changing axes size to 12

Author: leem44

classification1.Rmd

@@ -33,7 +33,7 @@ print_tidymodels <- function(tidymodels_object) {
   }
 }
 
-theme_update(axis.title = element_text(size = 14)) # modify axis label size in plots 
+theme_update(axis.title = element_text(size = 12)) # modify axis label size in plots 
 ```
 
 ## Overview 
@@ -240,7 +240,7 @@ perim_concav <- cancer |>
        color = "Diagnosis") +
   scale_color_manual(labels = c("Malignant", "Benign"), 
                      values = c("orange2", "steelblue2")) +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 perim_concav
 ```
 
@@ -1061,7 +1061,7 @@ ggplot(unscaled_cancer, aes(x = Area,
    facet_zoom(x = ( Area > 380 & Area < 420) , 
               y = (Smoothness > 0.08 & Smoothness < 0.14), zoom.size = 2) + 
     theme_bw() + 
-    theme(text = element_text(size = 14), legend.position="bottom")
+    theme(text = element_text(size = 12), legend.position="bottom")
 ```
 
 ### Balancing
@@ -1108,7 +1108,7 @@ rare_plot <- rare_cancer |>
        color = "Diagnosis") +
   scale_color_manual(labels = c("Malignant", "Benign"), 
                      values = c("orange2", "steelblue2")) +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 rare_plot
 ```
@@ -1404,7 +1404,7 @@ wkflw_plot <-
        y = "Smoothness (standardized)") +
   scale_color_manual(labels = c("Malignant", "Benign"), 
                      values = c("orange2", "steelblue2")) +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 wkflw_plot
 ```

clustering.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(warning = FALSE, fig.align = "default")
 cbbPalette <- c(brewer.pal(9, "Paired"))
 cbpalette <- c("darkorange3", "dodgerblue3", "goldenrod1")
 
-theme_update(axis.title = element_text(size = 14)) # modify axis label size in plots 
+theme_update(axis.title = element_text(size = 12)) # modify axis label size in plots 
 ```
 
 ## Overview 
@@ -174,7 +174,7 @@ ggplot(data, aes(x = flipper_length_standardized,
   geom_point() +
   xlab("Flipper Length (standardized)") +
   ylab("Bill Length (standardized)") + 
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 ```
 
 Based \index{ggplot}\index{ggplot!geom\_point} on the visualization 
@@ -473,7 +473,7 @@ for (i in 1:4) {
                color = "black", 
                fill = cbpalette) +
     annotate("text", x = -0.5, y = 1.5, label = paste0("Iteration ", i), size = 5)+ 
-    theme(text = element_text(size = 14))
+    theme(text = element_text(size = 12))
   
   if (i == 1 | i == 2) {
     plt_ctr <- plt_ctr +
@@ -507,7 +507,7 @@ for (i in 1:4) {
                color = "black", 
                fill = cbpalette) +
     annotate("text", x = -0.5, y = 1.5, label = paste0("Iteration ", i), size = 5)+ 
-    theme(text = element_text(size = 14))
+    theme(text = element_text(size = 12))
 
   if (i == 1 | i ==2) {
     plt_lbl <- plt_lbl +
@@ -646,7 +646,7 @@ for (i in 1:5) {
                color = "black", 
                fill = cbpalette) +
     annotate("text", x = -0.5, y = 1.5, label = paste0("Iteration ", i), size = 5) + 
-    theme(text = element_text(size = 14))
+    theme(text = element_text(size = 12))
 
   if (i == 1 | i == 2) {
     plt_ctr <- plt_ctr +
@@ -679,7 +679,7 @@ for (i in 1:5) {
                color = "black", 
                fill = cbpalette) +
     annotate("text", x = -0.5, y = 1.5, label = paste0("Iteration ", i), size = 5) + 
-    theme(text = element_text(size = 14))
+    theme(text = element_text(size = 12))
 
   if (i == 1 | i == 2) {
     plt_lbl <- plt_lbl +
@@ -942,7 +942,7 @@ cluster_plot <- ggplot(clustered_data,
   scale_color_manual(values = c("dodgerblue3",
                                 "darkorange3",  
                                 "goldenrod1")) + 
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 cluster_plot
 ```
@@ -1045,7 +1045,7 @@ elbow_plot <- ggplot(clustering_statistics, aes(x = k, y = tot.withinss)) +
   xlab("K") +
   ylab("Total within-cluster sum of squares") +
   scale_x_continuous(breaks = 1:9) + 
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 elbow_plot
 ```
@@ -1088,7 +1088,7 @@ elbow_plot <- ggplot(clustering_statistics, aes(x = k, y = tot.withinss)) +
   xlab("K") +
   ylab("Total within-cluster sum of squares") +
   scale_x_continuous(breaks = 1:9) + 
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 elbow_plot
 ```

inference.Rmd

@@ -21,7 +21,7 @@ min_x <- function(dist) {
   min(ggp_data$data[[1]]$xmin)
 }
 
-theme_update(axis.title = element_text(size = 14)) # modify axis label size in plots 
+theme_update(axis.title = element_text(size = 12)) # modify axis label size in plots 
 ```
 
 ## Overview
@@ -294,7 +294,7 @@ sampling_distribution <- ggplot(sample_estimates, aes(x = sample_proportion)) +
   geom_histogram(fill = "dodgerblue3", color = "lightgrey", bins = 12) +
   ylab("Count") +
   xlab("Sample proportions") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 sampling_distribution
 ```
@@ -343,7 +343,7 @@ population_distribution <- ggplot(airbnb, aes(x = price)) +
   geom_histogram(fill = "dodgerblue3", color = "lightgrey") +
   ylab("Count") + 
   xlab("Price per night (Canadian dollars)") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 population_distribution
 ```
@@ -389,7 +389,7 @@ sample_distribution <- ggplot(one_sample, aes(price)) +
   geom_histogram(fill = "dodgerblue3", color = "lightgrey") +
   ylab("Count") + 
   xlab("Price per night (Canadian dollars)") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 sample_distribution
 
@@ -438,7 +438,7 @@ sampling_distribution_40 <- ggplot(sample_estimates, aes(x = sample_mean)) +
   geom_histogram(fill = "dodgerblue3", color = "lightgrey") +
   ylab("Count") + 
   xlab("Sample mean price per night (Canadian dollars)") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 sampling_distribution_40
 ```
@@ -786,7 +786,7 @@ one_sample_dist <- ggplot(one_sample, aes(price)) +
   geom_histogram(fill = "dodgerblue3", color = "lightgrey") +
   ylab("Count") + 
   xlab("Price per night (Canadian dollars)") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 one_sample_dist
 ```
@@ -813,7 +813,7 @@ boot1_dist <- ggplot(boot1, aes(price)) +
   geom_histogram(fill = "dodgerblue3", color = "lightgrey") +
   ylab("Count") + 
   xlab("Price per night (Canadian dollars)") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 boot1_dist
 
@@ -855,7 +855,7 @@ ggplot(six_bootstrap_samples, aes(price)) +
   xlab("Price per night (Canadian dollars)") +
   ylab("Count") + 
   facet_wrap(~replicate) +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 ```
 
 We see in Figure \@ref(fig:11-bootstrapping-six-bootstrap-samples) how the
@@ -886,7 +886,7 @@ boot_est_dist <- ggplot(boot20000_means, aes(x = mean)) +
   geom_histogram(fill = "dodgerblue3", color = "lightgrey") +
   ylab("Count") +
   xlab("Sample mean price per night \n (Canadian dollars)") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 boot_est_dist
 ```

regression2.Rmd

@@ -25,7 +25,7 @@ print_tidymodels <- function(tidymodels_object) {
   }
 }
 
-theme_update(axis.title = element_text(size = 14)) # modify axis label size in plots 
+theme_update(axis.title = element_text(size = 12)) # modify axis label size in plots 
 ```
 
 ## Overview 
@@ -300,7 +300,7 @@ lm_plot_final <- ggplot(sacramento_train, aes(x = sqft, y = price)) +
   ylab("Price (USD)") +
   scale_y_continuous(labels = dollar_format()) +
   geom_smooth(method = "lm", se = FALSE) + 
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 lm_plot_final
 ```
@@ -371,7 +371,7 @@ knn_plot_final <- ggplot(sacr_preds, aes(x = sqft, y = price)) +
   geom_line(data = sacr_preds, aes(x = sqft, y = .pred), color = "blue") +
   ggtitle("KNN regression") +
   annotate("text", x = 3500, y = 100000, label = paste("RMSPE =", sacr_rmspe)) +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 lm_rmspe <- lm_test_results |>
   filter(.metric == "rmse") |>
@@ -381,7 +381,7 @@ lm_rmspe <- lm_test_results |>
 lm_plot_final <- lm_plot_final +
   annotate("text", x = 3500, y = 100000, label = paste("RMSPE =", lm_rmspe)) +
   ggtitle("linear regression") +
-  theme(text = element_text(size = 14))
+  theme(text = element_text(size = 12))
 
 grid.arrange(lm_plot_final, knn_plot_final, ncol = 2)
 ```