diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
index c111bea3..09aca861 100644
--- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
+++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd
@@ -226,6 +226,19 @@ baked_pumpkins %>%
   theme(legend.position = "none")
 ```
 
+
+```{r cat plot pumpkins-colors-variety}
+# Specify colors for each value of the hue variable
+palette <- c(ORANGE = "orange", WHITE = "wheat")
+
+# Create the bar plot
+ggplot(pumpkins, aes(y = Variety, fill = Color)) +
+  geom_bar(position = "dodge") +
+  scale_fill_manual(values = palette) +
+  labs(y = "Variety", fill = "Color") +
+  theme_minimal()
+```
+
 Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.
 
 ## 3. Build your model