R Programming Advanced

Course description:

In this R Programming Advanced course, you will master some more complex featured of this widely used open source language, including aggregation, plyr, dplyr, and parallel processing. With the knowledge gained in this course, you will be ready to take your R knowledge to another level. Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science. Every year, the number of R users grows by about 40%, and an increasing number of organizations are using it in their day-to-day activities. Continue your journey with R with us now!

R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. In this course we will start with some common data manipulation operations using various base R functions and packages like plyr, comparing the speed of in memory calculations. We’ll then demonstrate 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 we’ll introduce external memory and database techniques using bibmemory, ff, SciDB, dplyr and Hadoop.

Target audience:

• R programmers who already have an intermediate level of knowledge

Prerequisites for R Programming Advanced:

• Intermediate R level

Course module structure

Module 1: Reading XML Data
– Read HTML Table
– Use xpath for complex searches in HTML
– xmlToList for easier parsing
Hands-on exercises

Module 2: Faster Group Operations
– Aggregate normally
– apply
– ddply
– data.table
– dplyr
– ddply parallel
– foreach
– dplyr with a database
Hands-on exercises

Module 3: Rcpp for faster code
– Basics of C++ with R
– Writing a C++ function for R
– Using C++ code in an R package
Hands-on exercises

Module 4: Advanced Machine Learning
– Recommendation Engine with RecommenderLab
– Text Mining with RTextTools
Hands-on exercises

Module 5: Network Analysis
– igraph
– Reading edgelists
– Base plots
– tkplots
– rglplots
– Network metrics like diameter, shortest path
– Node metrics like centrality and betweenness
Hands-on exercises

Module 6: Advanced Graphics
– ggvis
– rCharts
Hands-on exercises

Module 7: Abstract data structures
– Abstract data structures
– Implementing concrete structures
– Asymptotic running time
– Experimental evaluation of algorithms
Hands-on exercises

Module 8: Immutable and persistent data
– List functions
– Trees
– Random access lists
Hands-on exercises

Module 9: Bags, stacks and queues

Module 10: Heaps
– Heaps
– Leftist heaps
– Binomial heaps
– Splay heaps
– Plotting heaps
– Sorting
Hands-on exercises

Note:

Every student has assigned to him his own virtual lab environment setup.

Additional details:

To attend this course, you need to have:
• PC/Laptop with internet access
• Updated web browser

€ 840

CATEGORY: Programming Course
DURATION: 2 days
SKILL LEVEL: Professional
LECTURES: 10 lessons