Alternative Backends for R LiveLessons teaches R programmers techniques for dealing with large data, both in memory and in databases.
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.
What You Will Learn
Who Should Take This Course
Table of Contents
Lesson 1: Reading XML Data
1.1. Read HTML Table
1.2. Use xpath for complex searches in HTML
1.3. xmlToList for easier parsing
Lesson 2: Faster Group Operations
2.1. Aggregate normally
2.6. ddply parallel
2.8. dplyr with a database
Lesson 3: Rcpp for faster code
3.1. Basics of C++ with R
3.2. Writing a C++ function for R
3.3. Using C++ code in an R package
Lesson 4: Advanced Machine Learning
4.1. Recommendation Engine with RecommenderLab
4.2. Text Mining with RTextTools
Lesson 5: Network Analysis
5.2. Reading edgelists
5.3. Base plots
5.6. Network metrics like diameter, shortest path
5.7. Node metrics like centrality and betweenness
Lesson 6: Advanced Graphics
About LiveLessons Video Training
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