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 0.57142862.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.colnames6.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 0.24929611.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
>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 3.44013.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$secondcolumn)- dist and cor
dist:
This function computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.
cor:
If x and y are matrices then the covariances (or correlations) between the columns of x and the columns of y are computed.
dist (and dist objects, which is what heatmap.2 is assuming it’s getting) assume that you’ve calculated the distance between rows, while using cor you are essentially calculating the distance between columns
16.grepl，类似于R的grep
grepl(pattern, x, ignore.case = FALSE, perl = FALSE, fixed = FALSE, useBytes = FALSE)
matirx[, grepl("Treat", colnames(matrix)) | grepl("Control", colnames(matrix))]17.substr, 去除矩阵的一部分
substr(x, start, stop)
substring(text, first, last = 1000000L)
substr(x, start, stop) <- value
substring(text, first, last = 1000000L) <- value18.优先级问题
":"的优先级高于四则运算，所以 1:3+1的结果是2，3，4，而不是1，2，3，419.退出R
quit()20.write.table输出时列名字一行通常会少一列，解决办法
write.table("filename.xls", sep="\t", col.names = NA, row.names = TRUE)| 21.R中and操作使用&，而不是&&, or操作使用 | 而不是 | 。 |
data[data$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$ok<3] <- 323.取出满足某一列符合条件的行（注意 ，的使用）
data_fdr <- data[data[,2]>=2,]24.排序矩阵
data <- data[order(data$col),]
T2 <- T2[order(T2$gene.index),]25.去除重复
data.frame <- data.frame[!duplicate(data.frame),]
data.frame <- unique(data.frame)26.字符串分割和提取
> countfiles
[1] "./treated1en.counts" "./treated2en.counts" "./treated3en.counts"
[4] "./untreated1en.counts" "./untreated2en.counts" "./untreated3en.counts"
[7] "./untreated4en.counts"
>strsplit(countfiles,'en')[[1]]
[1] "./treated1" ".counts"
[[2]]
[1] "./treated2" ".counts"
[[3]]
[1] "./treated3" ".counts"
[[4]]
[1] "./untreated1" ".counts"
[[5]]
[1] "./untreated2" ".counts"
[[6]]
[1] "./untreated3" ".counts"
[[7]]
[1] "./untreated4" ".counts"
> sapply(strsplit(countfiles,'en'),"[[",1)
[1] "./treated1" "./treated2" "./treated3" "./untreated1" "./untreated2"
[6] "./untreated3" "./untreated4"
> strsplit(sapply(strsplit(countfiles,'en'),"[[",1),"\\/")
[[1]]
[1] "." "treated1"
[[2]]
[1] "." "treated2"
[[3]]
[1] "." "treated3"
[[4]]
[1] "." "untreated1"
[[5]]
[1] "." "untreated2"
[[6]]
[1] "." "untreated3"
[[7]]
[1] "." "untreated4"
> sapply(strsplit(sapply(strsplit(countfiles,'en'),"[[",1),"\\/"),"[[",2)
[1] "treated1" "treated2" "treated3" "untreated1" "untreated2"
[6] "untreated3" "untreated4"27.翻转矩阵或数据框(turn matrix or data.frame upsiede-down)
> a
X1 X2 X3
ac 1 4 7
cb 2 5 8
bc 3 6 9
> a[rev(rownames(a)),]
X1 X2 X3
bc 3 6 9
cb 2 5 8
ac 1 4 728.替换矩阵中符合特定要求的值
> data <- matrix(c(1,2,3,4,0,1,2,3,4,5,6,0,0,2,0),nrow=3)
> data
[,1] [,2] [,3] [,4] [,5]
[1,] 1 4 2 5 0
[2,] 2 0 3 6 2
[3,] 3 1 4 0 0
> log2(data)
[,1] [,2] [,3] [,4] [,5]
[1,] 0.000000 2 1.000000 2.321928 -Inf
[2,] 1.000000 -Inf 1.584963 2.584963 1
[3,] 1.584963 0 2.000000 -Inf -Inf
> data_log <- log2(data)
> data_log[data_log==-Inf] = 0
> data_log
[,1] [,2] [,3] [,4] [,5]
[1,] 0.000000 2 1.000000 2.321928 0
[2,] 1.000000 0 1.584963 2.584963 1
[3,] 1.584963 0 2.000000 0.000000 0
> data.m$value[data.m$value < $small] <- 029.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.
