    32 manual(m5)   10.6     13.6 manual      
 4   1.6    23    29 manual(m5)    9.78    12.3 manual      
 5   1.6    24    32 auto(l4)     10.2     13.6 auto        
 6   1.8    18    29 auto(l5)      7.65    12.3 auto        
 7   1.8    21    29 manual(m5)    8.92    12.3 manual      
 8   1.8    18    26 manual(m5)    7.65    11.0 manual      
 9   1.8    16    25 auto(l5)      6.8     10.6 auto        
10   1.8    24    30 auto(l3)     10.2     12.8 auto        
# ℹ 107 more rows

mpgnew <- mpg %>% 
  filter(year == 1999) %>% 
  select(displ,cty, hwy,trans) %>% 
  arrange(displ) %>% 
  mutate(cty.kpl = 0.425*cty, 
               hwy.kpl = 0.425*hwy) %>% 
  mutate(transmission = 
           if_else(substring(trans, 1,4) == "auto", 
                   "auto","manual"))

二值转换 if_else()

#二值转换 if_else

mpg$transmission <- if_else(
  substring(mpg$trans, 1,4) == "auto", 
  "auto","manual") 

多值转换 case_when()

#多值转换 case_when
mpg$drive <- case_when(
  mpg$drv == "f" ~ "front-wheel",
  mpg$drv == "r" ~ "rear-wheel",
  mpg$drv == "4" ~ "four-wheel")

ggplot2——散点图

data(mpg)

mpg %>% 
  ggplot(aes(displ, cty))+
  geom_point()+
  geom_smooth(method = lm,
              se = F)

mpg %>% 
  ggplot(aes(displ, cty,
             color = drv,
             shape = drv))+
  geom_point()+
  geom_smooth(method = lm,
              se = F)

mpg %>% 
  ggplot(aes(displ, cty,
             size = cyl,
             shape = drv))+
  geom_point()+
  geom_smooth(method = lm,
              se = F)

mpg %>% 
  ggplot(aes(displ, cty,
             color = drv))+
  geom_point()+
  geom_smooth(method = lm,
              se = F)

mpg %>% 
  ggplot(aes(displ, cty))+
  geom_point(aes(color = drv))+
  geom_smooth(method = lm,
              se = F)

mpg %>% 
  ggplot(aes(displ, cty))+
  geom_point()+
  geom_smooth(aes(color = drv),
              method = lm,
              se = F)

mpg %>% 
  ggplot(aes(displ, cty,
             color = drv))+
  geom_point()+
  geom_smooth(method = lm,
              se = F)+
  labs(x = "Engine Size",
       y = "MPG on the Cityway",
       title = "Fuel Efficiency")+
  theme_light()+
  theme(text = element_text(face = "bold",
                            colour = "blue"),
        plot.title = element_text(hjust = 0.5))

描述性统计量的报告

summary(mpg)

 manufacturer          model               displ            year     
 Length:234         Length:234         Min.   :1.600   Min.   :1999  
 Class :character   Class :character   1st Qu.:2.400   1st Qu.:1999  
 Mode  :character   Mode  :character   Median :3.300   Median :2004  
                                       Mean   :3.472   Mean   :2004  
                                       3rd Qu.:4.600   3rd Qu.:2008  
                                       Max.   :7.000   Max.   :2008  
      cyl           trans               drv                 cty       
 Min.   :4.000   Length:234         Length:234         Min.   : 9.00  
 1st Qu.:4.000   Class :character   Class :character   1st Qu.:14.00  
 Median :6.000   Mode  :character   Mode  :character   Median :17.00  
 Mean   :5.889                                         Mean   :16.86  
 3rd Qu.:8.000                                         3rd Qu.:19.00  
 Max.   :8.000                                         Max.   :35.00  
      hwy             fl               class          
 Min.   :12.00   Length:234         Length:234        
 1st Qu.:18.00   Class :character   Class :character  
 Median :24.00   Mode  :character   Mode  :character  
 Mean   :23.44                                        
 3rd Qu.:27.00                                        
 Max.   :44.00                                        

library(psych)
describe(mpg)

              vars   n    mean    sd median trimmed   mad    min  max range
manufacturer*    1 234    7.76  5.13    6.0    7.68  5.93    1.0   15  14.0
model*           2 234   19.09 11.15   18.5   18.98 14.08    1.0   38  37.0
displ            3 234    3.47  1.29    3.3    3.39  1.33    1.6    7   5.4
year             4 234 2003.50  4.51 2003.5 2003.50  6.67 1999.0 2008   9.0
cyl              5 234    5.89  1.61    6.0    5.86  2.97    4.0    8   4.0
trans*           6 234    5.65  2.88    4.0    5.53  1.48    1.0   10   9.0
drv*             7 234    1.67  0.66    2.0    1.59  1.48    1.0    3   2.0
cty              8 234   16.86  4.26   17.0   16.61  4.45    9.0   35  26.0
hwy              9 234   23.44  5.95   24.0   23.23  7.41   12.0   44  32.0
fl*             10 234    4.63  0.70    5.0    4.77  0.00    1.0    5   4.0
class*          11 234    4.59  1.99    5.0    4.64  2.97    1.0    7   6.0
               skew kurtosis   se
manufacturer*  0.21    -1.63 0.34
model*         0.11    -1.23 0.73
displ          0.44    -0.91 0.08
year           0.00    -2.01 0.29
cyl            0.11    -1.46 0.11
trans*         0.29    -1.65 0.19
drv*           0.48    -0.76 0.04
cty            0.79     1.43 0.28
hwy            0.36     0.14 0.39
fl*           -2.25     5.76 0.05
class*        -0.14    -1.52 0.13

绘制学术论文中的表1

library(psych)
library(gt)
mpg %>% 
  select_if(is.numeric) %>% 
  describe() %>% 
  select(n,mean,sd, median, min,max) %>% 
  round(3) %>% 
  format(nsmall =3) %>% 
  cbind(variable = c("displ","year","cyl","cty","hwy"),.) %>% 
  gt() %>% 
  gtsave(filename = "table1.docx")

相关系数及其可视化

library(tidyverse)
library(corrplot)

data(mtcars)

mtcars %>% 
  select(mpg:qsec) %>% 
  cor() %>% 
  round(3) %>% 
  corrplot(addCoef.col = "white",
           number.cex = 0.8,
           number.digits = 3,
           tl.cex = 0.8,
           tl.col = 1,
           cl.length = 11,
           mar = c(1,1,1,1))

mtcars %>% 
  select(mpg:qsec) %>% 
  cor() %>% 
  round(3) %>% 
  corrplot(addCoef.col = "white",
           number.cex = 0.8,
           number.digits = 3,
           tl.cex = 0.8,
           tl.col = 1,
           cl.length = 11,
           type = "upper",
           method = "square")

library(psych)

gr <- colorRampPalette(c("#B52127", "white", "#2171B5"))

gr <- colorRampPalette(c("purple", "white", "cyan"))

mtcars %>% 
  select(mpg:qsec) %>% 
  cor.plot(cex = 0.6,
           stars = TRUE,
           gr = gr,
           cex.axis = 0.8)