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This post is following of the above post.
In this post, I will do PCA(Principal Component Analysis).
I refer below web site.
Principal Component Analysis (PCA) 101, using R | by Peter Nistrup | Towards Data Science
Firstly, I will make subset for PCA from df.
Now, I have subset_pca data frame which has 6 mumerical varibles, ppp, gdp, capi, l_gdp, l_capi and l_ppp.
Then, I use prcomp() function for PCA.
Above results tells that PC1 counts 39% of variables, PC2 counts 32% variable. So, PC1 and PC2 counts 72% variable.
Then, I make a plot to visualize PCA result using screeplot() function.
Let's make PC1 vs PC2 plot
Then, I add rownames to pca_result$x.
Then, let's make a hierarchial culstering dendrogram.
That's it. Thank you!.
The next post is
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