91、R语言编程基础

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1、查看当前工作空间

> getwd()[1] "C:/Users/P0079482.HHDOMAIN/Documents">

2、查看内存中有哪些对象

> ls() [1] "a"         "a1"        "b"         "bank"      "bank_full" "dat"       [7] "m1"        "tab"       "w"         "x"         "x1"        "x2"       >

3、把指定对象从内存中删除

> rm('a')> ls() [1] "a1"        "b"         "bank"      "bank_full" "dat"       "m1"        [7] "tab"       "w"         "x"         "x1"        "x2"       >

4、查看函数帮助

> help(matrix)>

5、创建向量和矩阵

> x1=c(2,4,6,8,0)> x2=c(1,3,5,7,9)
查看向量的长度> length(x1)[1] 5
查看向量的类型
> mode(x1)[1] "numeric">

6、按行将向量排列成矩阵

> rbind(x1,x2)[,1] [,2] [,3] [,4] [,5]x1 2 4 6 8 0x2 1 3 5 7 9

7、按列将向量排成矩阵

> cbind(x1,x2)     x1 x2[1,]  2  1[2,]  4  3[3,]  6  5[4,]  8  7[5,]  0  9

8、求向量的均值

> x=c(1:100)> mean(x)[1] 50.5>

9、求向量的和

> sum(x)[1] 5050>

10、

> max(x)  求最大值[1] 100> min(x)　　最小值[1] 1> var(x)　　方差[1] 841.6667> prod(x)    连乘[1] 9.332622e+157> sd(x)      标准差[1] 29.01149

11、生成矩阵

> a1=c(1:12)> matrix(a1,nrow=3,ncol=4)     [,1] [,2] [,3] [,4][1,]    1    4    7   10[2,]    2    5    8   11[3,]    3    6    9   12>

> matrix(a1,nrow=4,ncol=3)     [,1] [,2] [,3][1,]    1    5    9[2,]    2    6   10[3,]    3    7   11[4,]    4    8   12

> matrix(a1,nrow=4,ncol=3,byrow=T)     [,1] [,2] [,3][1,]    1    2    3[2,]    4    5    6[3,]    7    8    9[4,]   10    1    2根据行生成矩阵

12、矩阵的转置

> a=matrix(1:12,nrow=3,ncol=4)> a     [,1] [,2] [,3] [,4][1,]    1    4    7   10[2,]    2    5    8   11[3,]    3    6    9   12> t(a)     [,1] [,2] [,3][1,]    1    2    3[2,]    4    5    6[3,]    7    8    9[4,]   10   11   12>

13、矩阵的加减

> a=b=matrix(1:12,nrow=3,ncol=4)> a+b     [,1] [,2] [,3] [,4][1,]    2    8   14   20[2,]    4   10   16   22[3,]    6   12   18   24> a-b     [,1] [,2] [,3] [,4][1,]    0    0    0    0[2,]    0    0    0    0[3,]    0    0    0    0>

14、矩阵乘法

> a=matrix(1:12,nrow=3,ncol=4)> b=matrix(1:12,nrow=4,ncol=3)> a%*%b     [,1] [,2] [,3][1,]   70  158  246[2,]   80  184  288[3,]   90  210  330>

15、矩阵求对角元素

> a=matrix(1:16,nrow=4,ncol=4)> a     [,1] [,2] [,3] [,4][1,]    1    5    9   13[2,]    2    6   10   14[3,]    3    7   11   15[4,]    4    8   12   16> diag(a)[1]  1  6 11 16> diag(diag(a))     [,1] [,2] [,3] [,4][1,]    1    0    0    0[2,]    0    6    0    0[3,]    0    0   11    0[4,]    0    0    0   16> diag(4)     [,1] [,2] [,3] [,4][1,]    1    0    0    0[2,]    0    1    0    0[3,]    0    0    1    0[4,]    0    0    0    1>

16、生成随机矩阵

> a=matrix(rnorm(16),4,4)> a           [,1]         [,2]        [,3]       [,4][1,]  0.2353978 -1.168817665 -0.03914636 -0.4350940[2,]  0.5550182 -0.001076645 -1.92283070  1.1007430[3,]  0.2582714 -0.846160178  0.94940298 -0.6125362[4,] -2.1307575 -2.478207744 -0.44198013 -0.2581712>

17、矩阵求逆

> solve(a)           [,1]       [,2]       [,3]       [,4][1,]  0.2524842  0.3182516  0.5172485 -0.2958290[2,]  0.4128992 -0.5045100 -1.1071800 -0.2200014[3,] -1.7116774  0.4246262  1.9141867  0.1535275[4,] -3.1169442  1.4892720  3.0819024  0.4171374>

18、基本的数据结构，数据框

> x1=c(10,13,45,26,23,12,24,78,23,43,31,56)> x2=c(20,65,32,32,27,87,60,13,42,51,77,35)> x=data.frame(x1,x2)> x   x1 x21  10 202  13 653  45 324  26 325  23 276  12 877  24 608  78 139  23 4210 43 5111 31 7712 56 35>

> (x=data.frame('weight'=x1,'cost'=x2))   weight cost1      10   202      13   653      45   324      26   325      23   276      12   877      24   608      78   139      23   4210     43   5111     31   7712     56   35>

19、读取文本文件、

> (x=data.frame('weight'=x1,'cost'=x2))   weight cost1      10   202      13   653      45   324      26   325      23   276      12   877      24   608      78   139      23   4210     43   5111     31   7712     56   35> (x=read.table("abc.txt"))    V1 V21  175 672  183 753  165 564  145 455  178 676  187 907  156 438  176 589  173 6010 170 56

20、读取excell文件

> w<-read.table("test.prn",header = T)> w  X.. X...11   A     22   B     33   C     54   D     5>

21、用readxl包读excell

> library(readxl)> dat<-read_excel("test.xlsx")> dat# A tibble: 4 x 2  `商品` `价格`   <chr>  <dbl>1      A      22      B      33      C      54      D      5>

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