Mastering data analysis with R : gain clear insights into your data and solve real-world data science problems with R-- from data munging to modeling and visualization / Gergely Daróczi.

Format:

Book

Author/Creator:

Daróczi, Gergely, author.

Series:

Community experience distilled.

Community experience distilled

Language:

English

Subjects (All):

R (Computer program language).

Data mining.

Physical Description:

1 online resource (397 p.)

Edition:

1st edition

Other Title:

Gain clear insights into your data and solve real-world data science problems with R--from data munging to modeling and visualization

Place of Publication:

Birmingham : Packt Publishing, 2015.

Language Note:

English

System Details:

text file

Summary:

Gain sharp insights into your data and solve real-world data science problems with R-from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Contents:

Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Hello, Data!; Loading text files of a reasonable size; Data files larger than the physical memory; Benchmarking text file parsers; Loading a subset of text files; Filtering flat files before loading to R; Loading data from databases; Setting up the test environment; MySQL and MariaDB; PostgreSQL; Oracle database; ODBC database access; Using a graphical user interface to connect to databases; Other database backends; Importing data from other statistical systems

Loading Excel spreadsheetsSummary; Chapter 2: Getting Data from the Web; Loading datasets from the Internet; Other popular online data formats; Reading data from HTML tables; Reading tabular data from static Web pages; Scraping data from other online sources; R packages to interact with data source APIs; Socrata Open Data API; Finance APIs; Fetching time series with Quandl; Google documents and analytics; Online search trends; Historical weather data; Other online data sources; Summary; Chapter 3: Filtering and Summarizing Data; Drop needless data; Drop needless data in an efficient way

Drop needless data in another efficient wayAggregation; Quicker aggregation with base R commands; Convenient helper functions; High-performance helper functions; Aggregate with data.table; Running benchmarks; Summary functions; Adding up the number of cases in subgroups; Summary; Chapter 4: Restructuring Data; Transposing matrices; Filtering data by string matching; Rearranging data; dplyr versus data.table; Computing new variables; Memory profiling; Creating multiple variables at a time; Computing new variables with dplyr; Merging datasets; Reshaping data in a flexible way

Converting wide tables to the long table formatConverting long tables to the wide table format; Tweaking performance; The evolution of the reshape packages; Summary; Chapter 5: Building Models (authored by Renata Nemeth and Gergely Toth); The motivation behind multivariate models; Linear regression with continuous predictors; Model interpretation; Multiple predictors; Model assumptions; How well does the line fit in the data?; Discrete predictors; Summary; Chapter 6: Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth); The modeling workflow; Logistic regression

Data considerationsGoodness of model fit; Model comparison; Models for count data; Poisson regression; Negative binomial regression; Multivariate non-linear models; Summary; Chapter 7: Unstructured Data; Importing the corpus; Cleaning the corpus; Visualizing the most frequent words in the corpus; Further cleanup; Stemming words; Lemmatisation; Analyzing the associations among terms; Some other metrics; The segmentation of documents; Summary; Chapter 8: Polishing Data; The types and origins of missing data; Identifying missing data; By-passing missing values

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Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed January 6, 2016).

ISBN:

9781783982035

1783982039

OCLC:

926118236