df %>% 
  group_by(sp.name) %>% 
  mutate(new = (any(stage == 'adult') & any(stage == 'young') & stage == 'adult') * number)

#> # A tibble: 6 x 4
#> # Groups:   sp.name [4]
#>   sp.name number stage   new
#>   <chr>    <dbl> <chr> <dbl>
#> 1 a            2 adult     2
#> 2 a            2 young     0
#> 3 b            3 young     0
#> 4 b            3 adult     3
#> 5 c            4 adult     0
#> 6 d            4 young     0

使用ifelse条件：

df %>% 
mutate(new_column = ifelse( sp.name == "a" & stage %in% c("young", "adult"), number , 0))

  sp.name number stage new_column
1       a      2 adult          2
2       a      2 young          2
3       b      3 young          0
4       b      3 adult          0
5       c      4 adult          0
6       d      4 young          0

您需要具有stage == "young"和"adult"（组级条件）以及stage == "adult"（行级条件）的any组：

df %>%
  group_by(sp.name) %>%
  mutate(new_column = ifelse(any(stage == "young") & any(stage == "adult") & stage == "adult", 
                             number[stage == 'adult'], 0))

  sp.name number stage new_column
1 a            2 adult          2
2 a            2 young          0
3 b            3 young          0
4 b            3 adult          3
5 c            4 adult          0
6 d            4 young          0

使用data.table

library(data.table)
setDT(df)[, new_column := number *(all(c("young", "adult") %chin% stage) & 
    stage == "adult"), sp.name]

• 输出

> df
   sp.name number stage new_column
1:       a      2 adult          2
2:       a      2 young          0
3:       b      3 young          0
4:       b      3 adult          3
5:       c      4 adult          0
6:       d      4 young          0