ARROW-16511: [R] Preserve schema metadata in write_dataset()

[nealrichardson]

No description provided.

[jonkeane]

Looks good, thanks for the quick PR + a bit of cleanup along the way.

[jonkeane]

If we had one already, a jira would be nice here, but I'm sure we'll remember this is where it's going even without it, so let'snot

[paleolimbot]

Looks great! I checked the sfarrow example that failed and it works on this branch (and fails on the master branch, as expected):

Details

# remotes::install_github("apache/arrow/r#13105")
library(arrow, warn.conflicts = FALSE)
library(sfarrow)

# read spatial object
nc <- sf::st_read(system.file("shape/nc.shp", package="sf"), quiet = TRUE)

# create random grouping
nc$group <- sample(1:3, nrow(nc), replace = TRUE)

# use dplyr to group the dataset. %>% also allowed
nc_g <- dplyr::group_by(nc, group)

# write out to parquet datasets
tf <- tempfile()  # create temporary location

# partitioning determined by dplyr 'group_vars'
write_sf_dataset(nc_g, path = tf)
#> Warning: This is an initial implementation of Parquet/Feather file support and
#> geo metadata. This is tracking version 0.1.0 of the metadata
#> (https://github.com/geopandas/geo-arrow-spec). This metadata
#> specification may change and does not yet make stability promises.  We
#> do not yet recommend using this in a production setting unless you are
#> able to rewrite your Parquet/Feather files.

list.files(tf, recursive = TRUE)
#> [1] "group=1/part-0.parquet" "group=2/part-0.parquet" "group=3/part-0.parquet"

# open parquet files from dataset
ds <- arrow::open_dataset(tf)

# create a query. %>% also allowed
q <- dplyr::filter(ds, group == 1)

# read the dataset (piping syntax also works)
read_sf_dataset(dataset = q)
#> Simple feature collection with 31 features and 15 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -83.73952 ymin: 33.88199 xmax: -75.7637 ymax: 36.55716
#> Geodetic CRS:  NAD27
#> First 10 features:
#>     AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
#> 1  0.070     2.968  1831    1831   Currituck 37053  37053       27   508     1
#> 2  0.153     2.206  1832    1832 Northampton 37131  37131       66  1421     9
#> 3  0.109     1.325  1841    1841      Person 37145  37145       73  1556     4
#> 4  0.190     2.204  1846    1846     Halifax 37083  37083       42  3608    18
#> 5  0.081     1.288  1880    1880     Watauga 37189  37189       95  1323     1
#> 6  0.086     1.267  1893    1893      Yadkin 37197  37197       99  1269     1
#> 7  0.111     1.392  1904    1904    Alamance 37001  37001        1  4672    13
#> 8  0.059     1.319  1927    1927    Mitchell 37121  37121       61   671     0
#> 9  0.122     1.516  1932    1932    Caldwell 37027  37027       14  3609     6
#> 10 0.080     1.307  1936    1936      Yancey 37199  37199      100   770     0
#>    NWBIR74 BIR79 SID79 NWBIR79 group                       geometry
#> 1      123   830     2     145     1 MULTIPOLYGON (((-76.00897 3...
#> 2     1066  1606     3    1197     1 MULTIPOLYGON (((-77.21767 3...
#> 3      613  1790     4     650     1 MULTIPOLYGON (((-78.8068 36...
#> 4     2365  4463    17    2980     1 MULTIPOLYGON (((-77.33221 3...
#> 5       17  1775     1      33     1 MULTIPOLYGON (((-81.80622 3...
#> 6       65  1568     1      76     1 MULTIPOLYGON (((-80.49554 3...
#> 7     1243  5767    11    1397     1 MULTIPOLYGON (((-79.24619 3...
#> 8        1   919     2       4     1 MULTIPOLYGON (((-82.11885 3...
#> 9      309  4249     9     360     1 MULTIPOLYGON (((-81.32813 3...
#> 10      12   869     1      10     1 MULTIPOLYGON (((-82.27921 3...

^{Created on 2022-05-09 by the reprex package (v2.0.1)}

[github-actions]

https://issues.apache.org/jira/browse/ARROW-16511

[ursabot]

Benchmark runs are scheduled for baseline = 214135d and contender = d00caa9. d00caa9 is a master commit associated with this PR. Results will be available as each benchmark for each run completes.
Conbench compare runs links:
[Finished ⬇️0.0% ⬆️0.0%] ec2-t3-xlarge-us-east-2
[Finished ⬇️0.43% ⬆️0.0%] test-mac-arm
[Finished ⬇️0.36% ⬆️0.0%] ursa-i9-9960x
[Finished ⬇️0.16% ⬆️0.0%] ursa-thinkcentre-m75q
Buildkite builds:
[Finished] d00caa94 ec2-t3-xlarge-us-east-2
[Finished] d00caa94 test-mac-arm
[Finished] d00caa94 ursa-i9-9960x
[Finished] d00caa94 ursa-thinkcentre-m75q
[Finished] 214135d8 ec2-t3-xlarge-us-east-2
[Finished] 214135d8 test-mac-arm
[Finished] 214135d8 ursa-i9-9960x
[Finished] 214135d8 ursa-thinkcentre-m75q
Supported benchmarks:
ec2-t3-xlarge-us-east-2: Supported benchmark langs: Python, R. Runs only benchmarks with cloud = True
test-mac-arm: Supported benchmark langs: C++, Python, R
ursa-i9-9960x: Supported benchmark langs: Python, R, JavaScript
ursa-thinkcentre-m75q: Supported benchmark langs: C++, Java