Note: for more information on data storage and locations, see vignette("v020_datastorage"). The below workflow is an application and demonstrates current setup from R (output is mostly not included).

If you wish to follow by running the vignette’s code chunks, you can put a copy of the vignette in your working directory:

source <- system.file("doc", "v022_example.Rmd", package = "n2khab")
file.copy(source, ".")

Minimal setup for using read_soilmap()

Install and load n2khab

Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS = "true")
library(remotes)
install_github("inbo/n2khab",
               build_vignettes = TRUE,
               upgrade = TRUE)
library(n2khab)
#> Attaching n2khab version 0.7.0.
#> When working with raster objects returned by n2khab, you can safely
#> mute proj4string degradation warnings:
#> 
#> options(rgdal_show_exportToProj4_warnings = "none")
#> 
#> You must do this before using the n2khab functions depending on 
#> rgdal or raster, and before loading the latter (or sp).
#> 
#> Do note that those warnings are applicable: in the returned raster
#> objects, the proj4string is effectively degraded and should not be used.
#> See https://inbo.github.io/n2khab/#suppressing-rgdal-warnings-about-proj4string-degradation
#> for more information.

Create (or locate) the n2khab_data directory with 2 subdirectories

This will create n2khab_data in the working directory:

n2khab_data_path <- fileman_folders()

You can also provide a custom root ‘path’ to fileman_folders(), but then take a location that is a (grand-grand-…) parent of the current working directory. This is because reading functions of n2khab will by default use the first n2khab_data directory they can find, starting from the working directory and sequentially climbing up one directory level at a time.

If you already have an existing n2khab_data directory in place, instead run:

n2khab_data_path <- fileman_up("n2khab_data")

Download the soilmap_simple data source in the correct place

The soilmap_simple data source is a GeoPackage, provided and documented at Zenodo. It belongs to the collection of processed data (Zenodo-link).

soilmap_simple_path <- file.path(n2khab_data_path, "20_processed/soilmap_simple")
dir.create(soilmap_simple_path)
download_zenodo(doi = "10.5281/zenodo.3732903",
                path = soilmap_simple_path)

At some future time, the download will be performed automatically by read_soilmap() (if the soilmap_simple data source is missing).

Explore the soilmap_simple data source!

Inspect documentation of read_soilmap():

?read_soilmap

Read the data source and inspect its contents:

sm_simple <- read_soilmap()
sm_simple
#> Simple feature collection with 270550 features and 10 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: 22265.45 ymin: 153062.6 xmax: 258872.2 ymax: 244027.9
#> CRS:            31370
#> # A tibble: 270,550 x 11
#>    bsm_poly_id bsm_region bsm_converted bsm_mo_soilunit… bsm_mo_substr
#>  *       <dbl> <fct>      <lgl>         <fct>            <fct>        
#>  1      165740 Kunstmati… FALSE         OB               <NA>         
#>  2      176046 Kunstmati… FALSE         OB               <NA>         
#>  3      185239 Zandleems… FALSE         Ldc              <NA>         
#>  4      162400 Kunstmati… FALSE         OB               <NA>         
#>  5      173971 Kunstmati… FALSE         OB               <NA>         
#>  6      173087 Zandleems… FALSE         Ldp              <NA>         
#>  7      199453 Zandleems… FALSE         Lep              <NA>         
#>  8      176922 Zandleems… FALSE         Ldc              <NA>         
#>  9      227861 Zandleems… FALSE         Abp(c)           <NA>         
#> 10      185390 Zandleems… FALSE         Lca              <NA>         
#> # … with 270,540 more rows, and 6 more variables: bsm_mo_tex <fct>,
#> #   bsm_mo_drain <fct>, bsm_mo_prof <fct>, bsm_mo_parentmat <fct>,
#> #   bsm_mo_profvar <fct>, geom <MULTIPOLYGON [m]>
glimpse(sm_simple)
#> Rows: 270,550
#> Columns: 11
#> $ bsm_poly_id        <dbl> 165740, 176046, 185239, 162400, 173971, 173087, 19…
#> $ bsm_region         <fct> Kunstmatige gronden, Kunstmatige gronden, Zandleem…
#> $ bsm_converted      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…
#> $ bsm_mo_soilunitype <fct> OB, OB, Ldc, OB, OB, Ldp, Lep, Ldc, Abp(c), Lca, O…
#> $ bsm_mo_substr      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ bsm_mo_tex         <fct> NA, NA, L, NA, NA, L, L, L, A, L, NA, L, L, NA, NA…
#> $ bsm_mo_drain       <fct> NA, NA, d, NA, NA, d, e, d, b, c, NA, d, c, NA, NA…
#> $ bsm_mo_prof        <fct> NA, NA, c, NA, NA, p, p, c, p, a, NA, c, a, NA, NA…
#> $ bsm_mo_parentmat   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ bsm_mo_profvar     <fct> NA, NA, NA, NA, NA, NA, NA, NA, (c), NA, NA, NA, N…
#> $ geom               <MULTIPOLYGON [m]> MULTIPOLYGON (((204667.9 19..., MULTI…

You can also read the explanations for levels of the code variables bsm_mo_substr, bsm_mo_tex, bsm_mo_drain etc. from the data source and insert them as columns (with order of factor levels matching the corresponding code’s factor level order). To do that, you must read the data source with explan = TRUE:

sm_simple <- read_soilmap(explan = TRUE)
glimpse(sm_simple)
#> Rows: 270,550
#> Columns: 17
#> $ bsm_poly_id             <dbl> 165740, 176046, 185239, 162400, 173971, 17308…
#> $ bsm_region              <fct> Kunstmatige gronden, Kunstmatige gronden, Zan…
#> $ bsm_converted           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ bsm_mo_soilunitype      <fct> OB, OB, Ldc, OB, OB, Ldp, Lep, Ldc, Abp(c), L…
#> $ bsm_mo_substr           <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_substr_explan    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_tex              <fct> NA, NA, L, NA, NA, L, L, L, A, L, NA, L, L, N…
#> $ bsm_mo_tex_explan       <fct> NA, NA, zandleem, NA, NA, zandleem, zandleem,…
#> $ bsm_mo_drain            <fct> NA, NA, d, NA, NA, d, e, d, b, c, NA, d, c, N…
#> $ bsm_mo_drain_explan     <fct> NA, NA, "matig nat, matig gleyig", NA, NA, "m…
#> $ bsm_mo_prof             <fct> NA, NA, c, NA, NA, p, p, c, p, a, NA, c, a, N…
#> $ bsm_mo_prof_explan      <fct> NA, NA, "met sterk gevlekte textuur (bij lemi…
#> $ bsm_mo_parentmat        <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_parentmat_explan <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ bsm_mo_profvar          <fct> NA, NA, NA, NA, NA, NA, NA, NA, (c), NA, NA, …
#> $ bsm_mo_profvar_explan   <fct> NA, NA, NA, NA, NA, NA, NA, NA, Bedolven text…
#> $ geom                    <MULTIPOLYGON [m]> MULTIPOLYGON (((204667.9 19..., …
tibble(drain_levels = levels(sm_simple$bsm_mo_drain),
       drain_levels_explained = levels(sm_simple$bsm_mo_drain_explan))
#> # A tibble: 15 x 2
#>    drain_levels drain_levels_explained                                          
#>    <chr>        <chr>                                                           
#>  1 a            zeer droog, niet gleyig                                         
#>  2 a-b          complex van zeer droog, niet gleyig tot droog, niet gleyig      
#>  3 a-d          complex van zeer droog, niet gleyig tot matig nat, matig gleyig 
#>  4 b            droog, niet gleyig                                              
#>  5 c            matig droog, zwak gleyig                                        
#>  6 c-d          complex van droog, zwak gleyig tot matig droog, matig gleyig    
#>  7 d            matig nat, matig gleyig                                         
#>  8 e            nat, sterk gleyig met reductiehorizont                          
#>  9 e-f          complex van nat, matig gleyig tot zeer nat, zeer sterk gleyig m…
#> 10 e-i          complex nat, sterk gleyig met reductiehorizont tot zeer nat met…
#> 11 f            zeer nat, zeer sterk gleyig met reductiehorizont                
#> 12 g            uiterst nat, gereduceerd                                        
#> 13 h            nat met relatief hoge ligging, sterk gleyig                     
#> 14 h-i          complex van nat met relatief hoge ligging, sterk gleyig tot zee…
#> 15 i            zeer nat met relatief hoge ligging, zeer sterk gleyig

Note: for the following chunks, it doesn’t matter whether you used explan = TRUE or not.

How many polygons are available per region?

sm_simple %>%
    st_drop_geometry %>%
    count(bsm_region)

Wat is the average polygon area per region?

sm_simple %>%
    mutate(area = st_area(.)) %>%
    st_drop_geometry %>%
    group_by(bsm_region) %>%
    summarise(mean_area = mean(area))

Plot polygons of the ‘Zwin’ region:

zwin_map <-
    sm_simple %>%
    filter(bsm_region == "Zwin") %>%
    ggplot(aes(fill = bsm_mo_tex)) +
    geom_sf()
  • with WGS84 graticule (though plotted in Belgian Lambert 72 = CRS 31370):
zwin_map
  • with Belgian Lambert 72 graticule:
zwin_map + coord_sf(datum = st_crs(31370))
  • make an interactive map of it, providing two alternative basemaps:
sm_simple %>%
        filter(bsm_region == "Zwin") %>%
        mutate(bsm_mo_tex = as.character(bsm_mo_tex)) %>%
        mapview(zcol = "bsm_mo_tex",
                alpha.region = 0.2,
                map.types = c("OpenStreetMap", "OpenTopoMap"))

Clicking a feature on the above generated map reveals all attributes.

Access more information: the soilmap data source

Download the soilmap data source in the correct place

The digital soil map of the Flemish Region is published at DOV (Databank Ondergrond Vlaanderen). The soilmap data source is the (renamed) shapefile, stored (with versioning) at Zenodo in order to support the read_soilmap() function and to sustain long-term workflow reproducibility. The soilmap data source belongs to the raw data collection (Zenodo-link).

soilmap_path <- file.path(n2khab_data_path, "10_raw/soilmap")
dir.create(soilmap_path)
download_zenodo(doi = "10.5281/zenodo.3387008",
                path = soilmap_path,
                parallel = TRUE)

At some time in future, the download will be performed automatically by read_soilmap() (if the soilmap data source is missing).

Explore the soilmap data source

Read the data source – this takes a while (large dataset) – and inspect its contents:

sm <- read_soilmap(use_processed = FALSE)
sm
#> Simple feature collection with 270550 features and 37 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: 22265.45 ymin: 153062.6 xmax: 258872.2 ymax: 244027.9
#> CRS:            EPSG:31370
#> # A tibble: 270,550 x 38
#>    bsm_poly_id bsm_map_id bsm_region bsm_ge_region bsm_legend bsm_legend_title
#>  *       <dbl> <fct>      <fct>      <fct>         <fct>      <fct>           
#>  1      165740 61E        Kunstmati… <NA>          Antropoge… bodemserie OB   
#>  2      176046 78W        Kunstmati… <NA>          Antropoge… bodemserie OB   
#>  3      185239 95W        Zandleems… <NA>          Vochtig z… bodemseries Lda…
#>  4      162400 75E        Kunstmati… <NA>          Antropoge… bodemserie OB   
#>  5      173971 63W        Kunstmati… <NA>          Antropoge… bodemserie OB   
#>  6      173087 64W        Zandleems… <NA>          Vochtig z… bodemserie Ldp …
#>  7      199453 98E        Zandleems… <NA>          Nat zandl… bodemserie Lep …
#>  8      176922 81W        Zandleems… <NA>          Vochtig z… bodemseries Lda…
#>  9      227861 88W        Zandleems… <NA>          Droge leem bodemserie Abp …
#> 10      185390 95W        Zandleems… <NA>          Vochtig z… bodemseries Lca…
#> # … with 270,540 more rows, and 32 more variables: bsm_legend_explan <fct>,
#> #   bsm_soiltype_id <dbl>, bsm_soiltype <fct>, bsm_ge_typology <lgl>,
#> #   bsm_soiltype_region <fct>, bsm_soilseries <fct>,
#> #   bsm_soilseries_explan <fct>, bsm_mo_soilunitype <fct>, bsm_mo_substr <fct>,
#> #   bsm_mo_substr_explan <fct>, bsm_mo_tex <fct>, bsm_mo_tex_explan <fct>,
#> #   bsm_mo_drain <fct>, bsm_mo_drain_explan <fct>, bsm_mo_prof <fct>,
#> #   bsm_mo_prof_explan <fct>, bsm_mo_parentmat <fct>,
#> #   bsm_mo_parentmat_explan <fct>, bsm_mo_profvar <fct>,
#> #   bsm_mo_profvar_explan <fct>, bsm_mo_phase <fct>, bsm_ge_substr <fct>,
#> #   bsm_ge_substr_explan <fct>, bsm_ge_series <fct>,
#> #   bsm_ge_series_explan <fct>, bsm_ge_subseries <fct>,
#> #   bsm_ge_subseries_explan <fct>, bsm_map_url <fct>, bsm_book_url <fct>,
#> #   bsm_detailmap_url <fct>, bsm_profloc_url <fct>, geometry <MULTIPOLYGON [m]>

Extract features that belong to the ‘Middellandpolders’ region:

sm_mp <-
    sm %>%
    filter(bsm_region == "Middellandpolders")
dim(sm_mp)
#> [1] 3991   38
  • make an interactive map (which may open in your webbrowser), with two alternative base maps:
sm_mp %>%
    mutate(bsm_ge_series = as.character(bsm_ge_series)) %>%
    mapview(zcol = "bsm_ge_series",
            alpha.region = 0.2,
            map.types = c("Wikimedia", "CartoDB.Positron"),
            alpha = 0)
  • calculate surface area (ha) and polygon count per bsm_ge_series:
sm_mp %>%
    mutate(area = st_area(.) %>% set_units("ha")) %>%
    st_drop_geometry %>%
    group_by(bsm_ge_series, bsm_ge_series_explan) %>%
    summarise(area = sum(area) %>% round(2),
              nr_polygons = n()) %>%
    arrange(desc(area)) %>%
    kable
bsm_ge_series bsm_ge_series_explan area nr_polygons
D overdekte kreekruggronden - Middelland materiaal over Oudland kreekrugmateriaal 9126.95 [ha] 1387
E dekkleigronden - meer dan 100 cm Duinkerken III-klei 7160.28 [ha] 761
F overdekte poelgronden en overdekte oude kleiplaatgronden met storende laag op geringe diepte 4848.24 [ha] 1110
P overdekte Pleistocene gronden - gebroken poldermateriaal op Pleistoceen zand 3506.81 [ha] 582
G geulgronden - meer dan 100 cm zware Duinkerken III-klei in laaggelegen geulen 815.64 [ha] 72
A kreekruggronden - slibhoudend zand tot klei overgaand naar lichter materiaal 308.98 [ha] 27
A kreekruggronden - slibhoudend zand tot klei overgaand naar lichter materiaal + overdekte kreekrugronden - Middellland materiaal over Oudland kreekrugmateriaal 230.75 [ha] 33
M gronden van de Lage Moeren 163.11 [ha] 19
  • calculate surface area (ha) and polygon count per soilseries where bsm_ge_series == "D":
sm_mp %>%
    filter(bsm_ge_series == "D") %>%
    mutate(area = st_area(.) %>% set_units("ha")) %>%
    st_drop_geometry %>%
    group_by(bsm_soilseries, bsm_soilseries_explan) %>%
    summarise(area = sum(area) %>% round(2),
              nr_polygons = n()) %>%
    arrange(desc(area)) %>%
    kable
bsm_soilseries bsm_soilseries_explan area nr_polygons
m.D5 Overdekte kreekruggronden (Middellandpolders) 4628.42 [ha] 642
m.Dk5 Overdekte kreekruggronden - klei (Middellandpolders) 1287.51 [ha] 123
m.Dl5 Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) 1070.21 [ha] 105
m.D4 Overdekte kreekruggronden (Middellandpolders) 843.91 [ha] 259
m.D2 Overdekte kreekruggronden (Middellandpolders) 392.31 [ha] 75
m.Dl6 Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) 170.04 [ha] 37
m.D5l Overdekte kreekruggronden (Middellandpolders) 169.59 [ha] 16
m.Dl4 Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) 159.95 [ha] 29
m.Dl2 Overdekte kreekruggronden - slibhoudend zand (Middellandpolders) 127.59 [ha] 28
m.Dk6 Overdekte kreekruggronden - klei (Middellandpolders) 115.02 [ha] 19
m.D4l Overdekte kreekruggronden (Middellandpolders) 85.74 [ha] 6
m.D3 Overdekte kreekruggronden (Middellandpolders) 20.93 [ha] 4
m.Df1 Overdekte kreekruggronden - zware klei (Middellandpolders) 20.43 [ha] 23
m.Dk4 Overdekte kreekruggronden - klei (Middellandpolders) 15.08 [ha] 6
m.D1 Overdekte kreekruggronden (Middellandpolders) 13.74 [ha] 9
m.D5d Overdekte kreekruggronden (Middellandpolders) 6.46 [ha] 6
  • plot all features where bsm_soilseries == "m.D5" (providing all mapview’s default base maps):
sm_mp %>%
    filter(bsm_soilseries == "m.D5") %>%
    mapview(color = "red",
            alpha = 1,
            alpha.region = 0)