New PDF release: The Art of R Programming: A Tour of Statistical Software

By Norman Matloff

ISBN-10: 1593273843

ISBN-13: 9781593273842

R is the world's most well liked language for constructing statistical software program: Archaeologists use it to trace the unfold of historical civilizations, drug businesses use it to find which drugs are secure and powerful, and actuaries use it to evaluate monetary dangers and retain economies operating smoothly.

"The artwork of R Programming" takes you on a guided travel of software program improvement with R, from easy varieties and knowledge buildings to complicated issues like closures, recursion, and nameless features. No statistical wisdom is needed, and your programming abilities can variety from hobbyist to pro.

Along the best way, you'll know about sensible and object-oriented programming, working mathematical simulations, and rearranging complicated info into less complicated, extra important codecs. You'll additionally research to:
* Create crafty graphs to imagine advanced facts units and functions
* Write extra effective code utilizing parallel R and vectorization
* Interface R with C/C++ and Python for elevated velocity or functionality
* locate new applications for textual content research, photo manipulation, and hundreds of thousands more
* Squash demanding insects with complicated debugging techniques

Whether you're designing airplane, forecasting the elements, otherwise you simply have to tame your information, The artwork of R Programming is your advisor to harnessing the facility of statistical computing.

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Additional resources for The Art of R Programming: A Tour of Statistical Software Design

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I+k-1. Thus, we need to count the 1s among those days. Since we’re Vectors 37 working with 0 and 1 data, the number of 1s is simply the sum of x[j] among those days, which we can conveniently obtain as follows: sum(x[i:(i+(k-1))]) The use of sum() and vector indexing allow us to do this computation compactly, avoiding the need to write a loop, so it’s simpler and faster. This is typical R. The same is true for this expression, on line 9: mean(abs(pred-x[(k+1):n])) Here, pred contains the predicted values, while x[(k+1):n] has the actual values for the days in question.

The call form is rep(x,times), which creates a vector of times*length(x) elements—that is, times copies of x. Here is an example: > x <- rep(8,4) > x [1] 8 8 8 8 > rep(c(5,12,13),3) [1] 5 12 13 5 12 13 > rep(1:3,2) [1] 1 2 3 1 2 3 5 12 13 There is also a named argument each, with very different behavior, which interleaves the copies of x. 5 Using all() and any() The any() and all() functions are handy shortcuts. They report whether any or all of their arguments are TRUE. > x <- 1:10 > any(x > 8) [1] TRUE > any(x > 88) [1] FALSE > all(x > 88) [1] FALSE > all(x > 0) [1] TRUE For example, suppose that R executes the following: > any(x > 8) It first evaluates x > 8, yielding this: (FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE) The any() function then reports whether any of those values is TRUE.

We’ll look at some of R’s built-in help facilities and then at those available on the Internet. 1 The help() Function To get online help, invoke help(). seq 20 Chapter 1 Special characters and some reserved words must be quoted when used with the help() function. 2 The example() Function Each of the help entries comes with examples. One really nice feature of R is that the example() function will actually run those examples for you. 125 9 10 11 12 13 14 15 16 17 The seq() function generates various kinds of numeric sequences in arithmetic progression.

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The Art of R Programming: A Tour of Statistical Software Design by Norman Matloff

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