m3sm<-read.table("http://eagle.fish.washington.edu/cnidarian/BiGo_lar_M3_methratio_v9_A.txt", header=T, sep="\t", nrows=1000) classes<-sapply(m3sm,class) T3D3<-read.table("http://eagle.fish.washington.edu/cnidarian/BiGo_lar_T3D5_methratio_v9_A.txt", header=T, sep="\t", colClasses=classes) m15fold<-m1big[m1big$CT_count >= 5, c(1,4,5,8)] head(m15fold) m1_5xCGonly<-m15fold[grep(".{2}CG.{1}",m15fold$context), ] head(T3D3) plot(m1_5xCGonly, col =) T3D3_Mcg<-T3D3[T3D3$ratio > 0.5, ] head(T3D3_Mcg) hist(T3D3_Mcg$ratio, 100) T3D3_Mcg5<-T3D3[T3D3$ratio > 0.5,] T3D3_Mcg5<-T3D3[grepl(".{2}Cg.{1}",T3D3$context) & T3D3$CT_count >= 5 & T3D3$ratio > 0.5,] T3D3_Mcg5<-T3D3[grepl(".{2}Cg.{1}",T3D3$context) & T3D3$CT_count >= 5 & T3D3$ratio > 0.5,] T3D3_Mcg5<-T3D3[grepl(".{2}Cg.{1}",T3D3$context) & T3D3$CT_count >= 5 & T3D3$ratio > 0.5,] T3D3_Mct5<-T3D3[grepl(".{2}CT.{1}",T3D3$context) & T3D3$CT_count >= 5, ] T3D3_Mct5<-T3D3[grepl(".{2}CT.{1}",T3D3$context) & T3D3$CT_count >= 5, ] T3D3_Mct5<-T3D3[grepl(".{2}CT.{1}",T3D3$context) & T3D3$CT_count >= 5, ] #make a histogram, breaking it 100 hist(m1_duo$ratio, 100) write.table(m1_duo,"test.txt", sep="\t", row.names = F, quote = F)