Data and Statistical Analysis
Have you ever analyzed raw data, come back to your analysis months later, and struggled to figure out what you did? The importance of keeping a lab notebook during experiments is often drilled into researchers. However, the same cannot be said for keeping a detailed log of data and statistical analysis.Whether you use Excel, SPSS, Statistica, R, etc. it is important to keep track of how your raw data was analyzed. At Sengi Data we prefer to use R for a lot of reasons, outlined in another article. R also makes keeping track of your data and statistical analysis, if you use R Markdown.In this article, we will discuss how to use R Markdown to simplify research.
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Data and Statistical Analysis
There are so many statistical software packages that it is hard to choose. Should you use SAS, SPSS, Stata, Statistica, SigmaPlot, R, or even Excel for your data and statistical analysis?We are big advocates of using R. More than a piece of software, R is a language and environment for data, statistics, and graphics. It can perform just about any statistical technique (linear and nonlinear modelling, classification, clustering, time-series analysis, etc.) and be used to create graphics for research publication.There is a steep learning curve when learning any statistical software, but R seems to be known for being difficult to use at first. We encourage everyone to stick with it. Try using R Commander, a graphical user interface (GUI), if you are nervous about coding at first. When you get more comfortable, you can then transition to using RStudio (another GUI) and swirl (a tool that teaches R directly within the console). 
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