Fixing ggplot aes_string

.Rbuildignore

@@ -7,3 +7,4 @@
 ^Meta$
 cran_comments.md
 .github/
+^LICENSE\.md$

DESCRIPTION

@@ -2,7 +2,7 @@ Package: blockCV
 Type: Package
 Title: Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation
 Version: 3.0-0
-Date: 2023-01-01
+Date: 2023-01-28
 Authors@R: c(person("Roozbeh", "Valavi", role = c("aut", "cre"),
                     email = "[email protected]"),
              person("Jane", "Elith", role = "aut",
@@ -17,7 +17,7 @@ URL: https://github.com/rvalavi/blockCV
 BugReports: https://github.com/rvalavi/blockCV/issues
 Maintainer: Roozbeh Valavi <[email protected]>
 Description: Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
-License: GPL-3
+License: GPL (>= 3)
 Encoding: UTF-8
 LazyData: true
 Depends:

NAMESPACE

@@ -40,4 +40,5 @@ export(spatialBlock)
 import(sf)
 importFrom(Rcpp,sourceCpp)
 importFrom(grDevices,gray.colors)
+importFrom(rlang,.data)
 useDynLib(blockCV, .registration = TRUE)

R/cv_explorer.R

@@ -108,10 +108,9 @@ cv_block_size <- function(r, # priority
     colnames(map_df) <- c("x", "y", "value")
 
     base_plot <- ggplot2::ggplot() +
-      ggplot2::geom_tile(data = map_df,
-                         ggplot2::aes_string(x = "x",
-                                             y = "y",
-                                             fill = "value")) +
+      ggplot2::geom_tile(
+        data = map_df,
+        ggplot2::aes(x = .data$x, y = .data$y, fill = .data$value)) +
       ggplot2::scale_fill_gradientn(colours = gray.colors(20, alpha = 1)) +
       ggplot2::guides(fill = "none")
 
@@ -122,7 +121,8 @@ cv_block_size <- function(r, # priority
   if(!is.null(x)){
     geom_x <- ggplot2::geom_sf(
       data = x,
-      ggplot2::aes_string(colour = switch(!is.null(column), column, NULL)),
+      switch(!is.null(column), ggplot2::aes(colour = {{ column }}), NULL),
+      # ggplot2::aes(colour = {{ switch(!is.null(column), column, NULL) }}),
       inherit.aes = FALSE,
       alpha = 0.5
     )
@@ -154,9 +154,14 @@ cv_block_size <- function(r, # priority
   )
 
   # create shiny server and main code
-  server <- function(input, output){
+  server <- function(input, output, session){
     output$ggplot <- shiny::renderPlot({
 
+      # stop app after session ends
+      session$onSessionEnded(function() {
+        shiny::stopApp()
+      })
+
       plot_size <- if(sf::st_is_longlat(x_obj)) round(input$num) / 111325 else round(input$num)
       vis_block <- sf::st_make_grid(x_obj, cellsize = plot_size, what = "polygons")
 
@@ -178,5 +183,5 @@ cv_block_size <- function(r, # priority
     })
   }
   # starting the shiny app
-  shiny::shinyApp(ui = ui, server = server)
+  shiny::shinyApp(ui = ui, server = server, )
 }

R/cv_plot.R

@@ -20,6 +20,7 @@
 #' @param remove_na logical; whether to remove excluded points in \code{cv_buffer} from the plot
 #'
 #' @importFrom grDevices gray.colors
+#' @importFrom rlang .data
 #' @return a ggplot object
 #' @export
 #'
@@ -83,8 +84,9 @@ cv_plot <- function(
                                 na.rm = TRUE)
     colnames(map_df) <- c("x", "y", "value")
 
-    geom_rast <- ggplot2::geom_tile(data = map_df,
-                                    ggplot2::aes_string(x="x", y="y", fill="value"))
+    geom_rast <- ggplot2::geom_tile(
+      data = map_df,
+      ggplot2::aes(x = .data$x, y = .data$y, fill = .data$value))
     geom_rast_col <- ggplot2::scale_fill_gradientn(colours = raster_colors)
   }
   # make geom_sf for spatial blocks
@@ -100,18 +102,16 @@ cv_plot <- function(
 
   if(!missing(x)){
     x_long <- .x_to_long(x, cv, num_plot = num_plots)
-
     # exclude NAs from cv_buffer
-    if(methods::is(cv, "cv_buffer") && remove_na){
+    # if(methods::is(cv, "cv_buffer") && remove_na){
+    if(.is_loo(cv) && remove_na){
       x_long <- x_long[which(complete.cases(x_long$value)), ]
     }
-
   } else{
     # stop if x is missing for buffer and cluster
     if(!methods::is(cv, "cv_spatial")) stop("'x' is required for plotting cv_cluster and cv_buffer.")
   }
 
-
   if(missing(x)){
     if(methods::is(cv, "cv_spatial")){
 
@@ -122,8 +122,9 @@ cv_plot <- function(
                          fill = "orangered4",
                          alpha = 0.04,
                          size = 0.2) +
-        ggplot2::geom_sf_text(ggplot2::aes_string(label = "folds"),
-                              size = label_size, fun.geometry = sf::st_centroid) +
+        ggplot2::geom_sf_text(
+          ggplot2::aes(label = .data$folds),
+          size = label_size, fun.geometry = sf::st_centroid) +
         ggplot2::labs(x = "", y = "") + # or set the axes labes to NULL
         ggplot2::scale_x_continuous(guide = ggplot2::guide_axis(check.overlap = TRUE)) +
         ggplot2::theme_minimal() +
@@ -137,10 +138,10 @@ cv_plot <- function(
       switch(!is.null(r), geom_rast, NULL) +
       switch(!is.null(r), geom_rast_col, NULL) +
       switch(methods::is(cv, "cv_spatial"), geom_poly, NULL) +
-      ggplot2::geom_sf(ggplot2::aes_string(col = "value"),
+      ggplot2::geom_sf(ggplot2::aes(col = .data$value),
                        alpha = points_alpha) +
       ggplot2::scale_color_manual(values = points_colors, na.value = "#BEBEBE03") +
-      ggplot2::facet_wrap(~folds, nrow = nrow, ncol = ncol) +
+      ggplot2::facet_wrap(~.data$folds, nrow = nrow, ncol = ncol) +
       ggplot2::labs(x = "", y = "", col = "") + # set the axes labes to NULL
       ggplot2::theme_bw() +
       ggplot2::guides(fill = "none")
@@ -151,6 +152,11 @@ cv_plot <- function(
 }
 
 
+# is it a LOO CV object?
+.is_loo <- function(x){
+  methods::is(x, "cv_buffer") || methods::is(x, "cv_nndm")
+}
+
 # transform x and fold numbers for plotting
 .x_to_long <- function(x, cv, num_plot=1:10){
   # get the folds list
@@ -171,13 +177,14 @@ cv_plot <- function(
     num_plot <- num_plot[num_plot <= k]
   }
   # get the length of unique ids
-  if(methods::is(cv, "cv_buffer")){
+  # if(methods::is(cv, "cv_buffer")){
+  if(.is_loo(cv)){
     len <- length(unique(unlist(cv$folds_list)))
   } else{
     len <- length(unlist(folds_list[[1]]))
-    if(len != nrow(x)){
-      stop("Number of rows in 'x' does not match the folds in 'cv'!")
-    }
+  }
+  if(len != nrow(x)){
+    stop("Number of rows in 'x' does not match the folds in 'cv'!")
   }
   # create a dataframe temp
   df <- data.frame(id = seq_len(len))

R/cv_similarity.R

@@ -140,8 +140,9 @@ cv_similarity <- function(cv,
   # which geom to choose
   geom_exta <- if(methods::is(cv, "cv_buffer")) goem_buffer else geom_other
 
-  p1 <- ggplot2::ggplot(data = mes_reshp,
-                        ggplot2::aes_string(x = "folds", y =  "value", col = "value")) +
+  p1 <- ggplot2::ggplot(
+    data = mes_reshp,
+    ggplot2::aes(x = .data$folds, y = .data$value, col = .data$value)) +
     ggplot2::geom_hline(yintercept = 0, color = "grey50", linetype = 2) +
     geom_exta +
     switch(!methods::is(cv, "cv_buffer"), geom_vio, NULL) +

R/cv_spatial_autocor.R

@@ -285,10 +285,10 @@ summary.cv_spatial_autocor <- function(object, ...){
     #   stat = "identity", data = vario_data,) +
     ggplot2::geom_point(size = 4) +
     ggplot2::geom_segment(
-      ggplot2::aes_string(x = "layers",
-                          xend = "layers",
-                          y = 0,
-                          yend = "range"),
+      ggplot2::aes(x = .data$layers,
+                   xend = .data$layers,
+                   y = 0,
+                   yend = .data$range),
       size = 1.5
     ) +
     ggplot2::labs(x = "Variables", y = "Range (km)") +