{"height":"190","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.crosshyou.info%2Fentry%2F2022%2F01%2F01%2F205342\" title=\"OECD Purchasing power parities (PPP) data analysis 5 - PCA (Principal Component Analysis) using R. - R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","title":"OECD Purchasing power parities (PPP) data analysis 5 - PCA (Principal Component Analysis) using R.","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/cross_hyou/20220101/20220101200456.jpg","type":"rich","version":"1.0","description":"Photo by Aron Visuals on Unsplash www.crosshyou.info 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 PC\u2026","published":"2022-01-01 20:53:42","url":"https://www.crosshyou.info/entry/2022/01/01/205342","provider_url":"https://hatena.blog","width":"100%","author_url":"https://blog.hatena.ne.jp/cross_hyou/","author_name":"cross_hyou","provider_name":"Hatena Blog","blog_title":"R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0","categories":["Data_Analysis"],"blog_url":"https://www.crosshyou.info/"}