如何在不丢失R中的NA值的情况下有条件地从数据帧中删除观测值？

Question

In the data frame there is a variable called YOB . As you can see, there are 333 NA values.

> summary(train$YOB)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1880    1970    1983    1980    1993    2039     333 

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I identified some outliers and want to get rid of them. Anything less than 1900 and greater than 2003 shall be removed. I tried to do this by indexing.

train = train[which(train$YOB >= 1900 & train$YOB <= 2003),]

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Unfortunately observations whose YOB variable were NA are also removed.

> summary(train$YOB)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1900    1970    1983    1980    1993    2003 

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On a side note, I face the same problem when using subset command.

> train = subset(train, YOB >= 1900 & YOB <= 2003)
> summary(train$YOB)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1900    1970    1983    1980    1993    2003 

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I have also tried to use this condition in both attempts, but with no success, eg

> train = train[which(!is.na(train$YOB) & train$YOB >= 1900 & train$YOB <= 2003),]
> summary(train$YOB)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1900    1970    1983    1980    1993    2003 

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I would like to keep the observations that have NA in the YOB variable and only remove those that are numeric. The idea is in a second step to impute missing values.

Answer 1

The which will give the numeric index and skip all those NA rows. To avoid that, use the logical index without wrapping with which . The index will be NA in that way and that row will remain as NA even if there are other values that are non-NA.

res1 <- train[train$YOB >= 1900 & train$YOB <= 2003,]
res1[is.na(res1$YOB),]
#   YOB col2
#NA  NA   NA

The correct way would be to have another condition with is.na

res2 <- train[is.na(train$YOB)| (train$YOB >= 1900 & train$YOB <= 2003),]
res2[is.na(res2$YOB),]
#   YOB      col2
#42  NA 0.2258094

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Using a simple example

set.seed(25)
d1 <- data.frame(v1 = c(NA, 1, 5), v2 = rnorm(3))
d1$v1 >1
#[1]    NA FALSE  TRUE

Here, the NA value remains as such. If we use which

which(d1$v1 >1)
#[1] 3

we get only the index of the TRUE values. According to OP, both the NA and the rows that satisfy the logical condition should return. In that case,

d1[is.na(d1$v1)|d1$v1 > 1,]
# v1         v2
#1 NA -0.2118336
#3  5 -1.1533076

data

set.seed(29)
train <- data.frame(YOB = sample(c(NA, 1850:2015), 100, replace=TRUE), 
           col2 = rnorm(100))