How can I create the column using the number in the column name?-CodePudding

I want to modify the longitudinal data.

How can I create the column using the number in the column name(e.g. gdpPercap_1952, gdpPercap_1957, etc.)?

I try to divide by the number(year) and the letter(gdp) in those column(e.g. gdpPercap_1952, gdpPercap_1957, etc.). Then I try to make the new column "year".

Would you tell me how I can solve that? Or is there any other suitable way?

 continent      country gdpPercap_1952
1    Africa      Algeria      2449.0082
2    Africa       Angola      3520.6103
3    Africa        Benin      1062.7522
4    Africa     Botswana       851.2411
5    Africa Burkina Faso       543.2552
6    Africa      Burundi       339.2965
  gdpPercap_1957 gdpPercap_1962
1      3013.9760      2550.8169
2      3827.9405      4269.2767
3       959.6011       949.4991
4       918.2325       983.6540
5       617.1835       722.5120
6       379.5646       355.2032
  gdpPercap_1967 gdpPercap_1972
1      3246.9918      4182.6638
2      5522.7764      5473.2880
3      1035.8314      1085.7969

CodePudding user response：

This can be done with pivot_longer

library(tidyr)
pivot_longer(df1, cols = starts_with('gdp'), 
     names_to = c(".value", "year"), names_sep = "_")

CodePudding user response：

Or instead of names_sep, we could use names_pattern:

library(dplyr)
library(tidyr)

df %>% 
  pivot_longer(
    -c(continent, country),
    names_to = c(".value", "year"), 
    names_pattern = "(.*)_(\\d )"
  ) %>% 
  data.frame()

 continent      country year gdpPercap
1     Africa      Algeria 1952 2449.0082
2     Africa      Algeria 1957 3013.9760
3     Africa      Algeria 1962 2550.8169
4     Africa       Angola 1952 3520.6103
5     Africa       Angola 1957 3827.9405
6     Africa       Angola 1962 4269.2767
7     Africa        Benin 1952 1062.7522
8     Africa        Benin 1957  959.6011
9     Africa        Benin 1962  949.4991
10    Africa     Botswana 1952  851.2411
11    Africa     Botswana 1957  918.2325
12    Africa     Botswana 1962  983.6540
13    Africa Burkina Faso 1952  543.2552
14    Africa Burkina Faso 1957  617.1835
15    Africa Burkina Faso 1962  722.5120
16    Africa      Burundi 1952  339.2965
17    Africa      Burundi 1957  379.5646
18    Africa      Burundi 1962  355.2032

data:

structure(list(continent = c("Africa", "Africa", "Africa", "Africa", 
"Africa", "Africa"), country = c("Algeria", "Angola", "Benin", 
"Botswana", "Burkina Faso", "Burundi"), gdpPercap_1952 = c(2449.0082, 
3520.6103, 1062.7522, 851.2411, 543.2552, 339.2965), gdpPercap_1957 = c(3013.976, 
3827.9405, 959.6011, 918.2325, 617.1835, 379.5646), gdpPercap_1962 = c(2550.8169, 
4269.2767, 949.4991, 983.654, 722.512, 355.2032)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))