将列分离/拆分为三个单独的列，当存在NA值时。

huangapple
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英文:

Separate/unstack column into three separate columns when there are NA values

问题

我有一列数据，看起来像这样：

  1. value 
  2. Ford
  3. Focus
  4. 2,300
  5. Chevy
  6. 15
  7. Toyota
  8. 245
  9. Honda
  10. Nissan

我想要将它转换成这样的格式，希望使用tidyverse解决方案。谢谢！

  1. Make   Model     Value   
  2. Ford   Focus     2,300
  3. Chevy  NA        15
  4. Toyota NA        245
  5. Honda  NA        NA
  6. Nissan NA        NA
英文:

I have a column that looks like this

  1. value 
  2. Ford
  3. Focus
  4. 2,300
  5. Chevy
  6. 15
  7. Toyota
  8. 245
  9. Honda
  10. Nissan

How would I get it to look like this? Would like to use a tidyverse solution. Thanks!

  1. Make   Model     Value   
  2. Ford   Focus     2,300
  3. Chevy  NA        15
  4. Toyota NA        245
  5. Honda  NA        NA
  6. Nissan NA        NA

答案1

得分: 1

你可以这样做（这依赖于完整的Make数据）：

  1. library(dplyr)
  2. library(tidyr)
  4. make <- c("Ford", "Chevy", "Toyota", "Honda", "Nissan")
  6. df %>%
  7.   mutate(id = cumsum(value %in% make),
  8.          name = case_when(value %in% make ~ "Make",
  9.                           grepl("\\d", value) ~ "Value",
  10.                           TRUE ~ "Model")) %>%
  11.   pivot_wider(id_cols = id) %>%
  12.   select(-id)

输出结果

  1.   Make   Model Value
  2.   <chr>  <chr> <chr>
  3. 1 Ford   Focus 2,300
  4. 2 Chevy  NA    15   
  5. 3 Toyota NA    245  
  6. 4 Honda  NA    NA   
  7. 5 Nissan NA    NA
英文:

You could do something like (this relies on complete data Make data):

  1. library(dplyr)
  2. library(tidyr)
  4. make &lt;- c(&quot;Ford&quot;, &quot;Chevy&quot;, &quot;Toyota&quot;, &quot;Honda&quot;, &quot;Nissan&quot;)
  6. df |&gt;
  7.   mutate(id = cumsum(value %in% make),
  8.          name = case_when(value %in% make ~ &quot;Make&quot;,
  9.                           grepl(&quot;\\d&quot;, value) ~ &quot;Value&quot;,
  10.                           T ~ &quot;Model&quot;))|&gt;
  11.   pivot_wider(id_cols = id) |&gt;
  12.   select(-id)

Output

  1.   Make   Model Value
  2.   &lt;chr&gt;  &lt;chr&gt; &lt;chr&gt;
  3. 1 Ford   Focus 2,300
  4. 2 Chevy  NA    15   
  5. 3 Toyota NA    245  
  6. 4 Honda  NA    NA   
  7. 5 Nissan NA    NA

huangapple
  • 本文由 发表于 2023年8月9日 01:25:50
  • 转载请务必保留本文链接：https://go.coder-hub.com/76861905.html
  • dplyr
  • r
  • tidyr
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