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On the original example:

library(arrow)
#> 
#> Attaching package: 'arrow'
#> The following object is masked from 'package:utils':
#> 
#>     timestamp
library(sf)
#> Warning: package 'sf' was built under R version 4.0.2
#> Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1

fname <- system.file("shape/nc.shp", package="sf")
df_spatial <- st_read(fname)
#> Reading layer `nc' from data source `/Users/romainfrancois/.R/library/4.0/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
df_spatial
#> Simple feature collection with 100 features and 14 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
#> First 10 features:
#>     AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
#> 1  0.114     1.442  1825    1825        Ashe 37009  37009        5  1091     1
#> 2  0.061     1.231  1827    1827   Alleghany 37005  37005        3   487     0
#> 3  0.143     1.630  1828    1828       Surry 37171  37171       86  3188     5
#> 4  0.070     2.968  1831    1831   Currituck 37053  37053       27   508     1
#> 5  0.153     2.206  1832    1832 Northampton 37131  37131       66  1421     9
#> 6  0.097     1.670  1833    1833    Hertford 37091  37091       46  1452     7
#> 7  0.062     1.547  1834    1834      Camden 37029  37029       15   286     0
#> 8  0.091     1.284  1835    1835       Gates 37073  37073       37   420     0
#> 9  0.118     1.421  1836    1836      Warren 37185  37185       93   968     4
#> 10 0.124     1.428  1837    1837      Stokes 37169  37169       85  1612     1
#>    NWBIR74 BIR79 SID79 NWBIR79                       geometry
#> 1       10  1364     0      19 MULTIPOLYGON (((-81.47276 3...
#> 2       10   542     3      12 MULTIPOLYGON (((-81.23989 3...
#> 3      208  3616     6     260 MULTIPOLYGON (((-80.45634 3...
#> 4      123   830     2     145 MULTIPOLYGON (((-76.00897 3...
#> 5     1066  1606     3    1197 MULTIPOLYGON (((-77.21767 3...
#> 6      954  1838     5    1237 MULTIPOLYGON (((-76.74506 3...
#> 7      115   350     2     139 MULTIPOLYGON (((-76.00897 3...
#> 8      254   594     2     371 MULTIPOLYGON (((-76.56251 3...
#> 9      748  1190     2     844 MULTIPOLYGON (((-78.30876 3...
#> 10     160  2038     5     176 MULTIPOLYGON (((-80.02567 3...

tab <- Table$create(df_spatial)
roundtripped <- as.data.frame(tab)
roundtripped
#> Simple feature collection with 100 features and 14 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
#> First 10 features:
#>     AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
#> 1  0.114     1.442  1825    1825        Ashe 37009  37009        5  1091     1
#> 2  0.061     1.231  1827    1827   Alleghany 37005  37005        3   487     0
#> 3  0.143     1.630  1828    1828       Surry 37171  37171       86  3188     5
#> 4  0.070     2.968  1831    1831   Currituck 37053  37053       27   508     1
#> 5  0.153     2.206  1832    1832 Northampton 37131  37131       66  1421     9
#> 6  0.097     1.670  1833    1833    Hertford 37091  37091       46  1452     7
#> 7  0.062     1.547  1834    1834      Camden 37029  37029       15   286     0
#> 8  0.091     1.284  1835    1835       Gates 37073  37073       37   420     0
#> 9  0.118     1.421  1836    1836      Warren 37185  37185       93   968     4
#> 10 0.124     1.428  1837    1837      Stokes 37169  37169       85  1612     1
#>    NWBIR74 BIR79 SID79 NWBIR79                       geometry
#> 1       10  1364     0      19 MULTIPOLYGON (((-81.47276 3...
#> 2       10   542     3      12 MULTIPOLYGON (((-81.23989 3...
#> 3      208  3616     6     260 MULTIPOLYGON (((-80.45634 3...
#> 4      123   830     2     145 MULTIPOLYGON (((-76.00897 3...
#> 5     1066  1606     3    1197 MULTIPOLYGON (((-77.21767 3...
#> 6      954  1838     5    1237 MULTIPOLYGON (((-76.74506 3...
#> 7      115   350     2     139 MULTIPOLYGON (((-76.00897 3...
#> 8      254   594     2     371 MULTIPOLYGON (((-76.56251 3...
#> 9      748  1190     2     844 MULTIPOLYGON (((-78.30876 3...
#> 10     160  2038     5     176 MULTIPOLYGON (((-80.02567 3...

attributes(roundtripped$geometry[[1]])
#> $class
#> [1] "XY"           "MULTIPOLYGON" "sfg"
attributes(df_spatial$geometry[[1]])
#> $class
#> [1] "XY"           "MULTIPOLYGON" "sfg"

Created on 2020-10-29 by the reprex package (v0.3.0.9001)

@romainfrancois
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This increases again the weight of the metadata, which now has to include attributes for each element of a list column, aka a struct array, but this seems on this example to work.

Can't use sf on the tests, so using something simpler:

test_that("metadata of list elements (ARROW-10386)", {
  df <- data.frame(x = I(list(structure(1, foo = "bar"), structure(2, foo = "bar"))))
  tab <- Table$create(df)
  expect_identical(attr(as.data.frame(tab)$x[[1]], "foo"), "bar")
  expect_identical(attr(as.data.frame(tab)$x[[2]], "foo"), "bar")
})

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@petrbouchal
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Thanks - can confirm the sf object now roundtrips correctly on my machine as well using the current HEAD version.

@romainfrancois romainfrancois force-pushed the ARROW-10386/List_metadata branch from d716a74 to 4d1e73d Compare November 12, 2020 09:32
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This looks good to me, a few minor suggestions

apply_arrow_r_metadata(.x, .y)
})
x
}
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Could we move !is.null(columns_metadata) up to line 290 instead of having it on both 291 and 296?

df <- data.frame(x = I(list(structure(1, foo = "bar"), structure(2, foo = "bar"))))
tab <- Table$create(df)
expect_identical(attr(as.data.frame(tab)$x[[1]], "foo"), "bar")
expect_identical(attr(as.data.frame(tab)$x[[2]], "foo"), "bar")
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I wonder if it would be clearer to have different attributes on each item/row to make it super obvious that we're not picking the attributes of the first item/row and copying them for the whole column.

@nealrichardson
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This increases again the weight of the metadata, which now has to include attributes for each element of a list column

How much does it blow up the metadata? Is this going to scale to be able to handle normal/large shapefiles?

Is there a more efficient representation for this metadata (considering that we have to serialize it to a string)? Should we special-case sf objects?

@nealrichardson
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Closing in favor of #9182; relevant commits cherry-picked to there.

kou pushed a commit that referenced this pull request Feb 20, 2023
…Hub issue numbers (#34260)

Rewrite the Jira issue numbers to the GitHub issue numbers, so that the GitHub issue numbers are automatically linked to the issues by pkgdown's auto-linking feature.

Issue numbers have been rewritten based on the following correspondence.
Also, the pkgdown settings have been changed and updated to link to GitHub.

I generated the Changelog page using the `pkgdown::build_news()` function and verified that the links work correctly.

---
ARROW-6338	#5198
ARROW-6364	#5201
ARROW-6323	#5169
ARROW-6278	#5141
ARROW-6360	#5329
ARROW-6533	#5450
ARROW-6348	#5223
ARROW-6337	#5399
ARROW-10850	#9128
ARROW-10624	#9092
ARROW-10386	#8549
ARROW-6994	#23308
ARROW-12774	#10320
ARROW-12670	#10287
ARROW-16828	#13484
ARROW-14989	#13482
ARROW-16977	#13514
ARROW-13404	#10999
ARROW-16887	#13601
ARROW-15906	#13206
ARROW-15280	#13171
ARROW-16144	#13183
ARROW-16511	#13105
ARROW-16085	#13088
ARROW-16715	#13555
ARROW-16268	#13550
ARROW-16700	#13518
ARROW-16807	#13583
ARROW-16871	#13517
ARROW-16415	#13190
ARROW-14821	#12154
ARROW-16439	#13174
ARROW-16394	#13118
ARROW-16516	#13163
ARROW-16395	#13627
ARROW-14848	#12589
ARROW-16407	#13196
ARROW-16653	#13506
ARROW-14575	#13160
ARROW-15271	#13170
ARROW-16703	#13650
ARROW-16444	#13397
ARROW-15016	#13541
ARROW-16776	#13563
ARROW-15622	#13090
ARROW-18131	#14484
ARROW-18305	#14581
ARROW-18285	#14615
* Closes: #33631

Authored-by: SHIMA Tatsuya <[email protected]>
Signed-off-by: Sutou Kouhei <[email protected]>
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4 participants