你好，我有以下数据集：
```R
ID <- c(1,1,1,2,2,3,3,3,4,4,4)
diagnosis <- c("A","A","B","C","C","B","A","A","C","C","B")
df <- data.frame(ID,diagnosis)

<details>
<summary>英文:</summary>
Hi I have the dataset below:

I would like to count how many people had each type of diagnosis. Some people have the same diagnosis multiple times which I would like to have them count once. 
ie. Only two people would have diagnosis "A". (ID 1 and ID 3)
ie. Only two people would have diagnosis "C". (ID 2 and ID 4)
ie. Only three people would have diagnosis "B". (ID 1, ID 2 and ID 4)
I'm wondering if there's a way of summarizing the above into a table.
I would appreciate all the help there is! Thanks!!!
</details>
# 答案1
**得分**: 3
你可以使用 `group_by` 按照诊断进行分组，并使用 `summarise` 结合 `n_distinct` 来计算每个分组中的 ID 数量，就像这样：
``` r
library(dplyr)
df %>%
  group_by(diagnosis) %>%
  summarise(n = n_distinct(ID))

<sup>创建于2023年03月31日，使用 [reprex v2.0.2](https://reprex.tidyverse.org)。</sup>
<details>
<summary>英文:</summary>
You could `group_by` on diagnosis and `summarise` with `n_distinct` to count the ID&#39;s per group like this:
``` r
library(dplyr)
df %&gt;%
  group_by(diagnosis) %&gt;%
  summarise(n = n_distinct(ID))
#&gt; # A tibble: 3 &#215; 2
#&gt;   diagnosis     n
#&gt;   &lt;chr&gt;     &lt;int&gt;
#&gt; 1 A             2
#&gt; 2 B             3
#&gt; 3 C             2

# A B C 
# 2 3 2

> colSums(table(df) > 0)
A B C
2 3 2