https://craig.rbind.io/tags/data-analysis/Recent content in Data Analysis on Craig Hutton, PhDHugo -- gohugo.ioen-usCopyright © {year} by Craig P. HuttonSat, 22 Jun 2019 00:00:00 +0000https://craig.rbind.io/post/2019-06-22-asgr-1-getting-data-into-r/Sat, 22 Jun 2019 00:00:00 +0000https://craig.rbind.io/post/2019-06-22-asgr-1-getting-data-into-r/1 TL;DR 2 Introduction 3 Installing and Loading R Packages 4 How to Get Your Data Into R 4.1 Comma Delimited (.csv) Files 4.2 Tab Delimited (.txt) Files 4.3 Files With Other Delimiting Characters (also .txt) 4.4 Microsoft Excel Files (.xlsx, .xls) 4.5 Files from SPSS, SAS, or Stata 4.6 Fixed Width Files (.txt, .gz, .bz2, .xz, .zip, etc.) 4.7 html/xml files 4.8 JavaScript Object Notation (JSON) Files 4.https://craig.rbind.io/post/2019-05-17-asgr-basic-workflow/Fri, 17 May 2019 00:00:00 +0000https://craig.rbind.io/post/2019-05-17-asgr-basic-workflow/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.