Added centering to figures where it was missing

Author: ttimbers

clustering.Rmd

@@ -164,7 +164,7 @@ penguin_data
 Next, we can create a scatter plot using this data set 
 to see if we can detect subtypes or groups in our data set.
 
-```{r 10-toy-example-plot, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.cap = "Scatter plot of standardized bill length versus standardized flipper length."}
+```{r 10-toy-example-plot, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.align = "center", fig.cap = "Scatter plot of standardized bill length versus standardized flipper length."}
 ggplot(data, aes(x = flipper_length_standardized, 
                  y = bill_length_standardized)) +
   geom_point() +
@@ -198,7 +198,7 @@ This procedure will separate the data into groups;
 Figure \@ref(fig:10-toy-example-clustering) shows these groups
 denoted by colored scatter points.
 
-```{r 10-toy-example-clustering, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 5, fig.cap = "Scatter plot of standardized bill length versus standardized flipper length with colored groups."}
+```{r 10-toy-example-clustering, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 5, fig.align = "center", fig.cap = "Scatter plot of standardized bill length versus standardized flipper length with colored groups."}
 ggplot(data, aes(y = bill_length_standardized, 
                  x = flipper_length_standardized, color = cluster)) +
   geom_point() +
@@ -256,7 +256,7 @@ in Figure \@ref(fig:10-toy-example-clus1-center).
 
 (ref:10-toy-example-clus1-center) Cluster 1 from the `penguin_data` data set example. Observations are in blue, with the cluster center highlighted in red.
 
-```{r 10-toy-example-clus1-center, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.cap = "(ref:10-toy-example-clus1-center)"}
+```{r 10-toy-example-clus1-center, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.align = "center", fig.cap = "(ref:10-toy-example-clus1-center)"}
 base <- ggplot(data, aes(x = flipper_length_standardized, y = bill_length_standardized)) +
   geom_point() +
   xlab("Flipper Length (standardized)") +
@@ -303,7 +303,7 @@ These distances are denoted by lines in Figure \@ref(fig:10-toy-example-clus1-di
 
 (ref:10-toy-example-clus1-dists) Cluster 1 from the `penguin_data` 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.
 
-```{r 10-toy-example-clus1-dists, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.cap = "(ref:10-toy-example-clus1-dists)"}
+```{r 10-toy-example-clus1-dists, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.align = "center", fig.cap = "(ref:10-toy-example-clus1-dists)"}
 base <- ggplot(clus1) +
   geom_point(aes(y = bill_length_standardized, 
                  x = flipper_length_standardized),
@@ -342,7 +342,7 @@ Figure \@ref(fig:10-toy-example-all-clus-dists).
 
 (ref:10-toy-example-all-clus-dists) All clusters from the `penguin_data` 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.
 
-```{r 10-toy-example-all-clus-dists, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 5, fig.cap = "(ref:10-toy-example-all-clus-dists)"}
+```{r 10-toy-example-all-clus-dists, echo = FALSE, warning = FALSE, fig.height = 4, fig.width = 5, fig.align = "center", fig.cap = "(ref:10-toy-example-all-clus-dists)"}
 
 
 all_clusters_base <- data |>
@@ -408,7 +408,7 @@ and randomly assigning a roughly equal number of observations
 to each of the K clusters.
 An example random initialization is shown in Figure \@ref(fig:10-toy-kmeans-init).
 
-```{r 10-toy-kmeans-init, echo = FALSE, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.cap = "Random initialization of labels."}
+```{r 10-toy-kmeans-init, echo = FALSE, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 4.35, fig.align = "center", fig.cap = "Random initialization of labels."}
 set.seed(14)
 penguin_data["label"] <- factor(sample(1:3, nrow(penguin_data), replace = TRUE))
 
@@ -439,7 +439,7 @@ and the right column depicts the reassignment of data to clusters.
 
 (ref:10-toy-kmeans-iter) First four iterations of K-means clustering on the `penguin_data` example data set. Each row corresponds to an iteration, where the left column depicts the center update, and the right column depicts the reassignment of data to clusters. Cluster centers are indicated by larger points that are outlined in black.
 
-```{r 10-toy-kmeans-iter, echo = FALSE, warning = FALSE, fig.height = 16, fig.width = 8, fig.cap = "(ref:10-toy-kmeans-iter)"}
+```{r 10-toy-kmeans-iter, echo = FALSE, warning = FALSE, fig.height = 16, fig.width = 8, fig.align = "center", fig.cap = "(ref:10-toy-kmeans-iter)"}
 list_plot_cntrs <- vector(mode = "list", length = 4)
 list_plot_lbls <- vector(mode = "list", length = 4)
 
@@ -546,7 +546,7 @@ These, however, are beyond the scope of this book.
 Unlike the classification and regression models we studied in previous chapters, K-means \index{K-means!restart,nstart} can get "stuck" in a bad solution.
 For example, Figure \@ref(fig:10-toy-kmeans-bad-init) illustrates an unlucky random initialization by K-means.
 
-```{r 10-toy-kmeans-bad-init, echo = FALSE, warning = FALSE, message = FALSE, fig.height = 4, fig.width = 4.35, fig.cap = "Random initialization of labels."}
+```{r 10-toy-kmeans-bad-init, echo = FALSE, warning = FALSE, message = FALSE, fig.height = 4, fig.width = 4.35, fig.align = "center", fig.cap = "Random initialization of labels."}
 penguin_data <- penguin_data |>
   mutate(label = as_factor(c(3L, 3L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 
                              1L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 3L)))
@@ -567,7 +567,7 @@ Figure \@ref(fig:10-toy-kmeans-bad-iter) shows what the iterations of K-means wo
 
 (ref:10-toy-kmeans-bad-iter) First five iterations of K-means clustering on the `penguin_data` example data set with a poor random initialization. Each row corresponds to an iteration, where the left column depicts the center update, and the right column depicts the reassignment of data to clusters. Cluster centers are indicated by larger points that are outlined in black.
 
-```{r 10-toy-kmeans-bad-iter, echo = FALSE, warning = FALSE, fig.height = 20, fig.width = 8, fig.cap = "(ref:10-toy-kmeans-bad-iter)"}
+```{r 10-toy-kmeans-bad-iter, echo = FALSE, warning = FALSE, fig.height = 20, fig.width = 8, fig.align = "center", fig.cap = "(ref:10-toy-kmeans-bad-iter)"}
 list_plot_cntrs <- vector(mode = "list", length = 5)
 list_plot_lbls <- vector(mode = "list", length = 5)
 
@@ -959,7 +959,7 @@ but there is a trade-off that doing many clusterings
 could take a long time.
 So this is something that needs to be balanced.
 
-```{r 10-choose-k-nstart, fig.height = 4, fig.width = 4.35, message= F, warning = F, fig.cap = "A plot showing the total WSSD versus the number of clusters when K-means is run with 10 restarts."}
+```{r 10-choose-k-nstart, fig.height = 4, fig.width = 4.35, message= FALSE, warning = FALSE, fig.align = "center", fig.cap = "A plot showing the total WSSD versus the number of clusters when K-means is run with 10 restarts."}
 penguin_clust_ks <- tibble(k = 1:9) |>
   rowwise() |>
   mutate(penguin_clusts = list(kmeans(standardized_data, nstart = 10, k)),