## ----setup, echo=FALSE, results='hide', warning=FALSE---------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  fig.align = 'center',
  fig.width = 8,
  fig.height = 6, 
  tidy = FALSE,
  verbose = FALSE,
  # run when NASIS is defined or when R_SOILDB_SKIP_LONG_EXAMPLES is FALSE
  eval = isTRUE(try(local_NASIS_defined(), silent = TRUE)) ||
           !as.logical(Sys.getenv("R_SOILDB_SKIP_LONG_EXAMPLES", unset = "TRUE"))
)
options(width = 100, stringsAsFactors = FALSE)

## ----eval = FALSE---------------------------------------------------------------------------------
#  install.packages(c('soilDB', 'terra', 'sf'))

## ----eval = FALSE---------------------------------------------------------------------------------
#  install.packages(c('soilDB', 'terra', 'sf'),
#    repos = c('https://ncss-tech.r-universe.dev',
#              'https://rspatial.r-universe.dev',
#              'https://r-spatial.r-universe.dev')
#  )

## ----eval = FALSE---------------------------------------------------------------------------------
#  # select gSSURGO grid, 30m resolution
#  x <- mukey.wcs(aoi = aoi, db = 'gssurgo', ...)
#  
#  # select gNATSGO grid, 30m resolution
#  x <- mukey.wcs(aoi = aoi, db = 'gnatsgo', ...)
#  
#  # select RSS grid, 10m resolution
#  x <- mukey.wcs(aoi = aoi, db = 'RSS', ...)
#  
#  # select STATSGO2 grid, 300m resolution
#  x <- mukey.wcs(aoi = aoi, db = 'statsgo', ...)

## ----eval = FALSE---------------------------------------------------------------------------------
#  # select various ISSR-800 grids, details below
#  x <- ISSR800.wcs(aoi = aoi, var = 'paws')

## ----fig.width = 5, fig.height = 5----------------------------------------------------------------
#  library(terra)
#  library(soilDB)
#  
#  # example point, WGS84 coordinates
#  p <- vect(
#    data.frame(
#      lon = -118.55639,
#      lat = 36.52578
#    ),
#    crs = "EPSG:4326"
#  )
#  
#  # 1000m buffer applied to WGS84 coordinate
#  # radius defined in meters
#  b <- buffer(p, 1000)
#  
#  # query WCS
#  # result is in EPSG:5070
#  mu <- mukey.wcs(b, db = 'gSSURGO')
#  
#  # inspect
#  plot(mu, legend = FALSE, axes = FALSE, main = attr(mu, 'layer name'))
#  box()
#  
#  # add buffer, after transforming to mukey grid CRS
#  plot(project(b, "EPSG:5070"), add = TRUE)
#  
#  # add original point, after transforming to mukey grid CRS
#  plot(project(p, "EPSG:5070"), add = TRUE, pch = 16)

## ----fig.width = 8, fig.height = 7----------------------------------------------------------------
#  library(sf)
#  library(soilDB)
#  library(terra)
#  
#  # paste the five coordinates comprising the BBOX polygon here
#  bb <- '-118.6609 36.4820,-118.6609 36.5972,-118.3979 36.5972,-118.3979 36.4820,-118.6609 36.4820'
#  
#  # convert WKT string -> sfc geometry
#  wkt <- sprintf('POLYGON((%s))', bb)
#  x <- st_as_sfc(wkt)
#  
#  # set coordinate reference system as GCS/WGS84
#  st_crs(x) <- 4326
#  
#  # query WCS
#  mu <- mukey.wcs(x, db = 'gSSURGO')
#  
#  # inspect
#  plot(mu, legend = FALSE, axes = FALSE, main = attr(mu, 'layer name'))
#  box()
#  
#  # add original BBOX, after transforming to mukey grid CRS
#  plot(st_transform(x, 5070), add = TRUE)

## -------------------------------------------------------------------------------------------------
#  # make a bounding box and assign a CRS (4326: GCS, WGS84)
#  a <- st_bbox(
#    c(xmin = -114.16, xmax = -114.08, ymin = 47.65, ymax = 47.68),
#    crs = st_crs(4326)
#  )
#  
#  # fetch gSSURGO map unit keys at native resolution (30m)
#  mu <- mukey.wcs(aoi = a, db = 'gssurgo')
#  
#  # check:
#  print(mu)
#  
#  plot(
#    mu,
#    main = 'gSSURGO map unit keys',
#    sub = 'Albers Equal Area Projection',
#    axes = FALSE,
#    legend = FALSE
#  )

## -------------------------------------------------------------------------------------------------
#  # because mu is a SpatRaster, result is a SpatVector object (GCS WGS84)
#  p <- SDA_spatialQuery(mu, what = 'mupolygon', geomIntersection = TRUE)

## -------------------------------------------------------------------------------------------------
#  p <- project(p, crs(mu))

## ----fig.width = 8, fig.height = 7----------------------------------------------------------------
#  plot(mu,
#       main = 'gSSURGO Grid (WCS)\nSSURGO Polygons (SDA)',
#       axes = FALSE,
#       legend = FALSE)
#  plot(p, add = TRUE, border = 'white')
#  mtext('CONUS Albers Equal Area Projection (EPSG:5070)', side = 1, line = 1)

## -------------------------------------------------------------------------------------------------
#  # make a bounding box (in California) and assign a CRS (GCS WGS84 / EPSG:4326)
#  a.CA <- st_bbox(c(
#    xmin = -121,
#    xmax = -120,
#    ymin = 37,
#    ymax = 38
#  ), crs = st_crs(4326))
#  
#  # fetch gSSURGO map unit keys at ~800m
#  # nearest-neighbor resampling = this is a "preview"
#  # result is a SpatRaster object
#  x.800 <- mukey.wcs(aoi = a.CA, db = 'gssurgo', res = 800)
#  
#  plot(
#    x.800,
#    main = 'A Preview of gSSURGO Map Unit Keys',
#    sub = 'Albers Equal Area Projection (800m)\nnearest-neighbor resampling',
#    axes = FALSE,
#    legend = FALSE
#  )

## ----fig.width=8, fig.height=6--------------------------------------------------------------------
#  # Coweeta Hydrologic Laboratory extent; specified in EPSG:5070
#  a <- st_bbox(
#    c(xmin = 1129000, xmax = 1135000, ymin = 1403000, ymax = 1411000),
#    crs = st_crs(5070)
#  )
#  
#  # convert boundary sf polygon
#  a <- st_as_sfc(a)
#  
#  # gSSURGO grid: 30m resolution
#  (x <- mukey.wcs(a, db = 'gSSURGO', res = 30))
#  
#  # gNATSGO grid: 30m resolution
#  (y <- mukey.wcs(a, db = 'gNATSGO', res = 30))
#  
#  # RSS grid: 10m resolution
#  (z <- mukey.wcs(a, db = 'RSS', res = 10))
#  
#  # graphical comparison
#  par(mfcol = c(1, 3))
#  
#  
#  # gSSURGO
#  plot(
#    x,
#    axes = FALSE,
#    legend = FALSE,
#    main = attr(x, 'layer name')
#  )
#  plot(a, add = TRUE)
#  
#  # gNATSGO
#  plot(
#    y,
#    axes = FALSE,
#    legend = FALSE,
#    main = attr(y, 'layer name')
#  )
#  plot(a, add = TRUE)
#  
#  # RSS
#  plot(
#    z,
#    axes = FALSE,
#    legend = FALSE,
#    main = attr(z, 'layer name'),
#    ext = x
#  )
#  plot(a, add = TRUE)

## ----fig.width=8, fig.height=6--------------------------------------------------------------------
#  (statsgo <- mukey.wcs(a, db = 'statsgo', res = 300))
#  
#  # graphical comparison
#  par(mfcol = c(1, 2))
#  
#  # gSSURGO
#  plot(
#    x,
#    axes = FALSE,
#    legend = FALSE,
#    main = attr(x, 'layer name')
#  )
#  
#  # STATSGO
#  plot(
#    statsgo,
#    axes = FALSE,
#    legend = FALSE,
#    main = attr(statsgo, 'layer name')
#  )

## ----fig.width = 6.5, fig.height=5----------------------------------------------------------------
#  # paste your BBOX text here
#  bb <- '-159.7426 21.9059,-159.7426 22.0457,-159.4913 22.0457,-159.4913 21.9059,-159.7426 21.9059'
#  
#  # convert WKT string -> sfc geometry
#  wkt <- sprintf('POLYGON((%s))', bb)
#  x <- st_as_sfc(wkt, crs = 4326)
#  
#  # query WCS
#  mu <- mukey.wcs(x, db = 'hi_ssurgo')
#  
#  # make NA (the ocean) blue
#  plot(
#    mu,
#    legend = FALSE,
#    axes = FALSE,
#    main = attr(mu, 'layer name'),
#    colNA = 'royalblue'
#  )

## ----eval=FALSE, include=FALSE--------------------------------------------------------------------
#  # # check mu names
#  # .is <- format_SQL_in_statement(cats(mu)[[1]]$mukey)
#  # .sql <- sprintf("SELECT mukey, muname FROM mapunit WHERE mukey IN %s", .is)
#  # knitr::kable(SDA_query(.sql))

## ----fig.width = 6.5, fig.height=5----------------------------------------------------------------
#  # paste your BBOX text here
#  bb <- '-65.7741 18.1711,-65.7741 18.3143,-65.5228 18.3143,-65.5228 18.1711,-65.7741 18.1711'
#  
#  # convert WKT string -> sfc geometry
#  wkt <- sprintf('POLYGON((%s))', bb)
#  x <- st_as_sfc(wkt, crs = 4326)
#  
#  # query WCS
#  mu <- mukey.wcs(x, db = 'pr_ssurgo')
#  
#  # make missing data (NA; the ocean) blue
#  plot(
#    mu,
#    legend = FALSE,
#    axes = FALSE,
#    main = attr(mu, 'layer name'),
#    colNA = 'royalblue'
#  )

## ----eval=FALSE, include=FALSE--------------------------------------------------------------------
#  # # check mu names
#  # .is <- format_SQL_in_statement(cats(mu)[[1]]$mukey)
#  # .sql <- sprintf("SELECT mukey, muname FROM mapunit WHERE mukey IN %s", .is)
#  # knitr::kable(SDA_query(.sql))

## -------------------------------------------------------------------------------------------------
#  # make a bounding box and assign a CRS (4326: GCS, WGS84)
#  a <- st_bbox(
#    c(xmin = -114.16, xmax = -114.08, ymin = 47.65, ymax = 47.68),
#    crs = st_crs(4326)
#  )
#  
#  # convert bbox to sf geometry
#  a <- st_as_sfc(a)
#  
#  # fetch gSSURGO map unit keys at native resolution (~30m)
#  mu <- mukey.wcs(aoi = a, db = 'gssurgo')

## ----fig.width=8----------------------------------------------------------------------------------
#  # copy example grid
#  mu2 <- mu
#  
#  # extract raster attribute table for thematic mapping
#  (rat <- cats(mu2)[[1]])
#  
#  # optionally use convenience function:
#  # * returns all fields from muaggatt table
#  # * along with map unit name
#  # tab <- get_SDA_muaggatt(mukeys = as.numeric(rat$mukey), query_string = TRUE)
#  
#  .sql <- paste0(
#    "SELECT mukey, aws050wta, aws0100wta FROM muaggatt WHERE mukey IN ",
#    format_SQL_in_statement(as.numeric(rat$mukey))
#  )
#  
#  # run query, result is a data.frame
#  tab <- SDA_query(.sql)
#  
#  # check
#  head(tab)
#  
#  # set raster categories
#  levels(mu2) <- tab
#  
#  # convert grid + RAT -> stack of property grids
#  aws <- catalyze(mu2)
#  
#  # plot, set a common range [0, 20] for both layers
#  plot(
#    aws,
#    axes = FALSE,
#    cex.main = 0.7,
#    main = c(
#      'Plant Available Water Storage (cm)\nWeighted Mean over Components, 0-50cm',
#      'Plant Available Water Storage (cm)\nWeighted Mean over Components, 0-100cm'
#    ),
#    range = c(0, 20)
#  )

## -------------------------------------------------------------------------------------------------
#  # copy example grid
#  mu2 <- mu
#  
#  # extract RAT for thematic mapping
#  rat <- cats(mu2)[[1]]
#  
#  rules <- c('ENG - Construction Materials; Roadfill',
#             'AWM - Irrigation Disposal of Wastewater')
#  
#  tab <- get_SDA_interpretation(
#    rulename = rules,
#    method = "Weighted Average",
#    mukeys = as.numeric(rat$mukey)
#  )
#  
#  # check
#  head(tab)
#  
#  # set ordered factor levels (for nice label/legend order)
#  tab$class_ENGConstructionMaterialsRoadfill <- factor(
#    tab$class_ENGConstructionMaterialsRoadfill,
#    levels = c(
#      'Not suited',
#      'Poorly suited',
#      'Moderately suited',
#      'Moderately well suited',
#      'Well suited',
#      'Not Rated'
#    ),
#    ordered = TRUE
#  )
#  
#  par(mar = c(4, 12, 3, 3))
#  boxplot(
#    rating_ENGConstructionMaterialsRoadfill ~ class_ENGConstructionMaterialsRoadfill,
#    cex.main = 0.7,
#    main = 'ENG - Construction Materials; Roadfill',
#    ylab = "",
#    data = tab,
#    horizontal = TRUE, # fuzzy ratings on X axis
#    las = 1            # rotate axis labels 90 degrees
#  )

## ----fig.width=8----------------------------------------------------------------------------------
#  vars <- c(
#    'rating_ENGConstructionMaterialsRoadfill',
#    'rating_AWMIrrigationDisposalofWastewater'
#  )
#  
#  # set raster categories
#  levels(mu2) <- tab[, c('mukey', vars)]
#  
#  rating <- catalyze(mu2)
#  
#  # inspect
#  plot(
#    rating,
#    axes = FALSE,
#    cex.main = 0.7,
#    main = c(
#      'Construction Materials; Roadfill\nWeighted Mean over Components',
#      'Irrigation Disposal of Wastewater\nWeighted Mean over Components'
#    )
#  )

## ---- fig.width = 8, fig.height = 6---------------------------------------------------------------
#  # copy example grid
#  mu2 <- mu
#  
#  # extract RAT for thematic mapping
#  rat <- cats(mu2)[[1]]
#  
#  tab <- get_SDA_property(property = 'Corrosion of Steel',
#                          method = 'DOMINANT CONDITION',
#                          mukeys = as.integer(rat$mukey),
#                          miscellaneous_areas = TRUE)
#  
#  # get soil data viewer standard colors for corsteel
#  cols <- get_SDV_legend_elements("attributecolumnname = 'corsteel'")
#  
#  # set raster categories
#  levels(mu2) <- tab[, c('mukey', 'corsteel')]
#  
#  # set active category
#  activeCat(mu2) <- 'corsteel'
#  
#  # plot
#  plot(
#    mu2,
#    col = cols$hex,
#    axes = FALSE,
#    legend = "topleft"
#  )

## -------------------------------------------------------------------------------------------------
#  # https://casoilresource.lawr.ucdavis.edu/gmap/?loc=36.57666,-96.70175,z14
#  # make a bounding box and assign a CRS (4326: GCS, WGS84)
#  a <- st_bbox(
#    c(xmin = -96.7696, xmax = -96.6477,
#      ymin = 36.5477, ymax = 36.6139),
#    crs = st_crs(4326)
#  )
#  
#  # fetch gSSURGO map unit keys at native resolution (~30m)
#  mu <- mukey.wcs(aoi = a, db = 'gssurgo')
#  
#  plot(
#    mu,
#    legend = FALSE,
#    axes = FALSE,
#    cex.main = 0.7,
#    main = 'gSSURGO Map Unit Key Grid'
#  )

## ---- fig.width = 8, fig.height = 6---------------------------------------------------------------
#  # copy example grid
#  mu2 <- mu
#  
#  # extract RAT for thematic mapping
#  rat <- cats(mu2)[[1]]
#  
#  # simplified parent material group name
#  tab <- get_SDA_pmgroupname(mukeys = as.integer(rat$mukey),
#                             miscellaneous_areas = TRUE)
#  
#  # set raster categories
#  levels(mu2) <- tab[, c('mukey', 'pmgroupname')]
#  
#  # set active category
#  activeCat(mu2) <- 'pmgroupname'
#  
#  plot(mu2, legend = "topleft", axes = FALSE)

## ---- fig.width = 8, fig.height = 6---------------------------------------------------------------
#  # copy example grid
#  mu2 <- mu
#  
#  # extract RAT for thematic mapping
#  rat <- cats(mu2)[[1]]
#  
#  # simplified parent material group name
#  tab <- get_SDA_hydric(mukeys = as.integer(rat$mukey))
#  
#  levels(mu2) <- tab[, c('mukey', 'HYDRIC_RATING')]
#  
#  # set active category
#  activeCat(mu2) <- 'HYDRIC_RATING'
#  plot(mu2, legend = "topleft", axes = FALSE)

## -------------------------------------------------------------------------------------------------
#  # extract RAT for thematic mapping
#  rat <- cats(mu)[[1]]
#  
#  # variables of interest
#  vars <- c("dbthirdbar_r", "awc_r", "ph1to1h2o_r")
#  
#  # get / aggregate specific horizon-level properties from SDA
#  # be sure to see the manual page for this function
#  tab <- get_SDA_property(property = vars,
#                          method = "Dominant Component (Numeric)",
#                          mukeys = as.integer(rat$mukey),
#                          top_depth = 0,
#                          bottom_depth = 25)
#  
#  
#  # check
#  head(tab)
#  
#  # convert areasymbol into a factor easy plotting later
#  tab$areasymbol <- factor(tab$areasymbol)
#  
#  # set raster categories
#  levels(mu) <- tab[, c('mukey', vars)]
#  
#  # list variables in the RAT
#  names(cats(mu)[[1]])
#  
#  # convert categories associated with keys to values
#  mu2 <- catalyze(mu)

## ----fig.width = 6, fig.height = 4----------------------------------------------------------------
#  plot(mu2$awc_r)
#  box()

## -------------------------------------------------------------------------------------------------
#  plot(mu2[['dbthirdbar_r']], cex.main = 0.7,
#       main = '1/3 Bar Bulk Density (g/cm^3)\nDominant Component\n0-25cm')
#  
#  plot(mu2[['awc_r']], cex.main = 0.7,
#       main = 'AWC (cm/cm)\nDominant Component\n0-25cm')
#  
#  plot(mu2[['ph1to1h2o_r']], cex.main = 0.7,
#       main = 'pH 1:1 H2O\nDominant Component\n0-25cm')

## -------------------------------------------------------------------------------------------------
#  # extract a BBOX like this from SoilWeb by pressing "b"
#  bb <- '-91.6853 36.4617,-91.6853 36.5281,-91.5475 36.5281,-91.5475 36.4617,-91.6853 36.4617'
#  wkt <- sprintf('POLYGON((%s))', bb)
#  
#  # init sf object from WKT
#  x <- st_as_sfc(wkt, crs = 4326)
#  
#  # get gSSURGO grid here
#  mu <- mukey.wcs(aoi = x, db = 'gssurgo')
#  
#  # note SSA boundary
#  plot(mu, legend = FALSE, axes = FALSE)

## ----fig.width = 8, fig.height = 6----------------------------------------------------------------
#  # extract RAT for thematic mapping
#  rat <- cats(mu)[[1]]
#  
#  # variables of interest
#  vars <- c("sandtotal_r", "silttotal_r", "claytotal_r")
#  
#  # get thematic data from SDA
#  # dominant component
#  # depth-weighted average
#  # sand, silt, clay (RV)
#  tab <-  get_SDA_property(property = vars,
#                           method = "Dominant Component (Numeric)",
#                           mukeys = as.integer(rat$mukey),
#                           top_depth = 25,
#                           bottom_depth = 50)
#  
#  # check
#  head(tab)
#  
#  # set raster categories
#  levels(mu) <- tab[, c('mukey', vars)]
#  
#  # convert mukey grid + RAT -> stack of numerical grids
#  # retaining only sand, silt, clay via [[vars]]
#  ssc <- catalyze(mu)
#  
#  # create a copy of the grid
#  texture.class <- ssc[[1]]
#  names(texture.class) <- 'soil.texture'
#  
#  # assign soil texture classes for the fine earth fraction
#  # using sand and clay percentages
#  values(texture.class) <- aqp::ssc_to_texcl(
#    sand = values(ssc[['sandtotal_r']]),
#    clay = values(ssc[['claytotal_r']]),
#    droplevels = FALSE
#  )
#  r <- c(ssc, texture.class)
#  
#  # graphical check
#  plot(
#    r,
#    cex.main = 0.7,
#    main = paste0(names(r), " - 25-50cm\nDominant Component")
#  )

