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

VISUALIZATION IN R
1. 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 / longest
axes
5. Create different subsets of attributes (informative only, non-informative only, no non-
informative attributies, etc.) and visualize the data using the nonlinear projection method -
MDS
6. Present different visualizations, comment on the result.
Visualizations
pc <- 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 component
with
58.4%
58.4% of the variance explained. The 2nd principal component explains the variance
half as good as the 1st principal component (
24.8%
24.8%).