R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics)

by Brand: Addison Wesley Professional

AED 134

Retail Price:AED 260
You Save:48%

Order now to get it by: Wednesday July 05 - Saturday July 08

Expedited Shipping available

Get it on Sunday July 2nd with expedited shipping.

Select the expedited delivery option after adding this item to your cart.

Condition: New

Product ID: 286780

Delivery Information |Returns & Exchanges |Payment Methods


  • Used Book in Good Condition
  • Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals

    Using the open source R language, you can build powerful statistical models to answer many of your most challenging
    questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much
    knowledge to be of help. R for Everyone is the solution.

    Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the
    perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive,
    this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.

    Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code.
    You’ll download and install R; navigate and use the R environment; master basic program control, data import, and
    manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several
    complete models, both linear and nonlinear, and use some data mining techniques.

    By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical
    problems you care about most.


    • Exploring R, RStudio, and R packages

    • Using R for math: variable types, vectors, calling functions, and more

    • Exploiting data structures, including data.frames, matrices, and lists

    • Creating attractive, intuitive statistical graphics

    • Writing user-defined functions

    • Controlling program flow with if, ifelse, and complex checks

    • Improving program efficiency with group manipulations

    • Combining and reshaping multiple datasets

    • Manipulating strings using R’s facilities and regular expressions

    • Creating normal, binomial, and Poisson probability distributions

    • Programming basic statistics: mean, standard deviation, and t-tests

    • Building linear, generalized linear, and nonlinear models

    • Assessing the quality of models and variable selection

    • Preventing overfitting, using the Elastic Net and Bayesian methods

    • Analyzing univariate and multivariate time series data

    • Grouping data via K-means and hierarchical clustering

    • Preparing reports, slideshows, and web pages with knitr

    • Building reusable R packages with devtools and Rcpp

    • Getting involved with the R global community


    Bestsellers in Data Mining