Merge pull request #193 from oceanhackweek/emiliom-patch-3

Update learning_python_r.md

Author: emiliom

resources/prep/learning_python_r.md

@@ -4,8 +4,13 @@ While we anticipate that most participants will have some experience with Python
 Below are links to a few resources to refresh your skills in Python and R, as well as a tutorial video from a previous OHW event that covers a handful of Python packages.
 The material covered in these lessons is a good reflection of the level we expect participants to be at and should get you up-to-date on the basic programming skills needed for the workshop.
 
+## Python 
+
 - [Plotting and Programming in Python](https://swcarpentry.github.io/python-novice-gapminder/index.html) - This lesson is an introduction to programming in Python for people with little or no previous programming experience.
 - [Programming with Python](https://swcarpentry.github.io/python-novice-inflammation/) - This Python lesson teaches data analysis using a case study of inflammation in patients who have been given a new treatment for arthritis.
-- [R for Reproducible Scientific Analysis](https://swcarpentry.github.io/r-novice-gapminder/) - R is commonly used in many scientific disciplines for statistical analysis. This lesson teaches novice programmers to write modular code and covers best practices for using R for data analysis.
 - [Oceanhackweek 2020 recording: Jupyter, NumPy, Pandas, and Matplotlib](https://www.youtube.com/watch?v=CTUAgpvfze0) - As a part of OceanHackWeek 2020, Leticia Portella gave a pre-hackweek tutorial on Jupyter, NumPy, Pandas, and Matplotlib. The Jupyter notebooks are found [here](https://github.com/oceanhackweek/ohw-preweek/tree/master/data-analysis-modules).
-- [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)
+- [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)
+
+## R
+
+- [R for Reproducible Scientific Analysis](https://swcarpentry.github.io/r-novice-gapminder/) - R is commonly used in many scientific disciplines for statistical analysis. This lesson teaches novice programmers to write modular code and covers best practices for using R for data analysis.