R/data.R
In obrl-soil/slga: Data Access Tools for the Soil and Landscape Grid of Australia
#' SLGA Attribute Information
#'
#' A data frame containing information about the modelled soils attributes
#' available from the Soil and Landscape Grid of Australia.
#'
#' @format A data frame with 14 observations and 4 variables \describe{
#' \item{Name}{Attribute name}
#' \item{Code}{Short code for attribute}
#' \item{Units}{Attribute measurement units}
#' \item{Transformation}{Attribute measurement scaling}
#' \item{NAT}{Whether the attribute is available as part of this product.}
#' \item{NAT_3D}{Whether the attribute is available as part of this product.}
#' \item{SA}{Whether the attribute is available as part of this product.}
#' \item{TAS}{Whether the attribute is available as part of this product.}
#' \item{WA}{Whether the attribute is available as part of this product.}
#' }
#' @source See also
#' \url{https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html}
#'
"slga_attribute_info"
#' SLGA Product Information
#'
#' A data frame containing information about the products available from the
#' Soil and Landscape Grid of Australia.
#'
#' All datasets are projected in EPSG:4326 (WGS84). Grid parameters have been
#' retrieved from metadata viewable with WCS DescribeCoverage requests.
#'
#' @format A data frame with 23 observations and 14 variables \describe{
#' \item{Type}{Product Type - Soil or Landscape}
#' \item{Product}{Product Name}
#' \item{Short_Name}{Product short name}
#' \item{Code}{Product code}
#' \item{xmin}{left bounding longitude in decimal degrees}
#' \item{xmax}{right bounding longitude in decimal degrees}
#' \item{ymin}{bottom latitude in decimal degrees}
#' \item{ymax}{top bounding latitude in decimal degrees}
#' \item{offset_x}{Cell resolution in x dimension}
#' \item{offset_y}{Cell resolution in y dimension}
#' \item{origin_x}{x coordinate result of
#' \code{\link[raster:origin]{raster::origin()}} for this dataset.}
#' \item{origin_y}{y coordinate result of
#' \code{\link[raster:origin]{raster::origin()}} for this dataset.}
#' \item{ncol}{number of raster cells in x dimension}
#' \item{nrow}{number of raster cells in y dimension}
#' }
#' @source See also
#' \url{https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html}
#'
'slga_product_info'
#' Central Brisbane surface clay content
#'
#' A rasterStack containing modelled estimated percent clay content for central
#' Brisbane, in South East Queensland.
#'
#' The dataset was retrieved from the National Soil Attributes Clay WCS on
#' 2019/07/07 using the demonstration code in
#' \code{\link[slga:get_soils_data]{get_soils_data}}.
#'
#' The dataset has three named layers. The first is the estimated value, the
#' second is the 5\% confidence limit, and the third is the 95\% confidence
#' limit.
#'
#' The dataset is in WGS84 (EPSG:4326) and has a resolution of 3 arc seconds,
#' which is approximately 80x90m when projected into EPSG:28355 or EPSG:3577.
#'
#' Note that some off-shore areas have a value of 0 rather than NA. A coastline
#' masking layer will be required to safely remove these values.
#'
"bne_surface_clay"
## Generated with
#slga_attribute_info <-
# data.frame(
# "Name" =
# c('Available Water Capacity', 'Bulk Density (Fine Earth)',
# 'Bulk Density (Whole Earth)', 'Cation Exchange Capacity',
# 'Cation Exchange Capacity (Effective)', 'Clay', 'Coarse Fragments',
# 'Depth of Regolith', 'Depth of Soil', 'Electrical Conductivity',
# 'Organic Carbon', 'pH CaCl2', 'pH Water', 'Sand', 'Silt',
# 'Total Nitrogen', 'Total Phosphorus'),
# "Code" =
# c('AWC', 'BDF', 'BDW', 'CEC', 'ECE', 'CLY', 'CFG', 'DER', 'DES', 'ECD',
# 'SOC', 'PHC', 'PHW', 'SND', 'SLT', 'NTO', 'PTO'),
# "Units" =
# c('%', 'g/cm', 'g/cm', 'meq/100g', 'meq/100g', '%', '%', 'Meters',
# 'Meters', 'dS/m', '%', 'pH Units', 'pH Units', '%', '%', '%', '%'),
# "Transformation" =
# c('None', 'None', 'None', 'Log', 'Log', 'None', 'None', 'None', 'None',
# 'Log', 'Log', 'None', 'None', 'None', 'None', 'Log', 'Log'),
# 'NAT' =
# c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE,
# TRUE, FALSE, TRUE, TRUE, TRUE, TRUE),
# 'NAT_3D' =
# c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
# TRUE, FALSE, TRUE, TRUE, TRUE, TRUE),
# 'SA' =
# c(TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
# TRUE, FALSE, TRUE, TRUE, FALSE, FALSE),
# 'TAS' =
# c(FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
# FALSE, TRUE, TRUE, TRUE, FALSE, FALSE),
# 'WA' =
# c(TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE,
# FALSE, TRUE, TRUE, TRUE, FALSE, FALSE),
# stringsAsFactors = FALSE)
#
#slga_product_info <- data.frame(
# 'Type' = 'Soil',
# 'Product' = c("National_3D", "National", "South_Australia", "Tasmania",
# "Western_Australia"),
# 'Short_Name' = c('NAT_3D', 'NAT', 'SA', 'TAS', 'WA'),
# 'Code' = c("ACLEP_AU_TRN_N", "ACLEP_AU_NAT_C", "ACLEP_AU_SAT_D",
# "ACLEP_AU_TAS_N", "ACLEP_AU_WAT_D"),
# 'xmin' = c(112.9995833334, 112.9995833334, 131.58708333370001,
# 143.73458333389601, 112.99958333299942),
# 'ymin' = c(-44.000416667014399, -44.0004166670142, -38.129583333586297,
# -43.706250000342799, -35.134583335670328),
# 'xmax' = c(153.99958333406099, 153.99958333406099, 141.01791666718501,
# 148.65041666730801, 129.09541666600785),
# 'ymax' = c(-10.0004166664663, -10.0004166664663, -31.521250000146399,
# -39.377916666939697, -13.742916669435452),
# 'offset_x' = c(0.00083333333334676806, 0.00083333333334676806,
# 0.00083333333334673478, 0.00083333333334666821,
# 0.00083333333331651199),
# 'offset_y' = c(-0.00083333333334676709, -0.00083333333334676221,
# -0.00083333333334677153, -0.00083333333334676578,
# -0.00083333333331651253),
# 'origin_x' = c(0.00041666491159730867, 0.00041666491159730867,
# 0.00041666491719638543, 0.00041666492933245536, #
# -0.00041666471960866147),
# 'origin_y' = c(-0.00041666630509595848, -0.00041666630515457825,
# -0.00041666630476555611, -0.00041666630497161350,
# 0.00041666362047187988),
# 'ncol' = c(40800, 40800, 7930, 5194, 25670),
# 'nrow' = c(49200, 49200, 11317, 5899, 19315),
# stringsAsFactors = FALSE
#)
#
#slga_terrain_info <-
# data.frame(
# "Type" = 'Landscape',
# "Product" = c('Prescott Index', 'Net Radiation [January]', 'Net Radiation [July]',
# 'Total Shortwave Sloping Surf [January]', 'Total Shortwave Sloping Surf [July]',
# 'Slope [percent]', 'Slope [percent] Median 300m Radius', 'Slope Relief Class',
# 'Aspect', 'Relief [1000m radius]', 'Relief [300m radius]', 'Topographic Wetness Index',
# 'TPI Mask', 'SRTM_TopographicPositionIndex', 'Contributing Area [partial]',
# 'MrVBF', 'Plan Curvature', 'Profile Curvature'),
# "Short_Name" = c('PSIND', 'NRJAN', 'NRJUL', 'TSJAN', 'TSJUL', 'SLPPC', 'SLMPC',
# 'RELCL', 'ASPCT', 'REL1K', 'REL3C', 'TWIND', 'TPMSK', 'TPIND',
# 'CAPRT', 'MRVBF', 'PLNCV', 'PRFCV'),
# "Code" = 'SRTM_attributes_3s_ACLEP_AU',
# 'xmin' = c(rep(112.99958333333333, 5), 112.99958333340001, 112.99958333339997,
# 112.9995833334, 112.99958333340001, 112.99958333340001, 112.99958333340001,
# 112.99958333339997, 112.99958333340001, 112.99958333340001,
# 112.99958333339997, 112.9995833309998, 112.99958333340001, 112.99958333340001),
# 'ymin' = c(rep(-44.000416666666666,5), -44.000416667015159, -44.000416667014996,
# -44.000416665779959, -44.000416667015159, -44.000416667015159, -44.000416667015159,
# -44.000416667014996, -44.000416667015159, -44.000416667015159,
# -44.000416667014996, -44.000416667380001, -44.000416667015159, -44.000416667015159),
# 'xmax' = c(rep(154.00041666666667, 5), 153.99958333406073, 153.99958333406303,
# 153.99958333175948, 153.99958333406073, 153.99958333406073, 153.99958333406073,
# 153.99958333406303, 153.99958333406073, 153.99958333406073,
# 153.99958333406303, 153.99958333428009, 153.99958333406073, 153.99958333406073),
# 'ymax' = c(rep(-9.9995833333333337, 5), -10.001249999799626, -10.001249999799711,
# -10.000416667140001, -10.000416666466279, -10.001249999799626, -10.001249999799626,
# -10.001249999799711, -10.001249999799626, -10.001249999799626,
# -10.001249999799711, -10.000416664660001, -10.001249999799626, -10.001249999799626),
# 'offset_x' = c(rep(0.00083333333333333328, 5), 0.00083333333334676264,
# 0.00083333333334681024, 0.00083333333329998951, 0.00083333333334676264,
# 0.00083333333334676264, 0.00083333333334676264, 0.00083333333334681024,
# 0.00083333333334676264, 0.00083333333334676264, 0.00083333333334681024,
# 0.00083333333340000597, 0.00083333333334676264, 0.00083333333334676264),
# 'offset_y' = c(rep(-0.00083333333333333328, 5), -0.00083333333334678628,
# -0.0008333333333467802, -0.00083333333329999894, -0.00083333333334678628,
# -0.00083333333334678628, -0.00083333333334678628, -0.0008333333333467802,
# -0.00083333333334678628, -0.00083333333334678628, -0.0008333333333467802,
# -0.0008333333334, -0.00083333333334678628, -0.00083333333334678628),
# 'origin_x' = c(0.00041666666665207686, 0.00041666666665207686, 0.00041666666665207686,
# 0.00041666666665207686, 0.00041666666665207686, 0.00041666491235048397,
# 0.00041666490584191251, -0.00041666207857815607, 0.00041666491235048397,
# 0.00041666491235048397, 0.00041666491235048397, 0.00041666490584191251,
# 0.00041666491235048397, 0.00041666491235048397, 0.00041666490584191251,
# 0.00041665529238343879, 0.00041666491235048397, 0.00041666491235048397),
# 'origin_y' = c(0.00041666666666628771, 0.00041666666666628771, 0.00041666666666628771,
# 0.00041666666666628771, 0.00041666666666628771, -0.00041666630484371581,
# -0.00041666630500181157, 0.00041666579328669684, -0.00041666630484371581,
# -0.00041666630484371581, -0.00041666630484371581, -0.00041666630500181157,
# -0.00041666630484371581, -0.00041666630484371581, -0.00041666630500181157,
# -0.00041666386000116518,-0.00041666630484371581, -0.00041666630484371581),
# 'ncol' = c(rep(49200, 5), rep(49199, 13)),
# 'nrow' = c(rep(40800, 5), 40798, 40798, 40799, 40799, 40798, 40798, 40798,
# 40798, 40798, 40799, 40798, 40798, 40798),
# stringsAsFactors = FALSE
# )
#
#slga_product_info <- rbind(slga_product_info, slga_terrain_info)
#
# aoi <- c(152.95, -27.55, 153.07, -27.45)
# bne_surface_clay <- get_soils_data(product = 'NAT', attribute = 'CLY',
# component = 'ALL', depth = 1,
# aoi = aoi, write_out = FALSE)
#usethis::use_data(slga_attribute_info, overwrite = TRUE)
#usethis::use_data(slga_product_info, overwrite = TRUE)
#usethis::use_data(bne_surface_clay, overwrite = TRUE, compress = 'xz')
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#' SLGA Attribute Information
#'
#' A data frame containing information about the modelled soils attributes
#' available from the Soil and Landscape Grid of Australia.
#'
#' @format A data frame with 14 observations and 4 variables \describe{
#' \item{Name}{Attribute name}
#' \item{Code}{Short code for attribute}
#' \item{Units}{Attribute measurement units}
#' \item{Transformation}{Attribute measurement scaling}
#' \item{NAT}{Whether the attribute is available as part of this product.}
#' \item{NAT_3D}{Whether the attribute is available as part of this product.}
#' \item{SA}{Whether the attribute is available as part of this product.}
#' \item{TAS}{Whether the attribute is available as part of this product.}
#' \item{WA}{Whether the attribute is available as part of this product.}
#' }
#' @source See also
#' \url{https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html}
#'
"slga_attribute_info"
#' SLGA Product Information
#'
#' A data frame containing information about the products available from the
#' Soil and Landscape Grid of Australia.
#'
#' All datasets are projected in EPSG:4326 (WGS84). Grid parameters have been
#' retrieved from metadata viewable with WCS DescribeCoverage requests.
#'
#' @format A data frame with 23 observations and 14 variables \describe{
#' \item{Type}{Product Type - Soil or Landscape}
#' \item{Product}{Product Name}
#' \item{Short_Name}{Product short name}
#' \item{Code}{Product code}
#' \item{xmin}{left bounding longitude in decimal degrees}
#' \item{xmax}{right bounding longitude in decimal degrees}
#' \item{ymin}{bottom latitude in decimal degrees}
#' \item{ymax}{top bounding latitude in decimal degrees}
#' \item{offset_x}{Cell resolution in x dimension}
#' \item{offset_y}{Cell resolution in y dimension}
#' \item{origin_x}{x coordinate result of
#' \code{\link[raster:origin]{raster::origin()}} for this dataset.}
#' \item{origin_y}{y coordinate result of
#' \code{\link[raster:origin]{raster::origin()}} for this dataset.}
#' \item{ncol}{number of raster cells in x dimension}
#' \item{nrow}{number of raster cells in y dimension}
#' }
#' @source See also
#' \url{https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html}
#'
'slga_product_info'
#' Central Brisbane surface clay content
#'
#' A rasterStack containing modelled estimated percent clay content for central
#' Brisbane, in South East Queensland.
#'
#' The dataset was retrieved from the National Soil Attributes Clay WCS on
#' 2019/07/07 using the demonstration code in
#' \code{\link[slga:get_soils_data]{get_soils_data}}.
#'
#' The dataset has three named layers. The first is the estimated value, the
#' second is the 5\% confidence limit, and the third is the 95\% confidence
#' limit.
#'
#' The dataset is in WGS84 (EPSG:4326) and has a resolution of 3 arc seconds,
#' which is approximately 80x90m when projected into EPSG:28355 or EPSG:3577.
#'
#' Note that some off-shore areas have a value of 0 rather than NA. A coastline
#' masking layer will be required to safely remove these values.
#'
"bne_surface_clay"
## Generated with
#slga_attribute_info <-
# data.frame(
# "Name" =
# c('Available Water Capacity', 'Bulk Density (Fine Earth)',
# 'Bulk Density (Whole Earth)', 'Cation Exchange Capacity',
# 'Cation Exchange Capacity (Effective)', 'Clay', 'Coarse Fragments',
# 'Depth of Regolith', 'Depth of Soil', 'Electrical Conductivity',
# 'Organic Carbon', 'pH CaCl2', 'pH Water', 'Sand', 'Silt',
# 'Total Nitrogen', 'Total Phosphorus'),
# "Code" =
# c('AWC', 'BDF', 'BDW', 'CEC', 'ECE', 'CLY', 'CFG', 'DER', 'DES', 'ECD',
# 'SOC', 'PHC', 'PHW', 'SND', 'SLT', 'NTO', 'PTO'),
# "Units" =
# c('%', 'g/cm', 'g/cm', 'meq/100g', 'meq/100g', '%', '%', 'Meters',
# 'Meters', 'dS/m', '%', 'pH Units', 'pH Units', '%', '%', '%', '%'),
# "Transformation" =
# c('None', 'None', 'None', 'Log', 'Log', 'None', 'None', 'None', 'None',
# 'Log', 'Log', 'None', 'None', 'None', 'None', 'Log', 'Log'),
# 'NAT' =
# c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE,
# TRUE, FALSE, TRUE, TRUE, TRUE, TRUE),
# 'NAT_3D' =
# c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
# TRUE, FALSE, TRUE, TRUE, TRUE, TRUE),
# 'SA' =
# c(TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
# TRUE, FALSE, TRUE, TRUE, FALSE, FALSE),
# 'TAS' =
# c(FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
# FALSE, TRUE, TRUE, TRUE, FALSE, FALSE),
# 'WA' =
# c(TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE,
# FALSE, TRUE, TRUE, TRUE, FALSE, FALSE),
# stringsAsFactors = FALSE)
#
#slga_product_info <- data.frame(
# 'Type' = 'Soil',
# 'Product' = c("National_3D", "National", "South_Australia", "Tasmania",
# "Western_Australia"),
# 'Short_Name' = c('NAT_3D', 'NAT', 'SA', 'TAS', 'WA'),
# 'Code' = c("ACLEP_AU_TRN_N", "ACLEP_AU_NAT_C", "ACLEP_AU_SAT_D",
# "ACLEP_AU_TAS_N", "ACLEP_AU_WAT_D"),
# 'xmin' = c(112.9995833334, 112.9995833334, 131.58708333370001,
# 143.73458333389601, 112.99958333299942),
# 'ymin' = c(-44.000416667014399, -44.0004166670142, -38.129583333586297,
# -43.706250000342799, -35.134583335670328),
# 'xmax' = c(153.99958333406099, 153.99958333406099, 141.01791666718501,
# 148.65041666730801, 129.09541666600785),
# 'ymax' = c(-10.0004166664663, -10.0004166664663, -31.521250000146399,
# -39.377916666939697, -13.742916669435452),
# 'offset_x' = c(0.00083333333334676806, 0.00083333333334676806,
# 0.00083333333334673478, 0.00083333333334666821,
# 0.00083333333331651199),
# 'offset_y' = c(-0.00083333333334676709, -0.00083333333334676221,
# -0.00083333333334677153, -0.00083333333334676578,
# -0.00083333333331651253),
# 'origin_x' = c(0.00041666491159730867, 0.00041666491159730867,
# 0.00041666491719638543, 0.00041666492933245536, #
# -0.00041666471960866147),
# 'origin_y' = c(-0.00041666630509595848, -0.00041666630515457825,
# -0.00041666630476555611, -0.00041666630497161350,
# 0.00041666362047187988),
# 'ncol' = c(40800, 40800, 7930, 5194, 25670),
# 'nrow' = c(49200, 49200, 11317, 5899, 19315),
# stringsAsFactors = FALSE
#)
#
#slga_terrain_info <-
# data.frame(
# "Type" = 'Landscape',
# "Product" = c('Prescott Index', 'Net Radiation [January]', 'Net Radiation [July]',
# 'Total Shortwave Sloping Surf [January]', 'Total Shortwave Sloping Surf [July]',
# 'Slope [percent]', 'Slope [percent] Median 300m Radius', 'Slope Relief Class',
# 'Aspect', 'Relief [1000m radius]', 'Relief [300m radius]', 'Topographic Wetness Index',
# 'TPI Mask', 'SRTM_TopographicPositionIndex', 'Contributing Area [partial]',
# 'MrVBF', 'Plan Curvature', 'Profile Curvature'),
# "Short_Name" = c('PSIND', 'NRJAN', 'NRJUL', 'TSJAN', 'TSJUL', 'SLPPC', 'SLMPC',
# 'RELCL', 'ASPCT', 'REL1K', 'REL3C', 'TWIND', 'TPMSK', 'TPIND',
# 'CAPRT', 'MRVBF', 'PLNCV', 'PRFCV'),
# "Code" = 'SRTM_attributes_3s_ACLEP_AU',
# 'xmin' = c(rep(112.99958333333333, 5), 112.99958333340001, 112.99958333339997,
# 112.9995833334, 112.99958333340001, 112.99958333340001, 112.99958333340001,
# 112.99958333339997, 112.99958333340001, 112.99958333340001,
# 112.99958333339997, 112.9995833309998, 112.99958333340001, 112.99958333340001),
# 'ymin' = c(rep(-44.000416666666666,5), -44.000416667015159, -44.000416667014996,
# -44.000416665779959, -44.000416667015159, -44.000416667015159, -44.000416667015159,
# -44.000416667014996, -44.000416667015159, -44.000416667015159,
# -44.000416667014996, -44.000416667380001, -44.000416667015159, -44.000416667015159),
# 'xmax' = c(rep(154.00041666666667, 5), 153.99958333406073, 153.99958333406303,
# 153.99958333175948, 153.99958333406073, 153.99958333406073, 153.99958333406073,
# 153.99958333406303, 153.99958333406073, 153.99958333406073,
# 153.99958333406303, 153.99958333428009, 153.99958333406073, 153.99958333406073),
# 'ymax' = c(rep(-9.9995833333333337, 5), -10.001249999799626, -10.001249999799711,
# -10.000416667140001, -10.000416666466279, -10.001249999799626, -10.001249999799626,
# -10.001249999799711, -10.001249999799626, -10.001249999799626,
# -10.001249999799711, -10.000416664660001, -10.001249999799626, -10.001249999799626),
# 'offset_x' = c(rep(0.00083333333333333328, 5), 0.00083333333334676264,
# 0.00083333333334681024, 0.00083333333329998951, 0.00083333333334676264,
# 0.00083333333334676264, 0.00083333333334676264, 0.00083333333334681024,
# 0.00083333333334676264, 0.00083333333334676264, 0.00083333333334681024,
# 0.00083333333340000597, 0.00083333333334676264, 0.00083333333334676264),
# 'offset_y' = c(rep(-0.00083333333333333328, 5), -0.00083333333334678628,
# -0.0008333333333467802, -0.00083333333329999894, -0.00083333333334678628,
# -0.00083333333334678628, -0.00083333333334678628, -0.0008333333333467802,
# -0.00083333333334678628, -0.00083333333334678628, -0.0008333333333467802,
# -0.0008333333334, -0.00083333333334678628, -0.00083333333334678628),
# 'origin_x' = c(0.00041666666665207686, 0.00041666666665207686, 0.00041666666665207686,
# 0.00041666666665207686, 0.00041666666665207686, 0.00041666491235048397,
# 0.00041666490584191251, -0.00041666207857815607, 0.00041666491235048397,
# 0.00041666491235048397, 0.00041666491235048397, 0.00041666490584191251,
# 0.00041666491235048397, 0.00041666491235048397, 0.00041666490584191251,
# 0.00041665529238343879, 0.00041666491235048397, 0.00041666491235048397),
# 'origin_y' = c(0.00041666666666628771, 0.00041666666666628771, 0.00041666666666628771,
# 0.00041666666666628771, 0.00041666666666628771, -0.00041666630484371581,
# -0.00041666630500181157, 0.00041666579328669684, -0.00041666630484371581,
# -0.00041666630484371581, -0.00041666630484371581, -0.00041666630500181157,
# -0.00041666630484371581, -0.00041666630484371581, -0.00041666630500181157,
# -0.00041666386000116518,-0.00041666630484371581, -0.00041666630484371581),
# 'ncol' = c(rep(49200, 5), rep(49199, 13)),
# 'nrow' = c(rep(40800, 5), 40798, 40798, 40799, 40799, 40798, 40798, 40798,
# 40798, 40798, 40799, 40798, 40798, 40798),
# stringsAsFactors = FALSE
# )
#
#slga_product_info <- rbind(slga_product_info, slga_terrain_info)
#
# aoi <- c(152.95, -27.55, 153.07, -27.45)
# bne_surface_clay <- get_soils_data(product = 'NAT', attribute = 'CLY',
# component = 'ALL', depth = 1,
# aoi = aoi, write_out = FALSE)
#usethis::use_data(slga_attribute_info, overwrite = TRUE)
#usethis::use_data(slga_product_info, overwrite = TRUE)
#usethis::use_data(bne_surface_clay, overwrite = TRUE, compress = 'xz')
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