cumsum is.na rle 忽略 NA 的连续性
cumsum is.na with rle ignoring consectives 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
我只想使用 cumsum(is.na(variable)
计算缺失的变量但忽略连续缺失的变量所以我想要的输出看起来像:
group variable newvariable
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
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 groups 可以工作。请问有什么建议吗?
参考:
df %>%
group_by(group) %>%
mutate(new = with(rle(is.na(variable)), rep(cumsum(values), lengths))) %>%
ungroup()
另一种选择是在逻辑向量
上使用 diff
和 cumsum
library(data.table)
setDT(df)[, new := cumsum(c(TRUE, diff(is.na(variable)) > 0) ), group ]
或 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
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
我只想使用 cumsum(is.na(variable)
计算缺失的变量但忽略连续缺失的变量所以我想要的输出看起来像:
group variable newvariable
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
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 groups 可以工作。请问有什么建议吗?
参考:
df %>%
group_by(group) %>%
mutate(new = with(rle(is.na(variable)), rep(cumsum(values), lengths))) %>%
ungroup()
另一种选择是在逻辑向量
上使用diff
和 cumsum
library(data.table)
setDT(df)[, new := cumsum(c(TRUE, diff(is.na(variable)) > 0) ), group ]
或 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