Reproducibility Research
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Reproducibility is not just Research publishing your analysis code. The entire workflow of a research project – from formulating hypotheses to dissemination of your results – has decisions and steps that ideally should be reproducible. This extends far beyond just posting the code for your model. In particular, the data cleaning process is an important step in a research project that is often the hardest step to make reproducible, especially if you are dealing with, for example, messy text data, where it’s hard to generalize your cleaning. R Markdown, R packages, and git as example tools that help with producing reproducible research. However, these types of tools can be intimidating if you’ve never seen or used them before. But that doesn’t mean you shouldn’t do anything. Well-documented code goes a long way for someone to be able to reproduce your research! With each new project, aim to try and improve the reproducibility of your workflow in incremental ways.