<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Statistics on Craig Hutton, PhD</title><link>https://craig.rbind.io/categories/statistics/</link><description>Recent content in Statistics on Craig Hutton, PhD</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>Copyright &amp;copy; {year} by Craig P. Hutton</copyright><lastBuildDate>Fri, 17 May 2019 00:00:00 +0000</lastBuildDate><atom:link href="https://craig.rbind.io/categories/statistics/index.xml" rel="self" type="application/rss+xml"/><item><title>A Scientist&#39;s Guide to R: Introduction and Basic Workflow</title><link>https://craig.rbind.io/post/2019-05-17-asgr-basic-workflow/</link><pubDate>Fri, 17 May 2019 00:00:00 +0000</pubDate><guid>https://craig.rbind.io/post/2019-05-17-asgr-basic-workflow/</guid><description>1 Introduction 2 Workflow Outline 2.1 Navigation 2.2 Notes 1 Introduction This tutorial will be the first of many blog posts for new researchers and science program students/trainees on how to use R as an analytical and productivity tool in the process of conducting scientific research. Specifically, in this post I will explain why you might want to use R and provide a brief overview of the basic R workflow used in the analysis of experimentally obtained data.</description></item></channel></rss>