R nest/unnest of dataframe results in non-identical objects

Question

My first use of nest/unnest functions in R and I do not understand the result. I nest and immediately unnest and compare the before/after dataframes. Why are the dataframes not identical?

> library(tidyverse)  
> concentration_original <- readRDS("./Data/concentration.Rds")
> print(concentration_original, n=15)
# A tibble: 12 x 5
   SUBJID    WT  DOSE  TIME  CONC
    <dbl> <dbl> <dbl> <dbl> <dbl>
 1      1  79.6  4.02 0      0.74
 2      1  79.6  4.02 0.25   2.84
 3      1  79.6  4.02 0.570  6.57
 4      1  79.6  4.02 1.12  10.5 
 5      1  79.6  4.02 2.02   9.66
 6      1  79.6  4.02 3.82   8.58
 7      2  72.4  4.4  0      0   
 8      2  72.4  4.4  0.27   1.72
 9      2  72.4  4.4  0.52   7.91
10      2  72.4  4.4  1      8.31
11      2  72.4  4.4  1.92   8.33
12      2  72.4  4.4  3.5    6.85
> 
> concentration_nested <- concentration_original %>% nest(data = c(TIME, CONC))
> concentration_nested
# A tibble: 2 x 4
  SUBJID    WT  DOSE data            
   <dbl> <dbl> <dbl> <list>          
1      1  79.6  4.02 <tibble [6 × 2]>
2      2  72.4  4.4  <tibble [6 × 2]>
> 
> concentration_unnested <- unnest(concentration_nested, cols = c(data))
> print(concentration_unnested, n=15)
# A tibble: 12 x 5
   SUBJID    WT  DOSE  TIME  CONC
    <dbl> <dbl> <dbl> <dbl> <dbl>
 1      1  79.6  4.02 0      0.74
 2      1  79.6  4.02 0.25   2.84
 3      1  79.6  4.02 0.570  6.57
 4      1  79.6  4.02 1.12  10.5 
 5      1  79.6  4.02 2.02   9.66
 6      1  79.6  4.02 3.82   8.58
 7      2  72.4  4.4  0      0   
 8      2  72.4  4.4  0.27   1.72
 9      2  72.4  4.4  0.52   7.91
10      2  72.4  4.4  1      8.31
11      2  72.4  4.4  1.92   8.33
12      2  72.4  4.4  3.5    6.85
> 
> if (identical(concentration_unnested, concentration_original)) {
+   print("After nest/unnest, we have a dataframe which IS IDENTICAL to the original")
+ } else {
+   print("After nest/unnest, we have a dataframe which IS NOT IDENTICAL to the original")
+ }
[1] "After nest/unnest, we have a dataframe which IS NOT IDENTICAL to the original"
> 
> all.equal(concentration_unnested, concentration_original)
[1] "Attributes: < Length mismatch: comparison on first 2 components >"
> 

Note that I am using all.equal in order to see the problem may have something to do with attributes. If I use all_equal instead, the result is TRUE but I am still stuck with the identical function saying the dataframes are not the same. Thanks for any help with this!

Added dput of original df and nested/unnested df.

> dput(concentration_original)
structure(list(SUBJID = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2), 
    WT = c(79.6, 79.6, 79.6, 79.6, 79.6, 79.6, 72.4, 72.4, 72.4, 
    72.4, 72.4, 72.4), DOSE = c(4.02, 4.02, 4.02, 4.02, 4.02, 
    4.02, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4), TIME = c(0, 0.25, 0.57, 
    1.12, 2.02, 3.82, 0, 0.27, 0.52, 1, 1.92, 3.5), CONC = c(0.74, 
    2.84, 6.57, 10.5, 9.66, 8.58, 0, 1.72, 7.91, 8.31, 8.33, 
    6.85)), spec = structure(list(cols = list(SUBJID = structure(list(), class = c("collector_double", 
"collector")), WT = structure(list(), class = c("collector_double", 
"collector")), DOSE = structure(list(), class = c("collector_double", 
"collector")), TIME = structure(list(), class = c("collector_double", 
"collector")), CONC = structure(list(), class = c("collector_double", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"), row.names = c(NA, 
-12L), class = c("tbl_df", "tbl", "data.frame"))
> dput(concentration_unnested)
structure(list(SUBJID = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2), 
    WT = c(79.6, 79.6, 79.6, 79.6, 79.6, 79.6, 72.4, 72.4, 72.4, 
    72.4, 72.4, 72.4), DOSE = c(4.02, 4.02, 4.02, 4.02, 4.02, 
    4.02, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4), TIME = c(0, 0.25, 0.57, 
    1.12, 2.02, 3.82, 0, 0.27, 0.52, 1, 1.92, 3.5), CONC = c(0.74, 
    2.84, 6.57, 10.5, 9.66, 8.58, 0, 1.72, 7.91, 8.31, 8.33, 
    6.85)), row.names = c(NA, -12L), class = c("tbl_df", "tbl", 
"data.frame"))
> 

Additional information: I think I found the problem. The spec= info on the original tibble contains information related to when the tibble was created with read_csv. When the tibble goes through the nest/unnest transformation, the spec= info has been discarded. There is another thread which mentions the spec= info becoming out of sync with the content of the tibble: Remove attributes from data read in readr::read_csv. In that case they suggest removing the spec= attribute:

attr(df, 'spec') <- NULL

Answer 1

Based on what I was able to find, the reason that your original dataframe is not identical to the output is that the original is of the class col_spec and the output is not.

Using the new waldo package, part of tidyverse, I ran the following:

compare(df, df %>% nest(data = c(TIME, CONC)) %>% unnest(cols = c(data)))
`attr(old, 'spec')` is an S3 object of class <col_spec>
`attr(new, 'spec')` is absent

It appears that you read in the data using readr and the resulting df was an object of class col_spec. Nesting the original df removes this attribute.

attr(df %>% nest(data = c(TIME, CONC)), 'spec')
NULL

Therefore, when you unnest, the df's are not identical.