![]() ![]() That is 100000x easier in python and frankly I would recommend python for its web scraping capabilities alone. Also if you want to scrape data from the internet god bless your soul if you are building that original library in R. If you want to do something with the data, like send an email every time the mets win, Python is a better choice. The pitchRx package for scraping pitchFX data. ![]() Notable packages include The Lahman package that provides all the season-to-season stats for teams, pitchers, and batters. If you want to look at data and make graphs R is better and has prettier graphics. In this blog, weve talked about a number of helpful R packages for doing baseball work. It can run Fortran, R, C and C++ libraries dynamically and has access to an insane amount of machine learning software that would only be accessible in C++ or Fortran otherwise. It is the strongest tool to work across the most domains. To put it this way Python was the 5th language I learned and is now what i write 99% of my code in. there is a few patterns that make up how you use 99% of the libs and you just need to what words to use. Python is far more gramatical then R is as a language and far more internally consistent in how it works. A normal python installation wouldn't come with all the stuff you want to use but you can install a variant called anaconda It come with all the stat libs preinstalled into the system version so you can avoid learning about installing libs and all that until later on. Where Python is a programming language that happens to be really great at statistical work. I would say R is a statistical tool first and a programming language second. ![]() It has far better help online and frankly it works like 10000000000000000000 times better. My two cents though if you are learning this because you want to develop programming skills drop using R and use Python. You may also be able to leave it in your system path, but I am not sure how R handles this. Where the variable MYSQL_HOME points to should be where you place your cnf file. Here is a straight forward example of creating a cnf file The only other gotcha is you need to make sure that the cnf is specified in your environment variables. In general I would just type them in its technically bad practice cause you expose credentials but your coding locally that is the least of your concerns. It appears you are doing neither which is the issue. So according to the R documentation you can either use a cnf or specify the host, port and username. Reddit Markdown Primer - how to make charts, other stuff in reddit The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball The Bill James Historical Baseball Abstractīaseball Hacks: Tips & Tools for Analyzing and Winning with Statistics The Book: Playing the Percentages in BaseballĮxtra Innings: More Baseball Between the Numbers Sabermetrics 101: Introduction to Baseball Analytics Sabermetrics - The search for objective knowledge about baseball through the analysis of empirical evidence. ![]()
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