Beginning R: An Introduction to Statistical Programming by Dr. Joshua F. Wiley

By Dr. Joshua F. Wiley

Beginning R, moment Edition is a hands-on booklet displaying tips on how to use the R language, write and shop R scripts, learn in facts records, and write customized statistical capabilities in addition to use in-built services. This booklet indicates using R in particular instances comparable to one-way ANOVA research, linear and logistic regression, facts visualization, parallel processing, bootstrapping, and extra. It takes a hands-on, example-based technique incorporating top practices with transparent reasons of the records being performed. it's been thoroughly re-written because the first version to use the most recent programs and contours in R model 3.

R is a strong open-source language and programming setting for information and has turn into the de facto typical for doing, educating, and studying computational facts. R is either an object-oriented language and a practical language that's effortless to profit, effortless to exploit, and entirely loose. a wide neighborhood of devoted R clients and programmers presents a superb resource of R code, features, and information units, with a continually evolving environment of applications supplying new performance for information research. R has additionally turn into well known in advertisement use at businesses reminiscent of Microsoft, Google, and Oracle. Your funding in studying R is certain to repay within the long-term as R maintains to develop into the visit language for info research and research.

What you’ll learn

  • How to procure and set up R
  • Hot to import and export information and scripts
  • How to investigate information and generate graphics
  • How to application in R to put in writing customized functions
  • Hot to take advantage of R for interactive statistical explorations
  • How to behavior bootstrapping and different complex techniques

Who this ebook is for

Beginning R, moment Edition is an easy-to-read e-book that serves as an handbook and reference for operating pros, professors, and scholars who are looking to research and use R for uncomplicated data. it's the excellent publication for an individual desiring a unfastened, able, and robust instrument for exploring data and automating their use.

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Extra info for Beginning R: An Introduction to Statistical Programming

Example text

Although this is a rather uninteresting example, it shows the syntax of a for loop, which is for(names in values ) expression. Here’s our code and result: > x <- 1:5 > for (i in x) print (i * 10) [1] 10 [1] 20 [1] 30 [1] 40 [1] 50 We defined x as the integers from 1 to 5, and wrote the for statement in such a way that the names attribute i iterated through the values in x one at a time, while the expression print(i * 10) printed the result of multiplying each successive value in the x vector by 10.

It is better to use variables and even other functions in your code so that you can change a single value once rather than many times. This is the essence of the pragmatic programmer who writes with purpose. Programmers who come to R from other languages such as C++ or Python tend to think in loops. You are probably convinced by now that R’s vectorization allows us to avoid loops in many situations. As you saw in Chapter 4, looping is possible when it is needed. Efficient code allows us to automate as many tasks as we can so that we don’t repeat ourselves, and to avoid looping as much as possible.

This flies in the face of the carefully structured and highly managed data most of us have come to know and love. Data lakes solve the problem of independently managed information silos (an old problem in information technology), and the newer problem of dealing with Big Data projects, which typically require large amounts of highly varied data. If you are particularly interested in using R for cloud computing, I recommend Ajay Ohri’s book R for Cloud Computing: An Approach for Data Scientists.

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