Minor Changes for CRAN recommendations and website update

Author: Kluga

DESCRIPTION

@@ -21,7 +21,13 @@ Author: Alexander Klug [aut, cre, cph],
   Seda Bekar [ctb]
   Dirk Zeuss [aut]
 Maintainer: Alexander Klug <[email protected]>
-Description: Climate Modelling package providing an automated climate modelling workflow.
+Description: An automated and streamlined workflow for predictive climate 
+             mapping using climate station data. Works within an environment 
+             the user provides a destined path to (otherwise it's `tempdir()`).
+             Quick and relatively easy creation of resilient and reproduceable 
+             climate models, predictions and climate maps, shortening the
+             usually long and complicated work of predictive modelling. 
+             For more information, please find the provided URL.
 License: GPL (>= 3)
 Encoding: UTF-8
 LazyData: true
@@ -53,5 +59,5 @@ Suggests:
 VignetteBuilder: knitr
 RoxygenNote: 7.3.2
 Config/testthat/edition: 3
-URL: https://github.com/envima/climodr
+URL: https://envima.github.io/climodr/
 BugReports: https://github.com/envima/climodr/issues

R/Autocorrelation.R

@@ -17,7 +17,7 @@
 #' @export autocorr
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' # Test data for autocorrelation after running fin.csv
 #' autocorr(method = "monthly",
 #'          resp = 5,
@@ -160,7 +160,7 @@ autocorr <- function(
             )
   } # end corlist-loop
 
-print(
+message(
   paste0(
     "Calculated autocorrelations for these sensors [",
     sensor_names,

R/Environment.R

@@ -15,13 +15,13 @@
 #' @export envi.create
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' # create climodr environment and allow terra-functions to use 70% of RAM
-#' envi.create("C:/user/climodr_user/project_directory",
-#'              memfrac = 0.7)
+#' envi.create(proj_path = tempdir(),
+#'             memfrac = 0.7)
 #' }
 #'
-envi.create <- function(proj_path,
+envi.create <- function(proj_path = tempdir(),
                         memfrac = NULL,
                         ...) {
   projectDirList = c("input/dep/",
@@ -108,7 +108,7 @@ envi.create <- function(proj_path,
     terra::terraOptions(tempdir = envrmt$path_tmp,
                         memfrac = memfrac)
   }
-  print(
+  message(
     paste0(
       "Succesfully created an environment in ",
       proj_path,
@@ -140,8 +140,8 @@ envi.create <- function(proj_path,
 #' }
 #'
 clim.sample <- function(envrmt = .GlobalEnv$envrmt){
-  print("Loading example data for the climodr example..")
-  print(environment())
+  message("Loading example data for the climodr example..")
+  message(environment())
 # Input dep folder
   data("res_area", envir = environment())
   data("plot_description", envir = environment())
@@ -151,7 +151,7 @@ clim.sample <- function(envrmt = .GlobalEnv$envrmt){
   terra::writeRaster(res_area, file.path(envrmt$path_dep, "res_area.tif"), overwrite = TRUE)
   write.csv(plot_description, file.path(envrmt$path_dep, "plot_description.csv"))
 
-  print(paste0("Saved climodr example dependency files to {", envrmt$path_dep, "}."))
+  message(paste0("Saved climodr example dependency files to {", envrmt$path_dep, "}."))
   rm(list = setdiff(ls(), "envrmt"))
   gc()
 
@@ -164,7 +164,7 @@ clim.sample <- function(envrmt = .GlobalEnv$envrmt){
   terra::writeRaster(sch_201707, file.path(envrmt$path_raster, "sch_201707.tif"), overwrite = TRUE)
   terra::writeRaster(sch_dgm, file.path(envrmt$path_raster, "sch_dgm.tif"), overwrite = TRUE)
 
-  print(paste0("Saved climodr example raster files to {", envrmt$path_raster, "}."))
+  message(paste0("Saved climodr example raster files to {", envrmt$path_raster, "}."))
   rm(list = setdiff(ls(), "envrmt"))
   gc()
 
@@ -177,7 +177,7 @@ clim.sample <- function(envrmt = .GlobalEnv$envrmt){
     eval(call("write.csv", as.name(i), file.path(envrmt$path_tabular, paste0(i, ".csv")), row.names = FALSE))
   }
 
-  print(paste0("Saved climodr example tabular files to {", envrmt$path_tabular, "}."))
+  message(paste0("Saved climodr example tabular files to {", envrmt$path_tabular, "}."))
   rm(list = setdiff(ls(), "envrmt"))
   gc()
 
@@ -187,11 +187,11 @@ clim.sample <- function(envrmt = .GlobalEnv$envrmt){
 
   terra::writeVector(ext_vignette, file.path(envrmt$path_vector, "ext_vignette.gpkg"), overwrite = TRUE)
 
-  print(paste0("Saved climodr example vector files to {", envrmt$path_vector, "}."))
+  message(paste0("Saved climodr example vector files to {", envrmt$path_vector, "}."))
   rm(list = setdiff(ls(), "envrmt"))
   gc()
 
 # Talk to the User
-  print("Done loading all the example files. You are ready to continue.")
+  message("Done loading all the example files. You are ready to continue.")
 }
 

R/Modelling.R

@@ -45,7 +45,7 @@
 #' @export calc.model
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' # Create 48 different models (12 months x 4 classifiers) for every month in 2017
 #' calc.model(method = "monthly",
 #'            timespan = c(2017),
@@ -98,7 +98,7 @@ calc.model <- function(
   # Optional: activate for faster computing
   if (doParallel == TRUE){
     # talk to the user
-    print("Starting parallelization.")
+    message("Starting parallelization.")
 
     cr <- parallel::detectCores()
     cl <- parallel::makeCluster(cr * 0.75)
@@ -122,14 +122,14 @@ calc.model <- function(
     months <- unique(data_y$month)
 
     # talk to the user
-    print(paste0("Training models for ", y, ".  Year-Nr.: ", which(timespan == y), "/", length(timespan)))
+    message(paste0("Training models for ", y, ".  Year-Nr.: ", which(timespan == y), "/", length(timespan)))
 
 # Loop for monthly models ----------------------------------------- Month --- #
     for (m in months) try ({
       data_m <- data_y[c(which(data_y$month == m)), ]
 
       # talk to the user
-      print(paste0("Training monthly models for ", y,".  Month-Nr.: ", m))
+      message(paste0("Training monthly models for ", y,".  Month-Nr.: ", m))
 
 # Loop for the climate sensors --------------------------------- Climresp --- #
       for (s in climresp) try({
@@ -146,7 +146,7 @@ calc.model <- function(
 
         if(autocorrelation == "TRUE"){
           # talk to the user
-          print("Use autocorellation data for filtering..")
+          message("Use autocorellation data for filtering..")
           data <- data_m[stats::complete.cases(data_m), ]
           delect <-
             utils::read.csv(
@@ -188,7 +188,7 @@ calc.model <- function(
               spacevar = "plot"
               )
             # talk to the user
-            print(
+            message(
               paste0(
                 "Run with spatial folds for cross validation.  Fold-Nr.: ",
                 which(f == dofolds), "/", length(dofolds)
@@ -201,7 +201,7 @@ calc.model <- function(
               timevar = "datetime"
               )
             # talk to the user
-            print(
+            message(
               paste0(
                 "Run with temporal folds for cross validation.  Fold-Nr.: ",
                 which(f == dofolds), "/", length(dofolds)
@@ -215,7 +215,7 @@ calc.model <- function(
               spacevar = "plot"
             )
             # talk to the user
-            print(
+            message(
               paste0(
                 "Run with spatio-temporal folds for cross validation.  Fold-Nr.: ",
                 which(f == dofolds), "/", length(dofolds)
@@ -240,7 +240,7 @@ calc.model <- function(
           resps <- trainingDat[ ,s]
 
           # talk to the user
-          print(
+          message(
             paste0(
               "Calculate models for sensor: ",
               sensor_names[which(s == climresp)])
@@ -295,27 +295,27 @@ calc.model <- function(
                 savePredictions = TRUE)
               modclass <-"gbm"
               # talk to the user
-              print(paste0("Next model: Stochastic Gradient Boosting.  ", i, "/", length(classifier)))
+              message(paste0("Next model: Stochastic Gradient Boosting.  ", i, "/", length(classifier)))
             }
             if (method == "lm"){
               tuneLength <- 10
               modclass <- "lim"
               # talk to the user
-              print(paste0("Next model: Linear Regression.  ", i, "/", length(classifier)))
+              message(paste0("Next model: Linear Regression.  ", i, "/", length(classifier)))
             }
             if (method == "rf"){
               tuneLength <- 1
               tuneGrid <- expand.grid(mtry = 2)
               modclass <- "raf"
               # talk to the user
-              print(paste0("Next model: Random Forest.  ", i, "/", length(classifier)))
+              message(paste0("Next model: Random Forest.  ", i, "/", length(classifier)))
             }
             if (method == "pls"){
 #              preds <- data.frame(scale(preds))
               tuneLength <- 10
               modclass <- "pls"
               # talk to the user
-              print(paste0("Next model: Partial-Least-Squares.  ", i, "/", length(classifier)))
+              message(paste0("Next model: Partial-Least-Squares.  ", i, "/", length(classifier)))
             }
             if (method == "nnet"){
               tuneLength <- 1
@@ -324,11 +324,11 @@ calc.model <- function(
               )
               modclass <- "nnt"
               # talk to the user
-              print(paste0("Next model: Neural Networks.  ", i, "/", length(classifier)))
+              message(paste0("Next model: Neural Networks.  ", i, "/", length(classifier)))
             }
 
             # talk to the user
-            print("Computing model...")
+            message("Computing model...")
 
             # calculate model
             ffsmodel <- CAST::ffs(
@@ -405,7 +405,7 @@ calc.model <- function(
   }) # end timespan loop [y]
 
   #talk to the user
-  print("Done! Saving evaluation data frame.")
+  message("Done! Saving evaluation data frame.")
 
   # save total loop analytics for eval
   saveRDS(df_total, file.path(envrmt$path_statistics, paste0(mnote, "_mod_eval_df.rds")));
@@ -414,7 +414,7 @@ calc.model <- function(
   # stop paralellization, if it was activated
   if (doParallel == TRUE){
     #talk to the user
-    print("Ending parallelization.")
+    message("Ending parallelization.")
     parallel::stopCluster(cl)
   }
 

R/Plotting.R

@@ -20,7 +20,7 @@
 #' @importFrom grDevices adjustcolor colorRampPalette dev.off png
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' # Create a Temperature Map from the vignette model
 #' climplot(mnote = "vignette",
 #'          sensor = "Ta_200",
@@ -46,7 +46,7 @@ climplot <- function(
 
   for (s in 1:length(sensor)){
     preds <- all_preds[which(grepl(all_preds, pattern = paste0(mnote, "_", sensor[s])))]
-    print(paste0("Creating maps for sensor [", sensor[s], "]."))
+    message(paste0("Creating maps for sensor [", sensor[s], "]."))
 
     for (i in 1:length(preds)){
       filename <- gsub("_prediction.tif.*", "", preds[i])

R/PreProcessing.R

@@ -108,7 +108,7 @@ prep.csv <- function(envrmt = .GlobalEnv$envrmt,
         row.names = FALSE
       )
     }
-    print(
+    message(
       paste0(
         "Removed NAs from ",
         number_of_csvs,
@@ -620,7 +620,7 @@ spat.csv <- function(envrmt = .GlobalEnv$envrmt,
 #' @export fin.csv
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' csv_fin <- fin.csv(method = "monthly",
 #'                    save_output = TRUE)
 #' head(csv_fin)

R/Prediction.R

@@ -15,7 +15,7 @@
 #' @export climpred
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' climpred(method = "monthly",
 #'          mnote = "normal",
 #'          AOA = TRUE)
@@ -103,7 +103,7 @@ climpred <- function(
       )
     )
 
-    print(
+    message(
       paste0(
         "Making ",
         mod_df[i, ]$sensor,
@@ -134,12 +134,13 @@ climpred <- function(
     )
 
     if (isTRUE(AOA)) try({
-      oldw <- getOption("warn") # old settings
-      options(warn = -1) # turn off warnings
-
-      aoa <- CAST::aoa(
-        newdata = raster,
-        model = mod)
+      suppressWarnings(
+        suppressMessages(
+          aoa <- CAST::aoa(
+            newdata = raster,
+            model = mod)
+        )
+      )
       names(aoa$AOA) <- paste0(modname, "_aoa")
       terra::writeRaster(
         aoa$AOA,
@@ -152,8 +153,6 @@ climpred <- function(
         ),
         overwrite = TRUE
       )
-
-      options(warn = oldw) # return old warning settings
     })
   } # end i loop