Some helpful resources for getting started with statistics in ‘R’ and RStudio

The summer after my first-year in medical school, I worked in a Cardiology research lab that performed computational analyses of data from hospital monitors—i.e. patient’s vital signs and ECG tracings. During that summer, I worked on some of the data analysis, visualization, and modeling; the language that the lab introduced me to was ‘R’ (and we occasionally used MatLab).

For those who do not know what ‘R’ is, in short it is a statistical programming language that allows one to parse/format data, perform statistical analyses, and then create visualizations or regression models. There are tons of resources out there regarding R and RStudio (which is the graphic user interface for this language), but here are a couple resources that I found the most helpful when starting.

MarinStatsLectures this is a Youtube channel that has playlists for getting started with R and statistics, from installing R and RStudio to performing statistical analyses and regression using R. These lectures are great because he provides step-by-step instructions, with good examples and practice data sets available.

Coursera: Johns Hopkins Data Science Specialization this is free Coursera online course that covers not just R but other data science tools that are used in conjunction with R. It covers various data science techniques and methods in a more structured manner . The classes have video lectures, assignments, quizzes, tests, and even a Capstone project. The production value is superb, and there is a way to receive a certification for an additional cost.

I found these websites to be a great help, and was a great place to start. For additional R topics related to different packages that I had to use for my specific research project I used the website: StackOverflow.com for specific questions about syntax, debug issues, etc.

Since R is an open-source programming language, there are tons of packages available to do countless types of analyses from image/pattern analysis to GPS mapping. These tools are useful in a multitude of research fields, and is pretty fun and easy to learn. I wish you the best of luck on your next research project.

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