Dropped factors?

[eliocamp]

Please specify whether your issue is about:

• a possible bug
• a question about package functionality
• a suggested code or documentation change, improvement to the code, or feature request

This is shown in the readme, but is it intended behaviour for factor variables to be converted to text in a roundtrip?

library(csvy)
csvy::write_csvy(iris, "iris.csvy")
all.equal(iris, csvy::read_csvy("iris.csvy"))
#> [1] "Attributes: < Names: 1 string mismatch >"                            
#> [2] "Attributes: < Length mismatch: comparison on first 2 components >"   
#> [3] "Attributes: < Component 2: Modes: numeric, character >"              
#> [4] "Attributes: < Component 2: Lengths: 150, 1 >"                        
#> [5] "Attributes: < Component 2: target is numeric, current is character >"
#> [6] "Component \"Species\": 'current' is not a factor"

str(iris)
#> 'data.frame':    150 obs. of  5 variables:
#>  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#>  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#>  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#>  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#>  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
str(csvy::read_csvy("iris.csvy"))
#> 'data.frame':    150 obs. of  5 variables:
#>  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#>  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#>  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#>  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#>  $ Species     : chr  "setosa" "setosa" "setosa" "setosa" ...
#>   ..- attr(*, "levels")= chr  "setosa" "versicolor" "virginica"
#>  - attr(*, "profile")= chr "tabular-data-package"
#>  - attr(*, "name")= chr "iris"

Session info

devtools::session_info()
#> ─ Session info ──────────────────────────────────────────────────────────
#>  setting  value                         
#>  version  R version 3.6.2 (2019-12-12)  
#>  os       elementary OS 5.1 Hera        
#>  system   x86_64, linux-gnu             
#>  ui       X11                           
#>  language en_US                         
#>  collate  en_US.UTF-8                   
#>  ctype    en_US.UTF-8                   
#>  tz       America/Argentina/Buenos_Aires
#>  date     2019-12-16                    
#> 
#> ─ Packages ──────────────────────────────────────────────────────────────
#>  package     * version    date       lib source                         
#>  assertthat    0.2.1      2019-03-21 [1] CRAN (R 3.6.0)                 
#>  backports     1.1.5      2019-10-02 [1] CRAN (R 3.6.1)                 
#>  callr         3.3.2      2019-09-22 [1] CRAN (R 3.6.1)                 
#>  cli           1.1.0      2019-03-19 [1] CRAN (R 3.6.0)                 
#>  crayon        1.3.4      2017-09-16 [1] CRAN (R 3.6.0)                 
#>  csvy        * 0.3.0      2019-12-16 [1] Github (leeper/csvy@af0aa8d)   
#>  data.table    1.12.6     2019-10-18 [1] CRAN (R 3.6.1)                 
#>  desc          1.2.0      2018-05-01 [1] CRAN (R 3.6.0)                 
#>  devtools      2.2.0.9000 2019-09-17 [1] Github (r-lib/devtools@2765fbe)
#>  digest        0.6.23     2019-11-23 [1] CRAN (R 3.6.1)                 
#>  ellipsis      0.3.0      2019-09-20 [1] CRAN (R 3.6.1)                 
#>  evaluate      0.14       2019-05-28 [1] CRAN (R 3.6.0)                 
#>  fs            1.3.1      2019-05-06 [1] CRAN (R 3.6.1)                 
#>  glue          1.3.1.9000 2019-09-17 [1] Github (tidyverse/glue@71eeddf)
#>  highr         0.8        2019-03-20 [1] CRAN (R 3.6.0)                 
#>  htmltools     0.4.0      2019-10-04 [1] CRAN (R 3.6.1)                 
#>  jsonlite      1.6        2018-12-07 [1] CRAN (R 3.6.0)                 
#>  knitr         1.25       2019-09-18 [1] CRAN (R 3.6.1)                 
#>  magrittr      1.5        2014-11-22 [1] CRAN (R 3.6.0)                 
#>  memoise       1.1.0      2017-04-21 [1] CRAN (R 3.6.0)                 
#>  pkgbuild      1.0.6      2019-10-09 [1] CRAN (R 3.6.1)                 
#>  pkgload       1.0.2      2018-10-29 [1] CRAN (R 3.6.0)                 
#>  prettyunits   1.0.2      2015-07-13 [1] CRAN (R 3.6.0)                 
#>  processx      3.4.1      2019-07-18 [1] CRAN (R 3.6.1)                 
#>  ps            1.3.0      2018-12-21 [1] CRAN (R 3.6.0)                 
#>  R6            2.4.1      2019-11-12 [1] CRAN (R 3.6.1)                 
#>  Rcpp          1.0.3      2019-11-08 [1] CRAN (R 3.6.1)                 
#>  remotes       2.1.0      2019-06-24 [1] CRAN (R 3.6.1)                 
#>  rlang         0.4.1.9000 2019-11-12 [1] Github (r-lib/rlang@5a0b80a)   
#>  rmarkdown     1.16       2019-10-01 [1] CRAN (R 3.6.1)                 
#>  rprojroot     1.3-2      2018-01-03 [1] CRAN (R 3.6.0)                 
#>  sessioninfo   1.1.1      2018-11-05 [1] CRAN (R 3.6.0)                 
#>  stringi       1.4.3      2019-03-12 [1] CRAN (R 3.6.0)                 
#>  stringr       1.4.0      2019-02-10 [1] CRAN (R 3.6.0)                 
#>  testthat      2.3.0      2019-11-05 [1] CRAN (R 3.6.1)                 
#>  usethis       1.5.1      2019-07-04 [1] CRAN (R 3.6.1)                 
#>  withr         2.1.2      2018-03-15 [1] CRAN (R 3.6.0)                 
#>  xfun          0.11       2019-11-12 [1] CRAN (R 3.6.1)                 
#>  yaml          2.2.0      2018-07-25 [1] CRAN (R 3.6.0)                 
#> 
#> [1] /home/elio/R/x86_64-pc-linux-gnu-library/3.6
#> [2] /usr/local/lib/R/site-library
#> [3] /usr/lib/R/site-library
#> [4] /usr/lib/R/library

The relevant yaml section has this:

#- name: Species
#  type: string
#  levels:
#  - setosa
#  - versicolor
#  - virginica

Which is not really correct, as the type is actually factor. I'm not at all familiar with the csvy spec so "factor" might not be a posible type. In any case, this applies not only to factors. It seems that write/read_csvy drops class attributes.

For example,

library(csvy)

iris$col <- 1
class(iris$col) <- c("custom_cass", "numeric")
csvy::write_csvy(iris, "iris.csvy")
str(iris)
#> 'data.frame':    150 obs. of  6 variables:
#>  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#>  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#>  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#>  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#>  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ col         : 'custom_cass' num  1 1 1 1 1 1 1 1 1 1 ...
str(csvy::read_csvy("iris.csvy"))
#> 'data.frame':    150 obs. of  6 variables:
#>  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#>  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#>  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#>  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#>  $ Species     : chr  "setosa" "setosa" "setosa" "setosa" ...
#>   ..- attr(*, "levels")= chr  "setosa" "versicolor" "virginica"
#>  $ col         : chr  "1" "1" "1" "1" ...
#>  - attr(*, "profile")= chr "tabular-data-package"
#>  - attr(*, "name")= chr "iris"