如何根据NA与字母对值进行分组 [英] How to group values based on NA vs. alphabet

问题描述

我在按字母顺序排列的数据框中有一列字母值的列，部分插入了 NA ： >

I have a column of LETTER values in alphabetic order in a dataframe partly interspersed with NA:

df1 <- data.frame(
  phase = c(NA, "A", "B", "D", NA, "A", "B", "C", "E", "A", "B", "D")
)

字母值形成组： A 直到 下一个 NA 或下一个 A 是一个群组。我想创建一个新列以使这些组明确。

The LETTER values form groups: anything from A until either the next NA or the next A is a group. I'd like to create a new column to make these groups explicit.

预期结果是：

df1 <- data.frame(
  phase = c(NA, "A", "B", "D", NA, "A", "B", "C", "E", "A", "B", "D"),
  group = c(NA,"group1","group1","group1",NA, "group2","group2","group2","group2","group3","group3","group3")
)

如何创建此列？对于基于 dplyr 的其他建议，我深表感谢。

How can I create this column? I'm grateful for any advice, dplyr-based or otherwise.

到目前为止，我已经尝试过-仅部分成功（第三组与第二组之间没有用 NA 隔开）：

What I've tried so far--with only partial success (the third group, which is not separated from the second by NA, is missed):

df1 %>% 
  mutate(group = cumsum(is.na(phase)),
         group = ifelse(is.na(phase), NA, paste("group", group, sep = "")))

   phase  group
1   <NA>   <NA>
2      A group1
3      B group1
4      D group1
5   <NA>   <NA>
6      A group2
7      B group2
8      C group2
9      E group2
10     A group2
11     B group2
12     D group2

推荐答案

如果阶段为 A ; ，跳到下一组。然后，当阶段为 NA 时，将这些组替换为 NA 。

If phase is "A", jump to the next group. Then replace those groups with NA when phase is NA.

library(dplyr)

df1 %>%
  mutate(group = cumsum(phase == "A" & !is.na(phase)) %>%
                 paste0("group", .) %>% 
                 replace(is.na(phase), NA))

#    phase  group
# 1   <NA>   <NA>
# 2      A group1
# 3      B group1
# 4      D group1
# 5   <NA>   <NA>
# 6      A group2
# 7      B group2
# 8      C group2
# 9      E group2
# 10     A group3
# 11     B group3
# 12     D group3

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