how to get a data frame from split_sheet?

[benmarwick]

I'd like to get the output of jailbreakr::split_sheet() as a data frame. How can I do that?

Here's what I've tried:

mg <- rexcel::rexcel_read("mini-gap.xlsx")
mg_split_sheet <- jailbreakr::split_sheet(mg)
thing <- mg_split_sheet[[1]]$values()
thing
#>      [,1]           [,2]        [,3]   [,4]      [,5]    [,6]       
#> [1,] "country"      "continent" "year" "lifeExp" "pop"   "gdpPercap"
#> [2,] "Algeria"      "Africa"    1952   43.077    9279525 2449.008   
#> [3,] "Angola"       "Africa"    1952   30.015    4232095 3520.61    
#> [4,] "Benin"        "Africa"    1952   38.223    1738315 1062.752   
#> [5,] "Botswana"     "Africa"    1952   47.622    442308  851.2411   
#> [6,] "Burkina Faso" "Africa"    1952   31.975    4469979 543.2552

It looks like a matrix or array, but the output of str is list (is that normal for an array? I don't work with them much)...

str(thing)
#> List of 36
#>  $ : chr "country"
#>  $ : chr "Algeria"
#>  $ : chr "Angola"
#>  $ : chr "Benin"
#>  $ : chr "Botswana"
#>  $ : chr "Burkina Faso"
#>  $ : chr "continent"
#>  $ : chr "Africa"
#>  $ : chr "Africa"
#>  $ : chr "Africa"
#>  $ : chr "Africa"
#>  $ : chr "Africa"
#>  $ : chr "year"
#>  $ : num 1952
#>  $ : num 1952
#>  $ : num 1952
#>  $ : num 1952
#>  $ : num 1952
#>  $ : chr "lifeExp"
#>  $ : num 43.1
#>  $ : num 30
#>  $ : num 38.2
#>  $ : num 47.6
#>  $ : num 32
#>  $ : chr "pop"
#>  $ : num 9279525
#>  $ : num 4232095
#>  $ : num 1738315
#>  $ : num 442308
#>  $ : num 4469979
#>  $ : chr "gdpPercap"
#>  $ : num 2449
#>  $ : num 3521
#>  $ : num 1063
#>  $ : num 851
#>  $ : num 543
#>  - attr(*, "dim")= Named int [1:2] 6 6
#>   ..- attr(*, "names")= chr [1:2] "row" "col"

I see in https://github.com/rsheets/linen/blob/master/R/export.R that the values element is created by array(lapply(...), which is why I posted here, rather than at the jailbreakr repo.

I tried as.data.frame.array, but that gives a data frame of lists:

thing_asdfa <- as.data.frame.array(thing)

str(thing_asdfa)
#> 'data.frame':    6 obs. of  6 variables:
#>  $ V1:List of 6
#>   ..$ : chr "country"
#>   ..$ : chr "Algeria"
#>   ..$ : chr "Angola"
#>   ..$ : chr "Benin"
#>   ..$ : chr "Botswana"
#>   ..$ : chr "Burkina Faso"
#>  $ V2:List of 6
#>   ..$ : chr "continent"
#>   ..$ : chr "Africa"
#>   ..$ : chr "Africa"
#>   ..$ : chr "Africa"
#>   ..$ : chr "Africa"
#>   ..$ : chr "Africa"
#>  $ V3:List of 6
#>   ..$ : chr "year"
#>   ..$ : num 1952
#>   ..$ : num 1952
#>   ..$ : num 1952
#>   ..$ : num 1952
#>   ..$ : num 1952
#>  $ V4:List of 6
#>   ..$ : chr "lifeExp"
#>   ..$ : num 43.1
#>   ..$ : num 30
#>   ..$ : num 38.2
#>   ..$ : num 47.6
#>   ..$ : num 32
#>  $ V5:List of 6
#>   ..$ : chr "pop"
#>   ..$ : num 9279525
#>   ..$ : num 4232095
#>   ..$ : num 1738315
#>   ..$ : num 442308
#>   ..$ : num 4469979
#>  $ V6:List of 6
#>   ..$ : chr "gdpPercap"
#>   ..$ : num 2449
#>   ..$ : num 3521
#>   ..$ : num 1063
#>   ..$ : num 851
#>   ..$ : num 543

And data frame-style indexing gives unexpected results:

thing[1,]
#> [[1]]
#> [1] "country"
#> 
#> [[2]]
#> [1] "continent"
#> 
#> [[3]]
#> [1] "year"
#> 
#> [[4]]
#> [1] "lifeExp"
#> 
#> [[5]]
#> [1] "pop"
#> 
#> [[6]]
#> [1] "gdpPercap"

I've also tried reshape2::melt and plyr::adply, but had no luck with those.

What do you recommend for getting this into a basic data frame?

Session info

devtools::session_info()
#> Session info --------------------------------------------------------------
#>  setting  value                       
#>  version  R version 3.3.2 (2016-10-31)
#>  system   x86_64, mingw32             
#>  ui       RTerm                       
#>  language (EN)                        
#>  collate  English_Australia.1252      
#>  tz       America/Los_Angeles         
#>  date     2017-02-20
#> Packages ------------------------------------------------------------------
#>  package    * version    date       source                             
#>  assertthat   0.1        2013-12-06 CRAN (R 3.2.2)                     
#>  backports    1.0.5      2017-01-18 CRAN (R 3.3.2)                     
#>  cellranger   1.1.0.9000 2017-02-20 Github (rsheets/cellranger@024d5ba)
#>  devtools     1.12.0     2016-06-24 CRAN (R 3.3.2)                     
#>  digest       0.6.12     2017-01-27 CRAN (R 3.3.2)                     
#>  evaluate     0.10       2016-10-11 CRAN (R 3.3.1)                     
#>  htmltools    0.3.5      2016-03-21 CRAN (R 3.2.4)                     
#>  jailbreakr   0.0.1      2017-02-20 Github (rsheets/jailbreakr@2fbec5f)
#>  knitr        1.15.1     2016-11-22 CRAN (R 3.3.2)                     
#>  lazyeval     0.2.0.9000 2016-11-07 Github (hadley/lazyeval@c155c3d)   
#>  linen        0.0.4      2017-02-20 Github (rsheets/linen@7618a13)     
#>  magrittr     1.5        2014-11-22 CRAN (R 3.3.1)                     
#>  memoise      1.0.0      2016-01-29 CRAN (R 3.2.5)                     
#>  R6           2.2.0      2016-10-05 CRAN (R 3.3.1)                     
#>  Rcpp         0.12.9     2017-01-14 CRAN (R 3.3.2)                     
#>  rexcel       0.0.1      2017-02-20 Github (rsheets/rexcel@e8dd5d3)    
#>  rmarkdown    1.3.1      2017-02-16 Github (rstudio/rmarkdown@e672d41) 
#>  rprojroot    1.2        2017-01-16 CRAN (R 3.3.2)                     
#>  stringi      1.1.2      2016-10-01 CRAN (R 3.3.1)                     
#>  stringr      1.1.0      2016-08-19 CRAN (R 3.3.1)                     
#>  tibble       1.2        2016-08-26 CRAN (R 3.3.1)                     
#>  withr        1.0.2      2016-06-20 CRAN (R 3.3.0)                     
#>  xml2         1.1.1      2017-02-20 Github (hadley/xml2@c84db5e)       
#>  yaml         2.1.14     2016-11-12 CRAN (R 3.3.2)

p.s. thanks for this project, and for the reprex pkg for making it easier to post code and output!

[jennybc]

This is a question for @richfitz I think ...