我是rhadoop和r的新手。我有一个正常的r程序，其中有一个库（methylkit）。我想知道有没有人能给我一些关于如何在hadoop上运行这个r程序的见解。我需要在原来的r程序中修改什么？如果有人能给我一些建议，那会很有帮助的。
代码：

library(methylKit)
file.list=list( "new_sample1.txt","new_sample2.txt","n_sample3.txt")
myobj=read(file.list,sample.id=list("test1","test2","ctrl1"),assembly="hg19",treatment=c(1,1,0),context="CpG", pipeline=list(fraction=TRUE,chr.col=1,start.col=2,end.col=2,
coverage.col=6,strand.col=3,freqC.col=5 ))
getMethylationStats(myobj[[1]],plot=F,both.strands=F)
pdf("sample1_statistics.pdf")
getMethylationStats(myobj[[1]],plot=T,both.strands=F)
dev.off()
getMethylationStats(myobj[[2]],plot=F,both.strands=F)
pdf("sample2_statistics.pdf")
getMethylationStats(myobj[[2]],plot=T,both.strands=F)
dev.off()
getCoverageStats(myobj[[3]],plot=F,both.strands=F)
pdf("sample3_statistics.pdf")
getMethylationStats(myobj[[3]],plot=T,both.strands=F)
dev.off()
library("graphics")
pdf("sample1_coverage.pdf")
getCoverageStats(myobj[[1]], plot = T, both.strands = F)
dev.off()
pdf("sample2_coverage.pdf")
getCoverageStats(myobj[[2]], plot = T, both.strands = F)
dev.off()
pdf("sample3_coverage.pdf")
getCoverageStats(myobj[[3]], plot = T, both.strands = F)
dev.off()
meth=unite(myobj, destrand=FALSE)
pdf("correlation.pdf")
getCorrelation(meth,plot=T)
dev.off()
pdf("cluster.pdf")
clusterSamples(meth, dist="correlation",method="ward", plot=TRUE)
dev.off()
hc <- clusterSamples(meth, dist = "correlation", method = "ward",plot = FALSE)
pdf("pca.pdf")
PCASamples(meth, screeplot = TRUE)
PCASamples(meth)
myDiff=calculateDiffMeth(meth)
write.table(myDiff, "mydiff.txt", sep='\t')
myDiff25p.hyper <-get.methylDiff(myDiff,differenc=25,qvalue=0.01,type="hyper")
myDiff25p.hyper
write.table(myDiff25p.hyper,"hyper_methylated.txt",sep='\t')
myDiff25p.hypo <-get.methylDiff(myDiff,differenc=25,qvalue=0.01,type="hypo")
myDiff25p.hypo
write.table(myDiff25p.hypo,"hypo_methylated.txt",sep='\t')
myDiff25p <-get.methylDiff(myDiff,differenc=25,qvalue=0.01)
myDiff25p
write.table(myDiff25p,"differentialy_methylated.txt",sep='\t')
diffMethPerChr(myDiff,plot=FALSE,qvalue.cutoff=0.01,meth.cutoff=25)
pdf("diffMethPerChr.pdf")
diffMethPerChr(myDiff,plot=TRUE,qvalue.cutoff=0.01,meth.cutoff=25)
dev.off()
gene.obj <- read.transcript.features(system.file("extdata","refseq.hg18.bed.txt", package = "methylKit"))
write.table(gene.obj,"gene_obj.txt", sep='\t')
annotate.WithGenicParts(myDiff25p, gene.obj)
cpg.obj <- read.feature.flank(system.file("extdata","cpgi.hg18.bed.txt", package = "methylKit"),feature.flank.name = c("CpGi","shores"))
write.table(cpg.obj,"cpg_obj.txt", sep='\t')
diffCpGann <- annotate.WithFeature.Flank(myDiff25p,cpg.obj$CpGi, cpg.obj$shores, feature.name = "CpGi",flank.name = "shores")
write.table(diffCpGann,"diffCpCann.txt", sep='\t')
diffCpGann 
promoters <- regionCounts(myobj, gene.obj$promoters)
head(promoters[[1]])
write.table(promoters,"promoters.txt", sep='\t')
diffAnn <- annotate.WithGenicParts(myDiff25p, gene.obj)
head(getAssociationWithTSS(diffAnn))
diffAnn
write.table(getAssociationWithTSS(diffAnn),"diff_ann.txt", sep='\t')
getTargetAnnotationStats(diffAnn, percentage = TRUE,precedence = TRUE)
pdf("piechart1.pdf")
plotTargetAnnotation(diffAnn, precedence = TRUE, main ="differential methylation annotation")
dev.off()
pdf("piechart2.pdf")
plotTargetAnnotation(diffCpGann, col = c("green","gray", "white"), main = "differential methylation annotation")
dev.off()
getFeatsWithTargetsStats(diffAnn, percentage = TRUE)