过滤数据框以仅获取在列中具有值且在第一个值 R 之后的任何列中具有另一个值的行

Question

我正在寻找一种方法来过滤以下数据框，以便我最终只得到在包含 1 的列之后的某列中具有 1 且在任何其他列中具有 2 的行。我感谢任何帮忙！

data_rel1 <- structure(list(job1category = c(NA, 1, 2, 2, 1, 1, 2, 1, 1, 1, 
1, 1, 1, 1, NA, 1, 1, 4, 1, 1, NA, NA, 1, 1, 1, 1, 1, 1, 2, 1, 
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, NA, 7, NA, 5, 1, 
5, NA, 2, 5, 1, NA, 1, 5, 1, 1, 1, 1, 5, 1, 5, 4, 1, 4, 5, 4, 
NA, 5, NA, 5, 4, 3, 6, 1, 4, 4, 5, 4, 1, NA, 1, NA, 1, NA, 1, 
1, 1, 1, 1, 4, 1, 1, 1, NA, 1, NA), job2category = c(3, 2, 1, 
2, 3, 1, 2, 2, 1, 1, 1, NA, 2, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 5, 3, 3, 1, 1, 2, 4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 
1, 1, 7, 7, 1, 1, 1, NA, 4, 1, 1, NA, 2, 1, 1, 1, 1, NA, 5, NA, 
4, 5, 4, NA, 5, 2, 4, 4, 2, 7, 5, NA, 5, 2, NA, 4, NA, 1, 5, 
NA, 1, NA, 1, 1, 1, 1, 5, 2, NA, 4, 1, 1, 1, NA, 1, NA), job3category = c(3, 
2, 1, 2, 3, 1, 2, 2, 1, 1, 1, NA, 2, 1, NA, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 5, 3, 3, 1, 1, 2, 4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 
2, 1, 1, 1, 7, 7, 1, 1, 1, NA, 4, 1, 1, NA, 2, 1, 1, 1, 1, NA, 
5, NA, 4, 5, 4, NA, 5, 2, 4, 4, 2, 7, 5, NA, 5, 2, NA, 4, NA, 
1, 5, NA, 1, NA, 1, 1, 1, 1, 5, 2, NA, 4, 1, 1, 1, NA, 1, NA), 
    job4category = c(3, 2, 1, 2, 3, 1, 2, 2, 1, 1, 1, NA, 2, 
    1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 3, 3, 1, 1, 2, 
    4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 7, 7, 1, 1, 1, 
    NA, 4, 1, 1, NA, 2, 1, 1, 1, 1, NA, 5, NA, 4, 5, 4, NA, 5, 
    2, 4, 4, 2, 7, 5, NA, 5, 2, NA, 4, NA, 1, 5, NA, 1, NA, 1, 
    1, 1, 1, 5, 2, NA, 4, 1, 1, 1, NA, 1, NA)), row.names = c(NA, 
-100L), class = c("tbl_df", "tbl", "data.frame"))

Answer 1

你可以用apply试试这个-

data_rel1[apply(data_rel1, 1, function(x) {
  inds <- which(x == 1)
  length(inds) && any(which(x == 2)  > min(inds))
}), ]

#  job1category job2category job3category job4category
#         <dbl>        <dbl>        <dbl>        <dbl>
#1            1            2            2            2
#2            1            2            2            2
#3            1            2            2            2
#4            1            2            2            2
#5            1            2            2            2
#6            1            2            2            2
#7            1            2            2            2
#8            1            2            2            2