Default to scaling features to unit variance for PCA.

R/dim_reduction.R

@@ -106,7 +106,7 @@ dimensionality_reduction_server <- function(input, output, session, rv, session_
       idx.valid.rows <- which(is.finite(rowSums(tmp)))
       tmp <- tmp[idx.valid.rows,]
       if(input$dimReductionMethod == 'PCA') {
-        pca <- prcomp(tmp[,input$featuresToReduce], rank = 2, retx = TRUE)
+        pca <- prcomp(tmp[,input$featuresToReduce], rank = 2, retx = TRUE, scale = TRUE)
         tmp <- predict(pca, newdata = rv$data[, input$featuresToReduce, with = FALSE])
         if(all(c("PCA.PC1", "PCA.PC2") %in% colnames(rv$data))) {
           rv$data[, ("PCA.PC1") := list(tmp[,1])]