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R for data science

Author: Dan Toomey
Publisher: Birmingham, UK : Packt Publishing, 2014.
Series: Community experience distilled.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
R is a powerful, open source, functional programming language. It can be used for a wide range of programming tasks and is best suited to produce data and visual analytics through customizable scripts and commands. The purpose of the book is to explore the core topics that data scientists are interested in. This book draws from a wide variety of data sources and evaluates this data using existing publicly available  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Toomey, Dan.
R for data science : learn and explore the fundamentals of data science with R.
Birmingham, England : Packt Publishing, ©2014
xxv, 389 pages
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Dan Toomey
ISBN: 132256535X 9781322565354 9781784392659 1784392650 1784390860 9781784390860
OCLC Number: 900882999
Language Note: English.
Notes: "Learn and explore the fundamentals of data science with R."
Includes index.
Description: 1 online resource (1 volume) : illustrations.
Contents: Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Mining Patterns; Cluster analysis; K-means clustering; Usage; Example; K-medoids clustering; Usage; Example; Hierarchical clustering; Usage; Example; Expectation-maximization; Usage; List of model names; Example; Density estimation; Usage; Example; Anomaly detection; Show outliers; Example; Example; Another anomaly detection example; Calculating anomalies; Usage; Example 1; Example 2; Association rules; Mine for associations; Usage; Example; Questions; Summary. Chapter 2: Data Mining SequencesPatterns; Eclat; Usage; Using eclat to find similarities in adult behavior; Finding frequent items in a dataset; An example focusing on highest frequency; arulesNBMiner; Usage; Mining the Agrawal data for frequent sets; Apriori; Usage; Evaluating associations in a shopping basket; Determining sequences using TraMineR; Usage; Determining sequences in training and careers; Similarities in the sequence; Sequence metrics; Usage; Example; Questions; Summary; Chapter 3: Text Mining; Packages; Text processing; Example; Creating a corpus; Text clusters; Word graphics. Analyzing the XML textQuestions; Summary; Chapter 4: Data Analysis --
Regression Analysis; Packages; Simple regression; Multiple regression; Multivariate regression analysis; Robust regression; Questions; Summary; Chapter 5: Data Analysis --
Correlation; Packages; Correlation; Example; Visualizing correlations; Covariance; Pearson correlation; Polychoric correlation; Tetrachoric correlation; A heterogeneous correlation matrix; Partial correlation; Questions; Summary; Chapter 6: Data Analysis --
Clustering; Packages; K-means clustering; Example; Optimal number of clusters; Medoids clusters. The cascadeKM functionSelecting clusters based on Bayesian information; Affinity propagation clustering; Gap statistic to estimate the number of clusters; Hierarchical clustering; Questions; Summary; Chapter 7: Data Visualization --
R Graphics; Packages; Interactive graphics; The latticist package; Bivariate binning display; Mapping; Plotting points on a map; Plotting points on a world map; Google Maps; The ggplot2 package; Questions; Summary; Chapter 8: Data Visualization --
Plotting; Packages; Scatter plots; Regression line; A lowess line; scatterplot; Scatterplot matrices. Splom --
display matrix datacpairs --
plot matrix data; Density scatter plots; Bar charts and plots; Bar plot; Usage; Bar chart; ggplot2; Word cloud; Questions; Summary; Chapter 9: Data Visualization --
3D; Packages; Generating 3D graphics; Lattice Cloud --
3D scatterplot; scatterplot3d; scatter3d; cloud3d; RgoogleMaps; vrmlgenbar3D; Big Data; pbdR; bigmemory; Research areas; Rcpp; parallel; microbenchmark; pqR; SAP integration; roxygen2; bioconductor; swirl; pipes; Questions; Summary; Chapter 10: Machine Learning in Action; Packages; Dataset; Data partitioning; Model; Linear model; Prediction.
Series Title: Community experience distilled.
Responsibility: Dan Toomey.

Abstract:

R is a powerful, open source, functional programming language. It can be used for a wide range of programming tasks and is best suited to produce data and visual analytics through customizable scripts and commands. The purpose of the book is to explore the core topics that data scientists are interested in. This book draws from a wide variety of data sources and evaluates this data using existing publicly available R functions and packages. In many cases, the resultant data can be displayed in a graphical form that is more intuitively understood. You will also learn about the often needed and frequently used analysis techniques in the industry. By the end of the book, you will know how to go about adopting a range of data science techniques with R.

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