• R语言学习——数据合并及绘制密度分布曲线图



    setwd("E:/08_cooperation/07_X-lab/06-Crosstalk/Aadapter_primer")
    # 读取lane01.txt,并对其按列进行相加处理,然后对列进行进行命名
    d1=read.table("lane01.txt",header = FALSE,sep = ",")
    cyc001=d1$V3+d1$V4+d1$V5+d1$V6
    d1=cbind(d1,cyc001)
    names(d1)=c("cyc001x","cyc001y","cyc001A","cyc001T","cyc001C","cyc001G","cyc001")
    # 读取lane06.txt,并对其按列进行相加处理,然后对列进行进行命名
    d2=read.table("lane06.txt",header = FALSE,sep = ",")
    cyc001=d2$V3+d2$V4+d2$V5+d2$V6
    d2=cbind(d2,cyc001)
    names(d2)=c("cyc001x","cyc001y","cyc001A","cyc001T","cyc001C","cyc001G","cyc001")

    head(d1)
    cyc001x cyc001y cyc001A cyc001C cyc001G cyc001T cyc001
    1 29.20 2.94 798 697 831 1322 3648
    2 83.36 3.51 1379 575 455 3185 5594
    3 121.10 2.82 1049 377 371 4249 6046
    4 150.12 2.42 1093 1317 1275 1157 4842
    5 159.20 3.58 1124 993 428 5124 7669
    6 194.29 2.63 1178 1007 372 1328 3885

    head(d2)
    cyc001x cyc001y cyc001A cyc001C cyc001G cyc001T cyc001
    1 37.57 3.14 2374 6680 1337 1501 11892
    2 108.90 3.11 3469 3720 528 5688 13405
    3 270.51 4.34 6710 1868 1039 4087 13704
    4 136.98 4.11 1753 11892 873 1656 16174
    5 142.14 3.93 1677 2732 1366 3399 9174
    6 234.00 4.00 1657 7318 727 1524 11226

    #载入plyr包
    library(plyr)
    listA<-list()
    listA[[1]] <- data.frame(t(d1$cyc001A))
    listA[[2]] <- data.frame(t(d2$cyc001A))
    A<-t(rbind.fill(listA))
    colnames(A)<-c("lane01_A","lane06_A")
    write.table(A,file="intsfile_A.txt")
    listT<-list()
    listT[[1]]<-data.frame(t(d1$cyc001T))
    listT[[2]]<-data.frame(t(d2$cyc001T))
    T<-t(rbind.fill(listT))
    colnames(T)<-c("lane01_T","lane06_T")
    write.table(T,file="intsfile_T.txt")
    listC <- list()
    listC[[1]] <- data.frame(t(d1$cyc001C))
    listC[[2]] <- data.frame(t(d2$cyc001C))
    C<- t(rbind.fill(listC))
    colnames(C) <-c("lane01_C","lane06_C")
    write.table(C,file="intsfile_C.txt")
    listG <- list()
    listG[[1]] <- data.frame(t(d1$cyc001G))
    listG[[2]] <- data.frame(t(d2$cyc001G))
    G<- t(rbind.fill(listG))
    colnames(G) <-c("lane01_G","lane06_G")
    write.table(G,file="intsfile_G.txt")
    listCyc <- list()
    listCyc[[1]] <- data.frame(t(d1$cyc001))
    listCyc[[2]] <- data.frame(t(d2$cyc001))
    ATCG<- t(rbind.fill(listCyc))
    colnames(ATCG) <-c("lane01","lane06")
    write.table(ATCG,file="intsfile_ATCG.txt")
    list.files()
    [1] "201811271857_lane06_8mix_10A_100B2" "201904171659_B028___Lane01_03_05"
    [3] "intsfile_A.txt" "intsfile_ATCG.txt"
    [5] "intsfile_C.txt" "intsfile_G.txt"
    [7] "intsfile_T.txt" "lane01.txt"
    [9] "lane06.txt"
    library(wordcloud2)
    library(gcookbook)
    library(ggplot2)
    library(reshape2)
    data=read.table("intsfile_ATCG.txt",header = T)
    data1=melt(data,variable.name="lane",value.name="ints")
    ggplot(data1,aes(x=ints,colour=lane))+geom_density(adjust=1)+ggtitle("A Adapter VS A Primer(Base_A)")+theme(plot.title=element_text(size=rel(1.2),hjust = 0.5,family="Times"))+scale_x_continuous(limits = c(0,60000),breaks = c(0,5000,10000,15000,20000,30000,40000,50000,60000))+scale_y_continuous("desity(%)",limits = c(0,0.0003),breaks = c(0.00000,0.00005,0.00010,0.00015,0.00020,0.00030),labels = c(0.00000,0.00005,0.00010,0.00015,0.00020,0.00030)*100)

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  • 原文地址:https://www.cnblogs.com/caicai2019/p/10756436.html
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