Fix minor plot rendering issues

scripts/10_correction_batch_effects_unfiltered.Rmd

@@ -87,14 +87,15 @@ if (correction_variable_1 == "PLEASE FILL IN"){
                                    var_explained = 100*round(summary(PCA_object)$importance[2,], 2),
                                    cumm_var_explained = 100*round(summary(PCA_object)$importance[3,], 2))
   
-  variance_explained %>%
+screeplot <- variance_explained %>%
     dplyr::slice(1:10) %>%
     ggplot(aes(x = factor(PC, level = PC), y = var_explained, group = 1)) +
     geom_point() +
     geom_line() +
     geom_text(aes(label = cumm_var_explained), vjust = 1.5) +
     labs(x = "Principal Component", y = "Variance Explained (%)", title = "Scree Plot",
          subtitle = "Note: cumulative variance explained (%) is displayed as text")
+print(screeplot)
   
   R <- pc_cutoff(variance_explained) # automatically calculate number of PCs
   print(paste0(R, " PCs were included"))
@@ -231,7 +232,8 @@ if (correction_variable_1 == "PLEASE FILL IN"){
 ```
 
 ```{r, print p-value table for batch effects after correction of first batch effect variable}
-anova_lm_pvalue_df %>% paged_table()
+if (correction_variable_1 != "PLEASE FILL IN"){
+  anova_lm_pvalue_df %>% paged_table()}
 ```
 
 ```{r, correction of second batch effect and PCA, results='asis'}
@@ -250,14 +252,15 @@ if (correction_variable_2 == "PLEASE FILL IN"){
                                    var_explained = 100*round(summary(PCA_object)$importance[2,], 2),
                                    cumm_var_explained = 100*round(summary(PCA_object)$importance[3,], 2))
   
-  variance_explained %>%
+screeplot <- variance_explained %>%
     dplyr::slice(1:10) %>%
     ggplot(aes(x = factor(PC, level = PC), y = var_explained, group = 1)) +
     geom_point() +
     geom_line() +
     geom_text(aes(label = cumm_var_explained), vjust = 1.5) +
     labs(x = "Principal Component", y = "Variance Explained (%)", title = "Scree Plot",
          subtitle = "Note: cumulative variance explained (%) is displayed as text")
+print(screenplot)
   
   R <- pc_cutoff(variance_explained) # automatically calculate number of PCs
   print(paste0(R, " PCs were included"))
@@ -394,7 +397,8 @@ if (correction_variable_2 == "PLEASE FILL IN"){
 ```
 
 ```{r, print p-value table for batch effects after correction of second batch effect variable}
-anova_lm_pvalue_df %>% paged_table()
+if (correction_variable_2 != "PLEASE FILL IN"){
+  anova_lm_pvalue_df %>% paged_table()}
 ```
 
 ```{r, correction of third batch effect and PCA, results='asis'}
@@ -413,14 +417,15 @@ if (correction_variable_3 == "PLEASE FILL IN"){
                                    var_explained = 100*round(summary(PCA_object)$importance[2,], 2),
                                    cumm_var_explained = 100*round(summary(PCA_object)$importance[3,], 2))
   
-  variance_explained %>%
+screeplot <- variance_explained %>%
     dplyr::slice(1:10) %>%
     ggplot(aes(x = factor(PC, level = PC), y = var_explained, group = 1)) +
     geom_point() +
     geom_line() +
     geom_text(aes(label = cumm_var_explained), vjust = 1.5) +
     labs(x = "Principal Component", y = "Variance Explained (%)", title = "Scree Plot",
          subtitle = "Note: cumulative variance explained (%) is displayed as text")
+print(screeplot)
   
   R <- pc_cutoff(variance_explained) # automatically calculate number of PCs
   print(paste0(R, " PCs were included"))
@@ -557,7 +562,8 @@ if (correction_variable_3 == "PLEASE FILL IN"){
 ```
 
 ```{r, print p-value table for batch effects after correction of third batch effect variable}
-anova_lm_pvalue_df %>% paged_table()
+if (correction_variable_3 != "PLEASE FILL IN"){
+  anova_lm_pvalue_df %>% paged_table()}
 ```
 
 ```{r, conversion M to Beta and save, include=FALSE}