Geom_point(aes(color = cluster), alpha = 0. Geom_vline(xintercept = 0, color = 'gray70') + Geom_hline(yintercept = 0, color = 'gray70') + Videos (supported services include YouTube and Vimeo). Code exercises (R code chunks that users can edit and execute directly). Geom_text_repel(aes(label = rownames(carsDf))) + Tutorials can include any or all of the following: Narrative, figures, illustrations, and equations. # install.packages(c('ggplot2','ggrepel')) How to do this using RStudio and GitHub Go to the repository page on GitHub for the project you would like to improve, and click on the Fork button in the top right corner. # need to install ggplot2 and ggrepel packages first # plot the first 2 PCs with cluster membership

You will need to install the ggplot2 and ggrepel packages from CRAN # cluster carsĬarsHC <- hclust(dist(pcaCars$scores), method = 'ward.D2')ĬarsDf <- ame(pcaCars$scores, 'cluster' = factor(carsClusters))

Go to RStudio and add the following code to pca.R and save the file. The motivation for creating this branch is that we want test out some code to cluster the cars based on principal component scores.

Github rstudio tutorial