Hi @AlexisW, I have used this:
data_frame_1 <- data.frame(read.table("~/Documents/A.csv"))
data_frame_2 <- data.frame(read.table("~/Documents/B.csv"))
data_frame_3 <- data.frame(read.table("~/Documents/C.csv"))
data_frame_4 <- data.frame(read.table("~/Documents/D.csv"))
intersect <- Reduce(intersect, list(data_frame_1$V1, data_frame_2$V1, data_frame_3$V1, data_frame_4$V1))
And this reverts:
> Reduce(intersect, list(data_frame_1$V1, data_frame_2$V1, data_frame_3$V1, data_frame_4$V1))
[1] "Streptococcaceae" "Firmicutes_unclassified" "Eubacteriaceae" "Tannerellaceae"
[5] "Veillonellaceae" "Erysipelotrichaceae" "Clostridiaceae" "Rikenellaceae"
[9] "Ruminococcaceae" "Akkermansiaceae" "Eggerthellaceae" "Enterobacteriaceae"
[13] "Oscillospiraceae" "Desulfovibrionaceae" "Lactobacillaceae" "Clostridiales_unclassified"
[17] "Odoribacteraceae" "Barnesiellaceae" "Lachnospiraceae" "Sutterellaceae"
[21] "Acidaminococcaceae" "Prevotellaceae" "Selenomonadaceae" "Coriobacteriaceae"
[25] "Bacteroidaceae" "Bifidobacteriaceae"
it means, it reverts only the common features in the first column. But I need the common features across the four tables those match with the third column as well.