我想使用R根据Eco_Status分组ID。

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英文:

I Want to Group IDs According to Eco_Status Using R

问题

我想要另一个数据框，其中包含四个Eco_Status作为列，并且将每个ID按照它们在每个Eco_Status下出现的情况分组。

给定上述数据框df，我想要一个包含poor、average、rich和billionaire作为它们状态的ID的向量。

英文:

I want another data frame that has the four(4) Eco_Status as column and group each ID as each occurs under each Eco_Status

  1. # group ID_numbers according to their respective status
  2. df <- read.table(text =
  3.                    "ID Pof_Exp  Eco_Status Gender
  4.               31304  2  poor male
  5.               31310  4  poor female
  6.               31307  6  rich male
  7.               31302  8  average male
  8.               31301 10 billionaire male
  9.               31308  2  poor female
  10.               31316  4  poor male
  11.               31317  6  rich male
  12.               31312  8  average female
  13.               31306 10 average female
  14.               31314  2  poor female
  15.               31311  4  average male
  16.               31305  6  rich male
  17.               31303  8  average male
  18.               31309 10  average female
  19.               31324  2  poor male
  20.               31320  4  poor female
  21.               31327  6  average male
  22.               31322  8  average female
  23.               31321 10 billionaire    male",
  24.               header = TRUE)

Given the above data frame df, I want a vector of ID having poor, average, rich, and billionaire as their status.

答案1

得分: 2

这是另一个可能的解决方案：

  1. df %>%
  2.   select(-c(Pof_Exp, Gender)) %>%
  3.   group_by(Eco_Status) %>%
  4.   mutate(row = row_number()) %>%
  5.   tidyr::pivot_wider(names_from = Eco_Status, values_from = ID) %>%
  6.   select(-row)

这将产生以下结果：

  1.    poor  rich average billionaire
  2.   <int> <int>   <int>       <int>
  3. 1 31304 31307   31302       31301
  4. 2 31310 31317   31312       31321
  5. 3 31308 31305   31306          NA
  6. 4 31316    NA   31311          NA
  7. 5 31314    NA   31303          NA
  8. 6 31324    NA   31309          NA
  9. 7 31320    NA   31327          NA
  10. 8    NA    NA   31322          NA
英文:

here is another possible solution:

  1. df %&gt;% select(-c(Pof_Exp, Gender)) %&gt;% group_by(Eco_Status) %&gt;% 
  2.   mutate(row = row_number()) %&gt;%tidyr::pivot_wider(names_from = Eco_Status, values_from = ID) %&gt;% 
  3.   select(-row)

which gives:

  1. poor  rich average billionaire
  2.   &lt;int&gt; &lt;int&gt;   &lt;int&gt;       &lt;int&gt;
  3. 1 31304 31307   31302       31301
  4. 2 31310 31317   31312       31321
  5. 3 31308 31305   31306          NA
  6. 4 31316    NA   31311          NA
  7. 5 31314    NA   31303          NA
  8. 6 31324    NA   31309          NA
  9. 7 31320    NA   31327          NA
  10. 8    NA    NA   31322          NA

答案2

得分: 1

像这样吗？

  1. df %>%
  2.     group_by(Eco_Status) %>%
  3.     summarise(ID = list(ID))
  5. Eco_Status                                                     ID
  6. 1     average 31302, 31312, 31306, 31311, 31303, 31309, 31327, 31322
  7. 2 billionaire                                           31301, 31321
  8. 3        poor        31304, 31310, 31308, 31316, 31314, 31324, 31320
  9. 4        rich                                    31307, 31317, 31305
英文:

Something like this?

  1. df %&gt;%
  2.     group_by(Eco_Status) %&gt;%
  3.     summarise(ID = list(ID))
  5.    Eco_Status                                                     ID
  6. 1     average 31302, 31312, 31306, 31311, 31303, 31309, 31327, 31322
  7. 2 billionaire                                           31301, 31321
  8. 3        poor        31304, 31310, 31308, 31316, 31314, 31324, 31320
  9. 4        rich                                    31307, 31317, 31305

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  • 本文由 发表于 2023年7月12日 21:28:45
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