R语言学习 - 基础概念和矩阵操作

正文

R基本语法

获取帮助文档，查看命令或函数的使用方法、事例或适用范围

>>> ?command
>>> ??command #深度搜索或模糊搜索此命令




    


>>> example(command) #得到命令的例子

R中的变量

> # 数字变量
> a <- 10
> a
[1] 10
> 
> # 字符串变量
> a <- "abc"
> a
[1] "abc"
> 
> # 逻辑变量
> a <- TRUE
> 
> a
[1] TRUE
> 
> b <- T
> 
> b
[1] TRUE
> 
> d <- FALSE
> 
> d
[1] FALSE
> # 向量
> 
> a <- vector(mode="logical", length=5)
> a
[1] FALSE FALSE FALSE FALSE FALSE
> 
> a <- c(1,2,3,4)
# 判断一个变量是不是vector
> is.vector(a)
[1] TRUE
> 
> # 矩阵
> 
> a <- matrix(1:20,nrow=5,ncol=4,byrow=T)
> a
     [,1] [,2] [,3] [,4]
[1,]    1    2    3    4
[2,]    5    6    7    8
[3,]    9   10   11   12
[4,]   13   14   15   16
[5,]   17   18   19   20
> 
> is.matrix(a)
[1] TRUE
> 
> dim(a) #查看或设置数组的维度向量
[1] 5 4
> 
> # 错误的用法
> dim(a) <- c(4,4)
Error in dim(a) <- c(4, 4) : dims [product 16]与对象长度[20]不匹配
> 
> # 正确的用法
> a <- 1:20
> dim(a) <- c(5,4) #转换向量为矩阵
> a
     [,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
> 
> print(paste("矩阵a的行数", nrow(a)))
[1] "矩阵a的行数 5"
> print(paste("矩阵a的列数", ncol(a)))
[1] "矩阵a的列数 4"
> 
> #查看或设置行列名
> rownames(a)
NULL
> rownames(a) <- c('a','b','c','d','e')
> a
  [,1] [,2] [,3] [,4]
a    1    6   11   16
b    2    7   12   17
c    3    8   13   18
d    4    9   14   19
e    5   10   15   20

# R中获取一系列的字母
> letters[1:4]
[1] "a" "b" "c" "d"
> colnames(a) <- letters[1:4]
> a
  a  b  c  d
a 1  6 11 16
b 2  7 12 17
c 3  8 13 18
d 4  9 14 19
e 5 10 15 20
> 

# is系列和as系列函数用来判断变量的属性和转换变量的属性
# 矩阵转换为data.frame
> is.character(a)
[1] FALSE
> is.numeric(a)
[1] TRUE
> is.matrix(a)
[1] TRUE
> is.data.frame(a)
[1] FALSE
> is.data.frame(as.data.frame(a))
[1] TRUE

R中矩阵运算

# 数据产生
# rnorm(n, mean = 0, sd = 1) 正态分布的随机数
# runif(n, min = 0, max = 1) 平均分布的随机数
# rep(1,5) 把1重复5次
# scale(1:5) 标准化数据
> a <- c(rnorm(5), rnorm(5,1), runif(5), runif(5,-1,1), 1:5, rep(0,5), c(2




    
,10,11,13,4), scale(1:5)[1:5])
> a
 [1] -0.41253556  0.12192929 -0.47635888 -0.97171653  1.09162243  1.87789657
 [7] -0.11717937  2.92953522  1.33836620 -0.03269026  0.87540920  0.13005744
[13]  0.11900686  0.76663940  0.28407356 -0.91251181  0.17997973  0.50452258
[19]  0.25961316 -0.58052230  1.00000000  2.00000000  3.00000000  4.00000000
[25]  5.00000000  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
[31]  2.00000000 10.00000000 11.00000000 13.00000000  4.00000000 -1.26491106
[37] -0.63245553  0.00000000  0.63245553  1.26491106
> a <- matrix(a, ncol=5, byrow=T)
> a
           [,1]       [,2]       [,3]       [,4]        [,5]
[1,] -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243
[2,]  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026
[3,]  0.8754092  0.1300574  0.1190069  0.7666394  0.28407356
[4,] -0.9125118  0.1799797  0.5045226  0.2596132 -0.58052230
[5,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000
[6,]  0.0000000  0.0000000  0.0000000  0.0000000  0.00000000
[7,]  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000
[8,] -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106

# 求行的加和
> rowSums(a)
[1] -0.6470593  5.9959284  2.1751865 -0.5489186 15.0000000  0.0000000 40.0000000
[8]  0.0000000

## 注意检查括号的配对
> a <- a[rowSums(abs(a)!=0,]
错误: 意外的']' in "a <- a[rowSums(abs(a)!=0,]"

# 去除全部为0的行
> a <- a[rowSums(abs(a))!=0,]

# 另外一种方式去除全部为0的行
> #a[rowSums(a==0)
> a
           [,1]       [,2]       [,3]       [,4]        [,5]
[1,] -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243
[2,]  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026
[3,]  0.8754092  0.1300574  0.1190069  0.7666394  0.28407356
[4,] -0.9125118  0.1799797  0.5045226  0.2596132 -0.58052230
[5,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000
[6,]  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000
[7,] -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106

# 矩阵运算，R默认针对整个数据进行常见运算

# 所有值都乘以2

> a * 2
           [,1]       [,2]       [,3]       [,4]        [,5]
[1,] -0.8250711  0.2438586 -0.9527178 -1.9434331  2.18324487
[2,]  3.7557931 -0.2343587  5.8590704  2.6767324 -0.06538051
[3,]  1.7508184  0.2601149  0.2380137  1.5332788  0.56814712
[4,] -1.8250236  0.3599595  1.0090452  0.5192263 -1.16104460
[5,]  2.0000000  4.0000000  6.0000000  8.0000000 10.00000000
[6,]  4.0000000 20.0000000 22.0000000 26.0000000  8.00000000
[7,] -2.5298221 -1.2649111  0.0000000  1.2649111  2.52982213

# 所有值取绝对值，再取对数 （取对数前一般加一个数避免对0或负值取对数）
> log2(abs(a)+1)
          [,1]      [,2]      [,3]      [,4]      [,5]
[1,] 0.4982872 0.1659818 0.5620435 0.9794522 1.0646224




    

[2,] 1.5250147 0.1598608 1.9743587 1.2255009 0.0464076
[3,] 0.9072054 0.1763961 0.1622189 0.8210076 0.3607278
[4,] 0.9354687 0.2387621 0.5893058 0.3329807 0.6604014
[5,] 1.0000000 1.5849625 2.0000000 2.3219281 2.5849625
[6,] 1.5849625 3.4594316 3.5849625 3.8073549 2.3219281
[7,] 1.1794544 0.7070437 0.0000000 0.7070437 1.1794544

# 取出最大值、最小值、行数、列数
> max(a)
[1] 13
> min(a)
[1] -1.264911
> nrow(a)
[1] 7
> ncol(a)
[1] 5

# 增加一列或一行
# cbind: column bind
> cbind(a, 1:7)
           [,1]       [,2]       [,3]       [,4]        [,5] [,6]
[1,] -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243    1
[2,]  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026    2
[3,]  0.8754092  0.1300574  0.1190069  0.7666394  0.28407356    3
[4,] -0.9125118  0.1799797  0.5045226  0.2596132 -0.58052230    4
[5,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000    5
[6,]  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000    6
[7,] -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106    7
> cbind(a, seven=1:7)
                                                             seven
[1,] -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243     1
[2,]  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026     2
[3,]  0.8754092  0.1300574  0.1190069  0.7666394  0.28407356     3
[4,] -0.9125118  0.1799797  0.5045226  0.2596132 -0.58052230     4
[5,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000     5
[6,]  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000     6
[7,] -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106     7

# rbind: row bind
> rbind(a,1:5)
           [,1]       [,2]       [,3]       [,4]        [,5]
[1,] -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243
[2,]  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026
[3,]  0.8754092  0.1300574  0.1190069  0.7666394  0.28407356
[4,] -0.9125118  0.1799797  0.5045226  0.2596132 -0.58052230
[5,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000
[6,]  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000
[7,] -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106
[8,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000

# 计算每一行的mad (中值绝对偏差，一般认为比方差的鲁棒性更强，更少受异常值的影响，更能反映数据间的差异)
> apply(a,1,mad)
[1] 0.7923976 2.0327283 0.2447279 0.4811672 1.4826000 4.4478000 0.9376786

# 计算每一行的var (方差)
# apply表示对数据（第一个参数）的每一行 (第二个参数赋值为1) 或每一列 (2)操作
#      最后返回一个列表
> apply(a,1,var)
[1]  0.6160264  1.6811161  0.1298913  0.3659391  2.5000000 22.5000000  1.0000000

# 计算每一列的平均值
> apply(a,2,mean)
[1] 0.4519068 1.6689045 2.4395294 2.7179083




    
 1.5753421

# 取出中值绝对偏差大于0.5的行
> b = a[apply(a,1,mad)>0.5,]
> b
           [,1]       [,2]       [,3]       [,4]        [,5]
[1,] -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243
[2,]  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026
[3,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000
[4,]  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000
[5,] -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106

# 矩阵按照mad的大小降序排列
> c = b[order(apply(b,1,mad), decreasing=T),]
> c
           [,1]       [,2]       [,3]       [,4]        [,5]
[1,]  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000
[2,]  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026
[3,]  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000
[4,] -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106
[5,] -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243

> rownames(c) <- paste('Gene', letters[1:5], sep="_")
> colnames(c) <- toupper(letters[1:5])
> c
                A          B          C          D           E
Gene_a  2.0000000 10.0000000 11.0000000 13.0000000  4.00000000
Gene_b  1.8778966 -0.1171794  2.9295352  1.3383662 -0.03269026
Gene_c  1.0000000  2.0000000  3.0000000  4.0000000  5.00000000
Gene_d -1.2649111 -0.6324555  0.0000000  0.6324555  1.26491106
Gene_e -0.4125356  0.1219293 -0.4763589 -0.9717165  1.09162243

# 矩阵转置
> expr = t(c)
> expr
  Gene_a      Gene_b Gene_c     Gene_d     Gene_e
A      2  1.87789657      1 -1.2649111 -0.4125356
B     10 -0.11717937      2 -0.6324555  0.1219293
C     11  2.92953522      3  0.0000000 -0.4763589
D     13  1.33836620      4  0.6324555 -0.9717165
E      4 -0.03269026      5  1.2649111  1.0916224

# 矩阵值的替换
> expr2 = expr
> expr2[expr2<0] = 0
> expr2
  Gene_a   Gene_b Gene_c    Gene_d    Gene_e
A      2 1.877897      1 0.0000000 0.0000000
B     10 0.000000      2 0.0000000 0.1219293
C     11 2.929535      3 0.0000000 0.0000000
D     13 1.338366      4 0.6324555 0.0000000
E      4 0.000000      5 1.2649111 1.0916224

# 矩阵中只针对某一列替换
# expr2是个矩阵不是数据框，不能使用列名字索引
> expr2[expr2$Gene_b<1, "Gene_b"] <- 1
Error in expr2 operator is invalid for atomic vectors
# str是一个最为常用、好用的查看变量信息的工具，尤其是对特别复杂的变量，
#    可以看清其层级结构，便于提取数据
> str(expr2)
 num [1:5, 1:5] 2 10 11 13 4 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:5] "A" "B" "C" "D" ...
  ..$ : chr [1:5] "Gene_a" "Gene_b" "Gene_c" "Gene_d" ...

# 转换为数据库，再进行相应的操作
> expr2 <- as.data.frame(expr2)
> str(expr2)
'data.frame':    5 obs. of  5 variables:
 $ Gene_a: num  2 10 11 13 4
 $ Gene_b: num  1.88 1 2.93 1.34 1
 $ Gene_c: num  1 2 3 4 5
 $ Gene_d: num  0 0 0 0.632 1.265
 $ Gene_e: num  0 0.122 0 0 1.092
> expr2[expr2$Gene_b<1, "Gene_b"] <- 1
> expr2
> expr2
  Gene_a   Gene_b Gene_c    Gene_d    Gene_e
A      2 1.877897      1 0.0000000 0.0000000
B     10 1.000000      2 0.0000000 0.1219293
C     11 2.929535      3 0.0000000 0.0000000
D     13 1.338366      4 0.6324555 0.0000000
E      4 1.000000      5 1.2649111 1.0916224

R中矩阵筛选合并

# 读入样品信息
> sampleInfo = "Samp;Group;Genotype
+ A;Control;WT
+ B;Control;WT
+ D;Treatment;Mutant
+ C;Treatment;Mutant
+ E;Treatment;WT
+ F;Treatment;WT"
> phenoData = read.table(text=sampleInfo,sep=";", header=T, row.names=1, quote="")
> phenoData
      Group Genotype
A   Control       WT
B   Control       WT
D Treatment   Mutant
C Treatment   Mutant
E Treatment       WT
F Treatment       WT

# 把样品信息按照基因表达矩阵中的样品信息排序，并只保留有基因表达信息的样品
# match() returns a vector of the positions of (first) matches of
          its first argument in its second.
> phenoData[match(rownames(expr), rownames(phenoData)),]
      Group Genotype
A   Control       WT
B   Control       WT
C Treatment   Mutant
D Treatment   Mutant
E Treatment       WT

# ‘%in%’ is a more intuitive interface as a binary operator, which
     returns a logical vector indicating if there is a match or not for
     its left operand.

# 注意顺序，%in%比match更好理解一些
> phenoData = phenoData[rownames(phenoData) %in% rownames(expr),]
> phenoData
      Group Genotype
A   Control       WT
B   Control       WT
C Treatment   Mutant
D Treatment   Mutant
E Treatment       WT

# 合并矩阵
# by=0 表示按照行的名字排序
# by=columnname 表示按照共有的某一列排序
# 合并后多出了新的一列Row.names
> merge_data = merge(expr, phenoData, by=0, all.x=T)
> merge_data
  Row.names Gene_a      Gene_b Gene_c     Gene_d     Gene_e     Group Genotype
1         A      2  1.87789657      1 -1.2649111 -0.4125356   Control       WT
2         B     10 -0.11717937

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