fixing figure sizes in classification1

Author: leem44

classification1.Rmd

@@ -210,7 +210,7 @@ for light orange and `"steelblue2"` for light blue—and
 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 = 4, fig.width = 5, fig.cap= "Scatter plot of concavity versus perimeter colored by diagnosis label."}
+```{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 %>%
   ggplot(aes(x = Perimeter, y = Concavity, color = Class)) +
   geom_point(alpha = 0.6) +
@@ -286,7 +286,7 @@ new observation, with standardized perimeter of `r new_point[1]` and standardize
 diagnosis "Class" is unknown. This new observation is depicted by the red, diamond point in
 Figure \@ref(fig:05-knn-1).
 
-```{r 05-knn-1, echo = FALSE, fig.height = 4, fig.width = 5, fig.cap="Scatter plot of concavity versus perimeter with new observation represented as a red diamond."}
+```{r 05-knn-1, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap="Scatter plot of concavity versus perimeter with new observation represented as a red diamond."}
 perim_concav_with_new_point <-  bind_rows(cancer, 
                                           tibble(Perimeter = new_point[1], 
                                                  Concavity = new_point[2], 
@@ -318,7 +318,7 @@ then the perimeter and concavity values are similar, and so we may expect that
 they would have the same diagnosis. 
 
 
-```{r 05-knn-2, echo = FALSE, fig.height = 4, fig.width = 5, fig.cap="Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a malignant label."}
+```{r 05-knn-2, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap="Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a malignant label."}
 perim_concav_with_new_point +
   geom_segment(aes(
     x = new_point[1],
@@ -343,7 +343,7 @@ Does this seem like the right prediction to make for this observation? Probably
 not, if you consider the other nearby points...
 
 
-```{r 05-knn-4, echo = FALSE, fig.height = 4, fig.width = 5, fig.cap="Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a benign label."}
+```{r 05-knn-4, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap="Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a benign label."}
 
 perim_concav_with_new_point2 <- bind_rows(cancer, 
                                           tibble(Perimeter = new_point[1], 
@@ -383,7 +383,7 @@ see that the diagnoses of 2 of the 3 nearest neighbors to our new observation
 are malignant. Therefore we take majority vote and classify our new red, diamond
 observation as malignant. 
 
-```{r 05-knn-5, echo =  FALSE, fig.height = 4, fig.width = 5, fig.cap="Scatter plot of concavity versus perimeter with three nearest neighbors."}
+```{r 05-knn-5, echo =  FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap="Scatter plot of concavity versus perimeter with three nearest neighbors."}
 perim_concav_with_new_point2 + 
   geom_segment(aes(
     x = new_point[1], y = new_point[2],
@@ -433,7 +433,7 @@ You will see in the `mutate` \index{mutate} step below, we compute the straight-
 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.
 
-```{r 05-multiknn-1, echo = FALSE, fig.height = 4, fig.width = 5, fig.cap="Scatter plot of concavity versus perimeter with new observation represented as a red diamond."}
+```{r 05-multiknn-1, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap="Scatter plot of concavity versus perimeter with new observation represented as a red diamond."}
 perim_concav <- bind_rows(cancer, 
                           tibble(Perimeter = new_point[1], 
                                  Concavity = new_point[2], 
@@ -515,7 +515,7 @@ The result of this computation shows that 3 of the 5 nearest neighbors to our ne
 malignant (`M`); 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 = 4, fig.width = 5, fig.cap="Scatter plot of concavity versus perimeter with 5 nearest neighbors circled."}
+```{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."}
 perim_concav + annotate("path",
   x = new_point[1] + 1.4 * cos(seq(0, 2 * pi,
     length.out = 100
@@ -999,7 +999,7 @@ ggarrange(unscaled, scaled, ncol = 2, common.legend = TRUE, legend = "bottom")
 
 ```
 
-```{r 05-scaling-plt-zoomed, fig.height = 4, fig.width = 10, echo = FALSE, fig.cap = "Close up of three nearest neighbors for unstandardized data."}
+```{r 05-scaling-plt-zoomed, fig.height = 5, fig.width = 10, echo = FALSE, fig.cap = "Close up of three nearest neighbors for unstandardized data."}
 library(ggforce)
 ggplot(unscaled_cancer, aes(x = Area, 
                             y = Smoothness, 
@@ -1031,11 +1031,10 @@ ggplot(unscaled_cancer, aes(x = Area,
     x = unlist(new_obs[1]), y = unlist(new_obs[2]),
     xend = unlist(neighbors[3, attrs[1]]),
     yend = unlist(neighbors[3, attrs[2]])
-  ), color = "black") +   theme_light() +  
-# facet_zoom( xlim = c(399.7, 401.6), ylim = c(0.08, 0.14), zoom.size = 2) + 
+  ), color = "black") +  
    facet_zoom(x = ( Area > 380 & Area < 420) , 
               y = (Smoothness > 0.08 & Smoothness < 0.14), zoom.size = 2) + 
-  theme_bw()
+  theme_bw() + theme(legend.position="bottom", text = element_text(size = 16))
 ```
 
 ### Balancing
@@ -1060,14 +1059,14 @@ function, which takes two arguments: a data frame-like object,
 and the number of rows to select from the top (`n`).
 The new imbalanced data is shown in Figure \@ref(fig:05-unbalanced).
 
-```{r 05-unbalanced-seed, echo = FALSE, fig.height = 4, fig.width = 5, warning = FALSE, message = FALSE}
+```{r 05-unbalanced-seed, echo = FALSE, fig.height = 3.5, fig.width = 4.5, warning = FALSE, message = FALSE}
 # hidden seed here for reproducibility 
 # randomness shouldn't affect much in this use of step_upsample,
 # but just in case...
 set.seed(3)
 ```
 
-```{r 05-unbalanced, fig.height = 4, fig.width = 5, fig.cap = "Imbalanced data."}
+```{r 05-unbalanced, fig.height = 3.5, fig.width = 4.5, fig.cap = "Imbalanced data."}
 rare_cancer <- bind_rows(
       filter(cancer, Class == "B"),
       cancer |> filter(Class == "M") |> slice_head(n = 3)
@@ -1095,7 +1094,7 @@ benign, and the benign vote will always win. For example, Figure \@ref(fig:05-up
 shows what happens for a new tumor observation that is quite close to three observations
 in the training data that were tagged as malignant.
 
-```{r 05-upsample, echo=FALSE, fig.height = 4, fig.width = 5, fig.cap = "Imbalanced data with 7 nearest neighbors to a new observation highlighted."}
+```{r 05-upsample, echo=FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap = "Imbalanced data with 7 nearest neighbors to a new observation highlighted."}
 new_point <- c(2, 2)
 attrs <- c("Perimeter", "Concavity")
 my_distances <- table_with_distances(rare_cancer[, attrs], new_point)
@@ -1147,7 +1146,7 @@ each area of the plot to the predictions the $K$-nearest neighbor
 classifier would make. We can see that the decision is 
 always "benign," corresponding to the blue color.
 
-```{r 05-upsample-2, echo = FALSE, fig.height = 4, fig.width = 5, fig.cap = "Imbalanced data with background color indicating the decision of the classifier and the points represent the labeled data."}
+```{r 05-upsample-2, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap = "Imbalanced data with background color indicating the decision of the classifier and the points represent the labeled data."}
 
 knn_spec <- nearest_neighbor(weight_func = "rectangular", neighbors = 7) |>
   set_engine("kknn") |>
@@ -1225,7 +1224,7 @@ classifier would make. We can see that the decision is more reasonable; when the
 to those labeled malignant, the classifier predicts a malignant tumor, and vice versa when they are 
 closer to the benign tumor observations.
 
-```{r 05-upsample-plot, echo = FALSE, fig.height = 4, fig.width = 5, fig.cap = "Upsampled data with background color indicating the decision of the classifier."}
+```{r 05-upsample-plot, echo = FALSE, fig.height = 3.5, fig.width = 4.5, fig.cap = "Upsampled data with background color indicating the decision of the classifier."}
 knn_spec <- nearest_neighbor(weight_func = "rectangular", neighbors = 7) |>
   set_engine("kknn") |>
   set_mode("classification")
@@ -1335,7 +1334,7 @@ predict the label of each, and visualize the predictions with a colored scatter
 > textbook. It is included for those readers who would like to use similar
 > visualizations in their own data analyses. 
 
-```{r 05-workflow-plot-show, fig.height = 4, fig.width = 5, fig.cap = "Scatter plot of smoothness versus area where background color indicates the decision of the classifier."}
+```{r 05-workflow-plot-show, fig.height = 3.5, fig.width = 4.6, fig.cap = "Scatter plot of smoothness versus area where background color indicates the decision of the classifier."}
 # create the grid of area/smoothness vals, and arrange in a data frame
 are_grid <- seq(min(unscaled_cancer$Area), 
                 max(unscaled_cancer$Area),