Question

这是我对这个问题的尝试，但是我不确定自己在做什么错。

Gender <- c('Male','Male','Male', 'Female', 'Female', 'Female')
Height <- c('183', '180', '185', '170', '165', '167')

DF <- data.frame(Gender, Height)

Male <- DF[(DF$Gender == 'Male'),]

Exclude_male <- DF[!Male]

Answer 1

我不确定问题的确切含义，但是您可以做的一件事是定义一个逻辑向量，该逻辑向量对于男性行为TRUE，对于女性行为FALSE：

is_male = DF$Gender == 'Male'
# note this is the same code you used in your "Male <-" line, 
# I'm just giving a name to a smaller part of it so we can re-use it

# Now you can define the `Male` and `Female` subsets using `is_male`:
Male = DF[is_male, ]
Female = DF[!is_male, ]

Answer 2

另外两个解决方案，都带有subset。

Female1 <- subset(DF, Gender != 'Male')
Female2 <- subset(DF, Gender == 'Female')

identical(Female1, Female2)
#[1] TRUE

Answer 3

这是用于保留所需值的其他代码选项。

Only_female <- subset(DF, Gender == 'Female', select = c(Gender, Height))

如何在不使用以下代码的情况下排除R中数据帧DF的公行；女性<-DF [（DF $ Gender =='女性'），]

3 个答案: