# R HANDS ON VISUALIZATION.docx - VISUALIZATION IN R 1. Find...

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VISUALIZATION IN R1.Find principal components of your data (use: Matlab: pca, R: princomp, or other).2.Make visualizations in two projection.3.Make the attribute axis representation.4.Select the most informative/informative attributes possible according to the shortest / longestaxes5.Create different subsets of attributes (informative only, non-informative only, no non-informative attributies, etc.) and visualize the data using the nonlinear projection method -MDS6.Present different visualizations, comment on the result.Visualizationspc <- prcomp(data7, center =TRUE, scale =TRUE)paaisk <- round((pc\$sdev^2/ sum(pc\$sdev^2)*100),2)ggplot(as.data.frame(pc\$x),aes(x=PC1, y=PC2)) +labs(title ="Scatterplot according to the first two Pricipal components",x = paste0("PC1 (",paaisk[1],"%)"), y = paste0("PC2 (",paaisk[2],"%)"))+geom_point() + theme_classic()
Here we can see, that the variance is quite well depicted by the 1st principal componentwith58.4%58.4% of the variance explained. The 2nd principal component explains the variancehalf as good as the 1st principal component (24.8%24.8%).

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Term
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K means clustering