How to Delete Rows from a Dataframe That Contain N*Na

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 cfam
2 ENSG00000199674    0    2    2    2    2
6 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 cfam
2 ENSG00000199674    0    2    2    2    2
4 ENSG00000207604    0   NA   NA    1    2
6 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 cfam
2 ENSG00000199674    0    2    2    2    2
4 ENSG00000207604    0   NA   NA    1    2
6 ENSG00000221312    0    1    2    3    2

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

How to remove row if it has a NA value in one certain column

The easiest solution is to use is.na():

df[!is.na(df$B), ]

which gives you:

   A B  C
1 NA 2 NA
2  1 2  3
4  1 2  3

How can I delete all rows of a DataFrame that have an NA in a specific column?

I don't know whether what follows is the most elegant way of deleting all rows having an NA in a specific column, but that is one way.

Generating a toy DataFrame

julia> df = DataFrame(A = 1:10, B = 2:2:20)
10x2 DataFrame
| Row | A  | B  |
|-----|----|----|
| 1   | 1  | 2  |
| 2   | 2  | 4  |
| 3   | 3  | 6  |
| 4   | 4  | 8  |
| 5   | 5  | 10 |
| 6   | 6  | 12 |
| 7   | 7  | 14 |
| 8   | 8  | 16 |
| 9   | 9  | 18 |
| 10  | 10 | 20 |

julia> df[[1,4,8],symbol("B")] = NA
NA

julia> df
10x2 DataFrame
| Row | A  | B  |
|-----|----|----|
| 1   | 1  | NA |
| 2   | 2  | 4  |
| 3   | 3  | 6  |
| 4   | 4  | NA |
| 5   | 5  | 10 |
| 6   | 6  | 12 |
| 7   | 7  | 14 |
| 8   | 8  | NA |
| 9   | 9  | 18 |
| 10  | 10 | 20 |

Filtering out rows whose "B"-column element is NA

julia> df[~isna(df[:,symbol("B")]),:]
7x2 DataFrame
| Row | A  | B  |
|-----|----|----|
| 1   | 2  | 4  |
| 2   | 3  | 6  |
| 3   | 5  | 10 |
| 4   | 6  | 12 |
| 5   | 7  | 14 |
| 6   | 9  | 18 |
| 7   | 10 | 20 |

julia> df
10x2 DataFrame
| Row | A  | B  |
|-----|----|----|
| 1   | 1  | NA |
| 2   | 2  | 4  |
| 3   | 3  | 6  |
| 4   | 4  | NA |
| 5   | 5  | 10 |
| 6   | 6  | 12 |
| 7   | 7  | 14 |
| 8   | 8  | NA |
| 9   | 9  | 18 |
| 10  | 10 | 20 |

Deleting rows whose "B"-column element is NA

julia> deleterows!(df,find(isna(df[:,symbol("B")])))
7x2 DataFrame
| Row | A  | B  |
|-----|----|----|
| 1   | 2  | 4  |
| 2   | 3  | 6  |
| 3   | 5  | 10 |
| 4   | 6  | 12 |
| 5   | 7  | 14 |
| 6   | 9  | 18 |
| 7   | 10 | 20 |

julia> df
7x2 DataFrame
| Row | A  | B  |
|-----|----|----|
| 1   | 2  | 4  |
| 2   | 3  | 6  |
| 3   | 5  | 10 |
| 4   | 6  | 12 |
| 5   | 7  | 14 |
| 6   | 9  | 18 |
| 7   | 10 | 20 |

Python - Drop row if two columns are NaN

Any one of the following two:

df.dropna(subset=[1, 2], how='all')

or

df.dropna(subset=[1, 2], thresh=1)

Remove rows whose number is NA in R

You could subset with the help of is.na():

f <- f[!is.na(f$Current_status) & !is.na(f$Start_date), ]

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