spacing out kwd arguments cls2 bughunt

source/classification2.Rmd

@@ -245,7 +245,7 @@ Here, we pass in the number `1`.
 
 ```{r}
 set.seed(1)
-random_numbers1 <- sample(0:9, 10, replace=TRUE)
+random_numbers1 <- sample(0:9, 10, replace = TRUE)
 random_numbers1
 ```
 
@@ -255,7 +255,7 @@ we run the `sample` function again, we will
 get a fresh batch of 10 numbers that also look random.
 
 ```{r}
-random_numbers2 <- sample(0:9, 10, replace=TRUE)
+random_numbers2 <- sample(0:9, 10, replace = TRUE)
 random_numbers2
 ```
 
@@ -265,10 +265,10 @@ value.
 
 ```{r}
 set.seed(1)
-random_numbers1_again <- sample(0:9, 10, replace=TRUE)
+random_numbers1_again <- sample(0:9, 10, replace = TRUE)
 random_numbers1_again
 
-random_numbers2_again <- sample(0:9, 10, replace=TRUE)
+random_numbers2_again <- sample(0:9, 10, replace = TRUE)
 random_numbers2_again
 ```
 
@@ -278,10 +278,10 @@ obtain a different sequence of random numbers.
 
 ```{r}
 set.seed(4235)
-random_numbers1_different <- sample(0:9, 10, replace=TRUE)
+random_numbers1_different <- sample(0:9, 10, replace = TRUE)
 random_numbers1_different
 
-random_numbers2_different <- sample(0:9, 10, replace=TRUE)
+random_numbers2_different <- sample(0:9, 10, replace = TRUE)
 random_numbers2_different
 ```
 
@@ -512,10 +512,10 @@ cancer_acc_1 <- cancer_test_predictions |>
                 filter(.metric == 'accuracy')
 
 cancer_prec_1 <- cancer_test_predictions |>
-  precision(truth = Class, estimate = .pred_class, event_level="first")
+  precision(truth = Class, estimate = .pred_class, event_level = "first")
 
 cancer_rec_1 <- cancer_test_predictions |>
-  recall(truth = Class, estimate = .pred_class, event_level="first")
+  recall(truth = Class, estimate = .pred_class, event_level = "first")
 ```
 
 In the metrics data frame, we filtered the `.metric` column since we are
@@ -537,12 +537,12 @@ If the labels were in the other order, we would instead use `event_level="second
 
 ```{r 06-precision}
 cancer_test_predictions |>
-  precision(truth = Class, estimate = .pred_class, event_level="first")
+  precision(truth = Class, estimate = .pred_class, event_level = "first")
 ```
 
 ```{r 06-recall}
 cancer_test_predictions |>
-  recall(truth = Class, estimate = .pred_class, event_level="first")
+  recall(truth = Class, estimate = .pred_class, event_level = "first")
 ```
 
 The output shows that the estimated precision and recall of the classifier on the test data was