Transform multi-columns data in wide to long format

Question

I have this data

dw <- structure(list(ccssid = c(1000023L, 1000043L), base.age = c("22", 
"27"), fu1.age = c("30", "35"), fu2.age = c("33", "37"), fu3.age = c("35", 
"40"), fu7.age = c("38", "42"), fu5.age = c("44", "49"), fu6.age = c("48", 
"52"), base.bmi = c("25.1421", "21.6333"), fu2.bmi = c("25.7959", 
"23.5078"), fu7.bmi = c("25.105", "24.961"), fu5.bmi = c("24.366", 
"24.961"), fu2.MET = c("150", "0"), fu2.CDC = c("Yes", "No"), 
    fu5.MET = c("360", "120"), fu5.CDC = c("Yes", "No"), fu6.MET = c(NA_character_, 
    NA_character_), fu6.CDC = c(NA_character_, NA_character_), 
    base.smk = c(NA, "1"), fu2.smk = c("2", "1"), fu7.smk = c("2", 
    "1"), fu5.smk = c("2", "1"), base.riskydrk = c(NA, "No"), 
    fu7.riskydrk = c("Yes", "Yes"), fu5.riskydrk = c("No", "No"
    ), base.MET = c(NA, NA), base.CDC = c(NA, NA), fu1.bmi = c(NA, 
    NA), fu1.MET = c(NA, NA), fu1.CDC = c(NA, NA), fu1.smk = c(NA, 
    NA), fu1.riskydrk = c(NA, NA), fu2.riskydrk = c(NA, NA), 
    fu3.bmi = c(NA, NA), fu3.MET = c(NA, NA), fu3.CDC = c(NA, 
    NA), fu3.smk = c(NA, NA), fu3.riskydrk = c(NA, NA), fu7.MET = c(NA, 
    NA), fu7.CDC = c(NA, NA), fu6.bmi = c(NA, NA), fu6.smk = c(NA, 
    NA), fu6.riskydrk = c(NA, NA)), row.names = 1:2, class = "data.frame")

I tried this code below to transform this data, but I am not sure why the values of riskydrk column are switched with smk column in the output. The smk column should have values 1,2, but somehow, it is switched with the values of riskydrk column. Can someone please help me figure out the issue? Thanks!

reshape(dw, direction='long', 
        varying=c('base.age', 'base.bmi', "base.MET", 'base.CDC', "base.smk", "base.riskydrk",
                  'fu1.age', 'fu1.bmi', "fu1.MET", 'fu1.CDC', "fu1.smk", "fu1.riskydrk",
                  'fu2.age', 'fu2.bmi', "fu2.MET", 'fu2.CDC', "fu2.smk", "fu2.riskydrk",
                  'fu3.age', 'fu3.bmi', "fu3.MET", 'fu3.CDC', "fu3.smk", "fu3.riskydrk",
                  'fu7.age', 'fu7.bmi', "fu7.MET", 'fu7.CDC', "fu7.smk", "fu7.riskydrk",
                  'fu5.age', 'fu5.bmi', "fu5.MET", 'fu5.CDC', "fu5.smk", "fu5.riskydrk",
                  'fu6.age', 'fu6.bmi', "fu6.MET", 'fu6.CDC', "fu6.smk", "fu6.riskydrk"),
        timevar='var',
        times=c('base', 'fu1', 'fu2', 'fu3', 'fu7', 'fu5', 'fu6'),
        v.names=c('age', 'bmi', 'MET', 'CDC', 'smk', 'riskydrk'),
        idvar='ccssid')

The desired output should be like this:

Answer 1

I think your problem is, that your times should be suffixes, so reshape is able to guess, which would be way easier.

## make prefixes to suffixes
names(dw) <- strsplit(names(dw), '\\.') |> lapply(rev) |> sapply(paste, collapse='.')

reshape(dw, direction='l', idvar='ccssid', varying=sort(names(dw)[-1]))
#               ccssid time age     bmi  CDC  MET riskydrk  smk
# 1000023.base 1000023 base  22 25.1421 <NA> <NA>     <NA> <NA>
# 1000043.base 1000043 base  27 21.6333 <NA> <NA>       No    1
# 1000023.fu1  1000023  fu1  30    <NA> <NA> <NA>     <NA> <NA>
# 1000043.fu1  1000043  fu1  35    <NA> <NA> <NA>     <NA> <NA>
# 1000023.fu2  1000023  fu2  33 25.7959  Yes  150     <NA>    2
# 1000043.fu2  1000043  fu2  37 23.5078   No    0     <NA>    1
# 1000023.fu3  1000023  fu3  35    <NA> <NA> <NA>     <NA> <NA>
# 1000043.fu3  1000043  fu3  40    <NA> <NA> <NA>     <NA> <NA>
# 1000023.fu5  1000023  fu5  44  24.366  Yes  360       No    2
# 1000043.fu5  1000043  fu5  49  24.961   No  120       No    1
# 1000023.fu6  1000023  fu6  48    <NA> <NA> <NA>     <NA> <NA>
# 1000043.fu6  1000043  fu6  52    <NA> <NA> <NA>     <NA> <NA>
# 1000023.fu7  1000023  fu7  38  25.105 <NA> <NA>      Yes    2
# 1000043.fu7  1000043  fu7  42  24.961 <NA> <NA>      Yes    1

Answer 2

Though the question is not tagged dplyr or tidyr , here is a way with package tidyr .

suppressPackageStartupMessages({
  library(tidyr)
})

dw |>
  pivot_longer(
    cols = -ccssid,
    names_to = c("var", ".value"),
    names_pattern = "(.*)\\.(.*)"
  )
#> # A tibble: 14 × 8
#>     ccssid var   age   bmi     MET   CDC   smk   riskydrk
#>      <int> <chr> <chr> <chr>   <chr> <chr> <chr> <chr>   
#>  1 1000023 base  22    25.1421 <NA>  <NA>  <NA>  <NA>    
#>  2 1000023 fu1   30    <NA>    <NA>  <NA>  <NA>  <NA>    
#>  3 1000023 fu2   33    25.7959 150   Yes   2     <NA>    
#>  4 1000023 fu3   35    <NA>    <NA>  <NA>  <NA>  <NA>    
#>  5 1000023 fu7   38    25.105  <NA>  <NA>  2     Yes     
#>  6 1000023 fu5   44    24.366  360   Yes   2     No      
#>  7 1000023 fu6   48    <NA>    <NA>  <NA>  <NA>  <NA>    
#>  8 1000043 base  27    21.6333 <NA>  <NA>  1     No      
#>  9 1000043 fu1   35    <NA>    <NA>  <NA>  <NA>  <NA>    
#> 10 1000043 fu2   37    23.5078 0     No    1     <NA>    
#> 11 1000043 fu3   40    <NA>    <NA>  <NA>  <NA>  <NA>    
#> 12 1000043 fu7   42    24.961  <NA>  <NA>  1     Yes     
#> 13 1000043 fu5   49    24.961  120   No    1     No      
#> 14 1000043 fu6   52    <NA>    <NA>  <NA>  <NA>  <NA>

^{Created on 2022-12-01 with reprex v2.0.2}