How to remove rows with missing values NAs in a data frame

Question

I want to remove the rows with missing values(NAs). Below is my example data frame:

             u       v   w     x   y    z
1 ABCD00000207234    0   NA   NA   NA   NA
2 ABCD00000198674    0   2    2    2    2
3 ABCD00000223622    0   NA   NA   NA   NA
4 ABCD00000200604    0   NA   NA   1    2
5 ABCD00000201431    0   NA   NA   NA   NA
6 ABCD00000220312    0   1    2    3    2

Basically, I'd like to get a data frame such as the following.

             u       v    w    x    y   z
2 ABCD00000198674    0    2    2    2    2
6 ABCD00000220312    0    1    2    3    2

Also,If I want to filter only few columns? How will I do that?

            u        v   w    x    y    z
2 ABCD00000199674    0   2    2    2    2
4 ABCD00000200604    0   NA   NA   1    2
6 ABCD00000220312    0   1    2    3    2

Answer 1

You can use complete.cases in the following manner:

final[complete.cases(final), ]
             u       v    w    x    y    z
2 ABCD00000198674    0    2    2    2    2
6 ABCD00000220312    0    1    2    3    2

na.omit can also be chosen to remove all NA's. Also it is better than complete.cases as complete.cases allows partial selection i.e. it includes certain columns of the dataframe:

final[complete.cases(final[ , 5:6]),]
             u       v    w    x    y    z
2 ABCD00000198674    0    2    2    2    2
4 ABCD00000200604    0   NA   NA    1    2
6 ENSG00000220312    0    1    2    3    2

This is not the solution you want right? So to use is.na you have to use something like this:

final[rowSums(is.na(final[ , 5:6])) == 0, ]
             u       v    w    x    y    z
2 ABCD00000198674    0    2    2    2    2
4 ABCD00000200604    0   NA   NA    1    2
6 ABCD00000220312    0    1    2    3    2

Answer 2

Hi,

The below code returns rows without NA values.

dataframe[-which(is.na(dataframe)), ]

To filter out few you can add row numbers next to which() to display the NA as well as other filtered rows.

rows = c(-which(is.na(airquality)),row_numbers.....)

dataframe[-which(is.na(dataframe)), ]

Hope it helps!