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Paste a string to selected column names of a dataframe with dynamic number of columns

I have the dataframe below which will actually have a dynamic number of columns.I want to paste a string "-actual" from the second column name up to the last column name of the dataframe.

structure(list(month = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", 
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), `2020` = c(NA, 5.26666666666667, 
-15.5806451612903, -41.4333333333333, -27.1290322580645, -15.7666666666667, 
-14.8709677419355, -14.9677419354839, -14.9333333333333, -15.7741935483871, 
-19.8666666666667, -19.741935483871), `2021` = c(-25.5806451612903, 
-23.4285714285714, -10.8387096774194, -8.3, -5.41935483870968, 
-3.53333333333333, -3.87096774193548, -4.67741935483871, -6.1, 
-5.54838709677419, -7.43333333333333, -6.7741935483871), `2022` = c(-18.9677419354839, 
-12.3571428571429, -10.4193548387097, -8.66666666666667, -6.06451612903226, 
-6.83333333333333, -8.64516129032258, -7.09677419354839, -8.73333333333333, 
-8.53333333333333, NA, NA)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -12L))

>Solution :

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Using dplyr::rename_with():

library(dplyr)

df %>% 
  rename_with(\(x) paste0(x, "-actual"), .cols = !month)
# A tibble: 12 × 4
  month `2020-actual` `2021-actual` `2022-actual`
  <chr>         <dbl>         <dbl>         <dbl>
1 Jan           NA           -25.6         -19.0 
2 Feb            5.27        -23.4         -12.4 
3 Mar          -15.6         -10.8         -10.4 
4 Apr          -41.4          -8.3          -8.67
5 May          -27.1          -5.42         -6.06
6 Jun          -15.8          -3.53         -6.83
7 Jul          -14.9          -3.87         -8.65
8 Aug          -15.0          -4.68         -7.10
9 Sep          -14.9          -6.1          -8.73
10 Oct          -15.8          -5.55         -8.53
11 Nov          -19.9          -7.43         NA   
12 Dec          -19.7          -6.77         NA   

NB, since the cols argument uses the tidyselect DSL, you can get the same result using:

  • !month (all columns besides "month"),
  • `2020`:`2022` (all columns from "2020" through "2022"),
  • matches("^\\d+$") (all columns consisting of digits only), or
  • where(is.numeric) (all columns with numeric values)
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