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+---
+title: Integrate literate statistical programming into RStudio workflow
+image: /assets/image.jpg
+---
+
+The simplest way to share your RStudio project directory is by pushing it to a remote repo on something like Github, and cloning it to your machine when you next need to work on it. 
+
+This also has the additional advantage of allowing you to keep track of changes to your project.
+
+R has a similar solution to Python’s virtualenvs called packrat which allows you to keep track of your analyses’ dependencies.
+
+
+Be able to answer 5 questions about your research:
+1. What did I do?
+2. Why did I do it?
+3. How did I set up everything at the time of the analysis?
+4. When did I make changes, and what were they?
+5. Who needs to access it, and how can I get it to them? 
+
+# What is R Markdown? 
+
+Like Python, R has its own tooling for literate statistical programming called R Markdown. Just like with Juypter notebooks, you can write chunks of markdown text alongside R code, meaning you can create easy-to-read, meaningful annotations for your analysis. You can also include results, tables and charts, allowing you to create reports and other documents from one self-contained R Markdown script.