Introduction to R | R Programming JumpStart Course Details:

R is an open-source programming language used for statistical computing, data analysis, and graphics. It’s used by a growing number of business and data analysts, statisticians, engineers, and scientists. This is because it’s a language that many non-programmers can easily work with, naturally extending a skill set that is common to high-end Excel users. It also has an wide variety of packages for data mining and for optimizing models. It's the perfect tool for when you have a statistical, numerical, or probabilities problems based on real data and you’ve pushed Excel past its limits.

    No classes are currenty scheduled for this course.

    Call (919) 283-1653 to get a class scheduled online or in your area!


  • Making R more friendly, R and available GUIs
  • The R environment
  • Related software and documentation
  • R and statistics
  • Using R interactively
  • An introductory session
  • Getting help with functions and features
  • R commands, case sensitivity, etc.
  • Recall and correction of previous commands
  • Executing commands from or diverting output to a file
  • Data permanency and removing objects

Simple manipulations, numbers and vectors

  • Vectors and assignment
  • Vector arithmetic
  • Generating regular sequences
  • Logical vectors
  • Missing values
  • Character vectors
  • Index vectors, selecting and modifying subsets of a data set
  • Other types of objects

Objects, their modes and attributes

  • Intrinsic attributes: mode and length
  • Changing the length of an object
  • Getting and setting attributes
  • The class of an object

Ordered and unordered factors

  • A specific example
  • The function tapply() and ragged arrays
  • Ordered factors

Arrays and matrices

  • Arrays
  • Array indexing. Subsections of an array
  • Index matrices
  • The array() function
  • The outer product of two arrays
  • Generalized transpose of an array
  • Matrix facilities
  • Forming partitioned matrices, cbind() and rbind()
  • The concatenation function, (), with arrays
  • Frequency tables from factors

Lists and data frames

  • Lists
  • Constructing and modifying lists
  • Data frames

Reading data from files

  • The read.table()function
  • The scan() function
  • Accessing builtin datasets
  • Editing data

Probability distributions

  • R as a set of statistical tables
  • Examining the distribution of a set of data
  • One- and two-sample tests

Grouping, loops and conditional execution

  • Grouped expressions
  • Control statements

Writing your own functions

  • Simple examples
  • Defining new binary operators
  • Named arguments and defaults
  • The '...' argument
  • Assignments within functions
  • More advanced examples
  • Scope
  • Customizing the environment
  • Classes, generic functions and object orientation

Statistical models in R

  • Defining statistical models; formulae
  • Linear models
  • Generic functions for extracting model information
  • Analysis of variance and model comparison
  • Updating fitted models
  • Generalized linear models
  • Nonlinear least squares and maximum likelihood models
  • Some non-standard models

Graphical procedures

  • High-level plotting commands
  • Low-level plotting commands
  • Interacting with graphics
  • Using graphics parameters
  • Graphics parameters list
  • Device drivers
  • Dynamic graphics


  • Standard packages
  • Contributed packages and CRAN
  • Namespaces

*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.

Join an engaging hands-on learning environment, where you’ll:

  • Manipulate objects in R and read data
  • Access R packages
  • Write R functions
  • Develop informative graphs
  • How to analyze data using common statistical models
  • Use R software via the command line and a graphical user interface (GUI)

This course has a 40% hands-on labs to 60% lecture ratio with engaging instruction, demos, group discussions, labs, and project work



This “skills-centric” course is about 50% hands-on lab and 50% lecture, designed to train attendees in core R programming and data analytics skills, coupling the most current, effective techniques with the soundest industry practices. Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review.

Before attending this course, you should have:

  • Hands-on experience with another programming language
  • Exposure to working with statistics and probability
  • Experience working with Excel


Data Scientist, Data Analyst, Data Architect, Statistician, Data Engineer, Developer, and Database Administrators who need to leverage R for analytics.

Ready to Jumpstart Your IT Career?