support writing aspatial tables with st_write() #1396

edzer · 2020-05-16T13:54:52Z

addresses #1345. Reprex:

library(sf)
# Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 7.0.0
nc = system.file("gpkg/nc.gpkg", package = "sf")
tf = tempfile(fileext = ".gpkg")
file.copy(nc, tf)
# [1] TRUE
# how does an aspatial layer look like? NA geometry_type
st_layers(system.file("gpkg/nospatial.gpkg", package = "sf"))
# Driver: GPKG 
# Available layers:
#   layer_name geometry_type features fields
# 1  nospatial            NA        1      2

# demo:
a = data.frame(a = c(1L,-3L), b = c("foo", "bar"))
st_write(a, tf, 
         layer = "nonspatial_table1",
         driver = "GPKG",
         delete_layer = TRUE,
         layer_options = "ASPATIAL_VARIANT=GPKG_ATTRIBUTES")
# Deleting layer `nonspatial_table1' failed
# Writing layer `nonspatial_table1' to data source `/tmp/RtmpfMlTaz/file50a8704316a6.gpkg' using driver `GPKG'
# options:        ASPATIAL_VARIANT=GPKG_ATTRIBUTES 
# Writing 2 features with 2 fields without geometries.
st_layers(tf)
# Driver: GPKG 
# Available layers:
#          layer_name geometry_type features fields
# 1           nc.gpkg Multi Polygon      100     14
# 2 nonspatial_table1            NA        2      2
(out = read_sf(tf, "nonspatial_table1"))
# # A tibble: 2 x 2
#       a b    
#   <int> <chr>
# 1     1 foo  
# 2    -3 bar  
# Warning message:
# no simple feature geometries present: returning a data.frame or tbl_df

addresses #1345

florisvdh

👍 Thanks a lot @edzer for this fine and useful result! 🎉 Two colleagues have confirmed that the below reprex also works well in Windows (thanks @damianooldoni, @hansvancalster). Further, inspection of the resulting GPKG files shows that GDAL nicely follows the GPKG 1.2 specification for non-spatial attributes.

reprex using GEOS 3.5.1, GDAL 2.2.2, PROJ 4.9.2 (Linux)

# install

remotes::install_github("r-spatial/sf@gpkg")
#> Using github PAT from envvar GITHUB_PAT
#> Skipping install of 'sf' from a github remote, the SHA1 (76cf2872) has not changed since last install.
#>   Use `force = TRUE` to force installation

# creating gpkg file with 2 spatial layers

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(sf)
#> Linking to GEOS 3.5.1, GDAL 2.2.2, PROJ 4.9.2

inborutils::download_zenodo("10.5281/zenodo.3784149")
#> Will download 1 file (total size: 32.5 KiB) from https://doi.org/10.5281/zenodo.3784149 (Distribution of the Natura 2000 habitat type 7220 (Cratoneurion) in Flanders, Belgium (version 2020); version: habitatsprings_2020v2)
#> 
#> Verifying file integrity...
#> habitatsprings.geojson was downloaded and its integrity verified (md5sum: 64c3db07d17274da047b3962aab28e80)
inborutils::download_zenodo("10.5281/zenodo.3386815")
#> Will download 6 files (total size: 1.5 MiB) from https://doi.org/10.5281/zenodo.3386815 (Redistribution of the map with the Special Areas of Conservation in Flanders with respect to Directive 92/43/EEC (status 2013-01-18); version: sac_2013-01-18)
#> 
#> Verifying file integrity...
#> sac.dbf was downloaded and its integrity verified (md5sum: 1fc2a8b5f56ad4ba05a999697e31626e)
#> sac.prj was downloaded and its integrity verified (md5sum: e8c2db3e5567fbf7ef9c0f306fde20f2)
#> sac.sbn was downloaded and its integrity verified (md5sum: 945791898114a317581c5560d025e420)
#> sac.sbx was downloaded and its integrity verified (md5sum: 7d75b87ca3aad3ae275ff447a1b2d75c)
#> sac.shp was downloaded and its integrity verified (md5sum: ce6f5cbb37f35884f28053a4be765408)
#> sac.shx was downloaded and its integrity verified (md5sum: 91094173055ff88e77355930464853a9)

habsprings <- st_read("habitatsprings.geojson")
#> Reading layer `habitatsprings' from data source `/tmp/RtmpNoZzsi/reprex202e2a3563de/habitatsprings.geojson' using driver `GeoJSON'
#> Simple feature collection with 104 features and 10 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 2.757966 ymin: 50.70777 xmax: 5.904029 ymax: 50.92789
#> CRS:            4326
habsprings %>% st_write("layers.gpkg",
                        layer = "habsprings",
                        delete_dsn = TRUE)
#> Deleting source `layers.gpkg' failed
#> Writing layer `habsprings' to data source `layers.gpkg' using driver `GPKG'
#> Writing 104 features with 10 fields and geometry type Point.
(read_sf("layers.gpkg"))
#> Simple feature collection with 104 features and 10 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 2.757966 ymin: 50.70777 xmax: 5.904029 ymax: 50.92789
#> CRS:            4326
#> # A tibble: 104 x 11
#>       id source validity_status name  system_type habitattype unit_id area_m2
#>    <dbl> <chr>  <chr>           <chr> <chr>       <chr>         <dbl>   <dbl>
#>  1     1 kalkt… gecontroleerd   Stee… rivulet     7220              1      50
#>  2     2 kalkt… gecontroleerd   Stee… rivulet     7220              1      20
#>  3     3 kalkt… gecontroleerd   Duli… <NA>        7230             NA       0
#>  4     4 kalkt… gecontroleerd   Kapi… <NA>        gh               NA       0
#>  5     5 kalkt… gecontroleerd   Reme… rivulet     7220              2     200
#>  6     6 kalkt… gecontroleerd   Reme… rivulet     7220              2     500
#>  7     7 anb    te controleren  Kest… unknown     7220,gh          32       0
#>  8     8 kalkt… gecontroleerd   Krin… rivulet     7220              3     800
#>  9     9 kalkt… gecontroleerd   Bois… rivulet     7220              4      50
#> 10    10 kalkt… gecontroleerd   Kwin… mire        7220              5      10
#> # … with 94 more rows, and 3 more variables: year <dbl>, sbz <dbl>, geom <POINT
#> #   [°]>

sac <- st_read("sac.shp")
#> Reading layer `sac' from data source `/tmp/RtmpNoZzsi/reprex202e2a3563de/sac.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 616 features and 12 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: 22084.25 ymin: 153207.4 xmax: 258865 ymax: 243333
#> proj4string:    +proj=lcc +lat_1=49.8333339 +lat_2=51.16666723333333 +lat_0=90 +lon_0=4.367486666666666 +x_0=150000.01256 +y_0=5400088.4378 +ellps=intl +units=m +no_defs
sac %>% st_write("layers.gpkg",
                        layer = "sac")
#> Writing layer `sac' to data source `layers.gpkg' using driver `GPKG'
#> Writing 616 features with 12 fields and geometry type Polygon.

st_layers("layers.gpkg")
#> Driver: GPKG 
#> Available layers:
#>   layer_name geometry_type features fields
#> 1 habsprings         Point      104     10
#> 2        sac       Polygon      616     12
(read_sf("layers.gpkg", layer = "sac"))
#> Simple feature collection with 616 features and 12 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: 22084.25 ymin: 153207.4 xmax: 258865 ymax: 243333
#> proj4string:    +proj=lcc +lat_1=49.8333339 +lat_2=51.16666723333333 +lat_0=90 +lon_0=4.367486666666666 +x_0=150000.01256 +y_0=5400088.4378 +ellps=intl +units=m +no_defs 
#> # A tibble: 616 x 13
#>    OBJECTID   AREA PERIMETER POLY_ID VOLGNR GEBCODE DEELGEBIED GEBOPP_HA
#>       <int>  <dbl>     <dbl>   <int>  <int> <chr>   <chr>          <int>
#>  1        1 2.42e6    12389.       1     18 BE2100… BE2100020…       678
#>  2        2 5.48e5     7234.       2     18 BE2100… BE2100020…       678
#>  3        3 5.86e5     4973.       3     19 BE2100… BE2100024…      3627
#>  4        4 2.06e7    27230.       4     14 BE2100… BE2100015…      2064
#>  5        5 2.62e5     2082.       5     19 BE2100… BE2100024…      3627
#>  6        6 9.43e5     4989.       6     18 BE2100… BE2100020…       678
#>  7        7 2.18e5     2010.       7     19 BE2100… BE2100024…      3627
#>  8        8 3.34e5     2967.       8     19 BE2100… BE2100024…      3627
#>  9        9 1.21e7    23357.       9     19 BE2100… BE2100024…      3627
#> 10       10 1.01e6     5913.      10     19 BE2100… BE2100024…      3627
#> # … with 606 more rows, and 5 more variables: HECTARES <dbl>, NAAM <chr>,
#> #   Shape_Leng <dbl>, Shape_Area <dbl>, geom <POLYGON [m]>
(read_sf("layers.gpkg", layer = "habsprings"))
#> Simple feature collection with 104 features and 10 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 2.757966 ymin: 50.70777 xmax: 5.904029 ymax: 50.92789
#> CRS:            4326
#> # A tibble: 104 x 11
#>       id source validity_status name  system_type habitattype unit_id area_m2
#>    <dbl> <chr>  <chr>           <chr> <chr>       <chr>         <dbl>   <dbl>
#>  1     1 kalkt… gecontroleerd   Stee… rivulet     7220              1      50
#>  2     2 kalkt… gecontroleerd   Stee… rivulet     7220              1      20
#>  3     3 kalkt… gecontroleerd   Duli… <NA>        7230             NA       0
#>  4     4 kalkt… gecontroleerd   Kapi… <NA>        gh               NA       0
#>  5     5 kalkt… gecontroleerd   Reme… rivulet     7220              2     200
#>  6     6 kalkt… gecontroleerd   Reme… rivulet     7220              2     500
#>  7     7 anb    te controleren  Kest… unknown     7220,gh          32       0
#>  8     8 kalkt… gecontroleerd   Krin… rivulet     7220              3     800
#>  9     9 kalkt… gecontroleerd   Bois… rivulet     7220              4      50
#> 10    10 kalkt… gecontroleerd   Kwin… mire        7220              5      10
#> # … with 94 more rows, and 3 more variables: year <dbl>, sbz <dbl>, geom <POINT
#> #   [°]>

# adding non-spatial table

mydf <- tibble(a = rep(letters, 100),
               b = seq_along(a) %>% rev,
               c = b / 100,
               d = lubridate::dmy("1/1/2020") - b,
               e = lubridate::now() - b)

## without GDAL option already works beautifully (uses GDAL default):

mydf %>%
    st_write("layers.gpkg",
             layer = "mydf",
             delete_layer = TRUE)  # note: delete_layer also works after repeating
#> Deleting layer `mydf' failed
#> Writing layer `mydf' to data source `layers.gpkg' using driver `GPKG'
#> Writing 2600 features with 5 fields without geometries.

(read_sf("layers.gpkg", layer = "mydf")) # no warning
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-20 20:29:39
#>  2 b      2599  26.0 2012-11-19 2020-05-20 20:29:40
#>  3 c      2598  26.0 2012-11-20 2020-05-20 20:29:41
#>  4 d      2597  26.0 2012-11-21 2020-05-20 20:29:42
#>  5 e      2596  26.0 2012-11-22 2020-05-20 20:29:43
#>  6 f      2595  26.0 2012-11-23 2020-05-20 20:29:44
#>  7 g      2594  25.9 2012-11-24 2020-05-20 20:29:45
#>  8 h      2593  25.9 2012-11-25 2020-05-20 20:29:46
#>  9 i      2592  25.9 2012-11-26 2020-05-20 20:29:47
#> 10 j      2591  25.9 2012-11-27 2020-05-20 20:29:48
#> # … with 2,590 more rows
(st_read("layers.gpkg", layer = "mydf",  # returns warning = OK
         as_tibble = TRUE,
         stringsAsFactors = FALSE))
#> Reading layer `mydf' from data source `/tmp/RtmpNoZzsi/reprex202e2a3563de/layers.gpkg' using driver `GPKG'
#> Warning: no simple feature geometries present: returning a data.frame or tbl_df
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-20 20:29:39
#>  2 b      2599  26.0 2012-11-19 2020-05-20 20:29:40
#>  3 c      2598  26.0 2012-11-20 2020-05-20 20:29:41
#>  4 d      2597  26.0 2012-11-21 2020-05-20 20:29:42
#>  5 e      2596  26.0 2012-11-22 2020-05-20 20:29:43
#>  6 f      2595  26.0 2012-11-23 2020-05-20 20:29:44
#>  7 g      2594  25.9 2012-11-24 2020-05-20 20:29:45
#>  8 h      2593  25.9 2012-11-25 2020-05-20 20:29:46
#>  9 i      2592  25.9 2012-11-26 2020-05-20 20:29:47
#> 10 j      2591  25.9 2012-11-27 2020-05-20 20:29:48
#> # … with 2,590 more rows

(read_sf("layers.gpkg", layer = "mydf")) %>%
    mutate(e = as.character(e)) %>%
    all.equal(mydf %>%
                  mutate(e = as.character(e)))
#> [1] TRUE

## explicitly using GDAL option has no other effect except explicit confirmation
## of layer_options:

mydf %>%
    st_write("layers.gpkg",
             layer = "mydf2",
             layer_options = "ASPATIAL_VARIANT=GPKG_ATTRIBUTES")
#> Writing layer `mydf2' to data source `layers.gpkg' using driver `GPKG'
#> options:        ASPATIAL_VARIANT=GPKG_ATTRIBUTES 
#> Writing 2600 features with 5 fields without geometries.

(read_sf("layers.gpkg", layer = "mydf")) %>%
    all.equal(
        (read_sf("layers.gpkg", layer = "mydf2"))
    )
#> [1] TRUE

## creating new gpkg file with only non-spatial layers:

mydf %>%
    st_write("layers2.gpkg",
             layer = "mydf",
             delete_dsn = TRUE)
#> Deleting source `layers2.gpkg' failed
#> Writing layer `mydf' to data source `layers2.gpkg' using driver `GPKG'
#> Writing 2600 features with 5 fields without geometries.
mydf %>%
    st_write("layers2.gpkg",
             layer = "mydf2")
#> Writing layer `mydf2' to data source `layers2.gpkg' using driver `GPKG'
#> Writing 2600 features with 5 fields without geometries.


# again adding spatial layers to both gpkg files:

sac %>%
    filter(stringr::str_detect(NAAM, "Turnhout")) %>%
    st_write("layers.gpkg",
             layer = "turnhout")
#> Writing layer `turnhout' to data source `layers.gpkg' using driver `GPKG'
#> Writing 16 features with 12 fields and geometry type Polygon.
sac %>%
    filter(stringr::str_detect(NAAM, "Turnhout")) %>%
    st_write("layers2.gpkg",
             layer = "turnhout")
#> Writing layer `turnhout' to data source `layers2.gpkg' using driver `GPKG'
#> Writing 16 features with 12 fields and geometry type Polygon.

st_layers("layers.gpkg")
#> Driver: GPKG 
#> Available layers:
#>   layer_name geometry_type features fields
#> 1 habsprings         Point      104     10
#> 2        sac       Polygon      616     12
#> 3   turnhout       Polygon       16     12
#> 4       mydf            NA     2600      5
#> 5      mydf2            NA     2600      5
st_layers("layers2.gpkg")
#> Driver: GPKG 
#> Available layers:
#>   layer_name geometry_type features fields
#> 1   turnhout       Polygon       16     12
#> 2       mydf            NA     2600      5
#> 3      mydf2            NA     2600      5

(read_sf("layers2.gpkg", layer = "turnhout"))
#> Simple feature collection with 16 features and 12 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: 186568.8 ymin: 222860.1 xmax: 203303.3 ymax: 235490.7
#> proj4string:    +proj=lcc +lat_1=49.8333339 +lat_2=51.16666723333333 +lat_0=90 +lon_0=4.367486666666666 +x_0=150000.01256 +y_0=5400088.4378 +ellps=intl +units=m +no_defs 
#> # A tibble: 16 x 13
#>    OBJECTID   AREA PERIMETER POLY_ID VOLGNR GEBCODE DEELGEBIED GEBOPP_HA
#>       <int>  <dbl>     <dbl>   <int>  <int> <chr>   <chr>          <int>
#>  1        3 5.86e5     4973.       3     19 BE2100… BE2100024…      3627
#>  2        5 2.62e5     2082.       5     19 BE2100… BE2100024…      3627
#>  3        7 2.18e5     2010.       7     19 BE2100… BE2100024…      3627
#>  4        8 3.34e5     2967.       8     19 BE2100… BE2100024…      3627
#>  5        9 1.21e7    23357.       9     19 BE2100… BE2100024…      3627
#>  6       10 1.01e6     5913.      10     19 BE2100… BE2100024…      3627
#>  7       12 1.96e6     8857.      12     19 BE2100… BE2100024…      3627
#>  8       14 3.88e6    11600.      14     19 BE2100… BE2100024…      3627
#>  9       16 5.00e5     3818.      16     19 BE2100… BE2100024…      3627
#> 10       19 2.06e6     6995.      19     19 BE2100… BE2100024…      3627
#> 11       21 8.29e5     6660.      21     19 BE2100… BE2100024…      3627
#> 12       23 9.09e5     6601.      23     19 BE2100… BE2100024…      3627
#> 13       24 9.11e6    25703.      24     19 BE2100… BE2100024…      3627
#> 14       27 3.20e5     3266.      27     19 BE2100… BE2100024…      3627
#> 15       35 2.01e6    10756.      35     19 BE2100… BE2100024…      3627
#> 16       46 1.95e5     1890.      46     19 BE2100… BE2100024…      3627
#> # … with 5 more variables: HECTARES <dbl>, NAAM <chr>, Shape_Leng <dbl>,
#> #   Shape_Area <dbl>, geom <POLYGON [m]>
(read_sf("layers2.gpkg", layer = "mydf"))
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-20 20:29:39
#>  2 b      2599  26.0 2012-11-19 2020-05-20 20:29:40
#>  3 c      2598  26.0 2012-11-20 2020-05-20 20:29:41
#>  4 d      2597  26.0 2012-11-21 2020-05-20 20:29:42
#>  5 e      2596  26.0 2012-11-22 2020-05-20 20:29:43
#>  6 f      2595  26.0 2012-11-23 2020-05-20 20:29:44
#>  7 g      2594  25.9 2012-11-24 2020-05-20 20:29:45
#>  8 h      2593  25.9 2012-11-25 2020-05-20 20:29:46
#>  9 i      2592  25.9 2012-11-26 2020-05-20 20:29:47
#> 10 j      2591  25.9 2012-11-27 2020-05-20 20:29:48
#> # … with 2,590 more rows
(read_sf("layers2.gpkg", layer = "mydf2"))
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-20 20:29:39
#>  2 b      2599  26.0 2012-11-19 2020-05-20 20:29:40
#>  3 c      2598  26.0 2012-11-20 2020-05-20 20:29:41
#>  4 d      2597  26.0 2012-11-21 2020-05-20 20:29:42
#>  5 e      2596  26.0 2012-11-22 2020-05-20 20:29:43
#>  6 f      2595  26.0 2012-11-23 2020-05-20 20:29:44
#>  7 g      2594  25.9 2012-11-24 2020-05-20 20:29:45
#>  8 h      2593  25.9 2012-11-25 2020-05-20 20:29:46
#>  9 i      2592  25.9 2012-11-26 2020-05-20 20:29:47
#> 10 j      2591  25.9 2012-11-27 2020-05-20 20:29:48
#> # … with 2,590 more rows

^{Created on 2020-05-20 by the reprex package (v0.3.0)}

Session info

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 3.6.3 (2020-02-29)
#>  os       Linux Mint 18.1             
#>  system   x86_64, linux-gnu           
#>  ui       X11                         
#>  language (EN)                        
#>  collate  nl_BE.UTF-8                 
#>  ctype    nl_BE.UTF-8                 
#>  tz       Europe/Brussels             
#>  date     2020-05-20                  
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version    date       lib source                          
#>  assertable    0.2.7      2019-09-21 [1] CRAN (R 3.6.1)                  
#>  assertthat    0.2.1      2019-03-21 [1] CRAN (R 3.6.0)                  
#>  backports     1.1.7      2020-05-13 [1] CRAN (R 3.6.3)                  
#>  bit           1.1-15.2   2020-02-10 [1] CRAN (R 3.6.2)                  
#>  bit64         0.9-7      2017-05-08 [1] CRAN (R 3.6.0)                  
#>  blob          1.2.1      2020-01-20 [1] CRAN (R 3.6.2)                  
#>  callr         3.4.3      2020-03-28 [1] CRAN (R 3.6.3)                  
#>  class         7.3-17     2020-04-26 [4] CRAN (R 3.6.3)                  
#>  classInt      0.4-3      2020-04-07 [1] CRAN (R 3.6.3)                  
#>  cli           2.0.2      2020-02-28 [1] CRAN (R 3.6.3)                  
#>  colorspace    1.4-1      2019-03-18 [1] CRAN (R 3.6.0)                  
#>  crayon        1.3.4      2017-09-16 [1] CRAN (R 3.6.0)                  
#>  crosstalk     1.1.0.1    2020-03-13 [1] CRAN (R 3.6.3)                  
#>  curl          4.3        2019-12-02 [1] CRAN (R 3.6.2)                  
#>  data.table    1.12.8     2019-12-09 [1] CRAN (R 3.6.2)                  
#>  DBI           1.1.0      2019-12-15 [1] CRAN (R 3.6.2)                  
#>  desc          1.2.0      2018-05-01 [1] CRAN (R 3.6.0)                  
#>  devtools      2.3.0      2020-04-10 [1] CRAN (R 3.6.3)                  
#>  digest        0.6.25     2020-02-23 [1] CRAN (R 3.6.3)                  
#>  dplyr       * 0.8.5      2020-03-07 [1] CRAN (R 3.6.3)                  
#>  e1071         1.7-3      2019-11-26 [1] CRAN (R 3.6.2)                  
#>  ellipsis      0.3.0      2019-09-20 [1] CRAN (R 3.6.1)                  
#>  evaluate      0.14       2019-05-28 [1] CRAN (R 3.6.1)                  
#>  fansi         0.4.1      2020-01-08 [1] CRAN (R 3.6.2)                  
#>  fs            1.4.1      2020-04-04 [1] CRAN (R 3.6.3)                  
#>  generics      0.0.2      2018-11-29 [1] CRAN (R 3.6.0)                  
#>  geoaxe        0.1.0      2016-02-19 [1] CRAN (R 3.6.0)                  
#>  ggplot2       3.3.0      2020-03-05 [1] CRAN (R 3.6.3)                  
#>  glue          1.4.1      2020-05-13 [1] CRAN (R 3.6.3)                  
#>  gtable        0.3.0      2019-03-25 [1] CRAN (R 3.6.0)                  
#>  highr         0.8        2019-03-20 [1] CRAN (R 3.6.0)                  
#>  hms           0.5.3      2020-01-08 [1] CRAN (R 3.6.2)                  
#>  htmltools     0.4.0      2019-10-04 [1] CRAN (R 3.6.1)                  
#>  htmlwidgets   1.5.1      2019-10-08 [1] CRAN (R 3.6.1)                  
#>  httr          1.4.1      2019-08-05 [1] CRAN (R 3.6.1)                  
#>  inborutils    0.1.0.9081 2020-05-06 [1] Github (inbo/inborutils@6341166)
#>  iterators     1.0.12     2019-07-26 [1] CRAN (R 3.6.1)                  
#>  jsonlite      1.6.1      2020-02-02 [1] CRAN (R 3.6.2)                  
#>  KernSmooth    2.23-17    2020-04-26 [4] CRAN (R 3.6.3)                  
#>  knitr         1.28       2020-02-06 [1] CRAN (R 3.6.3)                  
#>  lattice       0.20-41    2020-04-02 [4] CRAN (R 3.6.3)                  
#>  lazyeval      0.2.2      2019-03-15 [1] CRAN (R 3.6.3)                  
#>  leaflet       2.0.3      2019-11-16 [1] CRAN (R 3.6.2)                  
#>  lifecycle     0.2.0      2020-03-06 [1] CRAN (R 3.6.3)                  
#>  lubridate     1.7.8      2020-04-06 [1] CRAN (R 3.6.3)                  
#>  magrittr      1.5        2014-11-22 [1] CRAN (R 3.6.0)                  
#>  memoise       1.1.0      2017-04-21 [1] CRAN (R 3.6.0)                  
#>  munsell       0.5.0      2018-06-12 [1] CRAN (R 3.6.0)                  
#>  oai           0.3.0      2019-09-07 [1] CRAN (R 3.6.1)                  
#>  odbc          1.2.2      2020-01-10 [1] CRAN (R 3.6.3)                  
#>  pillar        1.4.4      2020-05-05 [1] CRAN (R 3.6.3)                  
#>  pkgbuild      1.0.8      2020-05-07 [1] CRAN (R 3.6.3)                  
#>  pkgconfig     2.0.3      2019-09-22 [1] CRAN (R 3.6.1)                  
#>  pkgload       1.0.2      2018-10-29 [1] CRAN (R 3.6.0)                  
#>  plyr          1.8.6      2020-03-03 [1] CRAN (R 3.6.3)                  
#>  prettyunits   1.1.1      2020-01-24 [1] CRAN (R 3.6.3)                  
#>  processx      3.4.2      2020-02-09 [1] CRAN (R 3.6.3)                  
#>  ps            1.3.3      2020-05-08 [1] CRAN (R 3.6.3)                  
#>  purrr         0.3.4      2020-04-17 [1] CRAN (R 3.6.3)                  
#>  R6            2.4.1      2019-11-12 [1] CRAN (R 3.6.2)                  
#>  Rcpp          1.0.4.6    2020-04-09 [1] CRAN (R 3.6.3)                  
#>  readr         1.3.1      2018-12-21 [1] CRAN (R 3.6.2)                  
#>  remotes       2.1.1      2020-02-15 [1] CRAN (R 3.6.3)                  
#>  reprex        0.3.0      2019-05-16 [1] CRAN (R 3.6.1)                  
#>  rgbif         2.2.0      2020-03-09 [1] CRAN (R 3.6.3)                  
#>  rgeos         0.5-3      2020-05-08 [1] CRAN (R 3.6.3)                  
#>  rlang         0.4.6      2020-05-02 [1] CRAN (R 3.6.3)                  
#>  rmarkdown     2.1        2020-01-20 [1] CRAN (R 3.6.3)                  
#>  rprojroot     1.3-2      2018-01-03 [1] CRAN (R 3.6.0)                  
#>  RSQLite       2.2.0      2020-01-07 [1] CRAN (R 3.6.2)                  
#>  scales        1.1.1      2020-05-11 [1] CRAN (R 3.6.3)                  
#>  sessioninfo   1.1.1      2018-11-05 [1] CRAN (R 3.6.0)                  
#>  sf          * 0.9-4      2020-05-19 [1] Github (r-spatial/sf@76cf287)   
#>  sp            1.4-1      2020-02-28 [1] CRAN (R 3.6.3)                  
#>  stringi       1.4.6      2020-02-17 [1] CRAN (R 3.6.3)                  
#>  stringr       1.4.0      2019-02-10 [1] CRAN (R 3.6.0)                  
#>  testthat      2.3.2      2020-03-02 [1] CRAN (R 3.6.3)                  
#>  tibble        3.0.1      2020-04-20 [1] CRAN (R 3.6.3)                  
#>  tidyr         1.0.3      2020-05-07 [1] CRAN (R 3.6.3)                  
#>  tidyselect    1.1.0      2020-05-11 [1] CRAN (R 3.6.3)                  
#>  units         0.6-6      2020-03-16 [1] CRAN (R 3.6.3)                  
#>  usethis       1.6.1      2020-04-29 [1] CRAN (R 3.6.3)                  
#>  utf8          1.1.4      2018-05-24 [1] CRAN (R 3.6.0)                  
#>  vctrs         0.3.0      2020-05-11 [1] CRAN (R 3.6.3)                  
#>  whisker       0.4        2019-08-28 [1] CRAN (R 3.6.1)                  
#>  withr         2.2.0      2020-04-20 [1] CRAN (R 3.6.3)                  
#>  xfun          0.13       2020-04-13 [1] CRAN (R 3.6.3)                  
#>  xml2          1.3.2      2020-04-23 [1] CRAN (R 3.6.3)                  
#>  yaml          2.2.1      2020-02-01 [1] CRAN (R 3.6.2)                  
#> 
#> [1] /home/floris/lib/R/library
#> [2] /usr/local/lib/R/site-library
#> [3] /usr/lib/R/site-library
#> [4] /usr/lib/R/library

reprex using GEOS 3.8.0, GDAL 3.0.4, PROJ 7.0.0 (Linux)

# install

remotes::install_github("r-spatial/sf@gpkg")
#> Skipping install of 'sf' from a github remote, the SHA1 (76cf2872) has not changed since last install.
#>   Use `force = TRUE` to force installation

# creating gpkg file with 2 spatial layers

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 7.0.0

inborutils::download_zenodo("10.5281/zenodo.3784149")
#> Will download 1 file (total size: 32.5 KiB) from https://doi.org/10.5281/zenodo.3784149 (Distribution of the Natura 2000 habitat type 7220 (Cratoneurion) in Flanders, Belgium (version 2020); version: habitatsprings_2020v2)
#> 
#> Verifying file integrity...
#> habitatsprings.geojson was downloaded and its integrity verified (md5sum: 64c3db07d17274da047b3962aab28e80)
inborutils::download_zenodo("10.5281/zenodo.3386815")
#> Will download 6 files (total size: 1.5 MiB) from https://doi.org/10.5281/zenodo.3386815 (Redistribution of the map with the Special Areas of Conservation in Flanders with respect to Directive 92/43/EEC (status 2013-01-18); version: sac_2013-01-18)
#> 
#> Verifying file integrity...
#> sac.dbf was downloaded and its integrity verified (md5sum: 1fc2a8b5f56ad4ba05a999697e31626e)
#> sac.prj was downloaded and its integrity verified (md5sum: e8c2db3e5567fbf7ef9c0f306fde20f2)
#> sac.sbn was downloaded and its integrity verified (md5sum: 945791898114a317581c5560d025e420)
#> sac.sbx was downloaded and its integrity verified (md5sum: 7d75b87ca3aad3ae275ff447a1b2d75c)
#> sac.shp was downloaded and its integrity verified (md5sum: ce6f5cbb37f35884f28053a4be765408)
#> sac.shx was downloaded and its integrity verified (md5sum: 91094173055ff88e77355930464853a9)

habsprings <- st_read("habitatsprings.geojson")
#> Reading layer `habitatsprings' from data source `/tmp/Rtmpke0Jq1/reprex1c0d78b72965/habitatsprings.geojson' using driver `GeoJSON'
#> Simple feature collection with 104 features and 10 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 2.757966 ymin: 50.70777 xmax: 5.904029 ymax: 50.92789
#> geographic CRS: WGS 84
habsprings %>% st_write("layers.gpkg",
                        layer = "habsprings",
                        delete_dsn = TRUE)
#> Deleting source `layers.gpkg' failed
#> Writing layer `habsprings' to data source `layers.gpkg' using driver `GPKG'
#> Writing 104 features with 10 fields and geometry type Point.
(read_sf("layers.gpkg"))
#> Simple feature collection with 104 features and 10 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 2.757966 ymin: 50.70777 xmax: 5.904029 ymax: 50.92789
#> geographic CRS: WGS 84
#> # A tibble: 104 x 11
#>       id source validity_status name  system_type habitattype unit_id area_m2
#>    <dbl> <chr>  <chr>           <chr> <chr>       <chr>         <dbl>   <dbl>
#>  1     1 kalkt… gecontroleerd   Stee… rivulet     7220              1      50
#>  2     2 kalkt… gecontroleerd   Stee… rivulet     7220              1      20
#>  3     3 kalkt… gecontroleerd   Duli… <NA>        7230             NA       0
#>  4     4 kalkt… gecontroleerd   Kapi… <NA>        gh               NA       0
#>  5     5 kalkt… gecontroleerd   Reme… rivulet     7220              2     200
#>  6     6 kalkt… gecontroleerd   Reme… rivulet     7220              2     500
#>  7     7 anb    te controleren  Kest… unknown     7220,gh          32       0
#>  8     8 kalkt… gecontroleerd   Krin… rivulet     7220              3     800
#>  9     9 kalkt… gecontroleerd   Bois… rivulet     7220              4      50
#> 10    10 kalkt… gecontroleerd   Kwin… mire        7220              5      10
#> # … with 94 more rows, and 3 more variables: year <dbl>, sbz <dbl>, geom <POINT
#> #   [°]>

sac <- st_read("sac.shp")
#> Reading layer `sac' from data source `/tmp/Rtmpke0Jq1/reprex1c0d78b72965/sac.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 616 features and 12 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: 22084.25 ymin: 153207.4 xmax: 258865 ymax: 243333
#> projected CRS:  Belge 1972 / Belgian Lambert 72
sac %>% st_write("layers.gpkg",
                        layer = "sac")
#> Writing layer `sac' to data source `layers.gpkg' using driver `GPKG'
#> Writing 616 features with 12 fields and geometry type Polygon.

st_layers("layers.gpkg")
#> Driver: GPKG 
#> Available layers:
#>   layer_name geometry_type features fields
#> 1 habsprings         Point      104     10
#> 2        sac       Polygon      616     12
(read_sf("layers.gpkg", layer = "sac"))
#> Simple feature collection with 616 features and 12 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: 22084.25 ymin: 153207.4 xmax: 258865 ymax: 243333
#> projected CRS:  Belge 1972 / Belgian Lambert 72
#> # A tibble: 616 x 13
#>    OBJECTID   AREA PERIMETER POLY_ID VOLGNR GEBCODE DEELGEBIED GEBOPP_HA
#>       <int>  <dbl>     <dbl>   <int>  <int> <chr>   <chr>          <int>
#>  1        1 2.42e6    12389.       1     18 BE2100… BE2100020…       678
#>  2        2 5.48e5     7234.       2     18 BE2100… BE2100020…       678
#>  3        3 5.86e5     4973.       3     19 BE2100… BE2100024…      3627
#>  4        4 2.06e7    27230.       4     14 BE2100… BE2100015…      2064
#>  5        5 2.62e5     2082.       5     19 BE2100… BE2100024…      3627
#>  6        6 9.43e5     4989.       6     18 BE2100… BE2100020…       678
#>  7        7 2.18e5     2010.       7     19 BE2100… BE2100024…      3627
#>  8        8 3.34e5     2967.       8     19 BE2100… BE2100024…      3627
#>  9        9 1.21e7    23357.       9     19 BE2100… BE2100024…      3627
#> 10       10 1.01e6     5913.      10     19 BE2100… BE2100024…      3627
#> # … with 606 more rows, and 5 more variables: HECTARES <dbl>, NAAM <chr>,
#> #   Shape_Leng <dbl>, Shape_Area <dbl>, geom <POLYGON [m]>
(read_sf("layers.gpkg", layer = "habsprings"))
#> Simple feature collection with 104 features and 10 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: 2.757966 ymin: 50.70777 xmax: 5.904029 ymax: 50.92789
#> geographic CRS: WGS 84
#> # A tibble: 104 x 11
#>       id source validity_status name  system_type habitattype unit_id area_m2
#>    <dbl> <chr>  <chr>           <chr> <chr>       <chr>         <dbl>   <dbl>
#>  1     1 kalkt… gecontroleerd   Stee… rivulet     7220              1      50
#>  2     2 kalkt… gecontroleerd   Stee… rivulet     7220              1      20
#>  3     3 kalkt… gecontroleerd   Duli… <NA>        7230             NA       0
#>  4     4 kalkt… gecontroleerd   Kapi… <NA>        gh               NA       0
#>  5     5 kalkt… gecontroleerd   Reme… rivulet     7220              2     200
#>  6     6 kalkt… gecontroleerd   Reme… rivulet     7220              2     500
#>  7     7 anb    te controleren  Kest… unknown     7220,gh          32       0
#>  8     8 kalkt… gecontroleerd   Krin… rivulet     7220              3     800
#>  9     9 kalkt… gecontroleerd   Bois… rivulet     7220              4      50
#> 10    10 kalkt… gecontroleerd   Kwin… mire        7220              5      10
#> # … with 94 more rows, and 3 more variables: year <dbl>, sbz <dbl>, geom <POINT
#> #   [°]>

# adding non-spatial table

mydf <- tibble(a = rep(letters, 100),
               b = seq_along(a) %>% rev,
               c = b / 100,
               d = lubridate::dmy("1/1/2020") - b,
               e = lubridate::now() - b)

## without GDAL option already works beautifully (uses GDAL default):

mydf %>%
    st_write("layers.gpkg",
             layer = "mydf",
             delete_layer = TRUE)  # note: delete_layer also works after repeating
#> Deleting layer `mydf' failed
#> Writing layer `mydf' to data source `layers.gpkg' using driver `GPKG'
#> Writing 2600 features with 5 fields without geometries.

(read_sf("layers.gpkg", layer = "mydf")) # no warning
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-19 19:23:16
#>  2 b      2599  26.0 2012-11-19 2020-05-19 19:23:17
#>  3 c      2598  26.0 2012-11-20 2020-05-19 19:23:18
#>  4 d      2597  26.0 2012-11-21 2020-05-19 19:23:19
#>  5 e      2596  26.0 2012-11-22 2020-05-19 19:23:20
#>  6 f      2595  26.0 2012-11-23 2020-05-19 19:23:21
#>  7 g      2594  25.9 2012-11-24 2020-05-19 19:23:22
#>  8 h      2593  25.9 2012-11-25 2020-05-19 19:23:23
#>  9 i      2592  25.9 2012-11-26 2020-05-19 19:23:24
#> 10 j      2591  25.9 2012-11-27 2020-05-19 19:23:25
#> # … with 2,590 more rows
(st_read("layers.gpkg", layer = "mydf",  # returns warning = OK
         as_tibble = TRUE,
         stringsAsFactors = FALSE))
#> Reading layer `mydf' from data source `/tmp/Rtmpke0Jq1/reprex1c0d78b72965/layers.gpkg' using driver `GPKG'
#> Warning: no simple feature geometries present: returning a data.frame or tbl_df
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-19 19:23:16
#>  2 b      2599  26.0 2012-11-19 2020-05-19 19:23:17
#>  3 c      2598  26.0 2012-11-20 2020-05-19 19:23:18
#>  4 d      2597  26.0 2012-11-21 2020-05-19 19:23:19
#>  5 e      2596  26.0 2012-11-22 2020-05-19 19:23:20
#>  6 f      2595  26.0 2012-11-23 2020-05-19 19:23:21
#>  7 g      2594  25.9 2012-11-24 2020-05-19 19:23:22
#>  8 h      2593  25.9 2012-11-25 2020-05-19 19:23:23
#>  9 i      2592  25.9 2012-11-26 2020-05-19 19:23:24
#> 10 j      2591  25.9 2012-11-27 2020-05-19 19:23:25
#> # … with 2,590 more rows

(read_sf("layers.gpkg", layer = "mydf")) %>%
    mutate(e = as.character(e)) %>%
    all.equal(mydf %>%
                  mutate(e = as.character(e)))
#> [1] TRUE

## explicitly using GDAL option has no other effect except explicit confirmation
## of layer_options:

mydf %>%
    st_write("layers.gpkg",
             layer = "mydf2",
             layer_options = "ASPATIAL_VARIANT=GPKG_ATTRIBUTES")
#> Writing layer `mydf2' to data source `layers.gpkg' using driver `GPKG'
#> options:        ASPATIAL_VARIANT=GPKG_ATTRIBUTES 
#> Writing 2600 features with 5 fields without geometries.

(read_sf("layers.gpkg", layer = "mydf")) %>%
    all.equal(
        (read_sf("layers.gpkg", layer = "mydf2"))
    )
#> [1] TRUE

## creating new gpkg file with only non-spatial layers:

mydf %>%
    st_write("layers2.gpkg",
             layer = "mydf",
             delete_dsn = TRUE)
#> Deleting source `layers2.gpkg' failed
#> Writing layer `mydf' to data source `layers2.gpkg' using driver `GPKG'
#> Writing 2600 features with 5 fields without geometries.
mydf %>%
    st_write("layers2.gpkg",
             layer = "mydf2")
#> Writing layer `mydf2' to data source `layers2.gpkg' using driver `GPKG'
#> Writing 2600 features with 5 fields without geometries.


# again adding spatial layers to both gpkg files:

sac %>%
    filter(stringr::str_detect(NAAM, "Turnhout")) %>%
    st_write("layers.gpkg",
             layer = "turnhout")
#> Writing layer `turnhout' to data source `layers.gpkg' using driver `GPKG'
#> Writing 16 features with 12 fields and geometry type Polygon.
sac %>%
    filter(stringr::str_detect(NAAM, "Turnhout")) %>%
    st_write("layers2.gpkg",
             layer = "turnhout")
#> Writing layer `turnhout' to data source `layers2.gpkg' using driver `GPKG'
#> Writing 16 features with 12 fields and geometry type Polygon.

st_layers("layers.gpkg")
#> Driver: GPKG 
#> Available layers:
#>   layer_name geometry_type features fields
#> 1 habsprings         Point      104     10
#> 2        sac       Polygon      616     12
#> 3   turnhout       Polygon       16     12
#> 4       mydf            NA     2600      5
#> 5      mydf2            NA     2600      5
st_layers("layers2.gpkg")
#> Driver: GPKG 
#> Available layers:
#>   layer_name geometry_type features fields
#> 1   turnhout       Polygon       16     12
#> 2       mydf            NA     2600      5
#> 3      mydf2            NA     2600      5

(read_sf("layers2.gpkg", layer = "turnhout"))
#> Simple feature collection with 16 features and 12 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: 186568.8 ymin: 222860.1 xmax: 203303.3 ymax: 235490.7
#> projected CRS:  Belge 1972 / Belgian Lambert 72
#> # A tibble: 16 x 13
#>    OBJECTID   AREA PERIMETER POLY_ID VOLGNR GEBCODE DEELGEBIED GEBOPP_HA
#>       <int>  <dbl>     <dbl>   <int>  <int> <chr>   <chr>          <int>
#>  1        3 5.86e5     4973.       3     19 BE2100… BE2100024…      3627
#>  2        5 2.62e5     2082.       5     19 BE2100… BE2100024…      3627
#>  3        7 2.18e5     2010.       7     19 BE2100… BE2100024…      3627
#>  4        8 3.34e5     2967.       8     19 BE2100… BE2100024…      3627
#>  5        9 1.21e7    23357.       9     19 BE2100… BE2100024…      3627
#>  6       10 1.01e6     5913.      10     19 BE2100… BE2100024…      3627
#>  7       12 1.96e6     8857.      12     19 BE2100… BE2100024…      3627
#>  8       14 3.88e6    11600.      14     19 BE2100… BE2100024…      3627
#>  9       16 5.00e5     3818.      16     19 BE2100… BE2100024…      3627
#> 10       19 2.06e6     6995.      19     19 BE2100… BE2100024…      3627
#> 11       21 8.29e5     6660.      21     19 BE2100… BE2100024…      3627
#> 12       23 9.09e5     6601.      23     19 BE2100… BE2100024…      3627
#> 13       24 9.11e6    25703.      24     19 BE2100… BE2100024…      3627
#> 14       27 3.20e5     3266.      27     19 BE2100… BE2100024…      3627
#> 15       35 2.01e6    10756.      35     19 BE2100… BE2100024…      3627
#> 16       46 1.95e5     1890.      46     19 BE2100… BE2100024…      3627
#> # … with 5 more variables: HECTARES <dbl>, NAAM <chr>, Shape_Leng <dbl>,
#> #   Shape_Area <dbl>, geom <POLYGON [m]>
(read_sf("layers2.gpkg", layer = "mydf"))
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-19 19:23:16
#>  2 b      2599  26.0 2012-11-19 2020-05-19 19:23:17
#>  3 c      2598  26.0 2012-11-20 2020-05-19 19:23:18
#>  4 d      2597  26.0 2012-11-21 2020-05-19 19:23:19
#>  5 e      2596  26.0 2012-11-22 2020-05-19 19:23:20
#>  6 f      2595  26.0 2012-11-23 2020-05-19 19:23:21
#>  7 g      2594  25.9 2012-11-24 2020-05-19 19:23:22
#>  8 h      2593  25.9 2012-11-25 2020-05-19 19:23:23
#>  9 i      2592  25.9 2012-11-26 2020-05-19 19:23:24
#> 10 j      2591  25.9 2012-11-27 2020-05-19 19:23:25
#> # … with 2,590 more rows
(read_sf("layers2.gpkg", layer = "mydf2"))
#> # A tibble: 2,600 x 5
#>    a         b     c d          e                  
#>    <chr> <int> <dbl> <date>     <dttm>             
#>  1 a      2600  26   2012-11-18 2020-05-19 19:23:16
#>  2 b      2599  26.0 2012-11-19 2020-05-19 19:23:17
#>  3 c      2598  26.0 2012-11-20 2020-05-19 19:23:18
#>  4 d      2597  26.0 2012-11-21 2020-05-19 19:23:19
#>  5 e      2596  26.0 2012-11-22 2020-05-19 19:23:20
#>  6 f      2595  26.0 2012-11-23 2020-05-19 19:23:21
#>  7 g      2594  25.9 2012-11-24 2020-05-19 19:23:22
#>  8 h      2593  25.9 2012-11-25 2020-05-19 19:23:23
#>  9 i      2592  25.9 2012-11-26 2020-05-19 19:23:24
#> 10 j      2591  25.9 2012-11-27 2020-05-19 19:23:25
#> # … with 2,590 more rows

^{Created on 2020-05-19 by the reprex package (v0.3.0)}

Session info

devtools::session_info()
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#>  ctype    nl_BE.UTF-8                 
#>  tz       Europe/Brussels             
#>  date     2020-05-19                  
#> 
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#> 
#> [1] /home/floris/lib/R/library
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#> [3] /usr/lib/R/site-library
#> [4] /usr/lib/R/library

edzer added 5 commits May 16, 2020 15:47

support writing aspatial tables with st_write()

bbf95cf

addresses #1345

bump version; update NEWS

2efbc6d

Merge branch 'master' into gpkg

e9f7e99

tidy

d2734ea

add regression tests; stop warning when r/w aspatial tbls

76cf287

florisvdh added a commit to florisvdh/misc that referenced this pull request May 19, 2020

Script to test sf-gpkg for r-spatial/sf#1396

096f421

florisvdh approved these changes May 20, 2020

View reviewed changes

Merge branch 'master' into gpkg

9767a31

edzer merged commit 8946531 into master May 20, 2020

edzer mentioned this pull request May 20, 2020

Support for writing a non-spatial table to a GeoPackage #1345

Closed

edzer added a commit that referenced this pull request May 23, 2020

take out 4.0 character/factor confusion; #1396

4752cdb

edzer deleted the gpkg branch September 4, 2020 08:20

florisvdh mentioned this pull request Jan 23, 2023

Feature request: helper function for working with GeoPackage schema extension r-spatial/rgeopackage#4

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support writing aspatial tables with st_write() #1396

support writing aspatial tables with st_write() #1396

edzer commented May 16, 2020

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support writing aspatial tables with st_write() #1396

support writing aspatial tables with st_write() #1396

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