# Local Soil Survey Geographic (SSURGO) Databases

## Overview

The `soilDB` package provides tools for accessing and working with soil survey data from the USDA-NRCS. Two key functions, `downloadSSURGO()` and `createSSURGO()`, streamline the process of acquiring and preparing Soil Survey Geographic database (SSURGO) data into a local SQLite (GeoPackage) database–similar to the functionality offered by SSURGO Portal.

This vignette demonstrates how to use these functions to obtain and view data from Morton and Stanton counties, Kansas (`areasymbol = c("KS129", "KS187")`).

### Remote vs. Local Access

For queries directly against the USDA Soil Data Access (SDA) web service without downloading local data, see the SDA vignette. The SDA vignette covers remote REST-based queries, property aggregation methods, and soil interpretations. Once you have created a local SSURGO database with `createSSURGO()`, many of the same functions can be used with the `dsn` parameter to query your local database instead.

## Download SSURGO Data

`downloadSSURGO()` will download the official SSURGO data from Web Soil Survey for the specified `areasymbol` and return the path to the ZIP archives.

    library(soilDB)

    gpkg_dir <- tempdir()

    AREASYMBOLS <- c("KS129", "KS187")

    ssurgo_zip <- downloadSSURGO(
      areasymbol = AREASYMBOLS,
      destdir = gpkg_dir
    )

Here we specify just two soil survey areas of interest using the “area symbol.” Generally, this is a 2-letter state code and a 3-digit county FIPS code or other identifier.

There are more options to specify the area of interest. Any SQL WHERE clause that targets the `sacatalog` (Soil Survey Area Catalog) table can be used.

For example, to do all soil survey areas in Kansas, instead of `areasymbol` we could set `WHERE = "areasymbol LIKE 'KS%'"`. Alternately, `WHERE` can be any R spatial object, which is passed to `SDA_spatialQuery()` to determine area symbols of interest.

We specify `destdir` as the destination directory to download area-specific ZIP files.

If unspecified, `exdir,`the directory the ZIP files get extracted to, is the same as `destdir`. If you want to keep the ZIP files and extracted data, set `destdir`. If you only want the extracted data, set `exdir`. If you want both to be kept only temporarily in order to create a new database, leave `destdir` as the default (`tempdir()`).

## Create a Local SSURGO Database

The `createSSURGO()` function uses the `sf` and `RSQLite` packages internally to build a SQLite database. A suggested SQLite-based format to use is GeoPackage (`".gpkg"` file), as it is an open format with a well-defined standard.

    # Create a local GeoPackage from the downloaded ZIP
    gpkg_path <- file.path(gpkg_dir, "ssurgo.gpkg")

    createSSURGO(
      gpkg_path,
      exdir = gpkg_dir
    )

Here we pass `exdir` so `createSSURGO()` knows where to look for the data that `downloadSSURGO()` extracted from ZIP files. If we supply `con` argument instead of `filename` we can connect to arbitrary DBIConnection objects, which could include those for other database connection types such as DuckDB or PostgresSQL.

The resulting `.gpkg` file is a spatially-enabled SQLite database that can be used in GIS software or queried directly in R.

## Load and Explore the Database

Once the GeoPackage is created, you can connect to it using `DBI` and explore its contents. The database follows the SSURGO schema, which includes tables like `mapunit`, `component`, `chorizon`, and spatial layers such as the spatial map unit polygon layer, `mupolygon`.

    library(DBI)
    library(RSQLite)

    # Connect to the GeoPackage
    con <- dbConnect(SQLite(), gpkg_path)

    # List available tables
    dbListTables(con)

### View Table Structure

You can inspect the structure of a specific table, such as `mapunit`, which contains general information about each map unit.

    dbListFields(con, "mapunit")

### Basic Queries

You can write arbitrary queries to run against the SQLite connection to the local database.

For example, a query to look at the first `5` rows of the `mapunit` table:

    dbGetQuery(con, "SELECT * FROM mapunit LIMIT 5")

### Join Tables

You can join tables to explore relationships between map units and their components:

    query <- "
    SELECT mu.musym, mu.muname, c.compname, c.comppct_r
    FROM mapunit mu
    JOIN component c ON mu.mukey = c.mukey
    LIMIT 10
    "
    dbGetQuery(con, query)

### Load Spatial Data

To work with spatial data, we will use the `sf` package.

Here we demonstrate how to read the `mupolygon` layer, which contains the polygon geometries of delineations of specific map unit concepts:

    library(sf)

    # Read spatial map units
    spatial_mu <- st_read(gpkg_path, layer = "mupolygon")

    spatial_mu

    # Plot the map units
    plot(st_geometry(spatial_mu))

This spatial layer can be used for mapping or spatial joins with other geospatial data sets. The `mukey` column is the unique map unit identifier. Any SSURGO data that can be queried or aggregated to be 1:1 with `mukey` can be used in thematic mapping.

## Visualizing Soil Properties

You can visualize soil properties by joining tabular data with the `mupolygon` layer and plotting with base R graphics.

### Load Tabular Data

Here we look at the first few rows of `mapunit` and `component`, two critical tables in the SSURGO schema.

    # Read tabular data
    mapunit <- dbReadTable(con, "mapunit")
    head(mapunit)

    component <- dbReadTable(con, "component")
    head(component)

    # Disconnect when done
    dbDisconnect(con)

### Example 1: Dominant Component Percentage (`comppct_r`)

As a simple example of how map unit aggregation works, we will “manually” calculate a thematic variable using the `component` table and base R `aggregate()`.

    # Calculate dominant component per map unit
    dominant_comp <- aggregate(
      comppct_r ~ mukey,
      data = component,
      max
    )
    head(dominant_comp)

    # Match dominant component value for each mukey with mukey of spatial data
    spatial_mu$domcomppct_r <- dominant_comp$comppct_r[match(spatial_mu$mukey, dominant_comp$mukey)]

Here we see map units symbolized by the dominant component percentage. Numbers closer to 100% are more “pure” concepts.

    # Visualize
    plot(
      spatial_mu["domcomppct_r"],
      main = "Dominant Component Percentage (domcomppct_r)",
      breaks = seq(0, 100, 10),
      key.pos = 4,
      border = NA,
      pal = hcl.colors(10)
    )

Note that in the northern part of the map the map units have high dominant component percentages, generally greater than 80%. As we look to the southern part of the map we see that the dominant component percentages are lower, indicating multiple major components per map unit.

### Example 2: Hydrologic Group (`hydgrp`) and Drainage Class (`drainagecl`)

`soilDB` provides many functions that can perform aggregations of map unit component data. We will use the function `get_SDA_property()` to calculate the map unit dominant condition values for several component-level hydrologic properties: the Hydrologic Group and the Drainage Class.

For more information on `get_SDA_property()`, including all available aggregation methods (Dominant Component, Weighted Average, Min/Max, Dominant Condition, None), see the SDA vignette.

In this case we want to get everything in our local database, so we use the `WHERE` clause `"1=1"` which is true for all map units in the database.

    component_properties <- c("hydgrp", "drainagecl")

    # Get most common hydgrp and drainagecl per mukey
    hyd_tab <- get_SDA_property(
      component_properties,
      method = "dominant condition",
      dsn = gpkg_path,
      WHERE = "1=1"
    )
    head(hyd_tab)

`soilDB` also includes a function, `NASISChoiceList()`, that helps with converting categorical attributes to factors with standard labels. The function also handles where those labels have a defined natural order.

We pass a data.frame (or named list) of properties we want to convert to factor. The column or list element names are used to identify the domain of interest, which stores the different factor levels, their order, and other metadata.

We extract from, and replace into, our `hyd_tab` data.frame using `[`:

    # Convert to factor
    hyd_tab[component_properties] <- NASISChoiceList(hyd_tab[component_properties])

    # Inspect result
    str(hyd_tab)

Above we see that `hydgrp` and `drainagecl` are now factors and drainage class is an ordinal variable ranging from `"excessively"` drained at the driest to `"subaqueous"` at the wettest.

Let’s merge this processed tabular data into our `sf` data.frame object using merge.

Both `spatial_mu` and `hyd_tab` have `"mukey"` as a column in common, along with `"areasymbol"`, `"musym"`, and `"muname"`.

By default `merge()` will work on all columns that match exactly. If they differ or you only want to join on one specific column you specify `by`. `by.x`, or `by.y`.

    # Join with spatial data
    spatial_mu <- merge(spatial_mu, hyd_tab)

    # Inspect
    str(spatial_mu)

You may also need to specify `sort=FALSE` and `incomparables=NA` for certain applications where the original order matters or some keys contain `NA`.

#### Thematic Maps

Now that we have the spatial and tabular data joined, we will make some thematic maps.

First, we plot map unit dominant condition Hydrologic Group.

    plot(
      spatial_mu["hydgrp"],
      main = "Hydrologic Group (hydgrp)",
      key.pos = 4,
      border = NA,
      pal = rev(hcl.colors(7))
    )

Next, we see map unit dominant condition Drainage Class.

    plot(
      spatial_mu["drainagecl"],
      main = "Drainage Class (drainagecl)",
      border = NA,
      key.pos = 4,
      pal = rev(hcl.colors(8))
    )

The previous examples show how to visualize component-level properties using base R, `soilDB`, `DBI`, `RSQLite`, and `sf`.

The aggregation of dominant component to map unit level values is one of the simplest, and most common, transformations of the SSURGO data for creating maps. Dominant condition aggregation method, combining the components with the same classes in the property of interest, is extremely useful for any categorical data element, such as `hydgrp` and `drainagecl` shown in this demo.

You can adapt the aggregation logic depending on your needs (e.g., max, mean, or most frequent value). Many `soilDB` functions include default aggregations, and you can write custom queries of your own using `SDA_query()` or DBI `dbGetQuery()`.

## Query and Aggregation Functions

This section describes some existing options in the `soilDB` package for

querying and aggregating data for thematic maps. All of the `get_SDA_*()` “SSURGO On Demand” functions can be applied to local copies of the SSURGO database by passing the `dsn` argument (either a path to a SQLite database or a DBIConnection object). For detailed documentation on these functions, including usage patterns, aggregation methods, and interpretation rules, see the SDA vignette.

- “SSURGO On Demand” Queries
  - Hydric Soils: `get_SDA_hydric()`
  - Soil Interpretations: `get_SDA_interpretation()`
  - Map Unit Aggregate Attributes : `get_SDA_muaggatt()`
  - Parent Material Groups: `get_SDA_pmgroupname()`
  - Component and Horizon Properties: `get_SDA_property()`
  - Component Ecological Classes: `get_SDA_coecoclass()`
  - Component Surface Morphometry: `get_SDA_cosurfmorph()`

## Alternative Tools

For users who prefer a Python-based solution, the SSURGOPortal R package wraps the official SSURGO Portal Python code for use in R.