在R中获取所选行的数据框总和

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英文:

Getting the sum of selected rows in a data frame in R

问题

I'd like to combine the population number for the 15 and 18 groups into the 15 age group, the 20, 21, and 22 age groups into the 20 age group, the 60 and 62 age groups into the 60 age group, and the 65 and 67 age groups into the 65 age group.

我想将15和18岁年龄组的人口数量合并为15岁年龄组，将20、21和22岁年龄组的人口数量合并为20岁年龄组，将60和62岁年龄组的人口数量合并为60岁年龄组，将65和67岁年龄组的人口数量合并为65岁年龄组。

英文:

I'm creating a population age pyramid for my metro area from US Census data. Here's some sample data:

  1. Sex = rep(c("male", "female"), each = 23)
  2. Age_group = as.factor(rep(c(0, 5, 10, 15, 18, 20, 21, 22, 25, 30, 35, 40, 45, 50, 55, 60, 62, 65, 67, 70, 75, 80, 85), length = 46))
  3. pop = c(-24684, -24946, -26465, -16103, -11431, -6233, -6071, -14838, -27420, -26246, -24612, -22753, -23036, -24870, -27676, -11400, -14267, -9493, -12132, -17456, -10913, -7241, -5836, 23322, 23225, 25521, 15128, 11388, 5858, 5300, 15385, 27610, 26368, 25329, 23045, 24025, 25847, 28077, 12419, 16241, 10000, 14411, 20807, 14309, 10216, 11125)
  5. pop_pyramid <- data.frame(Sex, Age_group, pop)

My issue: While most of the data is in 5-year age groups, the 15, 18, 20, 21, 22, 60, 62, 65, and 67 age groups for each sex are not. I'd like to combine the population number for the 15 and 18 groups into the 15 age group, the 20, 21, and 22 age groups into the 20 age group, the 60 and 62 age groups the 60 age group, and the 65 and 67 age groups into the 65 age group.

Simply adding the rows I want fails as Age_group and Sex are factors; rowSums also fails for the same reason. I thought of aggregation but only need those select rows aggregated, not the entire data frame. Is there a way to do this without resorting to doing it by hand?

Thanks.

答案1

得分: 1

我建议将 Age_group 中的 5 取模操作移除，然后您可以继续进行计算：

  1. pop_pyramid$Age_group2 <- as.numeric(as.character(Age_group)) -
  2.   as.numeric(as.character(Age_group))%%5
  3. pop_pyramid
  5.       Sex Age_group    pop Age_group2
  6. 1    male         0 -24684          0
  7. 2    male         5 -24946          5
  8. 3    male        10 -26465         10
  9. 4    male        15 -16103         15
  10. 5    male        18 -11431         15
  11. 6    male        20  -6233         20
  12. 7    male        21  -6071         20
  13. 8    male        22 -14838         20
  14. 9    male        25 -27420         25
  15. 10   male        30 -26246         30
  16. 11   male        35 -24612         35
  17. 12   male        40 -22753         40
  18. 13   male        45 -23036         45
  19. 14   male        50 -24870         50
  20. 15   male        55 -27676         55
  21. 16   male        60 -11400         60
  22. 17   male        62 -14267         60
  23. 18   male        65  -9493         65
  24. 19   male        67 -12132         65
  25. 20   male        70 -17456         70
  26. 21   male        75 -10913         75
  27. 22   male        80  -7241         80
  28. 23   male        85  -5836         85
  29. 24 female         0  23322          0
  30. 25 female         5  23225          5
  31. 26 female        10  25521         10
  32. 27 female        15  15128         15
  33. 28 female        18  11388         15
  34. 29 female        20   5858         20
  35. 30 female        21   5300         20
  36. 31 female        22  15385         20
英文:

I would suggest to remove the modulus of 5 from Age_group then you can continue with the calculations:

  1. pop_pyramid$Age_group2 &lt;- as.numeric(as.character(Age_group)) -
  2.   as.numeric(as.character(Age_group))%%5
  3. pop_pyramid
  5.       Sex Age_group    pop Age_group2
  6. 1    male         0 -24684          0
  7. 2    male         5 -24946          5
  8. 3    male        10 -26465         10
  9. 4    male        15 -16103         15
  10. 5    male        18 -11431         15
  11. 6    male        20  -6233         20
  12. 7    male        21  -6071         20
  13. 8    male        22 -14838         20
  14. 9    male        25 -27420         25
  15. 10   male        30 -26246         30
  16. 11   male        35 -24612         35
  17. 12   male        40 -22753         40
  18. 13   male        45 -23036         45
  19. 14   male        50 -24870         50
  20. 15   male        55 -27676         55
  21. 16   male        60 -11400         60
  22. 17   male        62 -14267         60
  23. 18   male        65  -9493         65
  24. 19   male        67 -12132         65
  25. 20   male        70 -17456         70
  26. 21   male        75 -10913         75
  27. 22   male        80  -7241         80
  28. 23   male        85  -5836         85
  29. 24 female         0  23322          0
  30. 25 female         5  23225          5
  31. 26 female        10  25521         10
  32. 27 female        15  15128         15
  33. 28 female        18  11388         15
  34. 29 female        20   5858         20
  35. 30 female        21   5300         20
  36. 31 female        22  15385         20

答案2

得分: 0

以下是翻译后的代码部分：

  1. # 使用 `cut` 函数来创建每个年龄组的计数表，但首先需要将它们转换为数值型值：
  2. table(cut(as.numeric(as.character(pop_pyramid$Age_group)), 
  3.     breaks = seq(0, 100, 5),
  4.     labels = paste0(seq(0, 95, 5), "-", seq(5, 100, 5)),
  5.     include.lowest = TRUE))

输出：

  1.    0-5   5-10  10-15  15-20  20-25  25-30  30-35  35-40  40-45  45-50  50-55  55-60  60-65  65-70  70-75  75-80  80-85  85-90  90-95 95-100 
  2.      4      2      2      4      6      2      2      2      2      2      2      2      4      4      2      2      2      0      0      0

如果您更愿意重新分类它们，您可以进行如下调整：

  1. pop_pyramid$new_agecat <- cut(as.numeric(as.character(pop_pyramid$Age_group)), 
  2.                               breaks = seq(0, 100, 5),
  3.                               labels = paste0(seq(0, 95, 5), "-", seq(5, 100, 5)),
  4.                               include.lowest = TRUE)

输出：

  1. #       Sex Age_group    pop new_agecat
  2. # 1    male         0 -24684        0-5
  3. # 2    male         5 -24946        0-5
  4. # 3    male        10 -26465       5-10
  5. # 4    male        15 -16103      10-15
  6. # 5    male        18 -11431      15-20

希望这对您有所帮助。

英文:

You can use cut to create a table of counts for each age group, but you will want to convert them to numeric values first:

  1. table(cut(as.numeric(as.character(pop_pyramid$Age_group)), 
  2.     breaks = seq(0, 100, 5),
  3.     labels = paste0(seq(0, 95, 5),&quot;-&quot;, seq(5, 100, 5)),
  4.     include.lowest = TRUE) )

Output:

  1.    0-5   5-10  10-15  15-20  20-25  25-30  30-35  35-40  40-45  45-50  50-55  55-60  60-65  65-70  70-75  75-80  80-85  85-90  90-95 95-100 
  2.      4      2      2      4      6      2      2      2      2      2      2      2      4      4      2      2      2      0      0      0

If you would rather just recategorize them, you could tweak to do:

  1. pop_pyramid$new_agecat &lt;- cut(as.numeric(as.character(pop_pyramid$Age_group)), 
  2.                               breaks = seq(0, 100, 5),
  3.                               labels = paste0(seq(0, 95, 5),&quot;-&quot;, seq(5, 100, 5)),
  4.                               include.lowest = TRUE)

Output:

  1. #       Sex Age_group    pop new_agecat
  2. # 1    male         0 -24684        0-5
  3. # 2    male         5 -24946        0-5
  4. # 3    male        10 -26465       5-10
  5. # 4    male        15 -16103      10-15
  6. # 5    male        18 -11431      15-20

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  • 本文由 发表于 2023年6月30日 02:49:50
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  • aggregate
  • dataframe
  • r
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