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Introduction to R

Course Summary

The Introduction to R training course is designed to demonstrate the fundamentals of the R programming language required to perform data analytics.

The course begins by teaching the language fundamentals, commonly used libraries, and advanced concepts necessary for data analytics. Next, it explores common data analytics. The course concludes with graphing best practices.

Purpose
 Learn how to leverage R to perform data analytics and graph best practices.
Audience
 Developers, data scientists, and individuals responsible for creating analytics software.
Role
Software Developer
Skill Level
Intermediate
Style
Hack-a-thon - Learning Spikes - Workshops
Duration
3 Days
Related Technologies
R | Python

 

Productivity Objectives
  • Describe how R and the R environment can be leveraged to perform data analytics
  • Create a simple application using R
  • Evaluate R against other analytics strategies like SAS, SPSS, Stata, etc.

What You'll Learn:

In the Introduction to R training course, you'll learn:
  • What is Data Science
    • Understand the history of data science
    • Definition of Data Science
    • Commonly used tools and technologies
    • Areas of application
    • Understand the Data Science Process
  • Introduction to R Language
    • What is R?
    • R compared to other programming languages
    • Set up the R environment
  • R Language Fundamentals
    • Core R syntax concepts
    • Variables and Types
    • Control Structures (Loops / Conditionals)
    • Write a first R program
  • Working with R
    • R Scalars, vectors, and matrices
    • Define R vectors
    • String and text manipulation
    • File IO
  • Working with R Continued
  • Lists
  • Functions
    • Introduce functions
    • Closures
    • lapply/sapply functions
  • DataFrames
  • Working with Data
    • DataFrames and File I/O
    • Read data from files
    • Data preparation
    • Built-in datasets
  • Visualization
    • Graphics Package
    • plot()/barplot()/hist()/boxplot()/scatter plot
    • Heat Map
    • ggplot2 package (qplot(), ggplot())
    • Exploration With Dplyr
  • Statistical Modeling With R
    • Statistical Functions
    • Deal with NA
    • Distributions (Binomial, Poisson, Normal)
  • Regression
    • Understand linear regressions
    • Implement linear regressions within R
  • Recommendations
  • Text Processing (tm package / Wordclouds)
  • Clustering
    • Introduction to Clustering
    • KMeans
  • Classification
    • What is classification?
    • Naive bayes
    • Decision trees
    • Train using caret package
    • Evaluate Algorithms
  • Leveraging Big Data with R
    • Understand the Big Data Ecosystem
    • Introduction to Hadoop
    • Incorporate R with Hadoop using RHadoop
  • Q/A
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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