Import R data frame attributes as metadata

[nalimilan]

Use the metadata function that are going to be added to DataAPI and DataFrames (https://github.com/JuliaData/DataFrames.jl/pull/3055) to import R data.frame attributes and set them as DataFrame metadata. R stores per-column attributes in vector objects, while DataFrames.jl stores them in the DataFrame object, as there is no generic mechanism to attach metadata to an AbstractVector object.

The row.names attribute is skipped as it is not appropriate to store it as global metadata given that it will get out of sync after subsetting rows. We could provide a way to turn row names into a column instead.

Also add methods to check equality between two DictoVec object as these are useful for tests (haven commonly sets named numeric vectors to store value labels so this case deserves testing).

(Test pass locally when using DataAPI and DataFrame branches.)

[alyst]

Thanks! I'll try to review it in the next few days.

[bkamins]

@alyst - we still need to decide on the best API in https://github.com/JuliaData/DataAPI.jl/pull/48 before finalizing this PR, so if you have any thoughts on this please comment there. Thank you!

[codecov[bot]]

Codecov Report

Base: 85.75% // Head: 86.64% // Increases project coverage by +0.88% :tada:

Coverage data is based on head (2724f62) compared to base (5df74cf). Patch coverage: 97.14% of modified lines in pull request are covered.

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #93      +/-   ##
==========================================
+ Coverage   85.75%   86.64%   +0.88%     
==========================================
  Files          14       14              
  Lines         646      689      +43     
==========================================
+ Hits          554      597      +43     
  Misses         92       92              

Impacted Files	Coverage Δ
src/convert.jl	95.73% <96.42%> (+0.76%)	:arrow_up:
src/DictoVec.jl	96.20% <100.00%> (-1.02%)	:arrow_down:
src/RData.jl	100.00% <100.00%> (ø)
src/context.jl	100.00% <0.00%> (ø)
src/sxtypes.jl	76.13% <0.00%> (+0.27%)	:arrow_up:
src/identifier.jl	92.59% <0.00%> (+0.28%)	:arrow_up:
src/readers.jl	74.28% <0.00%> (+0.29%)	:arrow_up:
... and 3 more

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[nalimilan]

Ah, DataFrames 1.4 requires Julia 1.6, so we have to drop support for older Julia versions here too. But the convention is to bump the minor release when doing that, so that we would still be able to tag a bugfix release on 1.0 if necessary. Maybe take the occasion to tag RData 1.0?

[alyst]

I think tagging 1.0.0 is a good idea.

[nalimilan]

Thanks! See https://github.com/JuliaRegistries/General/pull/69824