Remove rows in a data.frame appearing in another data.frame by rowname() as id

Question

I know there are some similar questions. But the answer for them doesn't help in my case or I did understand them wrong.

My example code

set.seed(0)

df <- data.frame(A = seq(20),
                 B1 = sample(c(T, F), 20, replace=T),
                 B2 = sample(c(T, F), 20, replace=T))

rownames(df) <- df$A
df <- df[, c('B1', 'B2'), drop = FALSE]

result <- df[df$B1 == FALSE & df$B2 == FALSE, ]

print(result)

Results in this

> result
      B1    B2
1  FALSE FALSE
4  FALSE FALSE
10 FALSE FALSE
16 FALSE FALSE
18 FALSE FALSE

So you see 5 cases at the end. I want this 5 cases be removed from the original data.frame df without loosing the rownames() or name the rows. So the rows with names 1, 4, 10, 16, 18 should be removed without causing renameing of the others rows.

What I tried (based on other answers):

> library(dplyr)
> anti_join(df, result)
Joining, by = c("B1", "B2")
      B1    B2
1   TRUE FALSE
2   TRUE  TRUE
3  FALSE  TRUE
4   TRUE  TRUE
5  FALSE  TRUE
6  FALSE  TRUE
7  FALSE  TRUE
8   TRUE  TRUE
9   TRUE  TRUE
10  TRUE FALSE
11 FALSE  TRUE
12  TRUE  TRUE
13  TRUE FALSE
14 FALSE  TRUE
15  TRUE FALSE

The rownames are not correct here. They are simply from 1 to 15.

Another try make no sense to me

> setdiff(df, result)
     B1    B2
1  TRUE FALSE
2  TRUE  TRUE
3 FALSE  TRUE

Answer 1

An option with setdiff would be :

df[setdiff(rownames(df), rownames(result)), ]

#      B1    B2
#2   TRUE FALSE
#3   TRUE  TRUE
#5  FALSE  TRUE
#6   TRUE  TRUE
#7  FALSE  TRUE
#8  FALSE  TRUE
#9  FALSE  TRUE
#11  TRUE  TRUE
#12  TRUE  TRUE
#13  TRUE FALSE
#14 FALSE  TRUE
#15  TRUE  TRUE
#17  TRUE FALSE
#19 FALSE  TRUE
#20  TRUE FALSE

---

However, a more general approach I would suggest would be to calculate indices only once and use them.

inds <- df$B1 == FALSE & df$B2 == FALSE
result <- df[inds, ]
output <- df[!inds, ]

Answer 2

我们可以%in%使用!

df[!rownames(df) %in% rownames(result),]