如何按数字递增顺序对数据框进行排序？

huangapple
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英文:

How can I order a data frame so it increases numerically?

问题

我有下面的表格。我想按数字升序排序，从第2波到第31波。

  1.  波次                     平均_B
  2.    <chr>                     <dbl>
  3.  1 第10波 - 2020年12月    5.49 
  4.  2 第11波 - 2021年1月     5.52
  5.  3 第12波 - 2021年2月    5.52
  6.  4 第13波 - 2021年3月       5.45
  7.  5 第14波 - 2021年4月       5.53
  8.  6 第15波 - 2021年5月         5.46
  9.  7 第16波 - 2021年6月        5.51
  10.  8 第17波 - 2021年7月        5.63
  11.  9 第18波 - 2021年8月      5.54
  12. 10 第19波 - 2021年9月   5.49
  13. 11 第2波 - 2020年4月        5.67
  14. 12 第20波 - 2021年10月     5.55
  15. 13 第21波 - 2021年11月    5.43
  16. 14 第22波 - 2021年12月    5.35
  17. 15 第23波 - 2022年1月     5.46
  18. 16 第24波 - 2022年2月    5.41
  19. 17 第25波 - 2022年3月       5.30
  20. 18 第26波 - 2022年4月       5.38
  21. 19 第27波 - 2022年5月         5.39
  22. 20 第28波 - 2022年6月        5.55
  23. 21 第29波 - 2022年7月        5.51
  24. 22 第3波 - 2020年5月          5.72
  25. 23 第30波 - 2022年8月      5.52
  26. 24 第31波 - 2022年9月   5.54
  27. 25 第4波 - 2020年6月         5.62
  28. 26 第5波 - 2020年7月         5.54
  29. 27 第6波 - 2020年8月       5.61
  30. 28 第7波 - 2020年9月    5.60
  31. 29 第8波 - 2020年10月      5.54
  32. 30 第9波 - 2020年11月     5.60

我创建了这个表格，希望能自动按照以下代码中的数字顺序排序：

  1. mean_values <- Trial %>% 
  2.   group_by(Wave) %>% 
  3.   summarise(mean_B = mean(Weight_Answer, na.rm = TRUE))

然后我尝试了下面的代码：

  1. mean_values <- Trial %>%
  2.   group_by(Wave) %>%
  3.   summarise(mean_B = mean(Weight_Answer, na.rm = TRUE)) %>%
  4.   arrange(as.numeric(gsub("Wave ", "", Wave)))

但它出现了警告：

>警告信息：
arrange() 中有1个警告。
ℹ 参数：..1 = as.numeric(gsub("Wave ", "", Wave))。
引起警告的原因：
! 强制转换引入了NAs。

英文:

I have the table below. I want to order it such that it goes from Wave 2 - Wave 31 in numeric ascending order.

  1.  Wave                     mean_B
  2.    <chr>                     <dbl>
  3.  1 Wave 10 - December 2020    5.49 
  4.  2 Wave 11 - January 2021     5.52
  5.  3 Wave 12 - February 2021    5.52
  6.  4 Wave 13 - March 2021       5.45
  7.  5 Wave 14 - April 2021       5.53
  8.  6 Wave 15 - May 2021         5.46
  9.  7 Wave 16 - June 2021        5.51
  10.  8 Wave 17 - July 2021        5.63
  11.  9 Wave 18 - August 2021      5.54
  12. 10 Wave 19 - September 2021   5.49
  13. 11 Wave 2 - April 2020        5.67
  14. 12 Wave 20 - October 2021     5.55
  15. 13 Wave 21 - November 2021    5.43
  16. 14 Wave 22 - December 2021    5.35
  17. 15 Wave 23 - January 2022     5.46
  18. 16 Wave 24 - February 2022    5.41
  19. 17 Wave 25 - March 2022       5.30
  20. 18 Wave 26 - April 2022       5.38
  21. 19 Wave 27 - May 2022         5.39
  22. 20 Wave 28 - June 2022        5.55
  23. 21 Wave 29 - July 2022        5.51
  24. 22 Wave 3 - May 2020          5.72
  25. 23 Wave 30 - August 2022      5.52
  26. 24 Wave 31 - September 2022   5.54
  27. 25 Wave 4 - June 2020         5.62
  28. 26 Wave 5 - July 2020         5.54
  29. 27 Wave 6 - August 2020       5.61
  30. 28 Wave 7 - September 2020    5.60
  31. 29 Wave 8 - October 2020      5.54
  32. 30 Wave 9 - November 2020     5.60

I created the table expecting it to be ordered numerically automatically using the following code:

  1. mean_values <- Trial %>% 
  2.   group_by(Wave) %>%
  3.   summarise(mean_B = mean(Weight_Answer, na.rm = TRUE))

I then tried to the code:

  1. mean_values <- Trial %>%
  2.   group_by(Wave) %>%
  3.   summarise(mean_B = mean(Weight_Answer, na.rm = TRUE)) %>%
  4.   arrange(as.numeric(gsub("Wave ", "", Wave)))

However, it gave the warning:

>Warning message:
There was 1 warning in arrange().
ℹ In argument: ..1 = as.numeric(gsub("Wave ", "", Wave)).
Caused by warning:
! NAs introduced by coercion

I am quite new to R so not sure what this means and how to resolve it

答案1

得分: 1

你可以使用gtools包中的mixedsort函数。

  1. library(gtools)
  3. df[match(mixedsort(df$Wave), df$Wave),]

或者提取Wave数字并根据它进行排序。

  1. library(dplyr)
  3. df %>%
  4.   arrange(as.integer(sub("Wave (\\d+).*", "\", Wave)))

输出

  1.                        Wave mean_B
  2. 11      Wave 2 - April 2020   5.67
  3. 22        Wave 3 - May 2020   5.72
  4. 25       Wave 4 - June 2020   5.62
  5. 26       Wave 5 - July 2020   5.54
  6. 27     Wave 6 - August 2020   5.61
  7. 28  Wave 7 - September 2020   5.60
  8. 29    Wave 8 - October 2020   5.54
  9. 30   Wave 9 - November 2020   5.60
  10. 1   Wave 10 - December 2020   5.49
  11. 2    Wave 11 - January 2021   5.52
  12. 3   Wave 12 - February 2021   5.52
  13. 4      Wave 13 - March 2021   5.45
  14. 5      Wave 14 - April 2021   5.53
  15. 6        Wave 15 - May 2021   5.46
  16. 7       Wave 16 - June 2021   5.51
  17. 8       Wave 17 - July 2021   5.63
  18. 9     Wave 18 - August 2021   5.54
  19. 10 Wave 19 - September 2021   5.49
  20. 12   Wave 20 - October 2021   5.55
  21. 13  Wave 21 - November 2021   5.43
  22. 14  Wave 22 - December 2021   5.35
  23. 15   Wave 23 - January 2022   5.46
  24. 16  Wave 24 - February 2022   5.41
  25. 17     Wave 25 - March 2022   5.30
  26. 18     Wave 26 - April 2022   5.38
  27. 19       Wave 27 - May 2022   5.39
  28. 20      Wave 28 - June 2022   5.55
  29. 21      Wave 29 - July 2022   5.51
  30. 23    Wave 30 - August 2022   5.52
  31. 24 Wave 31 - September 2022   5.54

数据

  1. df <- structure(list(Wave = c("Wave 10 - December 2020", "Wave 11 - January 2021", 
  2. "Wave 12 - February 2021", "Wave 13 - March 2021", "Wave 14 - April 2021", 
  3. "Wave 15 - May 2021", "Wave 16 - June 2021", "Wave 17 - July 2021", 
  4. "Wave 18 - August 2021", "Wave 19 - September 2021", "Wave 2 - April 2020", 
  5. "Wave 20 - October 2021", "Wave 21 - November 2021", "Wave 22 - December 2021", 
  6. "Wave 23 - January 2022", "Wave 24 - February 2022", "Wave 25 - March 2022", 
  7. "Wave 26 - April 2022", "Wave 27 - May 2022", "Wave 28 - June 2022", 
  8. "Wave 29 - July 2022", "Wave 3 - May 2020", "Wave 30 - August 2022", 
  9. "Wave 31 - September 2022", "Wave 4 - June 2020", "Wave 5 - July 2020", 
  10. "Wave 6 - August 2020", "Wave 7 - September 2020", "Wave 8 - October 2020", 
  11. "Wave 9 - November 2020"), mean_B = c(5.49, 5.52, 5.52, 5.45, 
  12. 5.53, 5.46, 5.51, 5.63, 5.54, 5.49, 5.67, 5.55, 5.43, 5.35, 5.46, 
  13. 5.41, 5.3, 5.38, 5.39, 5.55, 5.51, 5.72, 5.52, 5.54, 5.62, 5.54, 
  14. 5.61, 5.6, 5.54, 5.6)), class = "data.frame", row.names = c(NA, 
  15. -30L))
英文:

You can use mixedsort from the gtools package.

  1. library(gtools)
  3. df[match(mixedsort(df$Wave), df$Wave),]

Or to extract the Wave number and arrange on that.

  1. library(dplyr)
  3. df |&gt; 
  4.   arrange(as.integer(sub(&quot;Wave (\\d+).*&quot;, &quot;\&quot;, Wave)))

Output

  1.                        Wave mean_B
  2. 11      Wave 2 - April 2020   5.67
  3. 22        Wave 3 - May 2020   5.72
  4. 25       Wave 4 - June 2020   5.62
  5. 26       Wave 5 - July 2020   5.54
  6. 27     Wave 6 - August 2020   5.61
  7. 28  Wave 7 - September 2020   5.60
  8. 29    Wave 8 - October 2020   5.54
  9. 30   Wave 9 - November 2020   5.60
  10. 1   Wave 10 - December 2020   5.49
  11. 2    Wave 11 - January 2021   5.52
  12. 3   Wave 12 - February 2021   5.52
  13. 4      Wave 13 - March 2021   5.45
  14. 5      Wave 14 - April 2021   5.53
  15. 6        Wave 15 - May 2021   5.46
  16. 7       Wave 16 - June 2021   5.51
  17. 8       Wave 17 - July 2021   5.63
  18. 9     Wave 18 - August 2021   5.54
  19. 10 Wave 19 - September 2021   5.49
  20. 12   Wave 20 - October 2021   5.55
  21. 13  Wave 21 - November 2021   5.43
  22. 14  Wave 22 - December 2021   5.35
  23. 15   Wave 23 - January 2022   5.46
  24. 16  Wave 24 - February 2022   5.41
  25. 17     Wave 25 - March 2022   5.30
  26. 18     Wave 26 - April 2022   5.38
  27. 19       Wave 27 - May 2022   5.39
  28. 20      Wave 28 - June 2022   5.55
  29. 21      Wave 29 - July 2022   5.51
  30. 23    Wave 30 - August 2022   5.52
  31. 24 Wave 31 - September 2022   5.54

Data

  1. df &lt;- structure(list(Wave = c(&quot;Wave 10 - December 2020&quot;, &quot;Wave 11 - January 2021&quot;, 
  2. &quot;Wave 12 - February 2021&quot;, &quot;Wave 13 - March 2021&quot;, &quot;Wave 14 - April 2021&quot;, 
  3. &quot;Wave 15 - May 2021&quot;, &quot;Wave 16 - June 2021&quot;, &quot;Wave 17 - July 2021&quot;, 
  4. &quot;Wave 18 - August 2021&quot;, &quot;Wave 19 - September 2021&quot;, &quot;Wave 2 - April 2020&quot;, 
  5. &quot;Wave 20 - October 2021&quot;, &quot;Wave 21 - November 2021&quot;, &quot;Wave 22 - December 2021&quot;, 
  6. &quot;Wave 23 - January 2022&quot;, &quot;Wave 24 - February 2022&quot;, &quot;Wave 25 - March 2022&quot;, 
  7. &quot;Wave 26 - April 2022&quot;, &quot;Wave 27 - May 2022&quot;, &quot;Wave 28 - June 2022&quot;, 
  8. &quot;Wave 29 - July 2022&quot;, &quot;Wave 3 - May 2020&quot;, &quot;Wave 30 - August 2022&quot;, 
  9. &quot;Wave 31 - September 2022&quot;, &quot;Wave 4 - June 2020&quot;, &quot;Wave 5 - July 2020&quot;, 
  10. &quot;Wave 6 - August 2020&quot;, &quot;Wave 7 - September 2020&quot;, &quot;Wave 8 - October 2020&quot;, 
  11. &quot;Wave 9 - November 2020&quot;), mean_B = c(5.49, 5.52, 5.52, 5.45, 
  12. 5.53, 5.46, 5.51, 5.63, 5.54, 5.49, 5.67, 5.55, 5.43, 5.35, 5.46, 
  13. 5.41, 5.3, 5.38, 5.39, 5.55, 5.51, 5.72, 5.52, 5.54, 5.62, 5.54, 
  14. 5.61, 5.6, 5.54, 5.6)), class = &quot;data.frame&quot;, row.names = c(NA, 
  15. -30L))

答案2

得分: 1

@benson23 给出了一个不错的答案。以下是一个 "base R" 的答案。

  1. # 提取波数
  2. waveNumbers <- sub("Wave (\\d+) - \\w+ \\d+", "\", dat$Wave) |> as.numeric()
  3. # 进行相应的排列
  4. dat2 <- dat[order(waveNumbers), ]

请注意，行的 "names"（1, 2, ...）也被排列了。你可以使用 rownames(dat2) <- NULL 来避免这种情况。

英文:

@benson23 gave a nice answer. Here is a "base R" answer.

  1. # extract wave numbers
  2. waveNumbers &lt;- sub(&quot;Wave (\\d+) \\- \\w+ \\d+&quot;, &quot;\&quot;, dat$Wave) |&gt; as.numeric()
  3. # permute accordingly
  4. dat2 &lt;- dat[order(waveNumbers), ]

Note that the row "names" (1, 2, ...) are permuted too. You can do rownames(dat2) &lt;- NULL to avoid that.

答案3

得分: 0

在你已经使用tidyverse的情况下，我们可以使用readr::parse_number：

  1. readr::parse_number("Wave 9 - November 2020     5.60")
  2. # [1] 9

这很容易添加到你的dplyr管道中：

  1. library(dplyr)
  2. df %>%
  3.   arrange(readr::parse_number(Wave))
  4. #                        Wave mean_B
  5. # 1       Wave 2 - April 2020   5.67
  6. # 2         Wave 3 - May 2020   5.72
  7. # 3        Wave 4 - June 2020   5.62
  8. # 4        Wave 5 - July 2020   5.54
  9. # 5      Wave 6 - August 2020   5.61
  10. # 6   Wave 7 - September 2020   5.60
  11. # 7     Wave 8 - October 2020   5.54
  12. # 8    Wave 9 - November 2020   5.60
  13. # 9   Wave 10 - December 2020   5.49
  14. # 10   Wave 11 - January 2021   5.52
  15. # 11  Wave 12 - February 2021   5.52
  16. # 12     Wave 13 - March 2021   5.45
  17. # 13     Wave 14 - April 2021   5.53
  18. # 14       Wave 15 - May 2021   5.46
  19. # 15      Wave 16 - June 2021   5.51
  20. # 16      Wave 17 - July 2021   5.63
  21. # 17    Wave 18 - August 2021   5.54
  22. # 18 Wave 19 - September 2021   5.49
  23. # 19   Wave 20 - October 2021   5.55
  24. # 20  Wave 21 - November 2021   5.43
  25. # 21  Wave 22 - December 2021   5.35
  26. # 22   Wave 23 - January 2022   5.46
  27. # 23  Wave 24 - February 2022   5.41
  28. # 24     Wave 25 - March 2022   5.30
  29. # 25     Wave 26 - April 2022   5.38
  30. # 26       Wave 27 - May 2022   5.39
  31. # 27      Wave 28 - June 2022   5.55
  32. # 28      Wave 29 - July 2022   5.51
  33. # 29    Wave 30 - August 2022   5.52
  34. # 30 Wave 31 - September 2022   5.54
英文:

Since you're already in the tidyverse, we can use readr::parse_number:

  1. readr::parse_number(&quot;Wave 9 - November 2020     5.60&quot;)
  2. # [1] 9

This is easy enough to add to your dplyr pipe:

  1. library(dplyr)
  2. df %&gt;%
  3.   arrange(readr::parse_number(Wave))
  4. #                        Wave mean_B
  5. # 1       Wave 2 - April 2020   5.67
  6. # 2         Wave 3 - May 2020   5.72
  7. # 3        Wave 4 - June 2020   5.62
  8. # 4        Wave 5 - July 2020   5.54
  9. # 5      Wave 6 - August 2020   5.61
  10. # 6   Wave 7 - September 2020   5.60
  11. # 7     Wave 8 - October 2020   5.54
  12. # 8    Wave 9 - November 2020   5.60
  13. # 9   Wave 10 - December 2020   5.49
  14. # 10   Wave 11 - January 2021   5.52
  15. # 11  Wave 12 - February 2021   5.52
  16. # 12     Wave 13 - March 2021   5.45
  17. # 13     Wave 14 - April 2021   5.53
  18. # 14       Wave 15 - May 2021   5.46
  19. # 15      Wave 16 - June 2021   5.51
  20. # 16      Wave 17 - July 2021   5.63
  21. # 17    Wave 18 - August 2021   5.54
  22. # 18 Wave 19 - September 2021   5.49
  23. # 19   Wave 20 - October 2021   5.55
  24. # 20  Wave 21 - November 2021   5.43
  25. # 21  Wave 22 - December 2021   5.35
  26. # 22   Wave 23 - January 2022   5.46
  27. # 23  Wave 24 - February 2022   5.41
  28. # 24     Wave 25 - March 2022   5.30
  29. # 25     Wave 26 - April 2022   5.38
  30. # 26       Wave 27 - May 2022   5.39
  31. # 27      Wave 28 - June 2022   5.55
  32. # 28      Wave 29 - July 2022   5.51
  33. # 29    Wave 30 - August 2022   5.52
  34. # 30 Wave 31 - September 2022   5.54

huangapple
  • 本文由 发表于 2023年5月24日 17:50:39
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  • dataframe
  • number-formatting
  • r
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