Please contact us
for GSA pricing.

Contract #

R Programming Training: Introduction to R Programming

4.4 out of 5 ( 49 reviews)

Course Number: RPROG-100
Duration: 4 days
view course outline

R Programming Training Overview

Accelebrate's Introduction to R Programming training course teaches attendees how to use R programming to compute statistics and generate charts, graphs, and other data representations.

Location and Pricing

Most Accelebrate courses are delivered as private, customized, on-site training at our clients' locations worldwide for groups of 3 or more attendees and are custom tailored to their specific needs. Please visit our client list to see organizations for whom we have delivered private in-house training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote for private training at your site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

R Programming Training Prerequisites

Students should have knowledge of basic statistics (t-test, chi-square-test, regression) and know the difference between descriptive and inferential statistics. No programming experience is needed.

Hands-on/Lecture Ratio

This R Programming training class is 70% hands-on, 30% lecture, with the longest lecture segments lasting for 15 minutes. Students "learn by doing," with immediate opportunities to apply the material they learn to real-world problems.

R Programming Training Materials

All R Programming training students receive a copy of Manning's R in Action and related courseware.

Software Needed on Each Student PC

  • R 3.0 or later with console
  • IDE or text editor of your choice (RStudio recommended)

R Programming Training Objectives

  • Master the use of the R Console
  • Learn R flow control and data structures
  • Use vectorized calculations
  • Write R functions
  • Understand basic R graphics
  • Be familiar with advanced graphics (Lattice, GGplot2)
  • Discover how to install R packages
  • Explore the R documentation
  • Learn to avoid R pitfalls
  • Use R for descriptive statistics
  • Use R for confirmatory/inferential statistics
  • Write R statistical models

R Programming Training Outline

  • Overview
    • History of R
    • Advantages and disadvantages
    • Downloading and installing
    • How to find documentation
  • Introduction
    • Using the R console
    • Getting help
    • Learning about the environment
    • Writing and executing scripts
    • Saving your work
  • Data Structures, Variables
    • Variables and assignment
    • Data types
    • Indexing, subsetting
    • Viewing data and summaries
    • Functions
    • Naming conventions
    • Objects
    • Models
    • Graphics
  • Control Flow
    • Truth testing
    • Branching
    • Looping
    • Vectorized calculations
  • Functions
    • Parameters
    • Return values
    • Variable scope
    • Exception Handling
  • Getting Data into the R environment
    • Builtin data
    • Reading local data
    • Web data
  • Overview of Statistics in R
    • Introduction to R Graphics
    • Model notation
  • Descriptive statistics
    • Continuous data
      • Scatter plot
      • Box plot
    • Categorical data
      • Mosaic plot
    • Correlation
  • Inferential statistics
    • T-test and non-parametric equivalents
    • Chi-squared test, logistic regression
    • Distribution testing
    • Power testing
  • Linear Regression
    • Linear models
    • Regression plots
    • ANOVA
  • Other Topics
    • Classification
    • Clustering
    • Time series
    • Dimensionality reduction
    • Machine Learning
  • Object Oriented R
    • Generic functions
    • S3/S4 classes
  • Installing Packages
    • Finding resources
    • Installing resources
  • More about Graphics
    • Labels
    • Exporting graphics
  • Sophisticated Graphics in R
    • Lattice
    • GGplot2
    • Interactive graphics
    • Animated GIF
    • rGGobi
  • R for Mapping and GIS
    • Choropleth maps
    • Layers
  • Conclusion