r-spatial / sf

Simple Features for R
https://r-spatial.github.io/sf/
Other
1.33k stars 293 forks source link

`st_read` reads compressed GPKG (>= GDAL 3.7) - should this be advertised? #2433

Open rsbivand opened 1 week ago

rsbivand commented 1 week ago

@Nowosad I've been looking at reading compressed vector files, such as this: GB_election_2024_sim.gpkg.zip

In terra:

> xx <- terra::vect("GB_election_2024_sim.gpkg.zip")
> xx
 class       : SpatVector 
 geometry    : polygons 
 dimensions  : 632, 19  (geometries, attributes)
 extent      : 5512.998, 655970.4, 5342.9, 1220287  (xmin, xmax, ymin, ymax)
 source      : GB_election_2024_sim.gpkg.zip (GB_election_2024_sim)
 coord. ref. : OSGB36 / British National Grid (EPSG:27700) 
 names       :     Constituency            Name Area_Code Area_Description
 type        :            <chr>           <chr>     <chr>            <chr>
 values      : Aberafan Maesteg Aberafan Maest~       WMC  Westminster Co~
                 Aberdeen North Aberdeen North~       WMC  Westminster Co~
                 Aberdeen South Aberdeen South~       WMC  Westminster Co~
       File_Name Feature_Serial_Number Collection_Serial_Number
           <chr>                 <int>                    <int>
 ABERAFAN_MAEST~                     1                        1
 ABERDEEN_NORTH~                   472                      472
 ABERDEEN_SOUTH~                   473                      473
 Global_Polygon_ID Admin_Unit_ID Census_Code (and 9 more)
             <int>         <int>       <chr>             
            146759        187003   W07000081             
            147374        187618   S14000060             
            147375        187619   S14000061        

In sf:

> sim <- st_read("GB_election_2024_sim.gpkg.zip")
Reading layer `GB_election_2024_sim' from data source 
  `/home/rsb/topics/packages/github-r-spatial/LICD/GB_election_2024_sim.gpkg.zip' 
  using driver `GPKG'
Simple feature collection with 632 features and 19 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 5512.998 ymin: 5342.9 xmax: 655970.4 ymax: 1220287
Projected CRS: OSGB36 / British National Grid
> all.equal(st_geometry(sim), st_geometry(st_cast(st_as_sf(xx), "MULTIPOLYGON")))
[1] TRUE
> all.equal(st_drop_geometry(sim), st_drop_geometry(st_as_sf(xx)))
[1] TRUE

Could we compress the GPKG in spData see https://github.com/Nowosad/spData/issues/62 ? Or are too many using GDAL < 3.7 ? This actually also works from GDAL >= 3.1 for ESRI Shapefiles too.

Nowosad commented 1 week ago

@rsbivand GDAL 3.7 was just released less than 1.5 years ago. Thus, my feeling is that it is too early to expect the majority of the users to have it...

rsbivand commented 1 week ago

OK, it does save a lot (~50%) on installed size. CRAN Windows and macOS are OK, current Fedora also, Ubuntu GH actions OK, CRAN Fedora devel checks not OK, because Fedora is still 36, so GDAL 3.4. CRAN macOS checks not OK 3.5 on both architectures. I'll see how to move CRAN forward, at least for feasible check platforms, with the benefit of smaller installed sizes for vector files.

rsbivand commented 1 week ago
GDAL37 <- as.numeric_version(unname(sf_extSoftVersion()["GDAL"])) >= "3.7.0"
file <- "aa.gpkg.zip"
zipfile <- system.file(file, package="aaa")
if (GDAL37) {
    out <- st_read(zipfile)
} else {
    td <- tempdir()
    bn <- sub(".zip", "", basename(file))
    target <- unzip(zipfile, files=bn, exdir=td)
    out <- st_read(target)
}