添加多个按一年滞后的列

huangapple
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英文:

Add multiple columns lagged by one year

问题

  1. 我需要在我的数据框中添加多列的1年滞后版本。以下是我的数据：
  3. ```R
  4. data<-data.frame(Year=c("2011","2011","2011","2012","2012","2012","2013","2013","2013"), 
  5.                  Country=c("America","China","India","America","China","India","America","China","India"),
  6.                  Value1=c(234,443,754,334,117,112,987,903,476),
  7.                  Value2=c(2,4,5,6,7,8,1,2,2))

我想要添加两列，包含Value1和Value2在t-1时的值，以使我的数据框看起来像这样：

添加多个按一年滞后的列

我该如何做到这一点？这样做是否是通过年份滞后我的变量的正确方式？

提前感谢！

  2. <details>
  3. <summary>英文:</summary>
  5. I need to add a 1-year-lagged version of multiple columns from my dataframe. Here&#39;s my data:

data<-data.frame(Year=c("2011","2011","2011","2012","2012","2012","2013","2013","2013"),
Country=c("America","China","India","America","China","India","America","China","India"),
Value1=c(234,443,754,334,117,112,987,903,476),
Value2=c(2,4,5,6,7,8,1,2,2))

  2. And I want to add two columns that contain Value1 and Value2 at t-1, so that my dataframe looks like this:
  4. [![enter image description here][1]][1]
  7.   [1]: https://i.stack.imgur.com/EFrpk.png
  9. How can I do this? Would this be the correct way to lag my variables by year?
  11. Thanks in advance!
  13. </details>
  16. # 答案1
  17. **得分**: 4
  19. 使用 *data.table*：
  21. ```R
  22. library(data.table)
  24. setDT(data)
  25. cols <- grep("^Value", colnames(data), value = TRUE)
  26. data[, paste0(cols, "_lag") := lapply(.SD, shift), .SDcols = cols, by = Country]
  27. #    Year Country Value1 Value2 Value1_lag Value2_lag
  28. # 1: 2011 America    234      2         NA         NA
  29. # 2: 2011   China    443      4         NA         NA
  30. # 3: 2011   India    754      5         NA         NA
  31. # 4: 2012 America    334      6        234          2
  32. # 5: 2012   China    117      7        443          4
  33. # 6: 2012   India    112      8        754          5
  34. # 7: 2013 America    987      1        334          6
  35. # 8: 2013   China    903      2        117          7
  36. # 9: 2013   India    476      2        112          8
英文:

Using data.table:

  1. library(data.table)
  3. setDT(data)
  4. cols &lt;- grep(&quot;^Value&quot;, colnames(data), value = TRUE)
  5. data[, paste0(cols, &quot;_lag&quot;) := lapply(.SD, shift), .SDcols = cols, by = Country]
  6. #    Year Country Value1 Value2 Value1_lag Value2_lag
  7. # 1: 2011 America    234      2         NA         NA
  8. # 2: 2011   China    443      4         NA         NA
  9. # 3: 2011   India    754      5         NA         NA
  10. # 4: 2012 America    334      6        234          2
  11. # 5: 2012   China    117      7        443          4
  12. # 6: 2012   India    112      8        754          5
  13. # 7: 2013 America    987      1        334          6
  14. # 8: 2013   China    903      2        117          7
  15. # 9: 2013   India    476      2        112          8

答案2

得分: 2

在dplyr中，按组使用lag：

  1. library(dplyr) #1.1.0
  2. data %>%
  3.   mutate(across(contains("Value"), lag, .names = "{col}_lagged"), .by = Country)
  5.   Year Country Value1 Value2 Value1_lagged Value2_lagged
  6. 1 2011 America 234 2 NA NA
  7. 2 2011 China 443 4 NA NA
  8. 3 2011 India 754 5 NA NA
  9. 4 2012 America 334 6 234 2
  10. 5 2012 China 117 7 443 4
  11. 6 2012 India 112 8 754 5
  12. 7 2013 America 987 1 334 6
  13. 8 2013 China 903 2 117 7
  14. 9 2013 India 476 2 112 8

在1.1.0以下版本：

  1. data %>%
  2.   group_by(Country) %>%
  3.   mutate(across(c(GDP, Population), lag, .names = "{col}_lagged")) %>%
  4.   ungroup()
英文:

In dplyr, use lag by group:

  1. library(dplyr) #1.1.0
  2. data %&gt;% 
  3.   mutate(across(contains(&quot;Value&quot;), lag, .names = &quot;{col}_lagged&quot;), .by = Country)
  5.   Year Country Value1 Value2 Value1_lagged Value2_lagged
  6. 1 2011 America    234      2            NA            NA
  7. 2 2011   China    443      4            NA            NA
  8. 3 2011   India    754      5            NA            NA
  9. 4 2012 America    334      6           234             2
  10. 5 2012   China    117      7           443             4
  11. 6 2012   India    112      8           754             5
  12. 7 2013 America    987      1           334             6
  13. 8 2013   China    903      2           117             7
  14. 9 2013   India    476      2           112             8

Below 1.1.0:

  1. data %&gt;% 
  2.   group_by(Country) %&gt;%
  3.   mutate(across(c(GDP, Population), lag, .names = &quot;{col}_lagged&quot;)) %&gt;%
  4.   ungroup()
  6. </details>
  10. # 答案3
  11. **得分**: 0
  13. 以下是您提供的代码的中文翻译：
  15. ```R
  16. 另一种使用`dplyr`来完成任务的方法。
  18.     library(dplyr)
  19.     data_lagged <- data %>%
  20.       group_by(Country) %>%
  21.       mutate(Value1_Lagged = lag(Value1),
  22.              Value2_Lagged = lag(Value2),
  23.              Year = as.integer(as.character(Year)) + 1)
  24.     data_final <- cbind(data, data_lagged[, c("Value1_Lagged", "Value2_Lagged")])
  25.     data_final

输出结果：

  1.   Year Country Value1 Value2 Value1_Lagged Value2_Lagged
  2. 1 2011 America    234      2            NA            NA
  3. 2 2011   China    443      4            NA            NA
  4. 3 2011   India    754      5            NA            NA
  5. 4 2012 America    334      6           234             2
  6. 5 2012   China    117      7           443             4
  7. 6 2012   India    112      8           754             5
  8. 7 2013 America    987      1           334             6
  9. 8 2013   China    903      2           117             7
  10. 9 2013   India    476      2           112             8
  2. 请注意，我已经将代码中的注释和代码部分翻译成中文。
  4. <details>
  5. <summary>英文:</summary>
  7. Another way using `dplyr` to ge tthe job done.
  9.     library(dplyr)
  10.     data_lagged &lt;- data %&gt;%
  11.       group_by(Country) %&gt;%
  12.       mutate(Value1_Lagged = lag(Value1),
  13.              Value2_Lagged = lag(Value2),
  14.              Year = as.integer(as.character(Year)) + 1)
  15.     data_final &lt;- cbind(data, data_lagged[, c(&quot;Value1_Lagged&quot;, &quot;Value2_Lagged&quot;)])
  16.     data_final
  19. Output:
  21.       Year Country Value1 Value2 Value1_Lagged Value2_Lagged
  22.     1 2011 America    234      2            NA            NA
  23.     2 2011   China    443      4            NA            NA
  24.     3 2011   India    754      5            NA            NA
  25.     4 2012 America    334      6           234             2
  26.     5 2012   China    117      7           443             4
  27.     6 2012   India    112      8           754             5
  28.     7 2013 America    987      1           334             6
  29.     8 2013   China    903      2           117             7
  30.     9 2013   India    476      2           112             8
  32. </details>

huangapple
  • 本文由 发表于 2023年2月8日 18:03:03
  • 转载请务必保留本文链接：https://go.coder-hub.com/75384121.html
  • dataframe
  • r
  • regression
  • time-series
  • variables
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