Local seed

DESCRIPTION

@@ -50,8 +50,7 @@ Imports:
     stats,
     terra,
     tidyr,
-    vegan,
-    withr
+    vegan
 Suggests: 
     ggExtra,
     ggplot2,

NAMESPACE

@@ -45,4 +45,3 @@ importFrom(terra,spatSample)
 importFrom(terra,vect)
 importFrom(tidyr,unnest)
 importFrom(vegan,decostand)
-importFrom(withr,local_seed)

NEWS.md

@@ -4,7 +4,7 @@
 * Update `sample_occurrences_from_raster()`
   - Use `lapply()` instead of for-loop
   - Randomise points in raster cells.
-* Fix issues (#76).
+* Fix issues (#37, #76).
 
 # gcube 0.3.0
 

R/apply_manual_sampling_bias.R

@@ -34,9 +34,6 @@
 #' # Get occurrence points
 #' occurrences_sf <- simulate_occurrences(plgn)
 #'
-#' # Set seed for reproducibility
-#' set.seed(123)
-#'
 #' # Create grid with bias weights
 #' grid <- st_make_grid(
 #'     plgn,

R/apply_polygon_sampling_bias.R

@@ -35,9 +35,6 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' # Set seed for reproducibility
-#' set.seed(123)
-#'
 #' # Simulate some occurrence data with coordinates and time points
 #' num_points <- 10
 #' occurrences <- data.frame(

R/create_spatial_pattern.R

@@ -12,7 +12,9 @@
 #' The default is `"random"`. `"clustered"` corresponds to a value of 10.
 #' See Details.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #' @param n_sim Number of simulations. Each simulation is a different layer in
 #' the raster. Default is 1.
 #'
@@ -35,7 +37,6 @@
 #' @importFrom stats predict
 #' @importFrom terra vect rast rasterize
 #' @importFrom gstat vgm gstat
-#' @importFrom withr local_seed
 #' @importFrom vegan decostand
 #'
 #' @family occurrence
@@ -149,7 +150,11 @@ create_spatial_pattern <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   # Use gstat object with vgm model to create spatial pattern

R/generate_taxonomy.R

@@ -15,7 +15,9 @@
 #' @param num_phyla Number of phyla to generate. Defaults to 1.
 #' @param num_kingdoms Number of kingdoms to generate. Defaults to 1.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @details The function works by randomly assigning species to genera, genera
 #' to families, families to orders, orders to classes, classes to phyla, and
@@ -32,7 +34,6 @@
 #'
 #' @import dplyr
 #' @import assertthat
-#' @importFrom withr local_seed
 #'
 #' @family multispecies
 #'
@@ -129,7 +130,11 @@ generate_taxonomy <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   # Assign species to genera

R/grid_designation.R

@@ -13,7 +13,9 @@
 #' `"row_names"` (the default), a new column `id` is created where the row names
 #' represent the unique IDs.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #' @param aggregate Logical. If `TRUE` (default), returns data cube in
 #' aggregated form (grid with the number of observations per grid cell).
 #' Otherwise, returns sampled points within the uncertainty circle.
@@ -51,8 +53,6 @@
 #' library(sf)
 #' library(dplyr)
 #'
-#' set.seed(123)
-#'
 #' # Create four random points
 #' n_points <- 4
 #' xlim <- c(3841000, 3842000)

R/sample_from_binormal_circle.R

@@ -13,7 +13,9 @@
 #' circle. Default is 0.95.
 #' See Details.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @details A new observation point is sampled from a bivariate Normal
 #' distribution with means equal to the X and Y coordinates of its original
@@ -40,8 +42,6 @@
 #' library(sf)
 #' library(dplyr)
 #'
-#' set.seed(123)
-#'
 #' # Create four random points
 #' n_points <- 4
 #' xlim <- c(3841000, 3842000)
@@ -86,7 +86,11 @@ sample_from_binormal_circle <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   # Set uncertainty to zero if column not present in data

R/sample_from_uniform_circle.R

@@ -8,7 +8,9 @@
 #' the function will assume no uncertainty (zero meters) around the observation
 #' points.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @returns An sf object with POINT geometry containing the locations of the
 #' sampled occurrences and a `coordinateUncertaintyInMeters` column containing
@@ -26,8 +28,6 @@
 #' @examples
 #' library(sf)
 #'
-#' set.seed(123)
-#'
 #' # Create four random points
 #' n_points <- 4
 #' xlim <- c(3841000, 3842000)
@@ -65,7 +65,11 @@ sample_from_uniform_circle <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   # Set uncertainty to zero if column not present in data

R/sample_observations.R

@@ -42,7 +42,9 @@
 #' Weights can be numeric values between 0 and 1 or positive integers, which
 #' will be rescaled to values between 0 and 1.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @returns An sf object with POINT geometry containing the locations of the
 #' occurrence with detection status. The object includes the following columns:
@@ -63,7 +65,6 @@
 #' @import dplyr
 #' @import assertthat
 #' @importFrom stats rbinom
-#' @importFrom withr local_seed
 #' @importFrom rlang .data
 #'
 #' @family main
@@ -73,9 +74,6 @@
 #' library(sf)
 #' library(dplyr)
 #'
-#' # Set seed for reproducibility
-#' set.seed(123)
-#'
 #' # Simulate some occurrence data with coordinates and time points
 #' num_points <- 10
 #' occurrences <- data.frame(
@@ -164,7 +162,11 @@ sample_observations <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   # Add detection probability

R/sample_occurrences_from_raster.R

@@ -7,7 +7,9 @@
 #' @param raster A SpatRaster object (see [terra::rast()]).
 #' @param time_series A vector with the number of occurrences per time point.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @returns An sf object with POINT geometry containing the locations of the
 #' simulated occurrences, a `time_point` column indicating the associated
@@ -110,7 +112,11 @@ sample_occurrences_from_raster <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   occ_pf_list <- lapply(seq_along(time_series), function(t) {

R/simulate_occurrences.R

@@ -22,7 +22,9 @@
 #' The default is `"random"`. `"clustered"` corresponds to a value of 10.
 #' See `create_spatial_pattern()`.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @returns An sf object with POINT geometry containing the locations of the
 #' simulated occurrences, a `time_point` column indicating the associated

R/simulate_random_walk.R

@@ -10,7 +10,9 @@
 #' @param sd_step A positive numeric value indicating the standard deviation of
 #' the random steps.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @returns A vector of integers of length `n_time_points` with the average
 #' number of occurrences.
@@ -19,7 +21,6 @@
 #'
 #' @import assertthat
 #' @importFrom stats rnorm
-#' @importFrom withr local_seed
 #'
 #' @family occurrence
 #'
@@ -62,7 +63,11 @@ simulate_random_walk <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   # Initialize an empty vector to store average abundance values

R/simulate_timeseries.R

@@ -14,7 +14,9 @@
 #' occurrences.
 #' @param ... Additional arguments to be passed to `temporal_function`.
 #' @param seed A positive numeric value setting the seed for random number
-#' generation to ensure reproducibility. If `NA` (default), no seed is used.
+#' generation to ensure reproducibility. If `NA` (default), then `set.seed()`
+#' is not called at all. If not `NA`, then the random number generator state is
+#' reset (to the state before calling this function) upon exiting this function.
 #'
 #' @returns A vector of integers of length `n_time_points` with the number of
 #' occurrences.
@@ -58,9 +60,6 @@
 #'   return(lambdas)
 #' }
 #'
-#' # Set seed for reproducibility
-#' set.seed(123)
-#'
 #' # Draw n_sim number of occurrences from Poisson distribution using
 #' # the custom function
 #' n_sim <- 10
@@ -135,7 +134,11 @@ simulate_timeseries <- function(
 
   # Set seed if provided
   if (!is.na(seed)) {
-    withr::local_seed(seed)
+    if (exists(".Random.seed", envir = .GlobalEnv)) {
+      rng_state_old <- get(".Random.seed", envir = .GlobalEnv)
+      on.exit(assign(".Random.seed", rng_state_old, envir = .GlobalEnv))
+    }
+    set.seed(seed)
   }
 
   # Check type of temporal_function

codemeta.json

@@ -332,21 +332,9 @@
       },
       "sameAs": "https://CRAN.R-project.org/package=vegan"
     },
-    "13": {
-      "@type": "SoftwareApplication",
-      "identifier": "withr",
-      "name": "withr",
-      "provider": {
-        "@id": "https://cran.r-project.org",
-        "@type": "Organization",
-        "name": "Comprehensive R Archive Network (CRAN)",
-        "url": "https://cran.r-project.org"
-      },
-      "sameAs": "https://CRAN.R-project.org/package=withr"
-    },
     "SystemRequirements": null
   },
-  "fileSize": "549.627KB",
+  "fileSize": "550.823KB",
   "citation": [
     {
       "@type": "SoftwareSourceCode",