R 对数据帧中的每个单元格应用条件替换函数

我试图通过检查每个值是否存在于特定的列表中并保留它来对R中的数据帧进行子集划分。例如，在以下数据帧中：

x <- data.frame(A = sample(1:5, 5),
                B = sample(1:5, 5),
                C = sample(1:5, 5))
  A B C
1 2 2 1
2 3 3 3
3 1 4 4
4 4 5 2
5 5 1 5

---

丢失的值会发生什么情况并不重要——如果更容易的话，可以将它们更改为NA。从浏览类似的问题来看，lapply似乎可以做到这一点，但作为新手，我正在努力将我所看到的应用到这个场景中

将每行折叠为匹配的数字，并将每行长度调整为ncol。假设您希望将数字左对齐，如预期输出所示

set.seed(47)
x <- data.frame(A = sample(1:5, 5),
                B = sample(1:5, 5),
                C = sample(1:5, 5))

# with lapply
keep_vals = c(1, 3, 4)
x[] = lapply(x, function(y) {
  y[! y %in% keep_vals] = NA
  return(y)
})

x
#    A  B  C
# 1  3  1  1
# 2  1 NA NA
# 3 NA NA  4
# 4  4  3 NA
# 5 NA  4  3

d <- setNames(as.data.frame(t(apply(d, 1, function(x) {
  x <- x[x %in% c(1, 3, 4)]
  `length<-`(x, ncol(d))
}))), names(d))
d
#    A  B  C
# 1  1 NA NA
# 2  3  3  3
# 3  1  4  4
# 4  4 NA NA
# 5 NA NA NA

使用dplyr:：bind_行

set.seed(47)  # reset data
x <- data.frame(A = sample(1:5, 5),
                B = sample(1:5, 5),
                C = sample(1:5, 5))

keep_vals = c(1, 3, 4)
for (i in 1:ncol(x)) {
  x[, i][!x[, i] %in% keep_vals] <- NA
}
x
#    A  B  C
# 1  3  1  1
# 2  1 NA NA
# 3 NA NA  4
# 4  4  3 NA
# 5 NA  4  3

x %>% mutate_all(
  ~replace(., !. %in% keep_vals, NA)
)
#    A  B  C
# 1  3  1  1
# 2  1 NA NA
# 3 NA NA  4
# 4  4  3 NA
# 5 NA  4  3

d <- setNames(as.data.frame(t(apply(d, 1, function(x) {
  x <- x[x %in% c(1, 3, 4)]
  `length<-`(x, ncol(d))
}))), names(d))
d
#    A  B  C
# 1  1 NA NA
# 2  3  3  3
# 3  1  4  4
# 4  4 NA NA
# 5 NA NA NA

d <- read.table(text="A B C
1 2 2 1
2 3 3 3
3 1 4 4
4 4 5 2
5 5 2 5", header=TRUE)

do.call(bind_rows,apply(x,1, function(a) a[a %in% c(1,3,4)]))

# A tibble: 5 x 3
      A     B     C
  <int> <int> <int>
1     4    NA    NA
2     1     1     1
3     3     3    NA
4    NA    NA     4
5    NA     4     3