1. java環(huán)境配置
2. R包安裝
#方法一
source("http://bioconductor.org/biocLite.R")
biocLite("kernlab")
#方法二
install.packages("kernlab")
#方法三
if(!suppressWarnings(require('kernlab')))
{
install.packages('kernlab')
require('kernlab')
}
#方法四（推薦）
library(BiocManager)
BiocManager::install("plot3D",ask = F,update = F)
3. R包卸載/卸除/查看加載
#卸載
remove.packages("rlang")
remove. packages(c("pkg1","pkg2") , lib = file .path("path", "to", "library"))
#卸除：在包使用函數沖突子刮，檢驗函數依賴時比較有用
detach("package:RMySQL")
#已加載
(.packages())
4. Rstudio設置
（1）ctrl+Alt+Shift+0 顯示四個窗口
（2）R版本共存
- https://segmentfault.com/a/1190000015332215?utm_source=tag-newest
- https://blog.csdn.net/ChenQihome9/article/details/81949965
- https://cran.r-project.org/bin/windows/base/old/
5. 文件讀取
- （1）讀寫excel
library(readxl)
data<-read_xlsx('E:/代碼/機器學習/winequality-red.xlsx', sheet = 'Sheet1')
library(openxlsx)
data<-read.xlsx('E:/代碼/機器學習/winequality-red.xlsx')
#install.packages("rJava")
#install.packages("xlsx")
#Sys.setenv(JAVA_HOME='C:/Program Files/Java/jdk1.8.0_171/jre')
library(rJava)
library(xlsxjars)
library(xlsx)
data<-read.xlsx(workbook, 1, encoding='UTF-8') #name sheet 防亂碼
write.xlsx(BP, paste('GO_BP_Module_', i, '.xlsx', sep=""), row.names = F)
write.xlsx(BP, 'target_GO_BP.xlsx')
- （2）讀寫txt
data<-read.table(file.choose(),header=T, sep="\t")
6. 常用數據處理函數/代碼
參考：https://www.cnblogs.com/zongfa/p/8537031.html （dplyr、tidyr包）
多向量取交集
A<-c()
B<-c()
C<-c()
result<-Reduce(intersect, list(A, B, C))
- 橫向輸出
library(tidyr)
data<-as.data.frame(t(data)) #任意向量
colnames(data)<-c(1:dim(data)[2])
result<-unite(data,"combine",colnames(data), sep="\t", remove = F)[,1] #combine可改窑睁，挺峡、\t空格間隔
- 拆分填充行
#excel 初步處理得到互作對
ncRDeath<- read.table(file.choose(),sep = "\t", stringsAsFactors = F,header = T, fill=TRUE, quote = "")
head(ncRDeath)
ncRDeath2<-ncRDeath[ncRDeath$Gene_Symbol != "" & ncRDeath$miRBase_mature_ID != "",]
final<-data.frame()
for (i in 1:dim(ncRDeath2)[1]){
#i=1
ncR_break<-data.frame(miRBase_mature_ID=unlist(strsplit(ncRDeath2[i,1], ",")),Gene_Symbol=ncRDeath2[i,2])
final<-rbind(final,ncR_break)
print(i)
}
dim(ncRDeath2) #2119
write.table(final,"6-ncRdeathDB_miRNA_break.txt",row.names = F,quote = F,sep="\t")
- 重置和變換
#（1）重置變量名
library(reshape)
rename(ncrdeathDB_miRNA, miRBase_mature_ID="miRNA", Gene_Symbol="target_mRNA")
#（2）重置數據框
inputData<-data.frame(cats[, c (2,3)], response = as.factor(cats$Sex))
#（3）正確添加表頭：將行名加入矩陣
datamatrix= cbind(rownames(datamatrix),datamatrix)
rownames(datamatrix)[1]<-'A'
7. 清除所有變量
rm(list = ls())
8. 計算運行時間
exeTimeTsne<-system.time(tsne)
9. 平臺對接
- （1）與SPSS對接
setwd('')
#install.packages('foreign')
library(foreign)
data.spss<-read.spss("car_sales.sav", to.data.frame = TRUE)
head(data.spss)
#install.packages('Hmisc')
library(Hmisc)
mydataframe<-spss.get("C:/Users/Administrator/Desktop/test.sav", use.value.lables=TRUE)
- （2）與python對接（不成功）
參考： - http://www.reibang.com/p/06dbd8dcc198
- https://stackoverflow.com/questions/59321283/reticulate-py-available-returns-false
#install.packages("reticulate")
library(reticulate)
