Subset rows based on number of occurrence in specific column

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英文:

Subset rows based on number of occurrence in specific column

问题

The gs_name 列的值是重复的，可能对应于hallmark数据框中的一个或多个行。我想保留只有在gs_name与少于25行或多于500行对应的数据框行。

  1. for (i in hallmark$gs_name) {
  2.   if (25 <= nrow(hallmark) >= 500) {
  3.     subset.df <- hallmark
  4.   }
  5. }

输入：

  1. > dput(hallmark[c(1:5,300:305),3:4])
  2. structure(list(gs_name = c("adipogenesis", "adipogenesis", "adipogenesis", 
  3. "adipogenesis", "adipogenesis", "bile_acid_metabolism", "bile_acid_metabolism", 
  4. "bile_acid_metabolism", "bile_acid_metabolism", "bile_acid_metabolism", 
  5. "bile_acid_metabolism"), gene_symbol = c("ABCA1", "ABCB8", "ACAA2", 
  6. "ACADL", "ACADM", "HSD17B4", "HSD17B6", "HSD3B1", "HSD3B7", "IDH1", 
  7. "IDH2")), row.names = c(NA, -11L), class = c("tbl_df", "tbl", 
  8. "data.frame"))
英文:

The gs_name column value is repetitive and may correspond to one or more rows in the hallmark df. I want to keep only the rows of the dataframe where gs_name corresponds with less than to 25 rows or more than 500 rows.

  1. for (i in hallmark$gs_name) {
  2.   if (25 <= nrow(hallmark) >= 500) {
  3.     subset.df <- hallmark
  4.   }
  5. }

Input:

  1. > dput(hallmark[c(1:5,300:305),3:4])
  2. structure(list(gs_name = c("adipogenesis", "adipogenesis", "adipogenesis", 
  3. "adipogenesis", "adipogenesis", "bile_acid_metabolism", "bile_acid_metabolism", 
  4. "bile_acid_metabolism", "bile_acid_metabolism", "bile_acid_metabolism", 
  5. "bile_acid_metabolism"), gene_symbol = c("ABCA1", "ABCB8", "ACAA2", 
  6. "ACADL", "ACADM", "HSD17B4", "HSD17B6", "HSD3B1", "HSD3B7", "IDH1", 
  7. "IDH2")), row.names = c(NA, -11L), class = c("tbl_df", "tbl", 
  8. "data.frame"))

答案1

得分: 1

  1. **data.table**
  3.     library(data.table)
  4.     setDT(hallmark )[, .SD[.N <= 25 | .N >= 500], by = gs_name]
英文:

additional solution option

data.table

  1. library(data.table)
  2. setDT(hallmark )[, .SD[.N <= 25 | .N >= 500], by = gs_name]

答案2

得分: 0

最简单的方法是按照 gs_name 对数据进行分组，并使用 dplyr 函数 n() 来满足你的两个条件，即：

  1. library(dplyr)
  3. hallmark <- structure(list(gs_name = c("adipogenesis", "adipogenesis", "adipogenesis", 
  4.                            "adipogenesis", "adipogenesis", "bile_acid_metabolism", "bile_acid_metabolism", 
  5.                            "bile_acid_metabolism", "bile_acid_metabolism", "bile_acid_metabolism", 
  6.                            "bile_acid_metabolism"), gene_symbol = c("ABCA1", "ABCB8", "ACAA2", 
  7.                                                                     "ACADL", "ACADM", "HSD17B4", "HSD17B6", "HSD3B1", "HSD3B7", "IDH1", 
  8.                                                                     "IDH2")), row.names = c(NA, -11L), class = c("tbl_df", "tbl", 
  9.                                                                                                                  "data.frame"))
  11. hallmark %>%
  12.   group_by(gs_name) %>%
  13.   filter(n() <= 25 | n() >= 500) %>%
  14.   ungroup()
  15. #> # A tibble: 11 × 2
  16. #>    gs_name              gene_symbol
  17. #>    <chr>                <chr>      
  18. #>  1 adipogenesis         ABCA1      
  19. #>  2 adipogenesis         ABCB8      
  20. #>  3 adipogenesis         ACAA2      
  21. #>  4 adipogenesis         ACADL      
  22. #>  5 adipogenesis         ACADM      
  23. #>  6 bile_acid_metabolism HSD17B4    
  24. #>  7 bile_acid_metabolism HSD17B6    
  25. #>  8 bile_acid_metabolism HSD3B1     
  26. #>  9 bile_acid_metabolism HSD3B7     
  27. #> 10 bile_acid_metabolism IDH1       
  28. #> 11 bile_acid_metabolism IDH2

<sup>创建于2023年07月18日，使用 reprex v2.0.2</sup>

英文:

Easiest way would be to group your data by gs_name and use the dplyr function n() for your two conditions, i.e.

  1. library(dplyr)
  3. hallmark &lt;- structure(list(gs_name = c(&quot;adipogenesis&quot;, &quot;adipogenesis&quot;, &quot;adipogenesis&quot;, 
  4.                            &quot;adipogenesis&quot;, &quot;adipogenesis&quot;, &quot;bile_acid_metabolism&quot;, &quot;bile_acid_metabolism&quot;, 
  5.                            &quot;bile_acid_metabolism&quot;, &quot;bile_acid_metabolism&quot;, &quot;bile_acid_metabolism&quot;, 
  6.                            &quot;bile_acid_metabolism&quot;), gene_symbol = c(&quot;ABCA1&quot;, &quot;ABCB8&quot;, &quot;ACAA2&quot;, 
  7.                                                                     &quot;ACADL&quot;, &quot;ACADM&quot;, &quot;HSD17B4&quot;, &quot;HSD17B6&quot;, &quot;HSD3B1&quot;, &quot;HSD3B7&quot;, &quot;IDH1&quot;, 
  8.                                                                     &quot;IDH2&quot;)), row.names = c(NA, -11L), class = c(&quot;tbl_df&quot;, &quot;tbl&quot;, 
  9.                                                                                                                  &quot;data.frame&quot;))
  11. hallmark %&gt;%
  12.   group_by(gs_name) %&gt;%
  13.   filter(n() &lt;= 25 | n() &gt;= 500) %&gt;%
  14.   ungroup()
  15. #&gt; # A tibble: 11 &#215; 2
  16. #&gt;    gs_name              gene_symbol
  17. #&gt;    &lt;chr&gt;                &lt;chr&gt;      
  18. #&gt;  1 adipogenesis         ABCA1      
  19. #&gt;  2 adipogenesis         ABCB8      
  20. #&gt;  3 adipogenesis         ACAA2      
  21. #&gt;  4 adipogenesis         ACADL      
  22. #&gt;  5 adipogenesis         ACADM      
  23. #&gt;  6 bile_acid_metabolism HSD17B4    
  24. #&gt;  7 bile_acid_metabolism HSD17B6    
  25. #&gt;  8 bile_acid_metabolism HSD3B1     
  26. #&gt;  9 bile_acid_metabolism HSD3B7     
  27. #&gt; 10 bile_acid_metabolism IDH1       
  28. #&gt; 11 bile_acid_metabolism IDH2

<sup>Created on 2023-07-18 with reprex v2.0.2</sup>

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  • 本文由 发表于 2023年7月18日 06:07:32
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