R语言学习记录-基本篇

1. prop.table(x, margin=NULL)

计算表格中各个数值在给定方向占得比例。1表示按行计算,2表示按列计算,默认按整个表格计算。

> m <- matrix(1:4,2)
> m
[,1] [,2]
[1,] 1 3
[2,] 2 4
> prop.table(m)
[,1] [,2]
[1,] 0.1 0.3
[2,] 0.2 0.4
> prop.table(m,1)
[,1] [,2]
[1,] 0.2500000 0.7500000
[2,] 0.3333333 0.6666667
> prop.table(m,2)
[,1] [,2]
[1,] 0.3333333 0.4285714
[2,] 0.6666667 0.5714286

2.去除矩阵中包含特定元素的行【https://stat.ethz.ch/pipermail/r-help/2008-November/179333.html】

d <- rbind(c(1,    0,    6,    4),
c(2,    5,   7,    5),
c(3,    6,    8,    6),
c(4,    0,    0,    0))
f <- as.matrix(d)
f[-which(rowSums(f==0)>0),]
rowSums(f==0):计算值为0的列数
which去除符合条件的值的坐标,-取反

data[rowSums(is.na(data))<ncol(data),]

去除和为0的行

data <- data[rowSums(data)!=0,]

3.矩阵转置

t(x) #转置后输出时,row.names 和col.names也会变化

aggregate(x, by=list(1,1,2,2), FUN=mean)

4.aggregate

Splits the data into subsets, computes summary statistics for each, and returns the result in a convenient form.

5.修改行或列的名字,rownames, colnames, paste

cluster.mean.colnames <- colnames(data2.cluster.mean)
cluster.mean.colnames[1] = paste('#',cluster.mean.colnames[1], sep='')
colnames(data2.cluster.mean) <- cluster.mean.colnames

6.R中字符串连接用paste

paste('1', '2', sep='\t')

7.在R交互式环境中执行写好的脚本

source('script.r')

8.read.table

data <- read.table(file='file', sep='\t', header=T, row.names=1)

row.names = 1 #not true but first column

If the first line have one item less than the total number of columns, header=TRUE autonomously.

If header=TRUE, col.names is the values in that line, no need to specify again.

If row.name and col.name starts with a number and check.names=TRUE(default), an X will be added before each name. One can turn it off to avoid adding characters.

9.R中if-else的使用,else需与if的右括号位于同一行。

if(is.na(result)[1]) {     print("NA") } else {     coef(result)[[1]] }

10.attach, 把数据表或列表的一列放入R的搜索空间,使其可以直接调用

>>> data[2:5,]

Gene hmc expression
2 NM_001001144 0.1999845 0.768915
3 NM_001001152 0.0000000 -0.663424
4 NM_001001160 0.1203579 -0.636796
5 NM_001001176 0.0000000 0.249296

>>>attach(data)

>>>hmc[2:5]

[1] 0.1999846 0.0000000 0.1203579 0.0000000

>>>expression[2:5]

[1] 0.768915 -0.663424 -0.636796 0.249296

11.lowess, 局部曲线拟合, This function performs the computations for the _LOWESS_ smoother which uses locally-weighted polynomial regression. http://210.75.224.29/wordpress/wp-content/gallery/for_article/scatter_plot_lowess.png

scatter_plot_lowess

>attach(mtcars)

>plot(wt, mpg, main="Scatter Example", xlab="Car Weight", ylab="Miles Per Gallon", pch=19)

> abline(lm(mpg~wt), col="red")
> lines(lowess(wt,mpg),col="blue")

12.R中得到list的多列

> mtcars
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2

> data <- mtcars(c(-2,-4,-7,-8))
> data <- mtcars(c(1,3,5,6))

> data
mpg disp drat wt
Mazda RX4 21.0 160.0 3.90 2.620
Mazda RX4 Wag 21.0 160.0 3.90 2.875
Datsun 710 22.8 108.0 3.85 2.320
Hornet 4 Drive 21.4 258.0 3.08 3.215
Hornet Sportabout 18.7 360.0 3.15 3.440

13.读取只含有一列的文件并把其转为向量

vector <- as.vector(read.table(file="file",sep="\t", header=F)$V1 )

14.重复向量中每个元素

rep(c(1,2,3), each=3)

rep(data$firstcolum, data$secondcolumn)
  1. dist and cor

dist:
This function computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.
cor:
If x and y are matrices then the covariances (or correlations) between the columns of x and the columns of y are computed.
dist (and dist objects, which is what heatmap.2 is assuming it’s getting) assume that you’ve calculated the distance between rows, while using cor you are essentially calculating the distance between columns

16.grepl,类似于R的grep

grepl(pattern, x, ignore.case = FALSE, perl = FALSE, fixed = FALSE, useBytes = FALSE)

matirx[, grepl("Treat", colnames(matrix)) | grepl("Control", colnames(matrix))]

17.substr, 去除矩阵的一部分

substr(x, start, stop)
substring(text, first, last = 1000000L)
substr(x, start, stop) <- value
substring(text, first, last = 1000000L) <- value

18.优先级问题

":"的优先级高于四则运算,所以 1:3+1的结果是234,而不是1234

19.退出R

quit()

20.write.table输出时列名字一行通常会少一列,解决办法

write.table("filename.xls", sep="\t", col.names = NA, row.names = TRUE)
21.R中and操作使用&,而不是&&, or操作使用 而不是  
data[data$ok<3] | data$nok == 'yes',]

22.修改矩阵或数据框中某一列符合条件的值

data$ok[data$ok<3] <- 3

23.取出满足某一列符合条件的行(注意 的使用)

data_fdr <- data[data[,2]>=2,]

24.排序矩阵

data <- data[order(data$col),]
T2 <- T2[order(T2$gene.index),]

25.去除重复

data.frame <- data.frame[!duplicate(data.frame),]
data.frame <- unique(data.frame)

26.字符串分割和提取

> countfiles
[1] "./treated1en.counts"   "./treated2en.counts"   "./treated3en.counts"
[4] "./untreated1en.counts" "./untreated2en.counts" "./untreated3en.counts"
[7] "./untreated4en.counts"

>strsplit(countfiles,'en')[[1]]
[1] "./treated1" ".counts"

[[2]]
[1] "./treated2" ".counts"

[[3]]
[1] "./treated3" ".counts"

[[4]]
[1] "./untreated1" ".counts"

[[5]]
[1] "./untreated2" ".counts"

[[6]]
[1] "./untreated3" ".counts"

[[7]]
[1] "./untreated4" ".counts"

> sapply(strsplit(countfiles,'en'),"[[",1)
[1] "./treated1"   "./treated2"   "./treated3"   "./untreated1" "./untreated2"
[6] "./untreated3" "./untreated4"
> strsplit(sapply(strsplit(countfiles,'en'),"[[",1),"\\/")
[[1]]
[1] "."        "treated1"

[[2]]
[1] "."        "treated2"

[[3]]
[1] "."        "treated3"

[[4]]
[1] "."          "untreated1"

[[5]]
[1] "."          "untreated2"

[[6]]
[1] "."          "untreated3"

[[7]]
[1] "."          "untreated4"

> sapply(strsplit(sapply(strsplit(countfiles,'en'),"[[",1),"\\/"),"[[",2)
[1] "treated1"   "treated2"   "treated3"   "untreated1" "untreated2"
[6] "untreated3" "untreated4"

27.翻转矩阵或数据框(turn matrix or data.frame upsiede-down)

> a
   X1 X2 X3
ac  1  4  7
cb  2  5  8
bc  3  6  9
> a[rev(rownames(a)),]
   X1 X2 X3
bc  3  6  9
cb  2  5  8
ac  1  4  7

28.替换矩阵中符合特定要求的值

> data <- matrix(c(1,2,3,4,0,1,2,3,4,5,6,0,0,2,0),nrow=3)
> data
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    4    2    5    0
[2,]    2    0    3    6    2
[3,]    3    1    4    0    0
> log2(data)
         [,1] [,2]     [,3]     [,4] [,5]
[1,] 0.000000    2 1.000000 2.321928 -Inf
[2,] 1.000000 -Inf 1.584963 2.584963    1
[3,] 1.584963    0 2.000000     -Inf -Inf
> data_log <- log2(data)
> data_log[data_log==-Inf] = 0
> data_log
         [,1] [,2]     [,3]     [,4] [,5]
[1,] 0.000000    2 1.000000 2.321928    0
[2,] 1.000000    0 1.584963 2.584963    1
[3,] 1.584963    0 2.000000 0.000000    0

> data.m$value[data.m$value < $small] <- 0

29.矩阵分割和选取

x[n,] # 取出第n行

x[,n] # 取出第n列

x[,c(1,3)] #取出第1和3列

x[-1,] #去除第一行

x['pattern'] #取出名为pattern的行

30.

100.

CHENTONG
版权声明:本文为博主原创文章,转载请注明出处。
alipay.png WeChatPay.png

CHENTONG

CHENTONG
积微,月不胜日,时不胜月,岁不胜时。凡人好敖慢小事,大事至,然后兴之务之。如是,则常不胜夫敦比于小事者矣!何也?小事之至也数,其悬日也博,其为积也大。大事之至也希,其悬日也浅,其为积也小。故善日者王,善时者霸,补漏者危,大荒者亡!故,王者敬日,霸者敬时,仅存之国危而后戚之。亡国至亡而后知亡,至死而后知死,亡国之祸败,不可胜悔也。霸者之善著也,可以时托也。王者之功名,不可胜日志也。财物货宝以大为重,政教功名者反是,能积微者速成。诗曰:德如毛,民鲜能克举之。此之谓也。

生信宝典文章集锦

### 程序学习心得* [生物信息之程序学习](http://mp.weixin.qq.com/s?__biz=MzI5MTcwNjA4NQ==&mid=2247483927&idx=1&sn=23adf2b9d13400f2081f790e674e...… Continue reading

生信宝典Linux学习系列文章整理

Published on October 18, 2017

在线画图

Published on October 17, 2017