在 previous post 中我得到了帮助，可以根据多个其他变量的条件更改单个变量。
但是，出现了更复杂的情况，因为我在分组变量中有多个缺失值。下面是一个示例数据框:
df2 <- data.frame(
ID = c(101:110),
Name = c("AA", "BB", "AA", "DD", "EE", "FF", "AA", "GG", "DD", "HH"),
Age = c(1, 56, 1, 72, 12, 43, 1, 32, 72, 99),
Gender = c("F", "M", "F", NA , NA, "M", "F", "M", NA, "M"),
Group = c(1, 2, 1, 2, 1, 4, 1, 3, 2, 4),
Date = seq(from = as.Date("2019-01-01"), to = as.Date("2019-01-10"), by = 'day'),
Order = c("re-do", "first", "first", "first", "re-do", "first", "re-do", "first", "re-do", "first"),
Site = c(2, 54, 2, 522, 3, 490, 2, 23, 522, 21)
)
>df2
ID Name Age Gender Group Date Order Site
1 101 AA 1 F 1 2019-01-01 re-do 2
2 102 BB 56 M 2 2019-01-02 first 54
3 103 AA 1 F 1 2019-01-03 first 2
4 104 DD 72 <NA> 2 2019-01-04 first 522
5 105 EE 12 <NA> 1 2019-01-05 re-do 3
6 106 FF 43 M 4 2019-01-06 first 490
7 107 AA 1 F 1 2019-01-07 re-do 2
8 108 GG 32 M 3 2019-01-08 first 23
9 109 DD 72 <NA> 2 2019-01-09 re-do 522
10 110 HH 99 M 4 2019-01-10 first 21
我有一个根据姓名、年龄、性别和组进行分组的函数，然后根据日期和顺序列更改 ID:
library(dplyr)
df2 %>%
group_by(Name, Age, Gender, Group, Site) %>%
mutate(first_date = ifelse(Order == "first",
Date,
Date[Order == "first"])) %>%
mutate(ID = ifelse(n() > 1 & Date >= first_date,
ID[Order == "first"],
ID)) %>%
select(-first_date)
但是，我遇到的问题是 NA 值仍然匹配和使用(请参阅下面第 4 行和第 9 行中复制的 ID 值):
ID Name Age Gender Group Date Order Site
<int> <fct> <dbl> <fct> <dbl> <date> <fct> <dbl>
1 101 AA 1 F 1 2019-01-01 re-do 2
2 102 BB 56 M 2 2019-01-02 first 54
3 103 AA 1 F 1 2019-01-03 first 2
4 104 DD 72 NA 2 2019-01-04 first 522
5 105 EE 12 NA 1 2019-01-05 re-do 3
6 106 FF 43 M 4 2019-01-06 first 490
7 103 AA 1 F 1 2019-01-07 re-do 2
8 108 GG 32 M 3 2019-01-08 first 23
9 104 DD 72 NA 2 2019-01-09 re-do 522
10 110 HH 99 M 4 2019-01-10 first 21
Warning messages:
1: Factor `Gender` contains implicit NA, consider using `forcats::fct_explicit_na`
2: Factor `Gender` contains implicit NA, consider using `forcats::fct_explicit_na`
3: Factor `Gender` contains implicit NA, consider using `forcats::fct_explicit_na`
4: Factor `Gender` contains implicit NA, consider using `forcats::fct_explicit_na`
我希望发生的是带有 NA 的行被忽略但没有被删除(这是我在管道中使用 na_omit() 设法获得的唯一结果)，因此它看起来像这样:
ID Name Age Gender Group Date Order Site
1 101 AA 1 F 1 2019-01-01 re-do 2
2 102 BB 56 M 2 2019-01-02 first 54
3 103 AA 1 F 1 2019-01-03 first 2
4 104 DD 72 <NA> 2 2019-01-04 first 522
5 105 EE 12 <NA> 1 2019-01-05 re-do 3
6 106 FF 43 M 4 2019-01-06 first 490
7 103 AA 1 F 1 2019-01-07 re-do 2
8 108 GG 32 M 3 2019-01-08 first 23
9 109 DD 72 <NA> 2 2019-01-09 re-do 522
10 110 HH 99 M 4 2019-01-10 first 21
最佳答案
我认为对 Gender 列中的 NA 值进行额外检查应该可以解决问题？
library(dplyr)
df2 %>%
group_by(Name, Age, Gender, Group, Site) %>%
mutate(first_date = ifelse(Order == "first",
Date,
Date[Order == "first"]),
ID = ifelse(n() > 1 & Date >= first_date & !is.na(Gender),
ID[Order == "first"],
ID)) %>%
select(-first_date)
# ID Name Age Gender Group Date Order Site
# <int> <fct> <dbl> <fct> <dbl> <date> <fct> <dbl>
# 1 101 AA 1 F 1 2019-01-01 re-do 2
# 2 102 BB 56 M 2 2019-01-02 first 54
# 3 103 AA 1 F 1 2019-01-03 first 2
# 4 104 DD 72 NA 2 2019-01-04 first 522
# 5 105 EE 12 NA 1 2019-01-05 re-do 3
# 6 106 FF 43 M 4 2019-01-06 first 490
# 7 103 AA 1 F 1 2019-01-07 re-do 2
# 8 108 GG 32 M 3 2019-01-08 first 23
# 9 109 DD 72 NA 2 2019-01-09 re-do 522
#10 110 HH 99 M 4 2019-01-10 first 21
关于r - 忽略(但不删除)dplyr group_by 函数中的 NA，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/55275194/