Cart () Loading...

    • Quantity:
    • Delivery:
    • Dates:
    • Location:


Introduction to R | R Programming JumpStart

Learn to program in R for statistical computing, data analysis, and graphics.

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.

GK# 9250 Vendor# TTDS6680
Vendor Credits:
No matching courses available.
Start learning as soon as today! Click Add To Cart to continue shopping or Buy Now to check out immediately.
Access Period:
Scheduling a custom training event for your team is fast and easy! Click here to get started.

Who Should Attend?

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

What You'll Learn

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



Course Outline


  • 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


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