From ab21406f8c2d81b90721c702e42856986ac778d9 Mon Sep 17 00:00:00 2001
From: John Ehrlinger <john.ehrlinger@gmail.com>
Date: Wed, 25 Jun 2025 10:16:59 -0400
Subject: [PATCH] remove cbind

---
 R/gg_partial.R    |  8 ++++----
 R/gg_partialpro.R | 29 +++++++++++++----------------
 2 files changed, 17 insertions(+), 20 deletions(-)

diff --git a/R/gg_partial.R b/R/gg_partial.R
index 2ac586e..09c2bdc 100644
--- a/R/gg_partial.R
+++ b/R/gg_partial.R
@@ -22,10 +22,10 @@ gg_partial = function(part_dta,
   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(
+      plt.df = dplyr::bind_cols(
         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
@@ -34,10 +34,10 @@ gg_partial = function(part_dta,
       
       ## Though VarPro works with logical or continuous only. Factors are
       ## one hot encoded internal to the varPro call.
-      plt.df = as.data.frame(cbind(
+      plt.df = dplyr::bind_cols(
         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
diff --git a/R/gg_partialpro.R b/R/gg_partialpro.R
index 5b318a4..3040e3e 100644
--- a/R/gg_partialpro.R
+++ b/R/gg_partialpro.R
@@ -24,17 +24,16 @@ gg_partialpro = function(part_dta,
   for (feature in seq(nvars)) {
     ## Format any continuous features (those with fewer than 10 unique values)
     if (length(part_dta[[feature]]$xvirtual) > cat_limit) {
-      plt.df = as.data.frame(
-        cbind(
-          variable = part_dta[[feature]]$xvirtual,
-          parametric = colMeans(part_dta[[feature]]$yhat.par, na.rm =
-                                  TRUE),
-          nonparametric = colMeans(part_dta[[feature]]$yhat.nonpar, na.rm =
-                                     TRUE),
-          causal = colMeans(part_dta[[feature]]$yhat.causal, na.rm =
-                              TRUE)
-        )
+      plt.df = dplyr::bind_cols(
+        variable = part_dta[[feature]]$xvirtual,
+        parametric = colMeans(part_dta[[feature]]$yhat.par, na.rm =
+                                TRUE),
+        nonparametric = colMeans(part_dta[[feature]]$yhat.nonpar, na.rm =
+                                   TRUE),
+        causal = colMeans(part_dta[[feature]]$yhat.causal, na.rm =
+                            TRUE)
       )
+      
       plt.df$name = names(part_dta)[[feature]]
       
       cont_list[[feature]] <- plt.df
@@ -46,12 +45,10 @@ gg_partialpro = function(part_dta,
       cat_feat = list()
       ## Each yhat has at least 2 columns, for logical values...
       for (ind in seq(length(unique(part_dta[[feature]]$xorg)))) {
-        cat_feat[[ind]] = as.data.frame(
-          cbind(
-            parametric = part_dta[[feature]]$yhat.par[, ind],
-            nonparametric = part_dta[[feature]]$yhat.nonpar[, ind],
-            causal = part_dta[[feature]]$yhat.causal[, ind]
-          )
+        cat_feat[[ind]] = dplyr::bind_cols(
+          parametric = part_dta[[feature]]$yhat.par[, ind],
+          nonparametric = part_dta[[feature]]$yhat.nonpar[, ind],
+          causal = part_dta[[feature]]$yhat.causal[, ind]
         )
         cat_feat[[ind]]$variable <-
           unique(part_dta[[feature]]$xorg)[ind]