我有一个数据集，其中一些数据以hh:mm格式和Excel序列号的混乱日期/时间格式存在。因此，我已将所有内容强制转换为字符串，并在一个大的`case_when`块内使用`stringr`和`readr`来识别不同的格式并正确处理它们。我认为我要么误解了我的`stringr`函数，要么误解了`case_when`，因为我得到了我期望的输出，但它会抛出解析失败和`NA`强制转换的警告，这些警告在最终产品中是不必要的。
这里是一些虚拟数据，其中包含我的数据集中每种格式的示例：
```R
dummy <- tibble(x = c("13:15:21", "02:03:17+01:00", "12:03", "0.1234"))

convert_times <- function(x){
  case_when(str_detect(x, "^0\\.")        ~ convert_excel_time(x), 
            str_detect(x, "\\+")          ~ parse_time(str_extract(x, ".+(?=\\+)")), 
            str_detect(x, ":")            ~ parse_time(x),
            .default = NA)
}
convert_excel_time <- function(x){
  as.numeric(x) * 24 * 60 * 60 %>%
  as_datetime() %>%
  hms::as_hms()
}

> dummy %>%
+   mutate(new = convert_time(x))
# A tibble: 4 × 2
  x              new        
  <chr>          <time>     
1 13:15:21       13:15:21.00
2 02:03:17+01:00 02:03:17.00
3 12:03          12:03:00.00
4 0.1234         02:57:41.76

[[1]]
<warning/rlang_warning>
Warning in `mutate()`:
ℹ In argument: `new = convert_time(x)`.
Caused by warning in `convert_excel_time()`:
! NAs introduced by coercion
---
Backtrace:
    ▆
 1. ├─dummy %>% mutate(new = convert_time(x))
 2. ├─dplyr::mutate(., new = convert_time(x))
 3. └─dplyr:::mutate.data.frame(., new = convert_time(x))
[[2]]
<warning/rlang_warning>
Warning in `mutate()`:
ℹ In argument: `new = convert_time(x)`.
Caused by warning:
! 2 parsing failures.
row col   expected         actual
  2  -- time like  02:03:17+01:00
  4  -- time like  0.1234        
---
Backtrace:
    ▆
 1. ├─dummy %>% mutate(new = convert_time(x))
 2. ├─dplyr::mutate(., new = convert_time(x))
 3. └─dplyr:::mutate.data.frame(., new = convert_time(x))

<details>
<summary>英文:</summary>
I have a data set with messy date/time formatting in some hh:mm formats and Excel serial numbers. So I&#39;ve coerced everything into a string and I&#39;m using `stringr` and `readr` within a large `case_when` block to identify different formats and process them properly. I think I&#39;m misunderstanding either my `stringr` functions or `case_when` because I&#39;m getting the output I expect, but it&#39;s throwing warnings of parsing failures and `NA` coercion that aren&#39;t in the final product.
Here are some dummy data with an example of each of the formats in my data set:

I've made a function to identify and parse each of these formats. It calls on another function to change excel serial codes into times. It uses regular expressions which I think are correct, but to summarise:
 - `^0\\.` should identify Excel serial numbers by the fact they are all
   decimals <1 
- `\\+` is identifying the times with the DST timezone
   indicator by searching for the plus sign then 
- `.+(?=\\+)` is
   extracting everything before the plus sign to parse 
- `:` is testing
   for a colon to make sure the result is some kind of time. This is a
   broader test so it's coming last after the pluses have already been
   matched

When I run it, I get the expected output, but the warnings that come along with it suggest to me I'm not understanding what's happening under the hood.

These are my errors

It seems to me, `convert_time` shouldn't be trying to parse those two observations at all since they are excluded by the left side of the `case_when` block. Similarly, I didn't expect `NA` coercion since the left hand side of the `case_when` prevents `convert_excel_time()` from seeing the hh:mm strings. Many thanks.
</details>
# 答案1
**得分**: 1
抱歉，我只返回翻译好的部分，不包括代码。以下是翻译好的内容：
"Gah! I didn't read all the documentation. The `dplyr` reference (https://dplyr.tidyverse.org/reference/case_when.html) clearly says `case_when` always solves all the RHS equations, which is why they throw a warning, but only uses the ones that match the LHS conditions.
# `case_when()` evaluates all RHS expressions, and then constructs its
# result by extracting the selected (via the LHS expressions) parts.
# In particular `NaN`s are produced in this case:
y &lt;- seq(-2, 2, by = .5)
case_when(
  y &gt;= 0 ~ sqrt(y),
  .default = y
)
#&gt; Warning: NaNs produced
#&gt; [1] -2.0000000 -1.5000000 -1.0000000 -0.5000000  0.0000000  0.7071068
#&gt; [7]  1.0000000  1.2247449  1.4142136"
<details>
<summary>英文:</summary>
Gah! I didn&#39;t read all the documentation. The `dplyr` reference (https://dplyr.tidyverse.org/reference/case_when.html) clearly says `case_when` always solves all the RHS equations, which is why they throw a warning, but only uses the ones that match the LHS conditions.

</details>