Merge pull request #59 from ehrlinger/copilot_refactor

Copilot refactor

Author: ehrlinger

DESCRIPTION

@@ -31,6 +31,8 @@ Suggests:
     lintr,
     datasets,
     rmarkdown, 
-    quarto
+    quarto, 
+    pkgdown,
+    pkgload
 VignetteBuilder: quarto
 RoxygenNote: 7.3.3

NAMESPACE

@@ -37,13 +37,12 @@ export(gg_vimp)
 export(kaplan)
 export(nelson)
 export(quantile_pts)
-export(r_data_types)
 export(surv_partial.rfsrc)
 export(varpro_feature_names)
-importFrom(dplyr,across)
+importFrom(dplyr,all_of)
+importFrom(dplyr,filter)
 importFrom(dplyr,mutate)
-importFrom(dplyr,n_distinct)
-importFrom(dplyr,where)
+importFrom(dplyr,select)
 importFrom(ggplot2,.data)
 importFrom(ggplot2,geom_line)
 importFrom(ggplot2,ggplot)

R/gg_partial_rfsrc.R

@@ -25,6 +25,7 @@
 #' prt_dta <- gg_partial_rfsrc(airq.obj,
 #'                        xvar.names = c("Wind"))
 #'
+#' @importFrom dplyr mutate filter select all_of
 #' @export
 gg_partial_rfsrc <- function(rf_model,
                              xvar.names = NULL,

R/r_data_types.R

@@ -21,14 +21,23 @@ The **ggRandomForests** package extracts tidy data objects from either
 `gg_error`, `gg_variable`, and `gg_vimp`—along with a small helper for
 building balanced conditioning intervals.
 
+```{r pkg-setup, include=FALSE}
+if (requireNamespace("ggRandomForests", quietly = TRUE)) {
+  library(ggRandomForests)
+} else if (requireNamespace("pkgload", quietly = TRUE)) {
+  pkgload::load_all(export_all = FALSE, helpers = FALSE, attach_testthat = FALSE)
+} else {
+  stop("Install ggRandomForests (or pkgload for dev builds) to render this vignette.")
+}
+```
+
 ## Error trajectories with `gg_error()`
 
 ```{r error-demo}
-library(ggRandomForests)
 library(randomForest)
 set.seed(42)
 rf_iris <- randomForest(Species ~ ., data = iris, ntree = 200, keep.forest = TRUE)
-err_df <- gg_error(rf_iris, training = TRUE)
+err_df <- ggRandomForests::gg_error(rf_iris, training = TRUE)
 head(err_df)
 ```
 
@@ -47,7 +56,7 @@ plot(err_df)
 set.seed(99)
 boston <- MASS::Boston
 rf_boston <- randomForest(medv ~ ., data = boston, ntree = 150)
-var_df <- gg_variable(rf_boston)
+var_df <- ggRandomForests::gg_variable(rf_boston)
 str(var_df[, c("lstat", "yhat")])
 ```
 
@@ -68,7 +77,7 @@ non-OOB predictions are available by setting `oob = FALSE`.
 ## Variable importance with `gg_vimp()`
 
 ```{r vimp-demo}
-vimp_df <- gg_vimp(rf_boston)
+vimp_df <- ggRandomForests::gg_vimp(rf_boston)
 head(vimp_df)
 plot(vimp_df)
 ```
@@ -82,7 +91,7 @@ placeholders so plots still render.
 ## Balanced conditioning cuts with `quantile_pts()`
 
 ```{r quantile-demo}
-rm_breaks <- quantile_pts(boston$rm, groups = 6, intervals = TRUE)
+rm_breaks <- ggRandomForests::quantile_pts(boston$rm, groups = 6, intervals = TRUE)
 rm_groups <- cut(boston$rm, breaks = rm_breaks)
 table(rm_groups)
 ```