library(tidyverse)

df  %>%
  pivot_longer(
    starts_with("X"),
    names_to = "Year",
    names_transform = list(Year = parse_number)
  ) %>%
  pivot_wider(names_from = Indicator, values_from = value) %>% 
  relocate(Year)

# A tibble: 9 × 6
   Year Country Population   GDP  Debt Currency
  <dbl> <chr>        <dbl> <dbl> <dbl>    <dbl>
1  2011 USA              1     1     1        2
2  2012 USA              4     4     4        5
3  2013 USA              8     8     8        9
4  2011 India            2     2     3        3
5  2012 India            5     5     6        6
6  2013 India            9     9    11       11
7  2011 China            3     4     4        4
8  2012 China            6     7     7        7
9  2013 China           11    12    12       12

这在tidyverse上是很直接的

library(tidyverse)
#> Warning: package 'tidyr' was built under R version 4.1.3
#> Warning: package 'readr' was built under R version 4.1.3
#> Warning: package 'dplyr' was built under R version 4.1.3

df<-data.frame(Country=c("USA","USA","USA","USA","India","India","India","India","China","China","China","China"),
               Indicator=rep(c("Population","GDP","Debt","Currency"),times=3),`2011`=rep(c(1,2,3,4),each=3),`2012`=rep(c(4,5,6,7),each=3),`2013`=rep(c(8,9,11,12),each=3))

df |> 
  pivot_longer(cols = contains('20')) |> 
  pivot_wider(names_from = Indicator,values_from = value)
#> # A tibble: 9 x 6
#>   Country name  Population   GDP  Debt Currency
#>   <chr>   <chr>      <dbl> <dbl> <dbl>    <dbl>
#> 1 USA     X2011          1     1     1        2
#> 2 USA     X2012          4     4     4        5
#> 3 USA     X2013          8     8     8        9
#> 4 India   X2011          2     2     3        3
#> 5 India   X2012          5     5     6        6
#> 6 India   X2013          9     9    11       11
#> 7 China   X2011          3     4     4        4
#> 8 China   X2012          6     7     7        7
#> 9 China   X2013         11    12    12       12

由reprex package（v2.0.1）于2023年1月19日创建

请尝试以下代码

df2 <- df %>% pivot_longer(c(starts_with('X')), names_to = 'year') %>% 
mutate(year=as.numeric(str_replace(year,'\\w',' '))) %>% 
pivot_wider(c('year','Country'), names_from = 'Indicator', values_from = 'value')

# A tibble: 9 × 6
   year Country Population   GDP  Debt Currency
  <dbl> <chr>        <dbl> <dbl> <dbl>    <dbl>
1  2011 USA              1     1     1        2
2  2012 USA              4     4     4        5
3  2013 USA              8     8     8        9
4  2011 India            2     2     3        3
5  2012 India            5     5     6        6
6  2013 India            9     9    11       11
7  2011 China            3     4     4        4
8  2012 China            6     7     7        7
9  2013 China           11    12    12       12