I have a data.frame that contains many columns. I want to keep the rows that have no NAs in 4 of these columns. The complication arises from the fact that I have other rows that are allowed have NAs in them so I can't use complete.cases or is.na. What's the most efficient way to do this?
我有一个包含许多列的data.frame。我要保留这些列中没有NAs的行。复杂的是，我有其他行允许有NAs，所以我不能使用complete。用例或is.na。最有效的方法是什么?
1 个解决方案
#1
17
You can still use complete.cases(). Just apply it to the desired columns (columns 1:4 in the example below) and then use the Boolean vector it returns to select valid rows from the entire data.frame.
仍然可以使用complete.cases()。只需将它应用到所需的列(下面示例中的第1 - 4列)，然后使用它返回的布尔向量从整个data.frame中选择有效的行。
set.seed(4)
x <- as.data.frame(replicate(6, sample(c(1:10,NA))))
x[complete.cases(x[1:4]),]
# V1 V2 V3 V4 V5 V6
# 1 7 4 6 8 10 5
# 2 1 2 5 5 1 2
# 5 6 8 4 10 6 6
# 6 2 6 9 3 4 4
# 7 4 3 3 1 2 1
# 9 8 5 2 7 7 3
# 10 10 10 1 2 5 NA
#1
17
You can still use complete.cases(). Just apply it to the desired columns (columns 1:4 in the example below) and then use the Boolean vector it returns to select valid rows from the entire data.frame.
仍然可以使用complete.cases()。只需将它应用到所需的列(下面示例中的第1 - 4列)，然后使用它返回的布尔向量从整个data.frame中选择有效的行。
set.seed(4)
x <- as.data.frame(replicate(6, sample(c(1:10,NA))))
x[complete.cases(x[1:4]),]
# V1 V2 V3 V4 V5 V6
# 1 7 4 6 8 10 5
# 2 1 2 5 5 1 2
# 5 6 8 4 10 6 6
# 6 2 6 9 3 4 4
# 7 4 3 3 1 2 1
# 9 8 5 2 7 7 3
# 10 10 10 1 2 5 NA