在GROUP_BY()之后获取count()以查找非缺失值

问题描述

我有一些缺少值的数据(即NA值)，简化格式如下(最后输入的代码)：

#>   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，我想取x的平均值和x的可用值的计数(即不是NA)，所以我使用了group_by，它适用于mean：

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

但当我尝试添加count()时，出现错误

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')"

我想要的输出是：

#>      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

可重现的代码如下：

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

推荐答案

count不是正确的函数。count的第一个参数是特定的DataFrame或Tibble。然而，你传递的是一个向量，所以你得到了错误。此外，count汇总了数据帧，因此每个组只有一行。例如，请参阅

library(dplyr)

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

#  country     n
#  <fct>   <int>
#1 JPN         2
#2 USA         2

如果要添加新列而不汇总，请改用add_count

df %>% 
  group_by(country) %>% 
  mutate(mean_x = mean(x, na.rm = TRUE)) %>%
  add_count(country)

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

然而，这两个函数并不能满足您的需要。要计算每个组的非NA值，您需要

df %>% 
  group_by(country) %>% 
  mutate(mean_x = mean(x, na.rm = TRUE), 
         count = length(na.omit(x)))
         #OR
         #count = sum(!is.na(x)))#as @Humpelstielzchen mentioned


#    id     x country mean_x count
#  <dbl> <dbl> <fct>    <dbl> <int>
#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