Reorganising dataframe by common variable-CodePudding

Apologies if this answer is already available, but I couldn't find an answer on here. I am trying to organise a data frame by grouping all columns with a year into one and then having the other data separate

My data currently look like this

| A Year | A Mean Temp | A Max Temp |A Min Temp| B Year | B Mean Temp| B Max Temp| B Min Temp|

etc

What I would like to end up with is a table like this:

Year| A Mean Temp| A Max Temp|A Min Temp|B Mean Temp|B Max Temp| B Min Temp

I believe the answer lies somewhere in the pivot_longer function but as of yet I am just getting error messages. Some data will have NA where there isn't data for that year.

Data

structure(list(Antarctica.Year.CE = 167:172, Antarctica.Temp..C. = c(0.33, 
0.31, 0.18, 0.08, -0.01, -0.11), Antarctica.Min..C. = c(-1.24, 
-1.26, -1.39, -1.48, -1.57, -1.67), Antarctica.Max..C. = c(1.89, 
1.87, 1.74, 1.64, 1.55, 1.45), Arctic.Year.CE = 1:6, Arctic.Temp..C. = c(-1.15, 
-0.96, -0.32, 0.1, -0.18, -0.61), Arctic.Min..C. = c(-1.92, -1.76, 
-1.38, -0.74, -1.08, -1.17), Arctic.Max..C. = c(-0.31, -0.11, 
0.48, 0.83, 0.73, 0.16), Asia.Year.CE = 800:805, Asia.Temp..C. = c(-0.31, 
-0.14, -0.36, -0.67, -0.78, -0.26), Asia.Min..C. = c(-1.4, -1.23, 
-1.45, -1.76, -1.87, -1.35), Asia.Max..C. = c(0.79, 0.96, 0.74, 
0.43, 0.31, 0.83), Australasia.Year.CE = 1001:1006, Australasia.Temp..C. = c(-0.24, 
-0.38, -0.29, -0.33, -0.34, -0.11), Australasia.Min..C. = c(-0.62, 
-0.79, -0.71, -0.73, -0.73, -0.56), Australasia.Max..C. = c(0.15, 
0.03, 0.13, 0.07, 0.05, 0.34), Europe.Year.CE = 1:6, Europe.Temp..C. = c(0.09, 
-0.26, -0.24, 0.22, 0.32, 0.67), Europe.Min..C. = c(-0.69, -1.14, 
-1.18, -0.66, -0.48, -0.11), Europe.Max..C. = c(0.88, 0.56, 0.61, 
1.07, 1.14, 1.5), North.America...Pollen.Year.CE = c(480L, 510L, 
540L, 570L, 600L, 630L), North.America...Pollen.Temp..C. = c(-0.25, 
-0.29, -0.33, -0.34, -0.34, -0.34), North.America...Pollen.Min..C. = c(-0.74, 
-0.7, -0.66, -0.65, -0.64, -0.64), North.America...Pollen.Max..C. = c(0.24, 
0.11, 0, -0.04, -0.04, -0.04), North.America...Trees.Year.CE = c(1204L, 
1214L, 1224L, 1234L, 1244L, 1254L), North.America...Trees.Temp..C. = c(-0.22, 
-0.45, -0.38, -0.87, -0.81, -0.06), North.America...Trees.Min..C. = c(-0.53, 
-0.72, -0.67, -1.12, -1.09, -0.35), North.America...Trees.Max..C. = c(0.04, 
-0.2, -0.11, -0.57, -0.52, 0.18), South.America.Year.CE = 857:862, 
    South.America.Temp..C. = c(-0.3, -0.21, -0.07, -0.38, -0.41, 
    -0.19), South.America.Min..C. = c(-1.12, -1, -0.88, -1.19, 
    -1.22, -0.98)), row.names = c(NA, 6L), class = "data.frame")

CodePudding user response：

We could use

library(tidyr)
pivot_longer(df1, cols= contains("Year"), names_to = "Country", 
    values_to = "Year")