Download e-book for iPad: Data Mining with R: Learning with Case Studies, Second by Luis Torgo

By Luis Torgo

Data Mining with R: studying with Case reviews, moment Edition makes use of sensible examples to demonstrate the facility of R and information mining. offering an in depth replace to the best-selling first variation, this new version is split into elements. the 1st half will characteristic introductory fabric, together with a brand new bankruptcy that offers an advent to info mining, to counterpoint the already present advent to R. the second one half comprises case reports, and the recent variation strongly revises the R code of the case stories making it extra up to date with contemporary programs that experience emerged in R.

The booklet doesn't suppose any past wisdom approximately R. Readers who're new to R and knowledge mining can be capable of stick to the case stories, and they're designed to be self-contained so the reader can begin anyplace within the record.

The booklet is observed by way of a suite of freely to be had R resource records that may be acquired on the book’s website. those documents comprise all of the code utilized in the case reviews, they usually facilitate the "do-it-yourself" technique within the book.

Designed for clients of information research instruments, in addition to researchers and builders, the e-book might be important for someone drawn to getting into the "world" of R and information mining.

About the Author

Luís Torgo is an affiliate professor within the division of laptop technological know-how on the college of Porto in Portugal. He teaches Data Mining in R in the NYU Stern college of industrial’ MS in company Analytics software. An lively researcher in desktop studying and knowledge mining for greater than two decades, Dr. Torgo can also be a researcher within the Laboratory of synthetic Intelligence and knowledge research (LIAAD) of INESC Porto LA.

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Extra resources for Data Mining with R: Learning with Case Studies, Second Edition

Example text

Let us see how to create factors in R. Suppose you have a vector with the sex of ten individuals: > g <- c("f", "m", "m", "m", "f", "m", "f", "m", "f", "f") > g [1] "f" "m" "m" "m" "f" "m" "f" "m" "f" "f" You can transform this vector into a factor by: 20 Data Mining with R: Learning with Case Studies > g <- factor(g) > g [1] f m m m f m f m f f Levels: f m Notice that you do not have a character vector anymore. 19 In this example, we have two levels, ‘f’ and ‘m’, which are represented internally as 1 and 2, respectively.

Function switch() for instance, allows us to compare the contents of a variable (to in the above code), against a set of options. For each option we can supply the value that will be the result of the function switch(). 3701. The function also allows to supply a return value in case the variable does not match any of the alternatives. In this case we are returning the special value NA. The goal here is to foresee situations where the user supplies a target unit that is unknown to this function.

This means that in our example we have decided that to calculate the standard error of the sample mean of a set of numbers it would be sufficient to execute the above 3 statements. The first of these calls the function var() with the content of the variable x. This variable is a parameter of the function. Parameters are special variables that will hold the values supplied in the arguments of the function when the user calls it. This means that whenever some user calls our se function he will have to supply a set of values in the first (and only) argument of this function.

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