Beginning R: The Statistical Programming Language by Mark Gardener

Beginning R: The Statistical Programming Language



Download Beginning R: The Statistical Programming Language

Beginning R: The Statistical Programming Language Mark Gardener ebook
Page: 504
Format: pdf
ISBN: 9781118164303
Publisher: Wrox Press, Inc.


When I was starting OrganizationView I knew that getting an easy to use and powerful analytic platform was critical and during my conversations with many statisticians and analysts about their choices one tool kept coming up – R. What's a pirate's favorite statistical programming language? €�I don't want to mess with that. 10 lines of Python will convince you to start. Book Description Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and wri. If you've listened to the show for a while or if you've been reading the paleocave blog from the beginning (like when we actually used to update it regularly), then you might know that I'm rather fascinated with statistics. It is the language I always use for the analysis of MCMC output. Although I'm not a big fan of “Numerical recipes”, it's not too bad a place to start. Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. Although R has many flaws, it is well suited to programming with data, and has a huge array of statistical libraries associated with it. R-help, a mailing list for dealing with problems with the statistical programming language R, has a somewhat scary reputation. We sat down to discuss why data scientists, statisticians, and programmers alike can use the R language to make data analysis easier and more powerful. R is a free statistical programming language that is rapidly becoming the standard in university departments and statistical teams in many big firms (especially in the 'Big Data' firms, life-sciences and finance sectors). Like many statisticians, I probably use R more than any other language in my day-to-day work.





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