Merge pull request #95 from Ludwigm6/master

cookfarm documentation

.Rbuildignore

@@ -8,3 +8,4 @@ vignettes/CAST-intro_files
 ^docs$
 ^pkgdown$
 ^\.github$
+data-raw/

NEWS.md

@@ -3,9 +3,11 @@
   * option of spatial error profiles (errorProfiles with variable="geodist")
   * normalize_DI for a more intuitive interpretation
   * geodist allows calculating temporal distances
+  * ffs now can be run in parallel (Linux only)
 * modifications:
   * function DItoErrormetric renamed to errorProfiles and allows for other dissimilarity measures
   * Improvement and homogenization of plotting methods for nndm, knndm and geodist objects
+  * aoa and trainDI `weight` now allows list input
 * deprecated:
   *plot_geodist (replaced by plot.geodist)
   *plot_ffs (replaced by plot.ffs)

R/cookfarm.R

@@ -0,0 +1,22 @@
+#' Cookfarm soil logger data
+#'
+#' spatio-temporal data of soil properties and associated predictors for the Cookfarm in South Africa
+#' @format
+#' A sf data.frame with 128545 rows and 17 columns:
+#' \describe{
+#'   \item{SOURCEID}{sPlotOpen Metadata}
+#'   \item{VW}{Response Variable - Soil Moisture}
+#'   \item{altitude}{Measurement depth of VW}
+#'   \item{Date, cdata}{Measurement Date, Cumulative Date}
+#'   \item{Easting, Northing}{Location Coordinates (EPSG:26911)}
+#'   \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum}{Predictor Variables}
+#' }
+#'
+#' @references \itemize{
+#' \item{Gash et al. 2015 - Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set \doi{https://doi.org/10.1016/j.spasta.2015.04.001}}
+#' \item{Meyer et al. 2018 - Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation \doi{https://doi.org/10.1016/j.envsoft.2017.12.001}}
+#' }
+#' @usage data(cookfarm)
+#'
+"cookfarm"
+

data-raw/create-cookfarm.R

@@ -0,0 +1,6 @@
+# This script creates the cookfarm dataset
+#
+
+
+cookfarm = readRDS("inst/extdata/Cookfarm.RDS")
+save(cookfarm, file = "data/cookfarm.rda")

data-raw/create-splotdata.R

@@ -0,0 +1,93 @@
+## This script creates the example dataset "splotdata" of the CAST package.
+## It downloads splotopen data points and associated worldclim predictors for South America.
+## A lower resolution predictor stack (terra rast) is also created for Chile.
+## For more information, please check out the Book Chapter and Repository CAST4Ecology
+
+library(geodata)
+library(rnaturalearth)
+library(terra)
+library(sf)
+library(tidyverse)
+library(geodata)
+
+
+##### Download Predictors --------------------------------
+## Warning: This downloads ~ 1 GB of data
+dir.create("data-raw/raw/")
+
+wcf = geodata::worldclim_global(var = "bio", path = "data-raw/raw/", res = 0.5)
+wc = geodata::worldclim_global(var = "bio", path = "data-raw/raw/", res = 5)
+elevf = geodata::elevation_global(res = 0.5, path = "data-raw/raw/")
+elev = geodata::elevation_global(res = 5, path = "data-raw/raw/")
+
+wcf = c(wcf, elevf)
+wc = c(wc, elev)
+
+##### Download sPlotOpen -------------------------------------
+if(!file.exists("data-raw/raw/splotopen")){
+  download.file("https://idata.idiv.de/ddm/Data/DownloadZip/3474?version=5779", destfile = "data-raw/raw/splotopen.zip")
+  unzip(zipfile = "data-raw/raw/splotopen.zip", exdir = "data-raw/raw/splotopen")
+  unzip(zipfile = "data-raw/raw/splotopen/sPlotOpen.RData(2).zip", exdir = "data-raw/raw/splotopen")
+}
+
+
+
+##### Clean up and save necessary files ----------------------------------
+# define region: all of south america
+region = rnaturalearth::ne_countries(continent = "South America", returnclass = "sf", scale = 110)
+
+
+# Predictor clean up
+wc = crop(wc, region)
+names(wc) = names(wc) |> str_remove(pattern = "wc2.1_5m_")
+p = c("bio_1", "bio_4", "bio_5", "bio_6", "bio_8", "bio_9", "bio_12", "bio_13", "bio_14", "bio_15", "elev")
+wc = wc[[p]]
+
+# worldclim in full resolution for extracting the training data
+wcf = crop(wcf, region)
+names(wcf) = names(wcf) |> str_remove(pattern = "wc2.1_30s_")
+wcf = wcf[[p]]
+wcf$lat = terra::init(wcf, "y")
+wcf$lon = terra::init(wcf, "x")
+
+
+# Gather Response Variable: sPlotOpen Species Richness for South America
+## see Appendix 1 of https://doi.org/10.1111/geb.13346
+load("data-raw/raw/splotopen/sPlotOpen.RData")
+
+splot = header.oa |>
+  #filter(Resample_1 == TRUE) |>
+  filter(Continent == "South America") |>
+  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) |>
+  left_join(CWM_CWV.oa |> select(c("PlotObservationID", "Species_richness"))) |>
+  select(c("PlotObservationID", "GIVD_ID", "Country", "Biome",
+           "Species_richness")) |>
+  na.omit()
+
+# extract predictor values and attach to response
+splot = terra::extract(wcf, splot, ID = FALSE, bind = TRUE) |>
+  st_as_sf() |>
+  na.omit()
+
+
+# only keep unique locations
+## some reference sample locations are in the same predictor stack pixel
+## this can lead to erroneous models and misleading validations
+splotdata = splot[!duplicated(c(splot$lat, splot$lon)),]
+splotdata = splotdata |> na.omit()
+splotdata$lat = NULL
+splotdata$lon = NULL
+
+
+# save splotdata
+splotdata$Biome = droplevels(splotdata$Biome)
+save(splotdata, file = "data/splotdata.rda")
+
+## save predictors for chile
+chile = rnaturalearth::ne_countries(country = "Chile", returnclass = "sf")
+wc = crop(wc, chile)
+writeRaster(wc, "inst/extdata/predictors_chile.tif", datatype = "INT2S", overwrite = TRUE)
+
+
+## Remove downloaded data
+unlink("data-raw/raw", recursive = TRUE)