A Short(-ish) Introduction to Using R Packages for Baseball Research, by @billpetti

http://www.hardballtimes.com/a-short-ish-introduction-to-using-r-for-baseball-research/

The article has great examples as well as links to other resources.

[A Short(-ish) Introduction to Using R Packages for Baseball Research, by @billpetti]

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Comparing R and Python

Again, Vik doesn’t just write about the pro’s and con’s of these two languages, but compares them practically by going through a number of activities side-by-side.

https://www.dataquest.io/blog/python-vs-r/

His conclusions, in short:

· R is more functional, Python is more object-oriented

· R has more data analysis built-ins, Python relies on packages

· Python has “main” packages for data analysis tasks, R has a larger ecosystem of small packages

· R has more statistical support in general

· It’s usually more straightforward to do non-statistical tasks in Python

· There are many parallels between the data analysis workflow in both

PS I forgot to add the link to the previously posted article about Python data visualization tools. It can be found here.

Link to nice list of resources for Data Analysis with R, from Udacity

https://www.udacity.com/wiki/ud651

Below is just a small subset of all the links provided:

R Basics