Learning about groups in data without prior information can be done using the following steps:

1. Load the data and run a PCA:

library(factoextra)
library(Biobase)

load(file.path(getwd(), "datasets", "ch1", "modencodefly_eset.RData") ) 
expr_pca <- prcomp(exprs(modencodefly.eset), scale=TRUE, center=TRUE ) fviz_screeplot(expr_pca)

2. Extract the principal components and estimate the optimal clusters:

main_components <- expr_pca$rotation[, 1:3]
fviz_nbclust(main_components, kmeans, method = "wss")

3. Perform k-means clustering and visualizing:

kmean_clus <- kmeans(main_components, 5, nstart=25, iter.max=1000)

fviz_cluster(kmean_clus, data = main_components,
             palette = RColorBrewer::brewer.pal(5, "Set2"),
             ggtheme = theme_minimal(),
             main = "k-Means Sample Clustering"
             )