Remove rows with all or some NAs (missing values) in data.frame

Also check complete.cases :

> final[complete.cases(final), ]             gene hsap mmul mmus rnor cfam2 ENSG00000199674    0    2    2    2    26 ENSG00000221312    0    1    2    3    2

na.omit is nicer for just removing all NA's. complete.cases allows partial selection by including only certain columns of the dataframe:

> final[complete.cases(final[ , 5:6]),]             gene hsap mmul mmus rnor cfam2 ENSG00000199674    0    2    2    2    24 ENSG00000207604    0   NA   NA    1    26 ENSG00000221312    0    1    2    3    2

Your solution can't work. If you insist on using is.na, then you have to do something like:

> final[rowSums(is.na(final[ , 5:6])) == 0, ]             gene hsap mmul mmus rnor cfam2 ENSG00000199674    0    2    2    2    24 ENSG00000207604    0   NA   NA    1    26 ENSG00000221312    0    1    2    3    2

but using complete.cases is quite a lot more clear, and faster.

Try na.omit(your.data.frame). As for the second question, try posting it as another question (for clarity).

tidyr has a new function drop_na:

library(tidyr)df %>% drop_na()#              gene hsap mmul mmus rnor cfam# 2 ENSG00000199674    0    2    2    2    2# 6 ENSG00000221312    0    1    2    3    2df %>% drop_na(rnor, cfam)#              gene hsap mmul mmus rnor cfam# 2 ENSG00000199674    0    2    2    2    2# 4 ENSG00000207604    0   NA   NA    1    2# 6 ENSG00000221312    0    1    2    3    2