Download Data Mining with R: Learning with Case Studies, Second by Luis Torgo PDF

By Luis Torgo

Data Mining with R: studying with Case reports, moment Edition makes use of functional examples to demonstrate the ability of R and knowledge mining. delivering an intensive 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 gives an creation to information mining, to enrich the already current advent to R. the second one half contains case experiences, and the recent version strongly revises the R code of the case stories making it extra up to date with fresh applications that experience emerged in R.

The ebook doesn't imagine any past wisdom approximately R. Readers who're new to R and information mining will be capable of persist with the case stories, and they're designed to be self-contained so the reader can begin anyplace within the rfile.

The publication is observed via a suite of freely on hand R resource documents that may be acquired on the book’s site. those documents contain the entire code utilized in the case stories, and so they facilitate the "do-it-yourself" technique within the book.

Designed for clients of information research instruments, in addition to researchers and builders, the booklet could be worthy for someone drawn to getting into the "world" of R and knowledge mining.

About the Author

Luís Torgo is an affiliate professor within the division of desktop technology on the college of Porto in Portugal. He teaches Data Mining in R in the NYU Stern university of commercial’ MS in company Analytics software. An lively researcher in computing device studying and knowledge mining for greater than twenty years, Dr. Torgo is usually a researcher within the Laboratory of man-made Intelligence and information research (LIAAD) of INESC Porto LA.

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

Example text

Length resulted in a vector! We subsetted a data frame and we obtained as result a different data structure. Length",drop=FALSE]. With tibbles this never happens. 2 Please be aware that this difference between tibbles and data frames may invalidate the use of some packages with tibbles. This will be the case if the functions of these packages somehow assume the above mentioned simplification after subsetting a single column of a Introduction to R 39 data frame. If you provide them with a tibble instead of a data frame they will not get the expected result and may return some error.

If that is the case then you need to call these functions with a standard data frame and not a tibble. The package dplyr defines the class tbl that can be regarded as a wrapper of the actual data source. These objects encapsulate the data source and provide a set of uniform data manipulation verbs irrespectively of these sources. The package currently covers several data sources like standard data frames (in the form of tibbles), several database management systems, and several other sources. The main advantages of this package are: (i) the encapsulation of the data source; (ii) providing a set of uniform data manipulation functions; and (iii) the computational efficiency of the provided functions.

The second statement uses function length() to obtain the number of values in x, that we store in another variable named n. Having these two quantities we are ready to calculate the standard error, by simply calculating the square root (function sqrt()) of the quotient of v by n. The result of this calculation is then returned back to the user by using the function return(). 3550299 In the above code we have used the function rnorm() to obtain a random sample of 100 numbers from a normal distribution with mean 20 and standard deviation 4.

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