[jira] [Assigned] (ARROW-8899) [R] Add R metadata like pandas metadata for round-trip fidelity

["Neal Richardson (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-8899?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Neal Richardson reassigned ARROW-8899:
--------------------------------------

    Assignee: Romain Francois

> [R] Add R metadata like pandas metadata for round-trip fidelity
> ---------------------------------------------------------------
>
>                 Key: ARROW-8899
>                 URL: https://issues.apache.org/jira/browse/ARROW-8899
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: R
>            Reporter: Neal Richardson
>            Assignee: Romain Francois
>            Priority: Critical
>             Fix For: 1.0.0
>
>
> Arrow Schema and Field objects have custom_metadata fields to store arbitrary strings in a key-value store. Pandas stores JSON in a "pandas" key and uses that to improve the fidelity of round-tripping data to Arrow/Parquet/Feather and back. https://pandas.pydata.org/docs/dev/development/developer.html#storing-pandas-dataframe-objects-in-apache-parquet-format describes this a bit.
> You can see this pandas metadata in the sample Parquet file:
> {code:r}
> tab <- read_parquet(system.file("v0.7.1.parquet", package="arrow"), as_data_frame = FALSE)
> tab
> # Table
> # 10 rows x 11 columns
> # $carat <double>
> # $cut <string>
> # $color <string>
> # $clarity <string>
> # $depth <double>
> # $table <double>
> # $price <int64>
> # $x <double>
> # $y <double>
> # $z <double>
> # $__index_level_0__ <int64>
> tab$metadata
> # $pandas
> # [1] "{\"index_columns\": [\"__index_level_0__\"], \"column_indexes\": [{\"name\": null, \"pandas_type\": \"string\", \"numpy_type\": \"object\", \"metadata\": null}], \"columns\": [{\"name\": \"carat\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"cut\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"color\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"clarity\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"depth\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"table\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"price\", \"pandas_type\": \"int64\", \"numpy_type\": \"int64\", \"metadata\": null}, {\"name\": \"x\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"y\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"z\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"__index_level_0__\", \"pandas_type\": \"int64\", \"numpy_type\": \"int64\", \"metadata\": null}], \"pandas_version\": \"0.20.1\"}"
> {code}
> We should do something similar in R: store the "attributes" for each column in a data.frame when we convert to Arrow, and restore those attributes when we read from Arrow. 
> Since ARROW-8703, you could naively do this all in R, something like:
> {code:r}
> tab$metadata$r <- lapply(df, attributes)
> {code}
> on the conversion to Arrow, and in as.data.frame(), do
> {code:r}
> if (!is.null(tab$metadata$r)) {
>   df[] <- mapply(function(col, meta) {
>     attributes(col) <- meta
>   }, col = df, meta = tab$metadata$r)
> }
> {code}
> However, it's trickier than this because:
> * {{tab$metadata$r}} needs to be serialized to string and deserialized on the way back. Pandas uses JSON but arrow doesn't currently have a JSON R dependency. We could {{dput()}} to dump the R attributes, but that could introduce risks since you have to parse/eval code to consume it. My best idea at the moment is to try {{rawToChar(serialize(x, ascii = TRUE))}} on the way out (ascii = TRUE doesn't mean it requires ASCII inputs, it's about how it serializes) and {{unserialize(charToRaw(x))}} on the way back. But maybe there's some lower-level way to do this better.
> * We'll need to do the same for all places where Tables and RecordBatches are created/converted
> * We'll need to make sure that nested types (structs) get the same coverage
> * This metadata only is attached to Schemas, meaning that Arrays/ChunkedArrays don't have a place to store extra metadata. So we probably want to attach to the R6 (Chunked)Array objects a metadata/attributes field so that if we convert an R vector to array, or if we extract an array out of a record batch, we don't lose the attributes.
> Doing this should resolve ARROW-4390 and make ARROW-8867 trivial as well.
> Finally, a note about this custom metadata vs. extension types. Extension types can be defined by [adding metadata to a Field|https://arrow.apache.org/docs/format/Columnar.html#extension-types] (in a Schema). I think this is out of scope here because we're only concerned with R roundtrip fidelity. If there were a type that (for example) R and Pandas both had that Arrow did not, we could define an extension type so that we could share that across the implementations. But unless/until there is value in establishing that extension type standard, let's not worry with it. (In other words, in R we should ignore pandas metadata; if there's anything that pandas wants to share with R, it will define it somewhere else.)



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