diff --git a/EDA.Rmd b/EDA.Rmd
index fd78a2f..aff0692 100644
--- a/EDA.Rmd
+++ b/EDA.Rmd
@@ -427,13 +427,7 @@ Then visualise with `geom_tile()` and the fill aesthetic:
 diamonds %>% 
   count(color, cut) %>%  
   ggplot(mapping = aes(x = color, y = cut)) +
-<<<<<<< HEAD
-    geom_tile(aes(fill = n))
-||||||| merged common ancestors
-  geom_tile(aes(fill = n))
-=======
-  geom_tile(mapping = aes(fill = n))
->>>>>>> 3eb371e1111d5ec11bacc14d8b4d38208a055bed
+    geom_tile(mapping = aes(fill = n))
 ```
 
 If the categorical variables are unordered, you might want to use the seriation package to simultaneously reorder the rows and columns in order to more clearly reveal interesting patterns. For larger plots, you might want to try the d3heatmap or heatmaply packages, which create interactive plots.