Note: Reference books: "153 minutes to learn the language R"
nonsense, nonsense ---- I really can not see do not know how many minutes, or foggy. .
head
Function and thetail
function of the matrix head and tail removed
> x=matrix(1:12,3,4)
> head(x = x)
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> head(x = x , 2)
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
> tail(x = x , 1)
[,1] [,2] [,3] [,4]
[3,] 3 6 9 12
- Removing the matrix of rows or columns
> #移除行或列的数据
> x <- data.frame(matrix(1:30,nrow = 5, byrow = TRUE))
> dim(x)
[1] 5 6 #5行6列
> print(x)
X1 X2 X3 X4 X5 X6
1 1 2 3 4 5 6
2 7 8 9 10 11 12
3 13 14 15 16 17 18
4 19 20 21 22 23 24
5 25 26 27 28 29 30
> x
X1 X2 X3 X4 X5 X6
1 1 2 3 4 5 6
2 7 8 9 10 11 12
3 13 14 15 16 17 18
4 19 20 21 22 23 24
5 25 26 27 28 29 30
#移除整行或整列元素
> new.x1 <- x[-c(1,4),]
> new.x1
X1 X2 X3 X4 X5 X6
2 7 8 9 10 11 12
3 13 14 15 16 17 18
5 25 26 27 28 29 30
> new.x2 <- x[,-c(2,3)]
> new.x2
X1 X4 X5 X6
1 1 4 5 6
2 7 10 11 12
3 13 16 17 18
4 19 22 23 24
5 25 28 29 30
- Two identical data frame it?
> al <- data.frame(num = 1:8, lib = letters[1:8])
> a2 <- al
> a2[[3,1]] <- 2
> al
num lib
1 1 a
2 2 b
3 3 c
4 4 d
5 5 e
6 6 f
7 7 g
8 8 h
> a2[[8,2]] <- "a"
> all(al == a2)#数据框a2的两个位置被改动
[1] FALSE
> any(al != a2)
[1] TRUE
> #比较两个数框是否相同,使用函数identical(element1,element2)
> identical(al,a2)
[1] FALSE
> #返回两个数据框不相同的位置
> which(al != a2 , arr.ind = TRUE)
row col
[1,] 3 1
[2,] 8 2
> #去掉相同的行或相同的列
> #使用unique函数去除重复元素
> x <- c(2:4,3:5)
> x
[1] 2 3 4 3 4 5
> xu <- x[!duplicated(x)]
> xu
[1] 2 3 4 5
> #duplicated函数返回元素是否重复的逻辑值
> unique(x)
[1] 2 3 4 5
- Dimension transformation uses
apern
function - Delete elements in the list
> #删除list中的元素,使用null(无效对象)
> lst <- list("a" = 1 , "b" = 2)
> lst$a <- NULL #注意是大写
> lst
$b
[1] 2
- Row or column of the matrix of accumulating
> mat
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20
> apply(mat , 2 , cumsum) #对列累加
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 3 13 23 33
[3,] 6 21 36 51
[4,] 10 30 50 70
[5,] 15 40 65 90
- Comparing two vector elements,
> x <- 1:10
> y <- rev(x) #把行向量倒过来
[1] 10 9 8 7 6 5 4 3 2 1
> pmax(x , y)
[1] 10 9 8 7 6 6 7 8 9 10
> pmin(x , y)
[1] 1 2 3 4 5 5 4 3 2 1
- Analyzing the data block is a number of columns
> sapply(x , is.numeric)
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
- sample function
> #随机抽取数字
> y = sample(5)
> y
[1] 2 1 5 3 4
> sample(y)
[1] 5 3 2 1 4
> sample(y , replace = TRUE)
[1] 2 2 3 4 2
> #解靴带法
> sample(y , 2 ) #非放回地 从y中抽取2项
[1] 3 5
> sample(y , 2 , replace = TRUE)
[1] 1 5
#merge函数的参数如果是向量的话,做笛卡尔积
#标准化和中心化
> x <- c(rnorm(2),2*rnorm(3))
> x
[1] -0.02973143 -0.29157917 -1.18222232 2.71501078
[5] 0.05909058
> m <- scale(x , scale = F) #只中心化
> m
[,1]
[1,] -0.2838451
[2,] -0.5456929
[3,] -1.4363360
[4,] 2.4608971
[5,] -0.1950231
attr(,"scaled:center")
[1] 0.2541137
> n <- scale(x , center = F) #只标准化
> n
[,1]
[1,] -0.01997882
[2,] -0.19593437
[3,] -0.79442571
[4,] 1.82442366
[5,] 0.03970748
attr(,"scaled:scale")
[1] 1.488147
#使用function函数定义二元函数的格式
> f <- function(x , y){r <- sqrt(x^2 + y^2) ; 10*sin(r)/r }
#如何把行向量转化成列向量,使用t(t(x))
> x <- 1:5
> x
[1] 1 2 3 4 5
> t(x)
[,1] [,2] [,3] [,4] [,5]
[1,] 1 2 3 4 5
> t(t(x))
[,1]
[1,] 1
[2,] 2
[3,] 3
[4,] 4
[5,] 5
#两个向量做内积,方法一
> y=t(t(x)) #y是一个列向量,x是行向量
> crossprod(x,y)
[,1]
[1,] 55
> crossprod(y , x)
[,1]
[1,] 55
#内积的方法二
> x%*%y
[,1]
[1,] 55
> y%*%x
[,1] [,2] [,3] [,4] [,5]
[1,] 1 2 3 4 5
[2,] 2 4 6 8 10
[3,] 3 6 9 12 15
[4,] 4 8 12 16 20
[5,] 5 10 15 20 25
> y%o%x
, , 1
[,1]
[1,] 1
[2,] 2
[3,] 3
[4,] 4
[5,] 5
, , 2
[,1]
[1,] 2
[2,] 4
[3,] 6
[4,] 8
[5,] 10
, , 3
[,1]
[1,] 3
[2,] 6
[3,] 9
[4,] 12
[5,] 15
, , 4
[,1]
[1,] 4
[2,] 8
[3,] 12
[4,] 16
[5,] 20
, , 5
[,1]
[1,] 5
[2,] 10
[3,] 15
[4,] 20
[5,] 25
> x<- -1+1i #定义复数,方法1
> y = complex(1 , 1) #定义复数,方法2
> x==y
[1] FALSE
> Mod(x) #计算复数x的模长
[1] 1.414214
> Conj(x) #取复数x的共轭
[1] -1-1i
> diag(x = x , nrow = 3)
[,1] [,2] [,3]
[1,] -1+1i 0+0i 0+0i
[2,] 0+0i -1+1i 0+0i
[3,] 0+0i 0+0i -1+1i
> #eigen()函数用于计算特征值
> #构造上三角或者下三角矩阵
> m <- matrix (1:16 , 4 ,4)
> lower.tri(m)
[,1] [,2] [,3] [,4]
[1,] FALSE FALSE FALSE FALSE
[2,] TRUE FALSE FALSE FALSE
[3,] TRUE TRUE FALSE FALSE
[4,] TRUE TRUE TRUE FALSE
> m[lower.tri(m)] <- 0
> m
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 0 6 10 14
[3,] 0 0 11 15
[4,] 0 0 0 16
#开立方根
> "^"(27 , 1/3)
[1] 3
> #幂函数运算
> #求余数使用%% ; %/%表示整除
> #求矩阵各行(列)的均值
> #方法一 使用apply函数> #方法二 使用colMeans 或者 rowMeans函数
> m <- 4 ; n <- 5
> A <- matrix(1:m*n , m , n)
> matrix(apply(A , 2 , mean))
[,1]
[1,] 12.5
[2,] 12.5
[3,] 12.5
[4,] 12.5
[5,] 12.5
> matrix(apply(A , 2 , mean) , m , n)
[,1] [,2] [,3] [,4] [,5]
[1,] 12.5 12.5 12.5 12.5 12.5
[2,] 12.5 12.5 12.5 12.5 12.5
[3,] 12.5 12.5 12.5 12.5 12.5
[4,] 12.5 12.5 12.5 12.5 12.5
> #计算组合数使用choose函数;
> choose(5,3)
[1] 10
> combn(5,3) #列出所有组合
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 1 1 1 1 1 1 2 2 2 3
[2,] 2 2 2 3 3 4 3 3 4 4
[3,] 3 4 5 4 5 5 4 5 5 5
> factorial(5) #计算阶乘
[1] 120
> #求导数,使用D(function , “variable_name”)
> f1 <- expression(sin(x)*x)
> D(f1 , "x")
cos(x) * x + sin(x)
#求方程的根,使用uniroot函数(二分法)
> f <- function(x)x^3 - 2*x - 1
> uniroot(f , c(0,2))
$root
[1] 1.618018
$f.root
[1] -9.17404e-05
$iter
[1] 6
$init.it
[1] NA
$estim.prec
[1] 6.103516e-05
#下面看一下uniroot函数的弊端--无法求本身是极值的零解
> f <- function(x)x^2 + 2*x +1
> uniroot(f , c(-2 , 2))
Error in uniroot(f, c(-2, 2)) : 位于极点边的f()值之正负号不相反
#解决方法,使用optimize函数
> optimize(f , c(-2 , 2)) #第一个参数是函数,第二个参数是初始区间
$minimum
[1] -1
$objective
[1] 0
> #可见,uninroot函数使用的是二分法求解根
> #在分布前的字母含义:d--概率密度函数;p--累积分布函数;r--产生该分布的随机数;q表示分位数函数
> help.search("distribution") #可以查各个分布的表达,列在下表
> #计算N(3,1)分布下,P(2<=X<=5)值
> pnorm(5 , 3 , 1) - pnorm(2 , 3 , 1)
[1] 0.8185946
#如何更改大小写
> x <- "MiXeD"
> tolower(x)
[1] "mixed"
> x <- "mIXed 123"
> toupper(x)
[1] "MIXED 123"
> letters[1:4]
[1] "a" "b" "c" "d"
#循环for的使用
> for(var in letters[1:5])
+ {x <- var; print(paste("foo" , var , sep = ""))}
[1] "fooa"
[1] "foob"
[1] "fooc"
[1] "food"
[1] "fooe"
#计数字符串中有多少个字母
> month.name[5]
[1] "May"
> nchar(month.name[4])
[1] 5
#取出子字符串,注意是闭区间!索引是从0开始!不同于python
> substr("abcde" , 2 , 4)
[1] "bcd"
> substr("abcde" , 2 , 3)
[1] "bc"
#取出子字符串,方法2
> substring("abdcdef" , 1:5 ,1:3)
[1] "a" "b" "d" "" ""
#正则表达式
> grep("J." , month.abb)
[1] 1 6 7 #January June July
> #使用“.”匹配任意字符;使用"\.来匹配"."
> help("regex") #regulation expression
> #使用as.Data(),as.POSIXet()函数将字符串日期转化成"Data"类型数据,可以进行运算
> d1 <- c("06/29/07")
> d1
[1] "06/29/07"
> D1 <- as.Date(d1 , "%m/%d/%y")
> D1
[1] "2007-06-29"
> d2 <- c("07/02/07")
> D2 <- as.Date(d2 , "%m/%d/%y")
> D2
[1] "2007-07-02"
> D2-D1 #在这种日期格式下,可以做加减
Time difference of 3 days
> difftime(D1 , D2 , units = "days")
Time difference of -3 days
> format(Sys.Date() , format = "%A , %d %B %Y") #在这种格式,返回当前系统时间
[1] "星期五 , 10 一月 2020"
> dev.new()
NULL
> hist(rnorm(25) , col = "VioletRed") #画出直方图
> dev.off()
RStudioGD
2
> dev.new()
NULL
> layout(matrix(c(1,1,1,2,3,4,2,3,4) , nr = 3 , byrow = T))
> hist(rnorm(25) , col = "VioletRed")
> hist(rnorm(25) , col = "VioletRed")
> hist(rnorm(25) , col = "VioletRed")
> hist(rnorm(25) , col = "VioletRed")
> hist(rnorm(25) , col = "VioletRed")
> dev.off()
RStudioGD
2
#绘图时使用par函数可以划分绘图区域
distributed | R function (parameter) |
---|---|
beta | beta(shape1 ,shape2) |
Two | binom(size,prob) |
x^2 | chisq(df) |
Uniform | unif(max,min) |
index | exp(rate) |
F | f(df1 ,df2) |
Gamma | gamma(shape,scale) |
Hypergeometric | hyper(m,n,k) |
Normality | norm(mean,std) |
Poisson | for (lambda) |
t | t(df) |
Weibull | weibull(shape,scale) |