Refactor code to use forward-pipe operator for better readability and consistency

M2/Clustering In Practice/Encoding.Rmd

@@ -7,7 +7,7 @@ library(dplyr)
 
 ```{r}
 df <- data.frame(
-  team = c('A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'),
+  team = c("A", "A", "B", "B", "B", "B", "C", "C"),
   points = c(25, 12, 15, 14, 19, 23, 25, 29)
 )
 
@@ -22,24 +22,24 @@ one_hot_data
 ```{r}
 train <- data.frame(
   target = c(10, 20, 15),
-  cat_col1 = c('city1', 'city2', 'city1'),
-  cat_col2 = c('james', 'adam', 'charles')
+  cat_col1 = c("city1", "city2", "city1"),
+  cat_col2 = c("james", "adam", "charles")
 )
 
 global_mean <- mean(train$target)
 alpha <- 10
 
-target_encoding <- train %>%
-  group_by(cat_col1) %>%
+target_encoding <- train |>
+  group_by(cat_col1) |>
   summarise(
     n = n(),
     sum_target = sum(target),
     cat_col1_te = (sum_target + (alpha * global_mean)) / (n + alpha),
     .groups = "drop"
-  ) %>%
+  ) |>
   select(cat_col1, cat_col1_te)
 
-train <- train %>% left_join(target_encoding, by = "cat_col1")
+train <- train |> left_join(target_encoding, by = "cat_col1")
 ```
 
 # Frequential Encoding
@@ -47,7 +47,7 @@ train <- train %>% left_join(target_encoding, by = "cat_col1")
 
 ```{r}
 df <- data.frame(
-  color = c('blue', 'red', 'blue', 'green'),
+  color = c("blue", "red", "blue", "green"),
   value = c(10, 20, 10, 30)
 )
 ```