9 R Most Popular O’Reilly Books
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data [Read online]
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
- Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI [Read online]
Machine learning has finally come of age. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work.
- R in a Nutshell: A Desktop Quick Reference (In a Nutshell (O’Reilly)) [Read online]
You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Updated for R 2. 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 R’s 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 Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O’Reilly Cookbooks) [Read online]
If you’re a beginner, R Cookbook will help get you started. “—Jeffrey Ryan, software consultant and R package author With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. “—Jeffrey Ryan, software consultant and R package author.
- Text Mining with R: A tidy approach
You’ll discover how tidy data principles can make text mining easier, more effective, and consistent by employing tools already in wide use. Along with tidy data methods, you’ll also examine several beginning-to-end tidy text analyses on data sources from Twitter to NASA datasets. Get real-world examples for implementing text mining using tidy R package Understand natural language processing concepts like sentiment analysis, tf-idf, and topic modeling Learn how to analyze unstructured, text-heavy data using R language and ecosystem.
- R Graphics Cookbook: Practical Recipes for Visualizing Data [Read online]
Use R’s default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and other examples Provide annotations to help viewers interpret data Control the overall appearance of graphics Render data groups alongside each other for easy comparison Use colors in plots Create network graphs, heat maps, and 3D scatter plots Structure data for graphing Q&A with Winston Chang, author of “R Graphics Cookbook: Practical Recipes for Visualizing Data” Q. Interest in R for data analysis and visualization has exploded in recent years. Use R’s default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and other examples Provide annotations to help viewers interpret data Control the overall appearance of graphics Render data groups alongside each other for easy comparison Use colors in plots Create network graphs, heat maps, and 3D scatter plots Structure data for graphing.
- Efficient R Programming: A Practical Guide to Smarter Programming [Read online]
There are many excellent R resources for visualization, data science, and package development. This hands-on book teaches novices and experienced R users how to write efficient R code. Get advice for setting up an R programming environment Explore general programming concepts and R coding techniques Understand the ingredients of an efficient R workflow Learn how to efficiently read and write data in R Dive into data carpentry—the vital skill for cleaning raw data Optimize your code with profiling, standard tricks, and other methods Determine your hardware capabilities for handling R computation Maximize the benefits of collaborative R programming Accelerate your transition from R hacker to R programmer.
- The Book of R: A First Course in Programming and Statistics [Read online]
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. Make The Book of R your doorway into the growing world of data analysis.
- Big Data Analytics with R [Read online]
R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. What you will learn Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e. Table of Contents The Era of Big Data Introduction to R Programming Language and Statistical Environment Unleashing the Power of R from Within Hadoop and MapReduce Framework for R R with Relational Database Management Systems (RDBMSs) R with Non-Relational (NoSQL) Databases Faster than Hadoop – Spark with R Machine Learning Methods for Big Data in R The Future of R – Big, Fast, and Smart Data.
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