问题

Here's the translated code portion:

我有一些（医疗）纵向数据，用于检测（或未检测到）支气管扩张症。我希望统计最新随访/检查时有无支气管扩张症的参与者人数。然后，我希望存储有支气管扩张症和没有支气管扩张症的患者人数。这样，我可以轻松地将这个数字/值插入到医学报告中（我还有其他需要这种方法的结果）。

我正在寻找是否有更简单/更准确的方法来完成我正在做的事情。

示例数据框：

| id | 随访日期 | 支气管扩张症 |
| --- | --- | --- |
| 1 | 2022年01月01日 | 否 |
| 1 | 2022年01月12日 | 是 |
| 2 | 2022年02月07日 | 是 |
| 2 | 2023年05月06日 | 是 |
| 3 | 2021年01月05日 | 否 |
| 3 | 2022年03月12日 | 否 |

这是我的当前代码：

```R
df1 <- df %>%
    group_by(id) %>%
    filter(date_of_followup == max(date_of_followup)) %>%
    filter(bronchiectasis == "是")

bronchiectasisyes <- sum(df1$bronchiectasis == "是")

df2 <- df %>%
    group_by(id) %>%
    filter(date_of_followup == max(date_of_followup)) %>%
    filter(bronchiectasis == "否")

bronchiectasisno <- sum(df2$bronchiectasis == "否")

这段代码可以获得我想要的结果，但可能有点混乱，我怀疑是否有更优雅/更健壮的方法。

<details>
<summary>英文:</summary>

I have some (medical) longitudinal data on the detection (or not) of bronchiectasis.  I wish to count the number of participants with and without bronchiectasis at their latest follow up/check.  I then wish to store the number of patients who do have bronchiectasis and the number who don&#39;t.  This is so I can then insert that number/value into a qmd report easily.  (I have other outcomes where I need this method).

Is there a simpler/more accurate method of what I&#39;m doing

Example df

| id | date_of_followup | bronchiectasis |
| --- | --- | --- |
| 1 | 01/01/2022 | N |
| 1 | 01/12/2022 | Y |
| 2 | 02/07/2022 | Y |
| 2 | 5/6/2023 | Y |
| 3 | 01/05/2021 | N |
| 3 | 3/12/2022 | N |

This is my current code

df1 <- df %>%
group_by(id) %>%
filter(date_of_followup == max(date_of_followup )) %>%
filter(bronchiectasis=="Y")

bronchiectasisyes <- sum(df1$bronchiectasis == "Y")

df2 <- df%>%
group_by(id) %>%
filter(date_of_followup == max(date_of_followup )) %>%
filter(bronchiectasis=="N")

bronchiectasisno <- sum(df2$bronchiectasis == "N")

This gets me the desired outcome, but perhaps is a bit messy and I suspect there is a more elegant/robust method


</details>


# 答案1
**得分**: 0

以下是翻译好的部分：

修复日期，按最大id筛选，然后进行汇总：

```r
library(dplyr)
df %>%
  mutate(date_of_followup = as.Date(date_of_followup, format="%m/%d/%Y")) %>%
  slice_max(date_of_followup, by = id) %>%
  count(bronchiectasis)
#   bronchiectasis n
# 1              N 1
# 2              Y 2

要确认这是否有效，请查看中间步骤：

df %>%
  mutate(date_of_followup = as.Date(date_of_followup, format="%m/%d/%Y")) %>%
  slice_max(date_of_followup, by = id)
#   id date_of_followup bronchiectasis
# 1  1       2022-01-12              Y
# 2  2       2023-05-06              Y
# 3  3       2022-03-12              N

（我假设您的日期使用m/d/Y格式...如有需要请进行修复。）

library(dplyr)
df %>%
  mutate(date_of_followup = as.Date(date_of_followup, format="%m/%d/%Y")) %>%
  slice_max(date_of_followup, by = id) %>%
  count(bronchiectasis)
#   bronchiectasis n
# 1              N 1
# 2              Y 2

df %>%
  mutate(date_of_followup = as.Date(date_of_followup, format="%m/%d/%Y")) %>%
  slice_max(date_of_followup, by = id)
#   id date_of_followup bronchiectasis
# 1  1       2022-01-12              Y
# 2  2       2023-05-06              Y
# 3  3       2022-03-12              N