Cleanup gg_partial

NAMESPACE

@@ -26,8 +26,8 @@ export(gg_error)
 export(gg_error.randomForest)
 export(gg_error.randomForest.formula)
 export(gg_error.rfsrc)
-export(gg_partial_df)
-export(gg_partialpro_df)
+export(gg_partial)
+export(gg_partialpro)
 export(gg_rfsrc)
 export(gg_roc)
 export(gg_survival)

R/gg_partial_df.R → R/gg_partial.R

@@ -2,29 +2,30 @@
 #' Split partial lots into continuous or categorical datasets
 #' @param part_dta partial plot data from \code{rfsrc::plot.variable}
 #' @param nvars how many of the partial plot variables to calculate
-#' @param cat_limit Categorical features are build when there are fewer than 
+#' @param cat_limit Categorical features are build when there are fewer than
 #'  cat_limit unique features.
-#' @param name a label name applied to all features. Useful when combining 
+#' @param model a label name applied to all features. Useful when combining
 #'  multiple partial plot objects in figures.
-#'    
+#'
 #' @export
-gg_partial_df = function(part_dta, nvars = NULL, cat_limit = 10, name=NULL) {
+gg_partial = function(part_dta,
+                      nvars = NULL,
+                      cat_limit = 10,
+                      model = NULL) {
   ## Prepare the partial dependencies data for panel plots
   if (is.null(nvars)) {
     nvars = length(part_dta$plotthis)
   }
 
   cont_list = list()
   cat_list = list()
   for (feature in seq(nvars)) {
     ## Format any continuous features (those with fewer than cat_limit unique values)
     if (length(unique(part_dta$plotthis[[feature]]$x)) > cat_limit) {
-      plt.df = as.data.frame(
-        cbind(
-          x = part_dta$plotthis[[feature]]$x,
-          yhat = part_dta$plotthis[[feature]]$yhat
-        )
-      )
+      plt.df = as.data.frame(cbind(
+        x = part_dta$plotthis[[feature]]$x,
+        yhat = part_dta$plotthis[[feature]]$yhat
+      ))
       plt.df$name = names(part_dta$plotthis)[[feature]]
 
       cont_list[[feature]] <- plt.df
@@ -33,12 +34,10 @@
 
       ## Though VarPro works with logical or continuous only. Factors are
       ## one hot encoded internal to the varPro call.
-      plt.df = as.data.frame(
-        cbind(
-          x = factor(part_dta$plotthis[[feature]]$x),
-          yhat = part_dta$plotthis[[feature]]$yhat
-        )
-      )
+      plt.df = as.data.frame(cbind(
+        x = factor(part_dta$plotthis[[feature]]$x),
+        yhat = part_dta$plotthis[[feature]]$yhat
+      ))
       plt.df$name = names(part_dta$plotthis)[[feature]]
 
       cat_list[[feature]] <- plt.df
@@ -47,12 +46,9 @@
   continuous = dplyr::bind_rows(cont_list)
   categorical = dplyr::bind_rows(cat_list)
 
-  if(!is.na(name)){
-    continuous$model <- categorical$model <- name
+  if (!is.null(model)) {
+    continuous$model <- categorical$model <- model
   }
 
-  return(list(
-    continuous = continuous,
-    categorical = categorical
-  ))
+  return(list(continuous = continuous, categorical = categorical))
 }

R/gg_partialpro_df.R → R/gg_partialpro.R

@@ -3,14 +3,17 @@
 #' Split partial lots into continuous or categorical datasets
 #' @param part_dta partial plot data from \code{varpro::partialpro}
 #' @param nvars how many of the partial plot variables to calculate
-#' @param cat_limit Categorical features are build when there are fewer than 
+#' @param cat_limit Categorical features are build when there are fewer than
 #'  cat_limit unique features.
-#' @param name a label name applied to all features. Useful when combining 
+#' @param model a label name applied to all features. Useful when combining
 #'  multiple partial plot objects in figures.
-#'    
+#'
 #' @export
-#' 
-gg_partialpro_df = function(part_dta, nvars = NULL, cat_limit=10, name=NULL) {
+#'
+gg_partialpro = function(part_dta,
+                         nvars = NULL,
+                         cat_limit = 10,
+                         model = NULL) {
   ## Prepare the partial pro  dependencies data for panel plots
   if (is.null(nvars)) {
     nvars = length(part_dta)
@@ -68,12 +71,9 @@
   continuous = dplyr::bind_rows(cont_list)
   categorical = dplyr::bind_rows(cat_list)
 
-  if(!is.na(name)){
-    continuous$model <- categorical$model <- name
+  if (!is.null(model)) {
+    continuous$model <- categorical$model <- model
   }
 
-  return(list(
-    continuous = continuous,
-    categorical = categorical
-  ))
+  return(list(continuous = continuous, categorical = categorical))
 }