{"provider_url":"https://hatena.blog","version":"1.0","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/cross_hyou/20240101/20240101102238.jpg","title":"OECD Total official and private flows data analysis 2 - Summary Statistics using R","url":"https://www.crosshyou.info/entry/2024/01/01/102258","published":"2024-01-01 10:22:58","width":"100%","author_name":"cross_hyou","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.crosshyou.info%2Fentry%2F2024%2F01%2F01%2F102258\" title=\"OECD Total official and private flows data analysis 2 - Summary Statistics 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>","blog_url":"https://www.crosshyou.info/","description":"Unsplash\u306eNastasia Kalinina\u304c\u64ae\u5f71\u3057\u305f\u5199\u771f www.crosshyou.info This post is following of the above post. Let's see summary statistics. First, let's see summary statistics by LOCATION. Let's see summary stats by TIME. I use group_by() function, then I use summarize() function and min(), quantile(), median(), m\u2026","type":"rich","categories":["Data_Analysis"],"height":"190","provider_name":"Hatena Blog","blog_title":"R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0","author_url":"https://blog.hatena.ne.jp/cross_hyou/"}