使用R tidyverse根据固定变量创建重复行

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英文:

R tidyverse create duplicate rows based on a fixed variable

问题

我有一个R数据框，其中包含每个id的start和end测量值。end - start的最小可能值是min_sz = 2（已知差异，但实际数据中可能并不发生）。我希望基于一个固定值创建“块”，并根据start和end重叠的块数为每个id创建重复的行。

如果我将min_sz = 2作为我的块大小，我的计算和结果将如下所示：

  1. min_sz = 2
  3. df = tibble(
  4.   id = c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j"),
  5.   start = c(0, 0, 0, 4, 4, 8, 10, 10, 16, 32),
  6.   end = c(4, 6, 16, 10, 22, 18, 36, 56, 42, 84),
  7. )
  9. df %>% 
  10.   mutate(dur = end - start) %>% 
  11.   mutate(n_min_chunks = dur / min_sz) %>% 
  12.   uncount(n_min_chunks) %>% 
  13.   group_by(id) %>% 
  14.   mutate(row = row_number()) %>% 
  15.   mutate(
  16.     chunk_start = start + (min_sz * (row - 1)),
  17.     chunk_end = start + (min_sz * row),
  18.   )
# id start end dur row chunk_start chunk_end
1 a 0 4 4 1 0 2
2 a 0 4 4 2 2 4
3 b 0 6 6 1 0 2
4 b 0 6 6 2 2 4
5 b 0 6 6 3 4 6
6 c 0 16 16 1 0 2
7 c 0 16 16 2 2 4
8 c 0 16 16 3 4 6
.. .. ... ... ... ... ... ...

我希望执行类似的操作，但对于可能的任何req_sz，它是min_sz的倍数，并包括部分重叠。例如，如果我使用req_sz = 20，则我的输出应该如下所示（请注意，#5和#6具有部分重叠“4-20”和“20-22”）：

# id start end dur chunk_start chunk_end
1 a 0 4 4 0 20
2 b 0 6 6 0 20
3 c 0 16 16 0 20
4 c 4 10 6 0 20
5 c 4 22 18 0 20
6 c 4 22 18 20 40
.. .. ... ... ... ... ...

但我一直无法找到一个允许我扩展这个“块分割”操作的数学解决方案。

任何帮助都将不胜感激！

英文:

I have an R dataframe that has start and end measurements for each id. The least possible value for end - start is min_sz = 2 (known difference, but may not actually occur in the data). I wish to create "chunks" based on a fixed value and create duplicate rows for each id, based on the number of chunks that start and end overlap.

If I were to use min_sz = 2 as my chunk size, my calculation and result would look something like this:

  1. min_sz = 2
  3. df = tibble(
  4.   id = c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j"),
  5.   start = c(0, 0, 0, 4, 4, 8, 10, 10, 16, 32),
  6.   end = c(4, 6, 16, 10, 22, 18, 36, 56, 42, 84),
  7. )
  9. df %>% 
  10.   mutate(dur = end - start) %>% 
  11.   mutate(n_min_chunks = dur / min_sz) %>% 
  12.   uncount(n_min_chunks) %>% 
  13.   group_by(id) %>% 
  14.   mutate(row = row_number()) %>% 
  15.   mutate(
  16.     chunk_start = start + (min_sz * (row - 1)),
  17.     chunk_end = start + (min_sz * row),
  18.   )
# id start end dur row chunk_start chunk_end
1 a 0 4 4 1 0 2
2 a 0 4 4 2 2 4
3 b 0 6 6 1 0 2
4 b 0 6 6 2 2 4
5 b 0 6 6 3 4 6
6 c 0 16 16 1 0 2
7 c 0 16 16 2 2 4
8 c 0 16 16 3 4 6
.. .. ... ... ... ... ... ...

I wish to apply a similar operation, but for potentially any req_sz that's a multiple of min_sz and includes partial overlaps. If I were to use req_sz = 20 for example, my output should look something like this (note #5 & #6 with partial overlaps "4-20" & "20-22"):

# id start end dur chunk_start chunk_end
1 a 0 4 4 0 20
2 b 0 6 6 0 20
3 c 0 16 16 0 20
4 c 4 10 6 0 20
5 c 4 22 18 0 20
6 c 4 22 18 20 40
.. .. ... ... ... ... ...

but I've been unable to come up with a mathematical solution that allows me to scale this "chunking" operation.

Any help would be greatly appreciated!

答案1

得分: 1

在基本的R语言中，您可以编写以下函数来实现相同的功能：

  1. explode <- function(data, by){
  2.   f <- function(...){ 
  3.     s <- list(...)[c('start', 'end')]
  4.     mat <- embed(seq((s$start %/% by) * by, (s$end %/% by + 1) * by, by), 2)
  5.     data.frame(...,dur = s$end - s$start, chk_st = mat[,2], chk_ed = mat[,1])
  6.   }
  7.   do.call(rbind, do.call(Map, c(f, data, by = by)))
  8. }
  10. explode(df, by=20)
  11.     id start end dur chunck_start chunk_end
  12. a    a     0   4   4            0        20
  13. b    b     0   6   6            0        20
  14. c    c     0  16  16            0        20
  15. d    d     4  10   6            0        20
  16. e.1  e     4  22  18            0        20
  17. e.2  e     4  22  18           20        40
  18. f    f     8  18  10            0        20
  19. g.1  g    10  36  26            0        20
  20. g.2  g    10  36  26           20        40
  21. h.1  h    10  56  46            0        20
  22. h.2  h    10  56  46           20        40
  23. h.3  h    10  56  46           40        60
  24. i.1  i    16  42  26            0        20
  25. i.2  i    16  42  26           20        40
  26. i.3  i    16  42  26           40        60
  27. j.1  j    32  84  52           20        40
  28. j.2  j    32  84  52           40        60
  29. j.3  j    32  84  52           60        80
  30. j.4  j    32  84  52           80       100

请注意，这是基于R语言的函数，用于实现您提供的功能，并对输入数据进行操作。

英文:

in base R you could write a function to accomplish the same:

  1. explode &lt;- function(data, by){
  2.   f &lt;- function(...){ 
  3.    s &lt;- list(...)[c(&#39;start&#39;, &#39;end&#39;)]
  4.    mat &lt;- embed(seq((s$start %/% by) * by, (s$end %/% by + 1) * by, by), 2)
  5.    data.frame(...,dur = s$end - s$start, chk_st = mat[,2], chk_ed = mat[,1])
  6.   }
  7.   do.call(rbind, do.call(Map, c(f, data, by = by)))
  8. }
  11.  explode(df, by=20)
  12.     id start end dur chunck_start chunk_end
  13. a    a     0   4   4            0        20
  14. b    b     0   6   6            0        20
  15. c    c     0  16  16            0        20
  16. d    d     4  10   6            0        20
  17. e.1  e     4  22  18            0        20
  18. e.2  e     4  22  18           20        40
  19. f    f     8  18  10            0        20
  20. g.1  g    10  36  26            0        20
  21. g.2  g    10  36  26           20        40
  22. h.1  h    10  56  46            0        20
  23. h.2  h    10  56  46           20        40
  24. h.3  h    10  56  46           40        60
  25. i.1  i    16  42  26            0        20
  26. i.2  i    16  42  26           20        40
  27. i.3  i    16  42  26           40        60
  28. j.1  j    32  84  52           20        40
  29. j.2  j    32  84  52           40        60
  30. j.3  j    32  84  52           60        80
  31. j.4  j    32  84  52           80       100

huangapple
  • 本文由 发表于 2023年7月11日 07:34:29
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  • r
  • tidyverse
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