replace reading of dataset

HannaMeyer · Mar 23, 2024 · 43c45e5 · 43c45e5
1 parent 38d1c26
commit 43c45e5
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Showing 22 changed files with 58 additions and 62 deletions.
diff --git a/R/aoa.R b/R/aoa.R
@@ -61,8 +61,8 @@
 #' library(viridis)
 #'
 #' # prepare sample data:
-#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-#' dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean)
+#' data(cookfarm)
+#' dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean)
 #' pts <- st_as_sf(dat,coords=c("Easting","Northing"))
 #' pts$ID <- 1:nrow(pts)
 #' set.seed(100)

diff --git a/R/calibrate_aoa.R b/R/calibrate_aoa.R
@@ -33,8 +33,8 @@
 #' library(latticeExtra)
 #'
 #' #' # prepare sample data:
-#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-#' dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean)
+#' data(cookfarm)
+#' dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean)
 #' pts <- st_as_sf(dat,coords=c("Easting","Northing"))
 #' pts$ID <- 1:nrow(pts)
 #' studyArea <- rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST"))[[1:8]]

diff --git a/R/geodist.R b/R/geodist.R
@@ -76,8 +76,8 @@
 #'############Distances in temporal space
 #' library(lubridate)
 #' library(ggplot2)
-#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-#' dat <- st_as_sf(dat,coords=c("Easting","Northing"))
+#' data(cookfarm)
+#' dat <- st_as_sf(cookfarm,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,]

diff --git a/R/global_validation.R b/R/global_validation.R
@@ -9,8 +9,8 @@
 #' @author Hanna Meyer
 #' @seealso \code{\link{CreateSpacetimeFolds}}
 #' @examples
-#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-#' dat <- dat[sample(1:nrow(dat),500),]
+#' data(cookfarm)
+#' dat <- cookfarm[sample(1:nrow(cookfarm),500),]
 #' indices <- CreateSpacetimeFolds(dat,"SOURCEID","Date")
 #' ctrl <- caret::trainControl(method="cv",index = indices$index,savePredictions="final")
 #' model <- caret::train(dat[,c("DEM","TWI","BLD")],dat$VW, method="rf", trControl=ctrl, ntree=10)

diff --git a/R/knndm.R b/R/knndm.R
@@ -145,9 +145,9 @@
 #' library(ggplot2)
 #'
 #' ### prepare sample data:
-#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-#' dat <- aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-#'    by=list(as.character(dat$SOURCEID)),mean)
+#' data(cookfarm)
+#' dat <- aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+#'    by=list(as.character(cookfarm$SOURCEID)),mean)
 #' pts <- dat[,-1]
 #' pts <- st_as_sf(pts,coords=c("Easting","Northing"))
 #' st_crs(pts) <- 26911

diff --git a/R/nndm.R b/R/nndm.R
@@ -107,9 +107,9 @@
 #' library(terra)
 #'
 #' ### prepare sample data:
-#' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-#' dat <- aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-#'    by=list(as.character(dat$SOURCEID)),mean)
+#' data(cookfarm)
+#' dat <- aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+#'    by=list(as.character(cookfarm$SOURCEID)),mean)
 #' pts <- dat[,-1]
 #' pts <- st_as_sf(pts,coords=c("Easting","Northing"))
 #' st_crs(pts) <- 26911

diff --git a/R/normalize_DI.R b/R/normalize_DI.R
@@ -14,7 +14,8 @@
 #'
 #' # prepare sample data:
 #' dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-#' dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean)
+#' data(cookfarm)
+#' dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean)
 #' pts <- st_as_sf(dat,coords=c("Easting","Northing"))
 #' pts$ID <- 1:nrow(pts)
 #' set.seed(100)

diff --git a/data-raw/create-cookfarm.R b/data-raw/create-cookfarm.R
diff --git a/inst/extdata/Cookfarm.RDS b/inst/extdata/Cookfarm.RDS
diff --git a/inst/extdata/bioclim_global.tif b/inst/extdata/bioclim_global.tif
diff --git a/man/aoa.Rd b/man/aoa.Rd
diff --git a/man/calibrate_aoa.Rd b/man/calibrate_aoa.Rd
diff --git a/man/geodist.Rd b/man/geodist.Rd
diff --git a/man/global_validation.Rd b/man/global_validation.Rd
diff --git a/man/knndm.Rd b/man/knndm.Rd
diff --git a/man/nndm.Rd b/man/nndm.Rd
diff --git a/man/normalize_DI.Rd b/man/normalize_DI.Rd
diff --git a/tests/testthat/test-aoa.R b/tests/testthat/test-aoa.R
@@ -1,7 +1,7 @@
 loaddata <- function() {
   # prepare sample data:
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean)
+  data(cookfarm)
+  dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- sf::st_as_sf(dat,coords=c("Easting","Northing"))
   pts$ID <- 1:nrow(pts)
   set.seed(100)

diff --git a/tests/testthat/test-geodist.R b/tests/testthat/test-geodist.R
@@ -262,8 +262,8 @@ test_that("geodist works with categorical variables in feature space", {
 
 test_that("geodist works in temporal space", {
 
-dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-dat <- sf::st_as_sf(dat,coords=c("Easting","Northing"))
+data(cookfarm)
+dat <- sf::st_as_sf(cookfarm,coords=c("Easting","Northing"))
 sf::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,]
@@ -282,8 +282,8 @@ expect_equal(mean_prediction_to_sample, 4674.37)
 })
 
 test_that("geodist works in temporal space and with CV", {
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- sf::st_as_sf(dat,coords=c("Easting","Northing"))
+  data(cookfarm)
+  dat <- sf::st_as_sf(cookfarm,coords=c("Easting","Northing"))
   sf::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,]

diff --git a/tests/testthat/test-knndm.R b/tests/testthat/test-knndm.R
@@ -195,9 +195,9 @@ test_that("kNNDM yields the expected results with SpatRast modeldomain", {
   set.seed(1234)
 
   # prepare sample data
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-                          by=list(as.character(dat$SOURCEID)),mean)
+  data(cookfarm)
+  dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+                          by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- dat[,-1]
   pts <- sf::st_as_sf(pts,coords=c("Easting","Northing"))
   sf::st_crs(pts) <- 26911
@@ -214,9 +214,9 @@ test_that("kNNDM works in feature space with kmeans clustering and raster as mod
   set.seed(1234)
 
   # prepare sample data
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-                          by=list(as.character(dat$SOURCEID)),mean)
+  data(cookfarm)
+  dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+                          by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- dat[,-1]
   pts <- sf::st_as_sf(pts,coords=c("Easting","Northing"))
   sf::st_crs(pts) <- 26911
@@ -238,9 +238,9 @@ test_that("kNNDM works in feature space with hierarchical clustering and raster
   set.seed(1234)
 
   # prepare sample data
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-                          by=list(as.character(dat$SOURCEID)),mean)
+  data(cookfarm)
+  dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+                          by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- dat[,-1]
   pts <- sf::st_as_sf(pts,coords=c("Easting","Northing"))
   sf::st_crs(pts) <- 26911
@@ -284,9 +284,9 @@ test_that("kNNDM works in feature space with categorical variables and predpoint
   set.seed(1234)
 
   # prepare sample data
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-                          by=list(as.character(dat$SOURCEID)),mean)
+  data(cookfarm)
+  dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+                          by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- dat[,-1]
   pts <- sf::st_as_sf(pts,coords=c("Easting","Northing"))
   sf::st_crs(pts) <- 26911
@@ -365,9 +365,9 @@ test_that("kNNDM works in feature space with Mahalanobis distance without cluste
   set.seed(1234)
 
   # prepare sample data
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-                          by=list(as.character(dat$SOURCEID)),mean)
+  data(cookfarm)
+  dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+                          by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- dat[,-1]
   pts <- sf::st_as_sf(pts,coords=c("Easting","Northing"))
   sf::st_crs(pts) <- 26911

diff --git a/tests/testthat/test-nndm.R b/tests/testthat/test-nndm.R
@@ -116,9 +116,9 @@ test_that("NNDM yields the expected results with SpatRast modeldomain", {
   set.seed(1234)
 
   # prepare sample data
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- terra::aggregate(dat[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
-                          by=list(as.character(dat$SOURCEID)),mean)
+  data(cookfarm)
+  dat <- terra::aggregate(cookfarm[,c("DEM","TWI", "NDRE.M", "Easting", "Northing","VW")],
+                          by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- dat[,-1]
   pts <- sf::st_as_sf(pts,coords=c("Easting","Northing"))
   sf::st_crs(pts) <- 26911

diff --git a/tests/testthat/test_trainDI.R b/tests/testthat/test_trainDI.R
@@ -1,7 +1,7 @@
 loaddata <- function() {
   # prepare sample data:
-  dat <- readRDS(system.file("extdata","Cookfarm.RDS",package="CAST"))
-  dat <- aggregate(dat[,c("VW","Easting","Northing")],by=list(as.character(dat$SOURCEID)),mean)
+  data("cookfarm")
+  dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],by=list(as.character(cookfarm$SOURCEID)),mean)
   pts <- sf::st_as_sf(dat,coords=c("Easting","Northing"))
   pts$ID <- 1:nrow(pts)
   set.seed(100)