From e09c8806685d1e11b521f5eff57968dbc77ab9e0 Mon Sep 17 00:00:00 2001 From: Ludwigm6 Date: Wed, 13 Mar 2024 10:34:58 +0100 Subject: [PATCH 1/3] dataset documentation --- R/cookfarm.R | 27 +++++++++++ data-raw/create-cookfarm.R | 6 +++ data-raw/create-splotdata.R | 93 ++++++++++++++++++++++++++++++++++++ data/cookfarm.rda | Bin 0 -> 653170 bytes data/splotdata.rda | Bin 43899 -> 43866 bytes man/cookfarm.Rd | 37 ++++++++++++++ 6 files changed, 163 insertions(+) create mode 100644 R/cookfarm.R create mode 100644 data-raw/create-cookfarm.R create mode 100644 data-raw/create-splotdata.R create mode 100644 data/cookfarm.rda create mode 100644 man/cookfarm.Rd diff --git a/R/cookfarm.R b/R/cookfarm.R new file mode 100644 index 00000000..e459a0ff --- /dev/null +++ b/R/cookfarm.R @@ -0,0 +1,27 @@ +#' 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 in EPSG:????} +#' \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} +#' } +#' @source \itemize{ +#' \item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} +#' \item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} +#' } +#' +#' @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" + diff --git a/data-raw/create-cookfarm.R b/data-raw/create-cookfarm.R new file mode 100644 index 00000000..ec2d215f --- /dev/null +++ b/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") diff --git a/data-raw/create-splotdata.R b/data-raw/create-splotdata.R new file mode 100644 index 00000000..c7cff000 --- /dev/null +++ b/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 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zYHyE6ZdRGDwsjoG4#$Y{k|wOSd#P%?d6grO3p%ej^r`_3yTcKrT#6Nu4O6e-V$O%0 zt^mTN6W%bXfdj<)le%ywr}u z%QcBK!IK^ENGsr*FUzLM-`N5S<8r&l9t#b=--#Q8cXcAF-ZtT(es4glF^a7{@=5pn zYpW+>mOnfMEd*K{g&E&1AGL1AcR6_&8~X)}^HtSwIRHniV8`w3Xm>6-3t$oi73KZz dM#F~>>KxmcF5unS+uHw$^B&BvB>(51{tt`_tpNZ4 diff --git a/man/cookfarm.Rd b/man/cookfarm.Rd new file mode 100644 index 00000000..e64ab5dd --- /dev/null +++ b/man/cookfarm.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/cookfarm.R +\docType{data} +\name{cookfarm} +\alias{cookfarm} +\title{Cookfarm soil logger data} +\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 in EPSG:????} + \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} +} +} +\source{ +\itemize{ +\item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} +\item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} +} +} +\usage{ +data(cookfarm) +} +\description{ +spatio-temporal data of soil properties and associated predictors for the Cookfarm in South Africa +} +\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}} + +} +} +\keyword{datasets} From 2328df0d590e0358e083a62b189941f752d862bb Mon Sep 17 00:00:00 2001 From: Ludwigm6 Date: Wed, 13 Mar 2024 10:48:41 +0100 Subject: [PATCH 2/3] data-raw in buildignore --- .Rbuildignore | 1 + NEWS.md | 2 ++ R/cookfarm.R | 4 ---- man/cookfarm.Rd | 6 ------ 4 files changed, 3 insertions(+), 10 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index eb54bd38..57f050d9 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -8,3 +8,4 @@ vignettes/CAST-intro_files ^docs$ ^pkgdown$ ^\.github$ +data-raw/ diff --git a/NEWS.md b/NEWS.md index 329f5ea5..245e8dfc 100644 --- a/NEWS.md +++ b/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) diff --git a/R/cookfarm.R b/R/cookfarm.R index e459a0ff..17df3a8e 100644 --- a/R/cookfarm.R +++ b/R/cookfarm.R @@ -11,10 +11,6 @@ #' \item{Easting, Northing}{Location in EPSG:????} #' \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} #' } -#' @source \itemize{ -#' \item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} -#' \item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} -#' } #' #' @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}} diff --git a/man/cookfarm.Rd b/man/cookfarm.Rd index e64ab5dd..406c670a 100644 --- a/man/cookfarm.Rd +++ b/man/cookfarm.Rd @@ -15,12 +15,6 @@ A sf data.frame with 128545 rows and 17 columns: \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} } } -\source{ -\itemize{ -\item{Plot with Species_richness from \href{https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13346}{sPlotOpen}} -\item{predictors acquired via R package \href{https://github.com/rspatial/geodata}{geodata}} -} -} \usage{ data(cookfarm) } From ea40f6ad070c83807ce21d79de5557d880c71c74 Mon Sep 17 00:00:00 2001 From: Ludwigm6 Date: Wed, 13 Mar 2024 11:13:59 +0100 Subject: [PATCH 3/3] Cookfarm documentation bugfix --- R/cookfarm.R | 5 ++--- man/cookfarm.Rd | 5 ++--- man/geodist.Rd | 1 + 3 files changed, 5 insertions(+), 6 deletions(-) diff --git a/R/cookfarm.R b/R/cookfarm.R index 17df3a8e..93b6c16f 100644 --- a/R/cookfarm.R +++ b/R/cookfarm.R @@ -8,14 +8,13 @@ #' \item{VW}{Response Variable - Soil Moisture} #' \item{altitude}{Measurement depth of VW} #' \item{Date, cdata}{Measurement Date, Cumulative Date} -#' \item{Easting, Northing}{Location in EPSG:????} -#' \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} +#' \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) #' diff --git a/man/cookfarm.Rd b/man/cookfarm.Rd index 406c670a..3e7f5d42 100644 --- a/man/cookfarm.Rd +++ b/man/cookfarm.Rd @@ -11,8 +11,8 @@ A sf data.frame with 128545 rows and 17 columns: \item{VW}{Response Variable - Soil Moisture} \item{altitude}{Measurement depth of VW} \item{Date, cdata}{Measurement Date, Cumulative Date} - \item{Easting, Northing}{Location in EPSG:????} - \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum} + \item{Easting, Northing}{Location Coordinates (EPSG:26911)} + \item{DEM, TWI, NDRE.M, NDRE.Sd, Precip_wrcc, MaxT_wrcc, MinT_wrcc, Precip_cum}{Predictor Variables} } } \usage{ @@ -25,7 +25,6 @@ spatio-temporal data of soil properties and associated predictors for the Cookfa \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}} - } } \keyword{datasets} diff --git a/man/geodist.Rd b/man/geodist.Rd index 4820c6d3..8838741e 100644 --- a/man/geodist.Rd +++ b/man/geodist.Rd @@ -118,6 +118,7 @@ dat <- st_as_sf(dat,coords=c("Easting","Northing")) st_crs(dat) <- 26911 trainDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2010,] predictionDat <- dat[dat$altitude==-0.3&lubridate::year(dat$Date)==2011,] +trainDat$week <- lubridate::week(trainDat$Date) cvfolds <- CreateSpacetimeFolds(trainDat,timevar = "week") dist <- geodist(trainDat,preddata = predictionDat,cvfolds = cvfolds$indexOut,type="time",time_unit="days")

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