rm(list=ls())library(ggplot2)setwd("C:\\Users\\Administrator\\Desktop\\Pei\\Trans_Kegg")data1<-read.table("1.txt",header=TRUE,sep="\t")data2<-read.table("2.txt",header=TRUE,sep=

rm(list=ls())setwd("C:\\Users\\Administrator\\Desktop\\machinelearning\\分组散点图")data("mpg")library(ggplot2)data<-read.table("data.txt",sep="\t",header=TRUE,row.names=1)#自定义刻度和标签cu

rm(list=ls())#清除所有变量setwd("C:\\Users\\Administrator\\Desktop\\NMDS\\Fun")#设置工作目录#1.导入所需的库。library(vegan)library(ggplot2)#2.定义所需的函数。mydata<-read.delim("mydata.txt",header=TRUE,sep="\t")my

setwd("C:\\Users\\Administrator\\Desktop")#加载所需的库library(data.table)#文件路径file_path<-"filtered_file_path_here.tsv"#替换为您的文件路径#从文件中读取数据data<-fread(file_path,sep="\t")#假设列名是"Pathway"，筛选

以上是学习的源头根据课题实际做了一些修改library(ggplot2)library(ggridges)library(reshape2)library(ggsci)setwd("C:\\Users\\Administrator\\Desktop")otu<-read.table('feature23.txt',header=TRUE,sep="\t")#对丰度标准化处理，并且转换成长数据