by | Released December 2015 | ISBN: 0134052706

Overview Alternative Backends for R LiveLessons teaches R programmers techniques for dealing with large data, both in memory and in databases. Description In this video training Jared starts with some common data manipulation operations using various base R functions and packages like plyr, comparing the speed of in memory calculations. He then demonstrates more advanced techniques for accomplishing the same task such as data.table, dplyr, Rcpp and parallel computation for increased speed. Finally, for when data size is an even bigger factor than speed he introduces external memory and database techniques using bibmemory, ff, SciDB, dplyr and Hadoop. About the Instructor Jared P. Lander is the Founder and CEO of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of  R for Everyone, a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble, and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. He is a member of the 2014 Strata New York selection committee.Skill Level Intermediate Advanced  What You Will Learn Basic Aggregation plyr dplyr data.table Rcpp Parallel Processing Code Benchmarking  Who Should Take This Course R programmers who already have an intermediate level of knowledge such as that gained from Reading  R for Everyone.  Course Requirements Basic Programming Skills Proficiency in R, including working with packages  Table of Contents Lesson 1: Reading XML Data1.1.  Read HTML Table1.2.  Use xpath for complex searches in HTML1.3.  xmlToList for easier parsing Lesson 2: Faster Group Operations2.1.  Aggregate normally2.2.  tapply2.3.  ddply2.4.  data.table2.5.  dplyr2.6.  ddply parallel2.7.  foreach2.8.  dplyr with a database Lesson 3: Rcpp for faster code3.1.  Basics of C++ with R3.2.  Writing a C++ function for R3.3.  Using C++ code in an R package Lesson 4: Advanced Machine Learning4.1.  Recommendation Engine with RecommenderLab4.2.  Text Mining with RTextTools Lesson 5: Network Analysis5.1.  igraph5.2.  Reading edgelists5.3.  Base plots5.4.  tkplots5.5.  rglplots5.6.  Network metrics like diameter, shortest path5.7.  Node metrics like centrality and betweenness Lesson 6: Advanced Graphics6.1.  ggvis6.2.  rCharts About LiveLessons Video Training LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more. View all LiveLessons on InformIT at: http://www.informit.com/livelessons. Show and hide more

1. Introduction
   • Advanced R Programming Part I LiveLessons: Introduction 00:01:17
2. Lesson 1: Reading XML Data
   • Learning Objectives 00:00:26
   • 1.1 Read an HTML table 00:03:22
   • 1.2 Use XPath for complex searches in HTML 00:18:50
   • 1.3 Use xmlToList for easier parsing 00:05:31
3. Lesson 2: Faster Group Operations
   • Learning Objectives 00:00:26
   • 2.1 Aggregate using formula notation with the aggregate function and tapply 00:04:29
   • 2.2 Use ddply for convenient aggregation 00:02:58
   • 2.3 Process in parallel with ddply’s parallel option 00:03:31
   • 2.4 Use data.table for faster aggregation 00:02:41
   • 2.5 Use dplyr for convenient and fast aggregation 00:04:46
   • 2.6 Operate in a database using dplyr 00:04:51
4. Lesson 3: Rcpp for Faster Code
   • Learning Objective 00:00:19
   • 3.1 Understand the basics of C++ with R 00:01:48
   • 3.2 Write a C++ function for R 00:04:35
   • 3.3 Using Rcpp Syntactic Sugar 00:05:51
   • 3.4 Summing in C++ 00:05:35
   • 3.5 Writing a package in R Refresher 00:09:37
   • 3.6 Writing a Package with C++ Code 00:06:05
5. Lesson 4: Advanced Machine Learning
   • Learning Objectives 00:00:18
   • 4.1 Build a recommendation engine with RecommenderLab 00:13:13
   • 4.2 Mine text with RTextTools 00:09:14
6. Lesson 5: Network Analysis
   • Learning Objectives 00:00:18
   • 5.1 Get started with igraph 00:08:16
   • 5.2 Read edgelists 00:07:12
   • 5.3 Common graph metrics 00:10:12
   • 5.4 Advanced graph plotting 00:06:00
   • 5.5 Centrality measures 00:04:16
   • 5.6 Interactive Graph Plot 00:02:51
7. Lesson 6: Web Graphics
   • Learning Objectives 00:00:20
   • 6.1 Build vega based web plots using ggvis 00:13:11
   • 6.2 Build D3 based web plots with rCharts 00:16:49
8. Lesson 7: Easier Presentations and Documents with RMarkdown
   • Learning Objectives 00:00:22
   • 7.1 Basics of RMarkdown 00:04:55
   • 7.2 Convert Markdown files to Word 00:01:30
   • 7.3 Convert Markdown to PDF 00:01:25
   • 7.4 Create slideshows with RMarkdown 00:03:10
   • 7.5 Write equations with RMarkdown 00:07:14
9. Summary
   • Advanced R Programming Part I LiveLessons: Summary 00:00:44
Show and hide more