R 语言基础知识

Posted 2022-12-04 大大大大大桃子

数据结构

先放一张 R 的基本数据结构,表被吓得吼 ☺️
代码地址: R-Basics

R 赋值

赋值 :

x <- 2

console :

[1] 2  

注: index 从 1 开始

向量 (vector)

只能包含同一类型的对象

# vector(mode = "logical", length = 0L) (类型,长度)
x <- vector("character",length = 4)

console :

chr [1:4] "" "" "" ""

或

x1 <- 1:4  

console :

 int [1:4] 1 2 3 4

或

x2 <- c(1,2,3,4)

console :

num[1:4] 1 2 3 4

如果 c() 传的类型不一样,会内隐强制转换字符型 :

x3 <- c(TRUE, 2, "A", "2L")

console :

chr [1:4] "TRUE" "2" "A" "2L"

显式类型转换

as.numeric(x3)
as.logical(x3)
as.character(x3)

属性 (attribute)

类型 (class)

查看类型 :

class(x)

console :

[1] "numeric"

1. “numeric” 数值型;
2. 2L: “integer”;
3. TRUE: 大写;
4. 2+2i: “complex”

长度 (length)

length(x3)

console :

[1] 4

维度 (dimensions: matrix, array)

如下, x3 是一个一维数组 :

matrix(x3)

console :

[1,] "TRUE"
[2,] "2"   
[3,] "A"   
[4,] "2L"

名称 (name)

names(x3) <- c("a","b","c","d")

console :

/*
a      b      c      d 
"TRUE"    "2"    "A"   "2L" 
*/

矩阵 (matrix)

向量 + 维度属性 (整数向量 : nrow,ncol)

# matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL)

x <- matrix(nrow = 3, ncol = 2)

console :

#   [,1] [,2]
# [1,]   NA   NA
# [2,]   NA   NA
# [3,]   NA   NA

dim(x)   

console :

# [1] 3 2   3行2列

维度

attributes(x)
#  $dim       
#  [1] 3 2

 y <- 1:6                          #创建一个向量
 dim(y) <- c(2,3)                  #添加维度信息

console :

#  > y
#  [,1] [,2] [,3]
#  [1,]    1    3    5
#  [2,]    2    4    6 

注意以上矩阵是按照列填充的

y2 <- matrix(1:6, nrow = 2,ncol = 3)    # 和上面 y  是一样的

行拼接

rbind(y,y2)

console :

#  [,1] [,2] [,3]
#  [1,]    1    3    5
#  [2,]    2    4    6
#  [3,]    1    3    5
#  [4,]    2    4    6

列拼接

cbind(y,y2)

console :

#  [,1] [,2] [,3] [,4] [,5] [,6]
#  [1,]    1    3    5    1    3    5
#  [2,]    2    4    6    2    4    6

数组

与矩阵类似,但是维度可以大于2 (矩阵维度只能等于2)

# array(data = NA, dim = length(data), dimnames = NULL)
x <- array(1:24, dim = c(4,6))

console :

# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,]    1    5    9   13   17   21
# [2,]    2    6   10   14   18   22
# [3,]    3    7   11   15   19   23
# [4,]    4    8   12   16   20   24

x1 <- array(1:24,dim = c(2,3,4))

console :

# , , 1

# [,1] [,2] [,3]
# [1,]    1    3    5
# [2,]    2    4    6

# , , 2

# [,1] [,2] [,3]
# [1,]    7    9   11
# [2,]    8   10   12

# , , 3

# [,1] [,2] [,3]
# [1,]   13   15   17
# [2,]   14   16   18

# , , 4

# [,1] [,2] [,3]
# [1,]   19   21   23
# [2,]   20   22   24

列表 (list)

可以包含不同类型的对象 list()

l <- list("a",2,10L,3+4i,TRUE)

console :

# [[1]]
# [1] "a"

# [[2]]
# [1] 2

# [[3]]
# [1] 10

# [[4]]
# [1] 3+4i

# [[5]]
# [1] TRUE

 l1 <- list(a = 1, b = 2, c = 3)

# $a
# [1] 1

# $b
# [1] 2

# $c
# [1] 3

 l2 <- list(c(1,2,3),c(4,5,6,7))

# [[1]]
# [1] 1 2 3

# [[2]]
# [1] 4 5 6 7

命名

x <- matrix(1:6,nrow = 2,ncol = 3)
dimnames(x) <- list(c("a","b"),c("c","d","e"))

console :

#  > x
#  c d e
#  a 1 3 5
#  b 2 4 6

因子 (factor)

 #  ---- 分类数据 / 有序 vs 无序
 #  ---- 整数向量 + 标签(label) (优于整数向量) 
 #         Male / Female vs 1 / 2
 #         常用于 lm(), glm()

# factor(x = character(), levels, labels = levels, exclude = NA, ordered = is.ordered(x), nmax = NA)
x <- factor(c("male","female","female","male","male"))

console :

# > x
# [1] male   female female male   male  
# Levels: female male

x2 <- factor(c("male","female","female","male","male"), levels = c("male","female"))

console :

# > x2
# [1] male   female female male   male  
# Levels: male female

注意: 以上 x 和 x2 的levels 先后顺序是不一样的

查看当前因子

table(x)

console :

# > table(x)
# x
# female   male 
# 2      3 

去掉因子属性

unclass(x)

console :

# > unclass(x)
# [1] 2 1 1 2 2
# attr(,"levels")
# [1] "female" "male" 

class(unclass(x))

console :

# > class(unclass(x))
# [1] "integer"

缺失值 (missing value)

#  ---  NA/NaN:NaN 属于NA, NA 不属于 NaN  (NaN表示数字的缺失值,NA范围更广)
#  ---  NA 是有类型属性的: integer NA, character NA
#  ---  is.na() is.nan() 判断是否有缺失值

 x <- c(1,NA,2,NA,3)
 is.na(x)
 is.nan(x)

console :

# >  is.na(x)
# [1] FALSE  TRUE FALSE  TRUE FALSE
# >  is.nan(x)
# [1] FALSE FALSE FALSE FALSE FALSE

数据框 (data frame)

 #  --- 存储表格数据 (tabular data)
 #  --- 视为各元素长度相同的列表
 #  ---    1. 每个元素代表一列数据
 #  ---    2. 每个元素的长度代表行数
 #  ---    3. 元素类型可以不同

# data.frame(..., row.names = NULL, check.rows = FALSE, check.names = TRUE, stringsAsFactors = default.stringsAsFactors())

 df <- data.frame(id = c(1,2,3,4), name = c("a","b","c","d"), gender = c(TRUE,TRUE,FALSE,TRUE))

console:

# > df
# id name gender
# 1  1    a   TRUE
# 2  2    b   TRUE
# 3  3    c  FALSE
# 4  4    d   TRUE

 nrow(df)
 ncol(df)

把数据框转换成矩阵

 df1 <- data.frame(id = c(1,2,3,4), score = c(5,6,7,8))
 data.matrix(df1)

console:

# id score
# [1,]  1     5
# [2,]  2     6
# [3,]  3     7
# [4,]  4     8

注意: 数据框和矩阵第一列标记是不一样的

日期和时间 (date, time)

 #  --- Date: 距离1970-01-01的天数 / date() /Sys.Date() / weekdays() / months() / quarters()

 x <- date()

console:

 #  [1] "Thu Jul 13 14:09:07 2017"

 class(x)   # [1] "character"

 x1 <- Sys.Date()   # [1] "2017-07-13"

 class(x1)     # [1] "Date"

 # 存储为date
 x2 <- as.Date("2017-07-13")
 class(x2)     # [1] "Date"
 weekdays(x2)    # [1] "星期四"
 months(x2)          # [1] "七月"
 quarters(x2)        # [1] "Q3"   季度
 julian(x2)     # 距离1970-01-01 过去了多少天

# [1] 17360
# attr(,"origin")
# [1] "1970-01-01"

 x3 <- as.Date("2016-11-11")
 x2 -x3

 # Time difference of 244 days

 as.numeric(x2-x3)   # [1] 244


 # 时间: POSIXct / POSIXlt
 #   ---  距离1970-01-01的秒数 / Sys.time()
 #           POSIXct: 整数,常用于存入数据库
 #           POSIXlt: 列表,还包含星期,年,月,日等信息

 x <- Sys.time()

 #  [1] "2017-07-13 14:22:14 CST"

 class(x)   # [1] "POSIXct" "POSIXt"

 # ct 和 lt 类型相互转换
  p <- as.POSIXlt(x)

 # [1] "2017-07-13 14:22:14 CST"

  class(p)   # [1] "POSIXlt" "POSIXt" 

 # 查看变量名称
  names(unclass(p))

#  [1] "sec"    "min"    "hour"   "mday"   "mon"    "year"   "wday"   "yday"   "isdst" 
#  [10] "zone"   "gmtoff"

 p$sec    # [1] 14.00388

 x1 <- "1991-05-15 03:45"
 strptime(x1, "%Y-%m-%d %H:%M")

 # [1] "1991-05-15 03:45:00 CDT"

RStudio 运行

# 运行代码
# 选中一段代码,点 Run