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Mary Rachel
Asked: 2025-01-08 05:11:14 +0800 CST

使用 summary_all() 获取多列中的值的数量

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我想创建一个表来统计数据集中每列非 NA 值的数量。我正在使用summarize_all()，但我很难获得非 NA 值的数量。

我参考了这个 StackOverflow 线程来获得一些见解，但它似乎没有帮助我获得计数：summarize_all with "n()" function。如果我通过，我将获得不同值的计数，或者如果我通过或如线程所引用，summarize_all(n_distinct)我将获得行数。summarize_all(list(n=~n()))summarize_all(list(n="length")

我的期望输出：

ID Female  Male Non_Binary
 5      5     4          3

我哪里做错了？

# Sample Code

test<-as_tibble(data.frame(`ID` = c("1","2","3","4","5"),
                           `Female` = c("Female","Female","Female","Female","Female"),
                           `Male` = c(NA,"Male","Male","Male","Male"),
                           `Non_Binary`=c("Non-Binary","Non-Binary","Non-Binary",NA,NA)))

## Attempt 1

summary<-test%>%
  summarize_all(list(n=~n()))

# A tibble: 1 × 4
   ID_n Female_n Male_n Non_Binary_n
  <int>    <int>  <int>        <int>
1     5        5      5            5

## Attempt 2

summary<-test%>%
  summarize_all(list(n="length"))

# A tibble: 1 × 4
   ID_n Female_n Male_n Non_Binary_n
  <int>    <int>  <int>        <int>
1     5        5      5            5

## Attempt 3

summary<-test%>%
  summarize_all(n_distinct)

# A tibble: 1 × 4
     ID Female  Male Non_Binary
  <int>  <int> <int>      <int>
1     5      1     2          2

### Desired Output

ID Female  Male Non_Binary
 5      5     4          3

1 个回答

  1. Best Answer

    n()和length()是这种情况下糟糕的选择 - 它们不会忽略NA它们所计算的值。计算满足条件（例如“非 NA”）的值的经典方法是满足sum条件。

    summarize_all()也已经弃用了几年。现在更倾向于使用across()。

    test |>
  summarize(across(everything(), \(x) sum(!is.na(x))))

# # A tibble: 1 × 4
#      ID Female  Male Non_Binary
#   <int>  <int> <int>      <int>
# 1     5      5     4          3
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