{"provider_name":"Hatena Blog","blog_title":"R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0","published":"2022-08-11 08:25:39","blog_url":"https://www.crosshyou.info/","author_name":"cross_hyou","provider_url":"https://hatena.blog","version":"1.0","height":"190","type":"rich","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/cross_hyou/20220811/20220811072604.jpg","description":"Photo by Ash from Modern Afflatus on Unsplash www.crosshyou.info This post is following of above post. In the above post, I made a dataframe which has basic statistics data for each locations. Let's look into it further, Firstly, let's see scatter plot matrix. Let's make correlation matrix. Some pai\u2026","categories":["Data_Analysis"],"url":"https://www.crosshyou.info/entry/2022/08/11/082539","author_url":"https://blog.hatena.ne.jp/cross_hyou/","width":"100%","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.crosshyou.info%2Fentry%2F2022%2F08%2F11%2F082539\" title=\"OECD Nutrient balance data analysis 4 - 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 Nutrient balance data analysis 4 - PCA(Principal Component Analysis) using R"}