Merge pull request #501 from UBC-DSCI/no-legend-editing

Remove legend editing, replace with renaming values in the df itself

Author: trevorcampbell

source/classification1.Rmd

@@ -190,29 +190,33 @@ glimpse(cancer)
 ```
 
 From the summary of the data above, we can see that `Class` is of type character
-(denoted by `<chr>`). Since we will be working with `Class` as a
-categorical statistical variable, we will convert it to a factor using the
-function `as_factor`. \index{factor!as\_factor}
-
+(denoted by `<chr>`). We can use the `distinct`\index{distinct} function to see all the unique
+values present in that column. We see that there are two diagnoses: benign, represented by "B",
+and malignant, represented by "M".
+```{r 05-distinct}
+cancer |>
+  distinct(Class)
+```
+Since we will be working with `Class` as a categorical
+variable, it is a good idea to convert it to a factor type using the `as_factor` function. \index{factor!as\_factor}
+We will also improve the readability of our analysis by renaming "M" to
+"Malignant" and "B" to "Benign" using the `fct_recode` method. The `fct_recode` method \index{factor!fct\_recode}
+is used to replace the names of factor values with other names. The arguments of `fct_recode` are the column that you
+want to modify, followed any number of arguments of the form `"new name" = "old name"` to specify the renaming scheme.
+ 
 ```{r 05-class}
 cancer <- cancer |>
-  mutate(Class = as_factor(Class))
+  mutate(Class = as_factor(Class)) |>
+  mutate(Class = fct_recode(Class, "Malignant" = "M", "Benign" = "B"))
 glimpse(cancer)
 ```
 
-Recall that factors have what are called "levels", which you can think of as categories. We
-can verify the levels of the `Class` column by using the `levels`\index{levels}\index{factor!levels} function.
-This function should return the name of each category in that column. Given
-that we only have two different values in our `Class` column (B for benign and M 
-for malignant), we only expect to get two names back.  Note that the `levels` function requires a *vector* argument; 
-so we use the `pull` function to extract a single column (`Class`) and 
-pass that into the `levels` function to see the categories 
-in the `Class` column. 
+Let's verify that we have successfully converted the `Class` column to a factor variable
+and renamed its values to "Benign" and "Malignant" using the `distinct` function once more.
 
-```{r 05-levels}
+```{r 05-distinct2}
 cancer |>
-  pull(Class) |>
-  levels()
+  distinct(Class)
 ```
 
 ### Exploring the cancer data
@@ -238,8 +242,6 @@ perimeter and concavity variables. Rather than use `ggplot's` default palette,
 we select our own colorblind-friendly colors&mdash;`"orange2"` 
 for light orange and `"steelblue2"` for light blue&mdash;and
  pass them as the `values` argument to the `scale_color_manual` function. 
-We also make the category labels ("B" and "M") more readable by 
-changing them to "Benign" and "Malignant" using the `labels` argument.
 
 ```{r 05-scatter, fig.height = 3.5, fig.width = 4.5, fig.cap= "Scatter plot of concavity versus perimeter colored by diagnosis label."}
 perim_concav <- cancer |>
@@ -248,8 +250,7 @@ perim_concav <- cancer |>
   labs(x = "Perimeter (standardized)", 
        y = "Concavity (standardized)",
        color = "Diagnosis") +
-  scale_color_manual(labels = c("Malignant", "Benign"), 
-                     values = c("orange2", "steelblue2")) +
+  scale_color_manual(values = c("orange2", "steelblue2")) +
   theme(text = element_text(size = 12))
 perim_concav
 ```
@@ -333,13 +334,10 @@ perim_concav_with_new_point <-  bind_rows(cancer,
   labs(color = "Diagnosis", x = "Perimeter (standardized)", 
        y = "Concavity (standardized)") +
   scale_color_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"), 
                      values = c("steelblue2", "orange2", "red")) +
   scale_shape_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(16, 16, 18))+ 
   scale_size_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(2, 2, 2.5))
 perim_concav_with_new_point
 ```
@@ -391,13 +389,10 @@ perim_concav_with_new_point2 <- bind_rows(cancer,
        x = "Perimeter (standardized)", 
        y = "Concavity (standardized)") +
  scale_color_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"), 
                      values = c("steelblue2", "orange2", "red")) +
   scale_shape_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(16, 16, 18))+ 
   scale_size_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(2, 2, 2.5))
 perim_concav_with_new_point2 +  
   geom_segment(aes(
@@ -488,13 +483,10 @@ perim_concav <- bind_rows(cancer,
                      breaks = seq(-2, 4, 1)) +
   labs(color = "Diagnosis") + 
   scale_color_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"), 
                      values = c("steelblue2", "orange2", "red")) +
   scale_shape_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(16, 16, 18))+ 
   scale_size_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(2, 2, 2.5))
 
 perim_concav
@@ -545,7 +537,7 @@ kable(math_table, booktabs = TRUE,
 ```
 
 The result of this computation shows that 3 of the 5 nearest neighbors to our new observation are
-malignant (`M`); since this is the majority, we classify our new observation as malignant. 
+malignant; since this is the majority, we classify our new observation as malignant. 
 These 5 neighbors are circled in Figure \@ref(fig:05-multiknn-3).
 
 ```{r 05-multiknn-3, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap="Scatter plot of concavity versus perimeter with 5 nearest neighbors circled."}
@@ -602,7 +594,8 @@ cancer |>
   slice(1:5) # take the first 5 rows
 ```
 
-Based on $K=5$ nearest neighbors with these three predictors we would classify the new observation as malignant since 4 out of 5 of the nearest neighbors are malignant class. 
+Based on $K=5$ nearest neighbors with these three predictors, we would classify 
+the new observation as malignant since 4 out of 5 of the nearest neighbors are from the malignant class. 
 Figure \@ref(fig:05-more) shows what the data look like when we visualize them 
 as a 3-dimensional scatter with lines from the new observation to its five nearest neighbors.
 
@@ -621,8 +614,7 @@ neighbors_3 <- cancer[order(my_distances_3$Distance), ]
 data <- neighbors_3 |> select(Perimeter, Concavity, Symmetry) |> slice(1:5)
 
 # add to the df
-scaled_cancer_3 <- bind_rows(cancer, new_obs_3) |> 
-  mutate(Class = fct_recode(Class, "Benign" = "B", "Malignant"= "M"))
+scaled_cancer_3 <- bind_rows(cancer, new_obs_3)
 
 plot_3d <- scaled_cancer_3 |>
   plot_ly() |>
@@ -637,7 +629,7 @@ plot_3d <- scaled_cancer_3 |>
             color = ~Class,
             opacity = 0.4,
             size = 2,
-            colors = c("orange2", "steelblue2", "red"), 
+            colors = c("steelblue2", "orange2", "red"), 
             symbol = ~Class, symbols = c('circle','circle','diamond'))
 
 x1 <- c(pull(new_obs_3[1]), data$Perimeter[1])
@@ -662,15 +654,15 @@ z5 <- c(pull(new_obs_3[3]), data$Symmetry[5])
 
 plot_3d <- plot_3d  |>
   add_trace(x = x1, y = y1, z = z1, type = "scatter3d", mode = "lines", 
-            name = "lines", showlegend = FALSE, color = I("steelblue2")) |>
+            name = "lines", showlegend = FALSE, color = I("orange2")) |>
   add_trace(x = x2, y = y2, z = z2, type = "scatter3d", mode = "lines", 
-            name = "lines", showlegend = FALSE, color =  I("steelblue2")) |>
+            name = "lines", showlegend = FALSE, color =  I("orange2")) |>
   add_trace(x = x3, y = y3, z = z3, type = "scatter3d", mode = "lines", 
-            name = "lines", showlegend = FALSE, color =  I("steelblue2")) |>
-  add_trace(x = x4, y = y4, z = z4, type = "scatter3d", mode = "lines", 
             name = "lines", showlegend = FALSE, color =  I("orange2")) |>
+  add_trace(x = x4, y = y4, z = z4, type = "scatter3d", mode = "lines", 
+            name = "lines", showlegend = FALSE, color =  I("steelblue2")) |>
   add_trace(x = x5, y = y5, z = z5, type = "scatter3d", mode = "lines", 
-            name = "lines", showlegend = FALSE, color =  I("steelblue2"))
+            name = "lines", showlegend = FALSE, color =  I("orange2"))
 
 if(!is_latex_output()){  
   plot_3d
@@ -786,7 +778,7 @@ Finally, we make the prediction on the new observation by calling the `predict`
 passing both the fit object we just created and the new observation itself. As above, 
 when we ran the $K$-nearest neighbors
 classification algorithm manually, the `knn_fit` object classifies the new observation as 
-malignant ("M"). Note that the `predict` function outputs a data frame with a single 
+malignant. Note that the `predict` function outputs a data frame with a single 
 variable named `.pred_class`.
 
 ```{r 05-predict}
@@ -837,12 +829,15 @@ is said to be *standardized*, \index{standardization!K-nearest neighbors} and al
 and a standard deviation of 1. To illustrate the effect that standardization can have on the $K$-nearest
 neighbor algorithm, we will read in the original, unstandardized Wisconsin breast
 cancer data set; we have been using a standardized version of the data set up
-until now. To keep things simple, we will just use the `Area`, `Smoothness`, and `Class`
+until now. As before, we will convert the `Class` variable to the factor type
+and rename the values to "Malignant" and "Benign." 
+To keep things simple, we will just use the `Area`, `Smoothness`, and `Class`
 variables:
 
 ```{r 05-scaling-1, message = FALSE}
 unscaled_cancer <- read_csv("data/unscaled_wdbc.csv") |>
   mutate(Class = as_factor(Class)) |>
+  mutate(Class = fct_recode(Class, "Benign" = "B", "Malignant" = "M")) |>
   select(Class, Area, Smoothness)
 unscaled_cancer
 ```
@@ -972,13 +967,10 @@ unscaled <- ggplot(unscaled_cancer, aes(x = Area,
                                         shape = Class, size = Class)) +
   geom_point(alpha = 0.6) + 
   scale_color_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"), 
                      values = c("steelblue2", "orange2", "red")) +
   scale_shape_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(16, 16, 18)) +
     scale_size_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values=c(2,2,2.5)) + 
   ggtitle("Unstandardized Data") +
   geom_segment(aes(
@@ -1015,13 +1007,10 @@ scaled <- ggplot(scaled_cancer, aes(x = Area,
                                     size = Class)) +
   geom_point(alpha = 0.6) + 
   scale_color_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"), 
                      values = c("steelblue2", "orange2", "red")) +
   scale_shape_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(16, 16, 18)) +
   scale_size_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                     values=c(2,2,2.5)) + 
   ggtitle("Standardized Data") +
   labs(x = "Area (standardized)", y = "Smoothness (standardized)") + 
@@ -1055,13 +1044,10 @@ ggplot(unscaled_cancer, aes(x = Area,
                             shape = Class)) +
   geom_point(size = 2.5, alpha = 0.6) + 
   scale_color_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"), 
                      values = c("steelblue2", "orange2", "red")) +
   scale_shape_manual(name = "Diagnosis", 
-                   labels = c("Benign", "Malignant", "Unknown"),
                      values= c(16, 16, 18)) +
     scale_size_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values = c(1, 1, 2.5)) + 
   ggtitle("Unstandardized Data") +
   geom_segment(aes(
@@ -1119,8 +1105,8 @@ set.seed(3)
 
 ```{r 05-unbalanced, fig.height = 3.5, fig.width = 4.5, fig.pos = "H", out.extra="", fig.cap = "Imbalanced data."}
 rare_cancer <- bind_rows(
-      filter(cancer, Class == "B"),
-      cancer |> filter(Class == "M") |> slice_head(n = 3)
+      filter(cancer, Class == "Benign"),
+      cancer |> filter(Class == "Malignant") |> slice_head(n = 3)
     ) |>
     select(Class, Perimeter, Concavity)
 
@@ -1130,8 +1116,7 @@ rare_plot <- rare_cancer |>
   labs(x = "Perimeter (standardized)", 
        y = "Concavity (standardized)",
        color = "Diagnosis") +
-  scale_color_manual(labels = c("Malignant", "Benign"), 
-                     values = c("orange2", "steelblue2")) +
+  scale_color_manual(values = c("orange2", "steelblue2")) +
   theme(text = element_text(size = 12))
 
 rare_plot
@@ -1164,18 +1149,15 @@ rare_plot <- bind_rows(rare_cancer,
        x = "Perimeter (standardized)", 
        y = "Concavity (standardized)") + 
   scale_color_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"), 
                      values = c("steelblue2", "orange2", "red")) +
   scale_shape_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(16, 16, 18))+ 
   scale_size_manual(name = "Diagnosis", 
-                     labels = c("Benign", "Malignant", "Unknown"),
                      values= c(2, 2, 2.5))
 
 for (i in 1:7) {
   clr <- "steelblue2"
-  if (neighbors$Class[i] == "M") {
+  if (neighbors$Class[i] == "Malignant") {
     clr <- "orange2"
   }
   rare_plot <- rare_plot +
@@ -1236,8 +1218,7 @@ rare_plot <-
   labs(color = "Diagnosis", 
        x = "Perimeter (standardized)", 
        y = "Concavity (standardized)") +
-  scale_color_manual(labels = c("Malignant", "Benign"), 
-                     values = c("orange2", "steelblue2"))
+  scale_color_manual(values = c("orange2", "steelblue2"))
 
 rare_plot
 ```
@@ -1308,8 +1289,7 @@ upsampled_plot <-
   labs(color = "Diagnosis", 
        x = "Perimeter (standardized)", 
        y = "Concavity (standardized)") +
-  scale_color_manual(labels = c("Malignant", "Benign"), 
-                     values = c("orange2", "steelblue2"))
+  scale_color_manual(values = c("orange2", "steelblue2"))
 
 upsampled_plot
 ```
@@ -1324,7 +1304,8 @@ First we will load the data, create a model, and specify a recipe for how the da
 # load the unscaled cancer data 
 # and make sure the response variable, Class, is a factor
 unscaled_cancer <- read_csv("data/unscaled_wdbc.csv") |>
-  mutate(Class = as_factor(Class))
+  mutate(Class = as_factor(Class)) |>
+  mutate(Class = fct_recode(Class, "Malignant" = "M", "Benign" = "B"))
 
 # create the KNN model
 knn_spec <- nearest_neighbor(weight_func = "rectangular", neighbors = 7) |>
@@ -1431,8 +1412,7 @@ wkflw_plot <-
   labs(color = "Diagnosis", 
        x = "Area", 
        y = "Smoothness") +
-  scale_color_manual(labels = c("Malignant", "Benign"), 
-                     values = c("orange2", "steelblue2")) +
+  scale_color_manual(values = c("orange2", "steelblue2")) +
   theme(text = element_text(size = 12))
 
 wkflw_plot

source/classification2.Rmd

@@ -234,16 +234,17 @@ set.seed(1)
 # load data
 cancer <- read_csv("data/unscaled_wdbc.csv") |>
   # convert the character Class variable to the factor datatype
-  mutate(Class = as_factor(Class)) 
+  mutate(Class = as_factor(Class))  |>
+  # rename the factor values to be more readable
+  mutate(Class = fct_recode(Class, "Malignant" = "M", "Benign" = "B"))
 
 # create scatter plot of tumor cell concavity versus smoothness,
 # labeling the points be diagnosis class
 perim_concav <- cancer |>
   ggplot(aes(x = Smoothness, y = Concavity, color = Class)) +
   geom_point(alpha = 0.5) +
   labs(color = "Diagnosis") +
-  scale_color_manual(labels = c("Malignant", "Benign"), 
-                     values = c("orange2", "steelblue2")) + 
+  scale_color_manual(values = c("orange2", "steelblue2")) + 
   theme(text = element_text(size = 12))
 
 perim_concav
@@ -268,7 +269,7 @@ in the data does not influence the data that ends up in the training and testing
 Second, it **stratifies** the \index{stratification} data by the class label, to ensure that roughly
 the same proportion of each class ends up in both the training and testing sets. For example,
 in our data set, roughly 63% of the
-observations are from the benign class (`B`), and 37% are from the malignant class (`M`),
+observations are from the benign class, and 37% are from the malignant class,
 so `initial_split` ensures that roughly 63% of the training data are benign, 
 37% of the training data are malignant,
 and the same proportions exist in the testing data.
@@ -958,8 +959,7 @@ for (i in 1:length(ks)) {
                size = 5.) +
     labs(color = "Diagnosis") +
     ggtitle(paste("K = ", ks[[i]])) +
-    scale_color_manual(labels = c("Malignant", "Benign"), 
-                       values = c("orange2", "steelblue2"))  +
+    scale_color_manual(values = c("orange2", "steelblue2"))  +
   theme(text = element_text(size = 18), axis.title=element_text(size=18)) 
   }