R语言学习 - 基础概念和矩阵操作
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] TRUER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)<ncol(a),]
> 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$Gene_b<1, "Gene_b"] <- 1
Error in expr2$Gene_b : $ 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$Gene_b<1, "Gene_b"] <- 1
> expr2
> expr2
Gene_a Gene_b Gene_c Gene_d Gene_e
A 2 1.877897 1 0.0000000 0.0000000
B 10 1.000000 2 0.0000000 0.1219293
C 11 2.929535 3 0.0000000 0.0000000
D 13 1.338366 4 0.6324555 0.0000000
E 4 1.000000 5 1.2649111 1.0916224R中矩阵筛选合并
# 读入样品信息
> sampleInfo = "Samp;Group;Genotype
+ A;Control;WT
+ B;Control;WT
+ D;Treatment;Mutant
+ C;Treatment;Mutant
+ E;Treatment;WT
+ F;Treatment;WT"
> phenoData = read.table(text=sampleInfo,sep=";", header=T, row.names=1, quote="")
> phenoData
Group Genotype
A Control WT
B Control WT
D Treatment Mutant
C Treatment Mutant
E Treatment WT
F Treatment WT
# 把样品信息按照基因表达矩阵中的样品信息排序，并只保留有基因表达信息的样品
# match() returns a vector of the positions of (first) matches of
its first argument in its second.
> phenoData[match(rownames(expr), rownames(phenoData)),]
Group Genotype
A Control WT
B Control WT
C Treatment Mutant
D Treatment Mutant
E Treatment WT
# ‘%in%’ is a more intuitive interface as a binary operator, which
returns a logical vector indicating if there is a match or not for
its left operand.
# 注意顺序，%in%比match更好理解一些
> phenoData = phenoData[rownames(phenoData) %in% rownames(expr),]
> phenoData
Group Genotype
A Control WT
B Control WT
C Treatment Mutant
D Treatment Mutant
E Treatment WT
# 合并矩阵
# by=0 表示按照行的名字排序
# by=columnname 表示按照共有的某一列排序
# 合并后多出了新的一列Row.names
> merge_data = merge(expr, phenoData, by=0, all.x=T)
> merge_data
Row.names Gene_a Gene_b Gene_c Gene_d Gene_e Group Genotype
1 A 2 1.87789657 1 -1.2649111 -0.4125356 Control WT
2 B 10 -0.11717937 2 -0.6324555 0.1219293 Control WT
3 C 11 2.92953522 3 0.0000000 -0.4763589 Treatment Mutant
4 D 13 1.33836620 4 0.6324555 -0.9717165 Treatment Mutant
5 E 4 -0.03269026 5 1.2649111 1.0916224 Treatment WT
> rownames(merge_data) <- merge_data$Row.names
> merge_data
Row.names Gene_a Gene_b Gene_c Gene_d Gene_e Group Genotype
A A 2 1.87789657 1 -1.2649111 -0.4125356 Control WT
B B 10 -0.11717937 2 -0.6324555 0.1219293 Control WT
C C 11 2.92953522 3 0.0000000 -0.4763589 Treatment Mutant
D D 13 1.33836620 4 0.6324555 -0.9717165 Treatment Mutant
E E 4 -0.03269026 5 1.2649111 1.0916224 Treatment WT
# 去除一列；-1表示去除第一列
> merge_data = merge_data[,-1]
> merge_data
Gene_a Gene_b Gene_c Gene_d Gene_e Group Genotype
A 2 1.87789657 1 -1.2649111 -0.4125356 Control WT
B 10 -0.11717937 2 -0.6324555 0.1219293 Control WT
C 11 2.92953522 3 0.0000000 -0.4763589 Treatment Mutant
D 13 1.33836620 4 0.6324555 -0.9717165 Treatment Mutant
E 4 -0.03269026 5 1.2649111 1.0916224 Treatment WT
# 提取出所有的数值列
> merge_data[sapply(merge_data, is.numeric)]
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 1.0916224str的应用
str: Compactly display the internal structure of an R object, a
diagnostic function and an alternative to ‘summary (and to some
extent, ‘dput’). Ideally, only one line for each ‘basic’
structure is displayed. It is especially well suited to compactly
display the (abbreviated) contents of (possibly nested) lists.
The idea is to give reasonable output for any R object. It
calls ‘args’ for (non-primitive) function objects.
str用来告诉结果的构成方式，对于不少Bioconductor的包，或者复杂的R函数的输出，都是一堆列表的嵌套，str(complex_result)会输出每个列表的名字，方便提取对应的信息。
# str的一个应用例子
> str(list(a = "A", L = as.list(1:100)), list.len = 9)
List of 2
$ a: chr "A"
$ L:List of 100
..$ : int 1
..$ : int 2
..$ : int 3
..$ : int 4
..$ : int 5
..$ : int 6
..$ : int 7
..$ : int 8
..$ : int 9
.. [list output truncated]
# 利用str查看pca的结果，具体的PCA应用查看http://mp.weixin.qq.com/s/sRElBMkyR9rGa4TQp9KjNQ
> pca_result <- prcomp(expr)
> pca_result
Standard deviations:
[1] 4.769900e+00 1.790861e+00 1.072560e+00 1.578391e-01 2.752128e-16
Rotation:
PC1 PC2 PC3 PC4 PC5
Gene_a 0.99422750 -0.02965529 0.078809521 0.01444655 0.06490461
Gene_b 0.04824368 -0.44384942 -0.885305329 0.03127940 0.12619948
Gene_c 0.08258192 0.81118590 -0.451360828 0.05440417 -0.35842886
Gene_d -0.01936958 0.30237826 -0.079325524 -0.66399283 0.67897952
Gene_e -0.04460135 0.22948437 -0.002097256 0.74496081 0.62480128
> str(pca_result)
List of 5
$ sdev : num [1:5] 4.77 1.79 1.07 1.58e-01 2.75e-16
$ rotation: num [1:5, 1:5] 0.9942 0.0482 0.0826 -0.0194 -0.0446 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:5] "Gene_a" "Gene_b" "Gene_c" "Gene_d" ...
.. ..$ : chr [1:5] "PC1" "PC2" "PC3" "PC4" ...
$ center : Named num [1:5] 8 1.229 3 0.379 0.243
..- attr(*, "names")= chr [1:5] "Gene_a" "Gene_b" "Gene_c" "Gene_d" ...
$ scale : logi FALSE
$ x : num [1:5, 1:5] -6.08 1.86 3.08 5.06 -3.93 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:5] "A" "B" "C" "D" ...
.. ..$ : chr [1:5] "PC1" "PC2" "PC3" "PC4" ...
- attr(*, "class")= chr "prcomp"
# 取出每个主成分解释的差异
> pca_result$sdev
[1] 4.769900e+00 1.790861e+00 1.072560e+00 1.578391e-01 2.752128e-16R的包管理
# 什么时候需要安装包
> library('unExistedPackage')
Error in library("unExistedPackage") :
不存在叫‘unExistedPackage’这个名字的程辑包
# 安装包
> install.packages("package_name")
# 指定安装来源
> install.packages("package_name", repo="http://cran.us.r-project.org")
# 安装Bioconductor的包
> source('https://bioconductor.org/biocLite.R')
> biocLite('BiocInstaller')
> biocLite(c("RUVSeq","pcaMethods"))
# 安装Github的R包
> install.packages("devtools")
> devtools::install_github("JustinaZ/pcaReduce")
# 手动安装, 首先下载包的源文件（压缩版就可），然后在终端运行下面的命令。
ct@ehbio:~$ R CMD INSTALL package.tar.gz
# 移除包
>remove.packages("package_name")
# 查看所有安装的包
>library()
# 查看特定安装包的版本
> installed.packages()[c("DESeq2"), c("Package", "Version")]
Package Version
"DESeq2" "1.14.1"
>
# 查看默认安装包的位置
>.libPaths()
# 调用安装的包
>library(package_name)
#devtools::install_github("hms-dbmi/scde", build_vignettes = FALSE)
#install.packages(c("mvoutlier","ROCR"))
#biocLite(c("RUVSeq","pcaMethods","SC3","TSCAN","monocle","MultiAssayExperiment","SummarizedExperiment"))
#devtools::install_github("satijalab/seurat")