{"version":"1.0","published":"2023-07-09 08:44:06","author_name":"cross_hyou","title":"OECD Crop production data analysis 4 - crop productivity ranking using R","height":"190","blog_url":"https://www.crosshyou.info/","categories":["Data_Analysis"],"provider_url":"https://hatena.blog","url":"https://www.crosshyou.info/entry/2023/07/09/084406","author_url":"https://blog.hatena.ne.jp/cross_hyou/","description":"www.crosshyou.info This post is following of the above post. In this post, I will make crop productivity ranking by country. Crop productivity is \"crop volume / crop field area\", it is measured as TONNE_HA. Let's start with MAIZE. ISR(Israel), NZL(New Zealand), CHL(Chile) and USA are thr top 4 count\u2026","blog_title":"R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0","provider_name":"Hatena Blog","type":"rich","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/cross_hyou/20230709/20230709080552.jpg","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.crosshyou.info%2Fentry%2F2023%2F07%2F09%2F084406\" title=\"OECD Crop production data analysis 4 - crop productivity ranking 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>","width":"100%"}