convert arrange/slice to slice_min in classification and regression

Author: trevorcampbell

source/classification1.Rmd

@@ -460,6 +460,14 @@ the $K=5$ neighbors that are nearest to our new point.
 You will see in the `mutate` \index{mutate} step below, we compute the straight-line
 distance using the formula above: we square the differences between the two observations' perimeter 
 and concavity coordinates, add the squared differences, and then take the square root.
+In order to find the $K=5$ nearest neighbors, we will use the `slice_min` function.
+
+> **Note:** Recall that in Chapter \@ref(intro), we used `arrange` followed by `slice` to
+> obtain the ten rows with the *largest* values of a variable. We could have instead used
+> the `slice_max` function for this purpose. The `slice_min` and `slice_max` functions
+> achieve the same goal as `arrange` followed by `slice`, but are slightly more efficient
+> because they are specialized for this purpose. In general, it is good to use more specialized
+> functions when they are available!
 
 ```{r 05-multiknn-1, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.pos = "H", out.extra="", fig.cap="Scatter plot of concavity versus perimeter with new observation represented as a red diamond."}
 perim_concav <- bind_rows(cancer, 
@@ -499,8 +507,7 @@ cancer |>
   select(ID, Perimeter, Concavity, Class) |>
   mutate(dist_from_new = sqrt((Perimeter - new_obs_Perimeter)^2 + 
                               (Concavity - new_obs_Concavity)^2)) |>
-  arrange(dist_from_new) |>
-  slice(1:5) # take the first 5 rows
+  slice_min(dist_from_new, n = 5) # take the 5 rows of minimum distance
 ```
 
 In Table \@ref(tab:05-multiknn-mathtable) we show in mathematical detail how
@@ -590,8 +597,7 @@ cancer |>
   mutate(dist_from_new = sqrt((Perimeter - new_obs_Perimeter)^2 + 
                               (Concavity - new_obs_Concavity)^2 +
                                 (Symmetry - new_obs_Symmetry)^2)) |>
-  arrange(dist_from_new) |>
-  slice(1:5) # take the first 5 rows
+  slice_min(dist_from_new, n = 5) # take the 5 rows of minimum distance
 ```
 
 Based on $K=5$ nearest neighbors with these three predictors, we would classify 

source/regression1.Rmd

@@ -233,13 +233,12 @@ sale price might be.
 For the example shown in Figure \@ref(fig:07-small-eda-regr), 
 we find and label the 5 nearest neighbors to our observation 
 of a house that is 2,000 square feet.
-\index{mutate}\index{slice}\index{arrange}\index{abs}
+\index{mutate}\index{slice\_min}\index{abs}
 
 ```{r 07-find-k3}
 nearest_neighbors <- small_sacramento |>
   mutate(diff = abs(2000 - sqft)) |>
-  arrange(diff) |>
-  slice(1:5) #subset the first 5 rows
+  slice_min(diff, n = 5)
 
 nearest_neighbors
 ```