nest() on grouped_df returns data frame instead of tibble

[tmastny]

nest applied to a grouped tibble returns a data.frame.

Related: #475

library(tidyr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
iris_tibble <- as_tibble(iris)
iris_tibble %>%
  group_by(Species) %>%
  nest(Sepal.Length = Sepal.Length) %>%
  class()
#> [1] "data.frame"

iris_tibble %>%
  nest(Sepal.Length = Sepal.Length) %>%
  class()
#> [1] "tbl_df"     "tbl"        "data.frame"

devtools::session_info()
#> ─ Session info ──────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 3.5.1 (2018-07-02)
#>  os       macOS  10.14.4              
#>  system   x86_64, darwin15.6.0        
#>  ui       X11                         
#>  language (EN)                        
#>  collate  en_US.UTF-8                 
#>  ctype    en_US.UTF-8                 
#>  tz       America/Chicago             
#>  date     2019-06-11                  
#> 
#> ─ Packages ──────────────────────────────────────────────────────────────
#>  package     * version     date       lib
#>  assertthat    0.2.1       2019-03-21 [1]
#>  backports     1.1.4       2019-04-10 [1]
#>  callr         3.1.1       2018-12-21 [1]
#>  cli           1.1.0       2019-03-19 [1]
#>  crayon        1.3.4       2017-09-16 [1]
#>  desc          1.2.0       2018-05-01 [1]
#>  devtools      2.0.1       2018-10-26 [1]
#>  digest        0.6.19      2019-05-20 [1]
#>  dplyr       * 0.8.1.9000  2019-06-11 [1]
#>  evaluate      0.12        2018-10-09 [1]
#>  fs            1.3.1       2019-05-06 [1]
#>  glue          1.3.1       2019-03-12 [1]
#>  highr         0.7         2018-06-09 [1]
#>  htmltools     0.3.6       2017-04-28 [1]
#>  knitr         1.21        2018-12-10 [1]
#>  magrittr      1.5.0.9000  2019-01-15 [1]
#>  memoise       1.1.0       2017-04-21 [1]
#>  pillar        1.4.1       2019-05-28 [1]
#>  pkgbuild      1.0.2       2018-10-16 [1]
#>  pkgconfig     2.0.2       2018-08-16 [1]
#>  pkgload       1.0.2       2018-10-29 [1]
#>  prettyunits   1.0.2       2015-07-13 [1]
#>  processx      3.2.1       2018-12-05 [1]
#>  ps            1.3.0       2018-12-21 [1]
#>  purrr         0.3.2.9000  2019-06-11 [1]
#>  R6            2.4.0       2019-02-14 [1]
#>  Rcpp          1.0.1       2019-03-17 [1]
#>  remotes       2.0.2       2018-10-30 [1]
#>  rlang         0.3.99.9003 2019-06-11 [1]
#>  rmarkdown     1.11        2018-12-08 [1]
#>  rprojroot     1.3-2       2018-01-03 [1]
#>  sessioninfo   1.1.1       2018-11-05 [1]
#>  stringi       1.4.3       2019-03-12 [1]
#>  stringr       1.4.0       2019-02-10 [1]
#>  testthat      2.0.1       2018-10-13 [1]
#>  tibble        2.1.3       2019-06-06 [1]
#>  tidyr       * 0.8.3.9000  2019-06-06 [1]
#>  tidyselect    0.2.5       2018-10-11 [1]
#>  usethis       1.4.0       2018-08-14 [1]
#>  vctrs         0.1.0.9004  2019-06-11 [1]
#>  withr         2.1.2       2018-03-15 [1]
#>  xfun          0.4         2018-10-23 [1]
#>  yaml          2.2.0       2018-07-25 [1]
#>  zeallot       0.1.0       2018-01-28 [1]
#>  source                             
#>  CRAN (R 3.5.1)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.1)                     
#>  CRAN (R 3.5.2)                     
#>  Github (tidyverse/dplyr@19aa821)   
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.1)                     
#>  Github (tidyverse/magrittr@4104d6b)
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  Github (tidyverse/purrr@e4d5539)   
#>  CRAN (R 3.5.1)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.0)                     
#>  Github (r-lib/rlang@e60f97f)       
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.2)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.1)                     
#>  Github (tidyverse/tidyr@7a2b843)   
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  Github (r-lib/vctrs@a2b48fc)       
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#>  CRAN (R 3.5.0)                     
#> 
#> [1] /Library/Frameworks/R.framework/Versions/3.5/Resources/library

^{Created on 2019-06-11 by the reprex package (v0.2.1.9000)}

[DavisVaughan]

I just hit this too while running some of the tidyr unnest() examples. I think the issue is with vec_cbind() in nest.tbl_df(). There isn't a vec_ptype2() method for grouped data frames yet so it just falls back to a data frame. I think that makes this related to tidyverse/dplyr#4426

library(tidyr)
library(vctrs)
library(dplyr, warn.conflicts = FALSE)

vec_ptype2.grouped_df <- function(x, y, ...) {
  UseMethod("vec_ptype2.grouped_df", y)
}

vec_ptype2.grouped_df.data.frame <- function(x, y, ...) {
  inner_type <- vec_ptype2(ungroup(x), y)
  groups <- mutate(group_data(x), .rows = list(integer()))
  new_grouped_df(inner_type, groups)
}

iris %>%
  group_by(Species) %>%
  nest(data = Sepal.Length)
#> # A tibble: 143 x 5
#> # Groups:   Species [3]
#>    Sepal.Width Petal.Length Petal.Width Species           data
#>  *       <dbl>        <dbl>       <dbl> <fct>   <list<df[,1]>>
#>  1         3.5          1.4         0.2 setosa         [1 × 1]
#>  2         3            1.4         0.2 setosa         [1 × 1]
#>  3         3.2          1.3         0.2 setosa         [2 × 1]
#>  4         3.1          1.5         0.2 setosa         [2 × 1]
#>  5         3.6          1.4         0.2 setosa         [1 × 1]
#>  6         3.9          1.7         0.4 setosa         [1 × 1]
#>  7         3.4          1.4         0.3 setosa         [1 × 1]
#>  8         3.4          1.5         0.2 setosa         [2 × 1]
#>  9         2.9          1.4         0.2 setosa         [1 × 1]
#> 10         3.1          1.5         0.1 setosa         [1 × 1]
#> # … with 133 more rows

^{Created on 2019-07-02 by the reprex package (v0.2.0.9000)}