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74309d7132 Full of example and guidelines. Link to the book shop here: R in Nutshell, 2nd Edition Note: This book review is in exchange of the O'Reilly Blogger Review Program ( Line Yes, I would recommend this to a friend(2 of 2 customers found this review helpful)Was this review helpful?Yes/No-You may also flag this reviewDisplaying reviews 1-9Back to top Immediate Access - Go Digitalwhat's this? Ebook: $41.99 Formats: DAISY, ePub, Mobi, PDF Print & Ebook: $54.99 Print: $49.99 Safari Books Online - Read now > Essential Links Register Your Book View/Submit Errata Media Praise Ask a Question Bulk Discounts & Licensing Twitter YouTube Slideshare Facebook Google Plus RSS View All RSS Feeds > 2016, O'Reilly Media, Inc. The eagle is named after the harpies of ancient Greek mythology,female wind spirits who were said to be human from the chest to theirankles and eagle from the neck up. Pinterest is using cookies to help give you the best experience we can. This is a good book if you want a reference for R.Bottom Line Yes, I would recommend this to a friend(2 of 2 customers found this review helpful)Was this review helpful?Yes/No-You may also flag this review11/9/2012(2 of 2 customers found this review helpful)5.0More than a "quick" reference book. I used to read guides online/other-books and then I used to combine information from this book to get stuff done. Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.
I have resorted back to just Googling my questions.I stuck with the book for about 2 weeks hoping as I learned its organization it would become useful. RStudio and ggplot2 are among them. Often another language will be used to format and prepare the data for analysis (to get an idea of how that process works, O'Reilly's Exploring Everyday Things with R and Ruby is a good place to start).There's also an entire section dedicated to one of R's specialties, data visualization! The book covers the built-in visualization tools, as well as popular ggplot2 package. One of the books that I used frequently during the research project was "R in nutshell". The examples are not clarifying. There are a lot of information on where getting the data and how tackle the visualization, important part for decoding and analysing many data of multidimensional space. Book Description:. Book covers most recent release of R (at least when it comes to Mac OS, I can't tell for the other systems). Get started quickly with an R tutorial and hundreds of examples Explore R syntax, objects, and other language details Find thousands of user-contributed R packages online, including Bioconductor Learn how to use R to prepare data for analysis Visualize your data with Rs graphics, lattice, and ggplot2 packages Use R to calculate statistical fests, fit models, and compute probability distributions Speed up intensive computations by writing parallel R programs for Hadoop Get a complete desktop reference to R R Basics Chapter 1 Getting and Installing R R Versions Getting and Installing Interactive R Binaries Chapter 2 The R User Interface The R Graphical User Interface The R Console Batch Mode Using R Inside Microsoft Excel RStudio Other Ways to Run R Chapter 3 A Short R Tutorial Basic Operations in R Functions Variables Introduction to Data Structures Objects and Classes Models and Formulas Charts and Graphics Getting Help Chapter 4 R Packages An Overview of Packages Listing Packages in Local Libraries Loading Packages Exploring Package Repositories Installing Packages From Other Repositories Custom Packages The R Language Chapter 5 An Overview of the R Language Expressions Objects Symbols Functions Objects Are Copied in Assignment Statements Everything in R Is an Object Special Values Coercion The R Interpreter Seeing How R Works Chapter 6 R Syntax Constants Operators Expressions Control Structures Accessing Data Structures R Code Style Standards Chapter 7 R Objects Primitive Object Types Vectors Lists Other Objects Attributes Chapter 8 Symbols and Environments Symbols Working with Environments The Global Environment Environments and Functions Exceptions Chapter 9 Functions The Function Keyword Arguments Return Values Functions as Arguments Argument Order and Named Arguments Side Effects Chapter 10 Object-Oriented Programming Overview of Object-Oriented Programming in R Object-Oriented Programming in R: S4 Classes Old-School OOP in R: S3 Working with Data Chapter 11 Saving, Loading, and Editing Data Entering Data Within R Saving and Loading R Objects Importing Data from External Files Exporting Data Importing Data From Databases Getting Data from Hadoop Chapter 12 Preparing Data Combining Data Sets Transformations Binning Data Subsets Summarizing Functions Data Cleaning Finding and Removing Duplicates Sorting Data Visualization Chapter 13 Graphics An Overview of R Graphics Graphics Devices Customizing Charts Chapter 14 Lattice Graphics History An Overview of the Lattice Package High-Level Lattice Plotting Functions Customizing Lattice Graphics Low-Level Functions Chapter 15 ggplot2 A Short Introduction The Grammar of Graphics A More Complex Example: Medicare Data Quick Plot Creating Graphics with ggplot2 Learning More Statistics with R Chapter 16 Analyzing Data Summary Statistics Correlation and Covariance Principal Components Analysis Factor Analysis Bootstrap Resampling Chapter 17 Probability Distributions Normal Distribution Common Distribution-Type Arguments Distribution Function Families Chapter 18 Statistical Tests Continuous Data Discrete Data Chapter 19 Power Tests Experimental Design Example t-Test Design Proportion Test Design ANOVA Test Design Chapter 20 Regression Models Example: A Simple Linear Model Details About the lm Function Subset Selection and Shrinkage Methods Nonlinear Models Survival Models Smoothing Machine Learning Algorithms for Regression Chapter 21 Classification Models Linear Classification Models Machine Learning Algorithms for Classification Chapter 22 Machine Learning Market Basket Analysis Clustering Chapter 23 Time Series Analysis Autocorrelation Functions Time Series Models Additional Topics Chapter 24 Optimizing R Programs Measuring R Program Performance Optimizing Your R Code Other Ways to Speed Up R Chapter 25 Bioconductor An Example Key Bioconductor Packages Data Structures Where to Go Next Chapter 26 R and Hadoop R and Hadoop Other Packages for Parallel Computation with R Where to Learn More Appendix R Reference base boot class cluster codetools foreign grDevices graphics grid KernSmooth lattice MASS methods mgcv nlme nnet rpart spatial splines stats stats4 survival tcltk tools utils Bibliography Colophon Title: R in a Nutshell, 2nd Edition By: Joseph Adler Publisher: O'Reilly Media Formats: Print Ebook Safari Books Online Print: October 2012 Ebook: September 2012 Pages: 724 Print ISBN: 978-1-4493-1208-4 ISBN 10: 1-4493-1208-X Ebook ISBN: 978-1-4493-1207-7 ISBN 10: 1-4493-1207-1 Joseph Adler Joseph Adler has many years of experience in data mining and data analysis at companies including DoubleClick, American Express, and VeriSign.