Graphic design updates

source/classification2.Rmd

@@ -94,8 +94,8 @@ labels for new observations without known class labels.
 > is. Imagine how bad it would be to overestimate your classifier's accuracy
 > when predicting whether a patient's tumor is malignant or benign!
 
-```{r 06-training-test, echo = FALSE, warning = FALSE, fig.cap = "Splitting the data into training and testing sets.", fig.retina = 2, out.width = "100%"}
-knitr::include_graphics("img/classification2/training_test.jpeg")
+```{r 06-training-test, echo = FALSE, warning = FALSE, fig.align = "center", fig.cap = "Splitting the data into training and testing sets.", fig.retina = 2, out.width = "100%"}
+knitr::include_graphics("img/classification2/training_test.png")
 ```
 
 How exactly can we assess how well our predictions match the actual labels for
@@ -108,7 +108,7 @@ test set is illustrated in Figure \@ref(fig:06-ML-paradigm-test).
 
 $$\mathrm{accuracy} = \frac{\mathrm{number \; of  \; correct  \; predictions}}{\mathrm{total \;  number \;  of  \; predictions}}$$
 
-```{r 06-ML-paradigm-test, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "Process for splitting the data and finding the prediction accuracy.", fig.retina = 2, out.width = "100%"}
+```{r 06-ML-paradigm-test, echo = FALSE, message = FALSE, warning = FALSE, fig.align = "center", fig.cap = "Process for splitting the data and finding the prediction accuracy.", fig.retina = 2, out.width = "100%"}
 knitr::include_graphics("img/classification2/ML-paradigm-test.png")
 ```
 
@@ -322,7 +322,7 @@ tumor cell concavity versus smoothness colored by diagnosis in Figure \@ref(fig:
 You will also notice that we set the random seed here at the beginning of the analysis
 using the `set.seed` function, as described in Section \@ref(randomseeds).
 
-```{r 06-precode, fig.height = 3.5, fig.width = 4.5, fig.cap="Scatter plot of tumor cell concavity versus smoothness colored by diagnosis label.", message = F, warning = F}
+```{r 06-precode, fig.height = 3.5, fig.width = 4.5, fig.align = "center", fig.cap="Scatter plot of tumor cell concavity versus smoothness colored by diagnosis label.", message = F, warning = F}
 # load packages
 library(tidyverse)
 library(tidymodels)
@@ -343,7 +343,7 @@ perim_concav <- cancer |>
   ggplot(aes(x = Smoothness, y = Concavity, color = Class)) +
   geom_point(alpha = 0.5) +
   labs(color = "Diagnosis") +
-  scale_color_manual(values = c("orange2", "steelblue2")) +
+  scale_color_manual(values = c("darkorange", "steelblue")) +
   theme(text = element_text(size = 12))
 
 perim_concav
@@ -793,7 +793,7 @@ Here, $C=5$ different chunks of the data set are used,
 resulting in 5 different choices for the **validation set**; we call this
 *5-fold* cross-validation.
 
-```{r 06-cv-image, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "5-fold cross-validation.", fig.pos = "H", out.extra="", fig.retina = 2, out.width = "100%"}
+```{r 06-cv-image, echo = FALSE, message = FALSE, warning = FALSE, fig.align = "center", fig.cap = "5-fold cross-validation.", fig.pos = "H", out.extra="", fig.retina = 2, out.width = "100%"}
 knitr::include_graphics("img/classification2/cv.png")
 ```
 
@@ -989,7 +989,7 @@ accuracies
 We can decide which number of neighbors is best by plotting the accuracy versus $K$,
 as shown in Figure \@ref(fig:06-find-k).
 
-```{r 06-find-k,  fig.height = 3.5, fig.width = 4, fig.cap= "Plot of estimated accuracy versus the number of neighbors."}
+```{r 06-find-k,  fig.height = 3.5, fig.width = 4, fig.align = "center", fig.cap= "Plot of estimated accuracy versus the number of neighbors."}
 accuracy_vs_k <- ggplot(accuracies, aes(x = neighbors, y = mean)) +
   geom_point() +
   geom_line() +
@@ -1049,7 +1049,7 @@ we vary $K$ from 1 to almost the number of observations in the training set.
 set.seed(1)
 ```
 
-```{r 06-lots-of-ks, message = FALSE, fig.height = 3.5, fig.width = 4, fig.cap="Plot of accuracy estimate versus number of neighbors for many K values."}
+```{r 06-lots-of-ks, message = FALSE, fig.height = 3.5, fig.width = 4, fig.align = "center", fig.cap="Plot of accuracy estimate versus number of neighbors for many K values."}
 k_lots <- tibble(neighbors = seq(from = 1, to = 385, by = 10))
 
 knn_results <- workflow() |>
@@ -1093,7 +1093,7 @@ new data: if we had a different training set, the predictions would be
 completely different.  In general, if the model *is influenced too much* by the
 training data, it is said to **overfit** the data.
 
-```{r 06-decision-grid-K, echo = FALSE, message = FALSE, fig.height = 10, fig.width = 10, fig.pos = "H", out.extra="", fig.cap = "Effect of K in overfitting and underfitting."}
+```{r 06-decision-grid-K, echo = FALSE, message = FALSE, fig.height = 10, fig.width = 10, fig.pos = "H", out.extra="", fig.align = "center", fig.cap = "Effect of K in overfitting and underfitting."}
 ks <- c(1, 7, 20, 300)
 plots <- list()
 
@@ -1137,7 +1137,7 @@ for (i in 1:length(ks)) {
                size = 5.) +
     labs(color = "Diagnosis") +
     ggtitle(paste("K = ", ks[[i]])) +
-    scale_color_manual(values = c("orange2", "steelblue2"))  +
+    scale_color_manual(values = c("darkorange", "steelblue"))  +
   theme(text = element_text(size = 18), axis.title=element_text(size=18))
   }
 
@@ -1256,8 +1256,8 @@ by maximizing estimated accuracy via cross-validation. After we have tuned the
 model we can use the test set to estimate its accuracy.
 The overall process is summarized in Figure \@ref(fig:06-overview).
 
-```{r 06-overview, echo = FALSE, message = FALSE, warning = FALSE, fig.pos = "H", out.extra="", fig.cap = "Overview of K-NN classification.", fig.retina = 2, out.width = "100%"}
-knitr::include_graphics("img/classification2/train-test-overview.jpeg")
+```{r 06-overview, echo = FALSE, message = FALSE, warning = FALSE, fig.pos = "H", out.extra="", fig.align = "center", fig.cap = "Overview of K-NN classification.", fig.retina = 2, out.width = "100%"}
+knitr::include_graphics("img/classification2/train-test-overview.png")
 ```
 
 The overall workflow for performing K-nearest neighbors classification using `tidymodels` is as follows:
@@ -1344,7 +1344,7 @@ variables there are, the more (random) influence they have, and the more they
 corrupt the set of nearest neighbors that vote on the class of the new
 observation to predict.
 
-```{r 06-performance-irrelevant-features, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "65%", fig.cap = "Effect of inclusion of irrelevant predictors."}
+```{r 06-performance-irrelevant-features, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "65%", fig.align = "center", fig.cap = "Effect of inclusion of irrelevant predictors."}
 # get accuracies after including k irrelevant features
 ks <- c(0, 5, 10, 15, 20, 40)
 fixedaccs <- list()
@@ -1418,7 +1418,7 @@ variables, the number of neighbors does not increase smoothly; but the general t
 Figure \@ref(fig:06-fixed-irrelevant-features) corroborates
 this evidence; if we fix the number of neighbors to $K=3$, the accuracy falls off more quickly.
 
-```{r 06-neighbors-irrelevant-features, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "65%", fig.cap = "Tuned number of neighbors for varying number of irrelevant predictors."}
+```{r 06-neighbors-irrelevant-features, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "65%", fig.align = "center", fig.cap = "Tuned number of neighbors for varying number of irrelevant predictors."}
 plt_irrelevant_nghbrs <- ggplot(res) +
               geom_line(mapping = aes(x=ks, y=nghbrs)) +
               labs(x = "Number of Irrelevant Predictors",
@@ -1428,15 +1428,15 @@ plt_irrelevant_nghbrs <- ggplot(res) +
 plt_irrelevant_nghbrs
 ```
 
-```{r 06-fixed-irrelevant-features, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "75%", fig.cap = "Accuracy versus number of irrelevant predictors for tuned and untuned number of neighbors."}
+```{r 06-fixed-irrelevant-features, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "75%", fig.align = "center", fig.cap = "Accuracy versus number of irrelevant predictors for tuned and untuned number of neighbors."}
 res_tmp <- res %>% pivot_longer(cols=c("accs", "fixedaccs"),
                                 names_to="Type",
                                 values_to="accuracy")
 
 plt_irrelevant_nghbrs <- ggplot(res_tmp) +
               geom_line(mapping = aes(x=ks, y=accuracy, color=Type)) +
               labs(x = "Number of Irrelevant Predictors", y = "Accuracy") +
-              scale_color_discrete(labels= c("Tuned K", "K = 3")) +
+              scale_color_manual(labels= c("Tuned K", "K = 3"), values = c("darkorange", "steelblue")) +
   theme(text = element_text(size = 17), axis.title=element_text(size=17))
 
 plt_irrelevant_nghbrs
@@ -1657,7 +1657,7 @@ predictors from the model! It is always worth remembering, however, that what cr
 is an *estimate* of the true accuracy; you have to use your judgement when looking at this plot to decide
 where the elbow occurs, and whether adding a variable provides a meaningful increase in accuracy.
 
-```{r 06-fwdsel-3, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "65%", fig.cap = "Estimated accuracy versus the number of predictors for the sequence of models built using forward selection.", fig.pos = "H"}
+```{r 06-fwdsel-3, echo = FALSE, warning = FALSE, fig.retina = 2, out.width = "65%", fig.align = "center", fig.cap = "Estimated accuracy versus the number of predictors for the sequence of models built using forward selection.", fig.pos = "H"}
 
 fwd_sel_accuracies_plot <- accuracies |>
   ggplot(aes(x = size, y = accuracy)) +

source/clustering.Rmd

@@ -44,7 +44,7 @@ hidden_print_cli <- function(x){
 # set the colors in the graphs,
 # some graphs with the code shown to students are hard coded
 cbbPalette <- c(brewer.pal(9, "Paired"))
-cbpalette <- c("darkorange3", "dodgerblue3", "goldenrod1")
+cbpalette <- c("darkorange", "steelblue", "goldenrod1")
 
 theme_update(axis.title = element_text(size = 12)) # modify axis label size in plots
 ```
@@ -143,7 +143,7 @@ Understanding this might help us with species discovery and classification in a
 way. Note that we have reduced the size of the data set to 18 observations and 2 variables;
 this will help us make clear visualizations that illustrate how clustering works for learning purposes.
 
-```{r 09-penguins, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "A Gentoo penguin.", out.width="60%", fig.align = "center", fig.retina = 2}
+```{r 09-penguins, echo = FALSE, message = FALSE, warning = FALSE, fig.align = "center", fig.cap = "A Gentoo penguin.", out.width="60%", fig.align = "center", fig.retina = 2}
 # image source: https://commons.wikimedia.org/wiki/File:Gentoo_Penguin._(8671680772).jpg
 knitr::include_graphics("img/clustering/gentoo.jpg")
 ```
@@ -258,7 +258,7 @@ ggplot(penguins_clustered, aes(y = bill_length_standardized,
   geom_point() +
   xlab("Flipper Length (standardized)") +
   ylab("Bill Length (standardized)") +
-  scale_color_manual(values= c("darkorange3", "dodgerblue3", "goldenrod1"))
+  scale_color_manual(values= c("darkorange", "steelblue", "goldenrod1"))
 ```
 
 What are the labels for these groups? Unfortunately, we don't have any. K-means,
@@ -308,7 +308,7 @@ In the first cluster from the example, there are `r nrow(clus1)` data points. Th
 (standardized flipper length `r round(mean(clus1$flipper_length_standardized),2)`, standardized bill length `r round(mean(clus1$bill_length_standardized),2)`) highlighted
 in Figure \@ref(fig:10-toy-example-clus1-center).
 
-(ref:10-toy-example-clus1-center) Cluster 1 from the `penguins_standardized` data set example. Observations are in blue, with the cluster center highlighted in red.
+(ref:10-toy-example-clus1-center) Cluster 1 from the `penguins_standardized` data set example. Observations are small blue points, with the cluster center highlighted as a large blue point with a black outline.
 
 ```{r 10-toy-example-clus1-center, echo = FALSE, warning = FALSE, fig.height = 3.25, fig.width = 3.5, fig.align = "center", fig.cap = "(ref:10-toy-example-clus1-center)"}
 base <- ggplot(penguins_clustered, aes(x = flipper_length_standardized, y = bill_length_standardized)) +
@@ -318,7 +318,7 @@ base <- ggplot(penguins_clustered, aes(x = flipper_length_standardized, y = bill
 
 base <- ggplot(clus1) +
   geom_point(aes(y = bill_length_standardized, x = flipper_length_standardized),
-  col = "dodgerblue3") +
+  col = "steelblue") +
   labs(x = "Flipper Length (standardized)", y = "Bill Length (standardized)") +
   xlim(c(
     min(clus1$flipper_length_standardized) - 0.25 *
@@ -334,8 +334,11 @@ base <- ggplot(clus1) +
   )) +
   geom_point(aes(y = mean(bill_length_standardized),
                  x = mean(flipper_length_standardized)),
-             color = "#F8766D",
-             size = 5) +
+               size = 4,
+               shape = 21,
+               stroke = 1,
+               color = "black",
+               fill = "steelblue")+
   theme(legend.position = "none")
 
 base
@@ -354,7 +357,7 @@ S^2 = \left((x_1 - \mu_x)^2 + (y_1 - \mu_y)^2\right) + \left((x_2 - \mu_x)^2 + (
 
 These distances are denoted by lines in Figure \@ref(fig:10-toy-example-clus1-dists) for the first cluster of the penguin data example.
 
-(ref:10-toy-example-clus1-dists) Cluster 1 from the `penguins_standardized` data set example. Observations are in blue, with the cluster center highlighted in red. The distances from the observations to the cluster center are represented as black lines.
+(ref:10-toy-example-clus1-dists) Cluster 1 from the `penguins_standardized` data set example. Observations are small blue points, with the cluster center highlighted as a large blue point with a black outline. The distances from the observations to the cluster center are represented as black lines.
 
 ```{r 10-toy-example-clus1-dists, echo = FALSE, warning = FALSE, fig.height = 3.25, fig.width = 3.5, fig.align = "center", fig.cap = "(ref:10-toy-example-clus1-dists)"}
 base <- ggplot(clus1)
@@ -373,13 +376,16 @@ for (i in 1:nrow(clus1)) {
 base <- base +
   geom_point(aes(y = mean(bill_length_standardized),
                  x = mean(flipper_length_standardized)),
-             color = "#F8766D",
-             size = 5)
+               size = 4,
+               shape = 21,
+               stroke = 1,
+               color = "black",
+               fill = "steelblue")
 
 base <- base +
   geom_point(aes(y = bill_length_standardized,
                  x = flipper_length_standardized),
-             col = "dodgerblue3") +
+             col = "steelblue") +
   labs(x = "Flipper Length (standardized)", y = "Bill Length (standardized)") +
   theme(legend.position = "none")
 
@@ -397,7 +403,7 @@ this means adding up all the squared distances for the 18 observations.
 These distances are denoted by black lines in
 Figure \@ref(fig:10-toy-example-all-clus-dists).
 
-(ref:10-toy-example-all-clus-dists) All clusters from the `penguins_standardized` data set example. Observations are in orange, blue, and yellow with the cluster center highlighted in red. The distances from the observations to each of the respective cluster centers are represented as black lines.
+(ref:10-toy-example-all-clus-dists) All clusters from the `penguins_standardized` data set example. Observations are small orange, blue, and yellow points with cluster centers denoted by larger points with a black outline. The distances from the observations to each of the respective cluster centers are represented as black lines.
 
 ```{r 10-toy-example-all-clus-dists, echo = FALSE, warning = FALSE, fig.height = 3.25, fig.width = 4.25, fig.align = "center", fig.cap = "(ref:10-toy-example-all-clus-dists)"}
 all_clusters_base <- ggplot(penguins_clustered)
@@ -434,20 +440,20 @@ all_clusters_base <- all_clusters_base +
              color = cluster)) +
   xlab("Flipper Length (standardized)") +
   ylab("Bill Length (standardized)") +
-  scale_color_manual(values= c("darkorange3",
-                               "dodgerblue3",
+  scale_color_manual(values= c("darkorange",
+                               "steelblue",
                                "goldenrod1"))
 
 all_clusters_base <- all_clusters_base +
   geom_point(aes(y = cluster_centers$y[1],
                  x = cluster_centers$x[1]),
-             color = "#F8766D", size = 3) +
+             color = "black", fill = "darkorange", size = 4, stroke = 1, shape = 21) +
   geom_point(aes(y = cluster_centers$y[2],
                  x = cluster_centers$x[2]),
-             color = "#F8766D", size = 3) +
+             color = "black", fill = "steelblue", size = 4, stroke = 1, shape = 21) +
   geom_point(aes(y = cluster_centers$y[3],
                  x = cluster_centers$x[3]),
-             color = "#F8766D", size = 3)
+             color = "black", fill = "goldenrod1", size = 4, stroke = 1, shape = 21)
 
 all_clusters_base
 ```
@@ -821,7 +827,7 @@ Figure \@ref(fig:10-toy-kmeans-vary-k) illustrates the impact of K
 on K-means clustering of our penguin flipper and bill length data
 by showing the different clusterings for K's ranging from 1 to 9.
 
-```{r 10-toy-kmeans-vary-k, echo = FALSE, warning = FALSE, fig.height = 6.25, fig.width = 6, fig.pos = "H", out.extra="", fig.cap = "Clustering of the penguin data for K clusters ranging from 1 to 9. Cluster centers are indicated by larger points that are outlined in black."}
+```{r 10-toy-kmeans-vary-k, echo = FALSE, warning = FALSE, fig.height = 6.25, fig.width = 6, fig.pos = "H", out.extra="", fig.align = "center", fig.cap = "Clustering of the penguin data for K clusters ranging from 1 to 9. Cluster centers are indicated by larger points that are outlined in black."}
 set.seed(3)
 
 kclusts <- tibble(k = 1:9) |>
@@ -885,7 +891,7 @@ decrease the total WSSD, but by only a *diminishing amount*. If we plot the tota
 clusters, we see that the decrease in total WSSD levels off (or forms an "elbow shape") \index{elbow method} when we reach roughly
 the right number of clusters (Figure \@ref(fig:10-toy-kmeans-elbow)).
 
-```{r 10-toy-kmeans-elbow, echo = FALSE, warning = FALSE, fig.align = 'center', fig.height = 3.25, fig.width = 4.25, fig.pos = "H", out.extra="", fig.cap = "Total WSSD for K clusters ranging from 1 to 9."}
+```{r 10-toy-kmeans-elbow, echo = FALSE, warning = FALSE, fig.align = "center", fig.height = 3.25, fig.width = 4.25, fig.pos = "H", out.extra="", fig.cap = "Total WSSD for K clusters ranging from 1 to 9."}
 p2 <- ggplot(clusterings, aes(x = k, y = tot.withinss)) +
   geom_point(size = 2) +
   geom_line() +
@@ -894,7 +900,7 @@ p2 <- ggplot(clusterings, aes(x = k, y = tot.withinss)) +
                    xend = 3.1,
                    yend = 6),
                arrow = arrow(length = unit(0.2, "cm"))) +
-  annotate("text", x = 4.4, y = 19, label = "Elbow", size = 7, color = "blue") +
+  annotate("text", x = 4.4, y = 19, label = "Elbow", size = 5) +
   labs(x = "Number of Clusters", y = "Total WSSD") +
   #theme(text = element_text(size = 20)) +
   scale_x_continuous(breaks = 1:9)
@@ -1009,8 +1015,8 @@ cluster_plot <- ggplot(clustered_data,
   labs(x = "Flipper Length",
        y = "Bill Length",
        color = "Cluster") +
-  scale_color_manual(values = c("dodgerblue3",
-                                "darkorange3",
+  scale_color_manual(values = c("steelblue",
+                                "darkorange",
                                 "goldenrod1")) +
   theme(text = element_text(size = 12))