在group_by()之后对不缺少的值执行count()

我有一些缺少值的数据(即NA值),简化格式如下(末尾输入代码):

#>   id   x country
#> 1  1 2.0     USA
#> 2  2 4.0     USA
#> 3  3 3.5     JPN
#> 4  4  NA     JPN

对于每个国家,我都想 x ,以及可用值的计数 十 (即不是NA),所以我使用 group_by ,它对 mean 以下内容:

df <- df %>% group_by(country) %>% 
  mutate(mean_x = mean(x, na.rm = TRUE),
        #count_x = count(x)) 
        )

df
#> # A tibble: 4 x 4
#> # Groups:   country [2]
#>      id     x country mean_x
#>   <dbl> <dbl> <fct>    <dbl>
#> 1     1   2   USA        3  
#> 2     2   4   USA        3  
#> 3     3   3.5 JPN        3.5
#> 4     4  NA   JPN        3.5

但是当我试图添加 count() ,我有个错误

library(tidyverse)
df <- data.frame(id = c(1, 2, 3, 4),
                  x = c(2, 4, 3.5, NA),
                  country = c("USA", "USA", "JPN", "JPN")
                 )
df
df <- df %>% group_by(country) %>% 
  mutate(mean_x = mean(x, na.rm = TRUE),
        count_x = count(x)) 
        )

df

#> Error in UseMethod("summarise_") : no applicable method for 'summarise_' applied to an 
#> object of class "c('double', 'numeric')"

我想要的结果是:

#>      id     x country mean_x  count
#>   <dbl> <dbl> <fct>    <dbl>
#> 1     1   2   USA        3     2
#> 2     2   4   USA        3     2
#> 3     3   3.5 JPN        3.5   1
#> 4     4  NA   JPN        3.5   1

可复制代码如下:

library(tidyverse)
df <- data.frame(id = c(1, 2, 3, 4),
                  x = c(2, 4, 3.5, NA),
                  country = c("USA", "USA", "JPN", "JPN")
                 )
df
df <- df %>% group_by(country) %>% 
  mutate(mean_x = mean(x, na.rm = TRUE),
        count_x = count(x)) 
        )

df