在data.frame中删除具有NAs（缺少值）的行 [英] Remove rows with NAs (missing values) in data.frame

问题描述

我想删除所有列中包含 NA 的数据框中的行。下面是我的示例数据框。

I'd like to remove the lines in this data frame that contain NAs across all columns. Below is my example data frame.

             gene hsap mmul mmus rnor cfam
1 ENSG00000208234    0   NA   NA   NA   NA
2 ENSG00000199674    0   2    2    2    2
3 ENSG00000221622    0   NA   NA   NA   NA
4 ENSG00000207604    0   NA   NA   1    2
5 ENSG00000207431    0   NA   NA   NA   NA
6 ENSG00000221312    0   1    2    3    2

基本上，我想得到一个数据框架，如下所示。

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

             gene hsap mmul mmus rnor cfam
2 ENSG00000199674    0   2    2    2    2
6 ENSG00000221312    0   1    2    3    2

此外，我想知道如何只对一些列进行过滤，所以我也可以得到如下的数据框： / p>

Also, I'd like to know how to only filter for some columns, so I can also get a data frame like this:

             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

推荐答案

还要检查 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 更好的是删除所有NA。 complete.cases 允许通过使用部分数据框进行部分选择：

na.omit is nicer for just removing all NA's. complete.cases allows partial selection by using part 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

您的解决方案无法正常工作。如果您坚持使用is.na，则必须执行以下操作：

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

但使用complete.cases相当清楚，更快。

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

这篇关于在data.frame中删除具有NAs（缺少值）的行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！