fledge: CRAN release v2.3.4 (#248)

Author: pat-s

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3spatiotempcv
 Title: Spatiotemporal Resampling Methods for 'mlr3'
-Version: 2.3.3.9000
+Version: 2.3.4
 Authors@R: c(
     person("Patrick", "Schratz", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0748-6624")),
@@ -65,7 +65,7 @@ Encoding: UTF-8
 LazyData: true
 NeedsCompilation: no
 Roxygen: list(markdown = TRUE, r6 = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 Collate: 
     'aaa.R'
     'ResamplingRepeatedSpCVBlock.R'

NEWS.md

@@ -1,6 +1,6 @@
 <!-- NEWS.md is maintained by https://fledge.cynkra.com, contributors should not edit this file -->
 
-# mlr3spatiotempcv 2.3.3.9000
+# mlr3spatiotempcv 2.3.4
 
 ## Chore
 

R/ResamplingRepeatedSpCVknndm.R

@@ -13,29 +13,30 @@
 #'
 #' @export
 #' @examples
-#' library(mlr3)
-#' library(mlr3spatial)
-#' set.seed(42)
-#' simarea = list(matrix(c(0, 0, 0, 100, 100, 100, 100, 0, 0, 0), ncol = 2, byrow = TRUE))
-#' simarea = sf::st_polygon(simarea)
-#' train_points = sf::st_sample(simarea, 1000, type = "random")
-#' train_points = sf::st_as_sf(train_points)
-#' train_points$target = as.factor(sample(c("TRUE", "FALSE"), 1000, replace = TRUE))
-#' pred_points = sf::st_sample(simarea, 1000, type = "regular")
+#' if (requireNamespace(c("mlr3spatial", "mlr3"))) {
+#'   library(mlr3)
+#'   library(mlr3spatial)
+#'   set.seed(42)
+#'   simarea = list(matrix(c(0, 0, 0, 100, 100, 100, 100, 0, 0, 0), ncol = 2, byrow = TRUE))
+#'   simarea = sf::st_polygon(simarea)
+#'   train_points = sf::st_sample(simarea, 1000, type = "random")
+#'   train_points = sf::st_as_sf(train_points)
+#'   train_points$target = as.factor(sample(c("TRUE", "FALSE"), 1000, replace = TRUE))
+#'   pred_points = sf::st_sample(simarea, 1000, type = "regular")
 #'
-#' task = mlr3spatial::as_task_classif_st(sf::st_as_sf(train_points), "target", positive = "TRUE")
+#'   task = mlr3spatial::as_task_classif_st(sf::st_as_sf(train_points), "target", positive = "TRUE")
 #'
-#' cv_knndm = rsmp("repeated_spcv_knndm", predpoints = pred_points, repeats = 2)
-#' cv_knndm$instantiate(task)
-
-#' #' ### Individual sets:
-#' # cv_knndm$train_set(1)
-#' # cv_knndm$test_set(1)
-#' # check that no obs are in both sets
-#' intersect(cv_knndm$train_set(1), cv_knndm$test_set(1)) # good!
+#'   cv_knndm = rsmp("repeated_spcv_knndm", predpoints = pred_points, repeats = 2)
+#'   cv_knndm$instantiate(task)
+#'   ### Individual sets:
+#'   # cv_knndm$train_set(1)
+#'   # cv_knndm$test_set(1)
+#'   # check that no obs are in both sets
+#'   intersect(cv_knndm$train_set(1), cv_knndm$test_set(1)) # good!
 #'
-#' # Internal storage:
-#' # cv_knndm$instance # table
+#'   # Internal storage:
+#'   # cv_knndm$instance # table
+#' }
 ResamplingRepeatedSpCVKnndm = R6Class("ResamplingRepeatedSpCVKnndm",
   inherit = mlr3::Resampling,
   public = list(

README.md

@@ -45,7 +45,7 @@ To cite the package in publications, use the output of `citation("mlr3spatiotemp
 
 ## Resources
 
-- [Recorded talk about mlr3spatiotempcv and mlr3spatial at OpenDataScience Europe Conference 2021 in Wageningen, NL](https://av.tib.eu/media/55271)
+- [Recorded talk about mlr3spatiotempcv and mlr3spatial at OpenDataScience Europe Conference 2021 in Wageningen, NL](https://www.youtube.com/watch?v=lWOMsmeWfEo)
 - [List of scientific articles related to spatiotemporal modeling and/or spatial partitioning](https://pat-s.notion.site/Spatial-autocorrelation-in-modeling-b62e1bc904b546b9a489b171913a3551)
 
 # Other spatiotemporal resampling packages

cran-comments.md

@@ -1,4 +1,4 @@
-mlr3spatiotempcv 2.3.3
+mlr3spatiotempcv 2.3.4
 
 ## Cran Repository Policy
 

man/mlr_resamplings_repeated_spcv_knndm.Rd

@@ -23,7 +23,7 @@ test_that("plot() works for 'sptcv_cstf' 2D - time_var", {
 
   expect_true(is_ggplot(p1))
   expect_true(is_ggplot(p2))
-  expect_list(p3)
+  expect_s3_class(p3, "patchwork")
 
   p4 = autoplot(rsp, tsk_cookfarm_sub, repeats_id = 2, sample_fold_n = 3L)
   p5 = autoplot(rsp, tsk_cookfarm_sub, fold_id = 1, repeats_id = 2,
@@ -66,7 +66,7 @@ test_that("plot() works for 'sptcv_cstf' 2D - space_var", {
 
   expect_true(is_ggplot(p1))
   expect_true(is_ggplot(p2))
-  expect_list(p3)
+  expect_s3_class(p3, "patchwork")
 
   p4 = autoplot(rsp, tsk_cookfarm_sub, repeats_id = 2, sample_fold_n = 3L)
   p5 = autoplot(rsp, tsk_cookfarm_sub, fold_id = 1, repeats_id = 2,
@@ -232,7 +232,7 @@ test_that("plot() works for 'spcv_disc'", {
 
   expect_true(is_ggplot(p1))
   expect_true(is_ggplot(p2))
-  expect_list(p3)
+  expect_s3_class(p3, "patchwork")
   expect_true(is_ggplot(p4))
 
   p5 = autoplot(rsp, task, repeats_id = 2)
@@ -260,6 +260,10 @@ test_that("plot() works for 'spcv_disc'", {
 
 test_that("train_color and test_color parameters work for resampling methods", {
   skip_if_not_installed("ggplot2")
+  skip_if_not_installed("vdiffr")
+  skip_if_not_installed("sf")
+  skip_if_not_installed("patchwork")
+  skip_if_not_installed("ggtext")
 
   set.seed(42)
 
@@ -323,7 +327,7 @@ test_that("plot() works for 'spcv_tiles'", {
 
   expect_true(is_ggplot(p1))
   expect_true(is_ggplot(p2))
-  expect_list(p3)
+  expect_s3_class(p3, "patchwork")
   expect_true(is_ggplot(p4))
 
   p5 = autoplot(rsp, task, c(1, 2), repeats_id = 2)
@@ -376,7 +380,7 @@ test_that("plot() works for 'spcv_knndm'", {
 
   expect_true(is_ggplot(p1))
   expect_true(is_ggplot(p2))
-  expect_list(p3)
+  expect_s3_class(p3, "patchwork")
 
   p5 = autoplot(rsp, task, repeats_id = 2)
   p6 = autoplot(rsp, task, fold_id = 1, repeats_id = 2)