Showing posts with label r language. Show all posts
Showing posts with label r language. Show all posts

Using R for Introductory Statistics Review

Using R for Introductory Statistics
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Using R for Introductory Statistics ReviewThis book doesn't show up under most listings for books about R, but it should. It's a very solid introduction to using R -- including installation, configuration, and some progrmaming -- for basic statistical work. My only complaint is that it wasn't quite comprehensive enough -- not enough examples were given and not enough discussion on important functions and parameters were present. Also, the index is atrocious.
I would recommend it as a good book to get going, but for in depth work you'll be referring to the HTML help a lot.Using R for Introductory Statistics OverviewThe cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models.This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. This is an ideal text for integrating the study of statistics with a powerful computational tool.

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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies Review

Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies
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Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies ReviewQuite introductory material, good for beginners (students as well as teachers planning a course) and strong in showing the links between critical discourse analysis and corpus linguistics.Introducing Electronic Text Analysis: A Practical Guide for Language and Literary Studies OverviewIntroducing Electronic Text Analysis is a practical and much needed introduction to corpora-bodies of linguistic data. Written specifically for students studying this topic for the first time, the book begins with a discussion of the underlying principles of electronic text analysis. It then examines how these corpora enhance our understanding of literary and non-literary works. In the first section the author introduces the concepts of concordance and lexical frequency, concepts whichare then applied to a range of areas of language study. Key areas examined are the use of on-line corpora to complement traditional stylistic analysis, and the ways in which methods such as concordance and frequency counts can reveal a particular ideology within a text. Presenting an accessible and thorough understanding of the underlying principles of electronic text analysis, the book contains abundant illustrative examples and a glossary with definitions of main concepts. Itwill also besupported by a companion website with links to on-line corpora so that students can apply their knowledge to further study. The accompanying website to this book can be found at http://www.routledge.com/textbooks/0415320216

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R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) Review

R Programming for Bioinformatics (Chapman and Hall/CRC Computer Science and Data Analysis)
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R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) ReviewThis is a strange little book in that it seems somewhat directed toward statisticians who want to develop R packages. The OOP section takes up 50 pages and discusses "S3 and S4" implementations of OOP in R in great detail, all of which is not doubt important for those few dozen accomplished statisticians who wish to write packages. However, by the time you are ready to actually write an R function that other people will use I can't imagine you wouldn't already be familiar with some of the basic commands discussed elsewhere in this book. So I am wondering who the intended audience is.
I think the majority of R users (biologists and programmers) want to run through some common statistical routines in a procedural fashion and produce reports that perform some analysis and show some graphs. The difficulty with R is learning how to massage data into a form that an existing statistical function will accept. That will invariably involve helper R-specific helper functions that do not exist in programming languages (e.g. unsplit) or that require a precise understanding of input (e.g. xtabs), and statistical routines that almost never return meaningful errors (glm). Manipulating data structures in R is not particularly intuitive (e.g. as.numeric(levels(f))[f]), so tons of examples are a must. However this book simply does not include enough R code - probably fewer than 250 lines.
In some instances commands are discussed at length in the space it would take to simply show the command. For example, a beginner would want to know how to save a data frame. Instead of providing a useful example like:
save(myDataFrame,file="myDataFrame.frame.RData",compress=TRUE)
there is a bizarre paragraph called "Working with R's binary format", in which save and load are discussed in theory as if they are planned for a distant release.
There is no chapter on using Sweave to develop pdf reports despite the book being actually written in Sweave. The author is more focused on "vignettes" which appear to be for documentation akin to POD files.
This book does include excellent sections on string manipulation, connecting to databases, and C integration. I learned some things about some neat Bioconductor functions available but a dedicated chapter would be nice.
At no point do you ever sense the author does not know what he is talking about - he just doesn't know who he is talking to. I hope in the future "R Programming For Bioinformatics" is split this into two more comprehensive books: "Developing R Packages" and "R for Biologists"R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) OverviewDue to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.Drawing on the author's first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code.With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.

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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) Review

From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library)
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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) ReviewIt is easy to read with lots of examples. Gives planty of ideas if you are interested in corpus study.From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) OverviewFrom Corpus to Classroom summarises and makes accessible recent work in corpus research, focusing particularly on spoken data. It is based on analysis of corpora such as CANCODE and Cambridge International Corpus, and written with particular reference to the development of corpus-informed pedagogy.The book explains how corpora can be designed and used, and focuses on what they tell us about language teaching. It examines the relevance of corpora to materials writers, course designers and language teachers and considers the needs of the learner in relation to authentic data. It shows how the answers to key questions such as 'Is there a basic, everyday vocabulary for English?', 'How should idioms be taught?' and 'What are the most common spoken language chunks?' are best explored by means of a clearer understanding of the workings of language in context.

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