cumsum is.na，带rle忽略连续性na'；s

简单的问题。假设我有以下数据：

library(tidyverse)
df <- data.frame(group = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2),
                     variable = c(NA, "a", NA, "b", "c", NA, NA, NA, NA, "a", NA, "c", NA, NA, "d", NA, NA, "a"))
df
   group variable
1      1     <NA>
2      1        a
3      1     <NA>
4      1        b
5      1        c
6      1     <NA>
7      1     <NA>
8      1     <NA>
9      1     <NA>
10     1        a
11     1     <NA>
12     1        c
13     1     <NA>
14     1     <NA>
15     1        d
16     2     <NA>
17     2     <NA>
18     2        a

我想我需要将

rle

合并到我的代码中：

df %>%
  group_by(group, na_group = {na_group = rle(variable); rep(seq_along(na_group$lengths), na_group$lengths)}) %>%
  mutate(newvariable = cumsum((is.na(variable)))) #?

也许

map

over group可以起作用。有什么建议吗

参考文献：

---

另一个选项是在逻辑向量上使用

diff

和

cumsum

library(data.table)
setDT(df)[, new := cumsum(c(TRUE, diff(is.na(variable)) > 0) ), group ]

---

或使用

dplyr

library(dplyr)
df %>%
   group_by(group) %>%
   mutate(new = cumsum(c(TRUE, diff(is.na(variable)) > 0)))
# A tibble: 18 x 3
# Groups:   group [2]
#   group variable   new
#   <dbl> <fct>    <int>
# 1     1 <NA>         1
# 2     1 a            1
# 3     1 <NA>         2
# 4     1 b            2
# 5     1 c            2
# 6     1 <NA>         3
# 7     1 <NA>         3
# 8     1 <NA>         3
# 9     1 <NA>         3
#10     1 a            3
#11     1 <NA>         4
#12     1 c            4
#13     1 <NA>         5
#14     1 <NA>         5
#15     1 d            5
#16     2 <NA>         1
#17     2 <NA>         1
#18     2 a            1

库（dplyr）
df%>%
分组依据（分组）%>%
变异（new=cumsum（c（TRUE，diff（is.na（variable））>0）））
#一个tibble:18x3
#分组：分组[2]
#组变量新
#        
# 1     1          1
#2 1 a 1
# 3     1          2
#4 1 b 2
#5 1 c 2
# 6     1          3
# 7     1          3
# 8     1          3
# 9     1          3
#101A3
#11     1          4
#12 1 c 4
#13     1          5
#14     1          5
#15 1 d 5
#16     2          1
#17     2          1
#18 2 a 1

也相关：和

library(data.table)
setDT(df)[, new := cumsum(c(TRUE, diff(is.na(variable)) > 0) ), group ]

library(dplyr)
df %>%
   group_by(group) %>%
   mutate(new = cumsum(c(TRUE, diff(is.na(variable)) > 0)))
# A tibble: 18 x 3
# Groups:   group [2]
#   group variable   new
#   <dbl> <fct>    <int>
# 1     1 <NA>         1
# 2     1 a            1
# 3     1 <NA>         2
# 4     1 b            2
# 5     1 c            2
# 6     1 <NA>         3
# 7     1 <NA>         3
# 8     1 <NA>         3
# 9     1 <NA>         3
#10     1 a            3
#11     1 <NA>         4
#12     1 c            4
#13     1 <NA>         5
#14     1 <NA>         5
#15     1 d            5
#16     2 <NA>         1
#17     2 <NA>         1
#18     2 a            1