Introduction to R Programming | R Programming JumpStart

JumpStart R! Program in R, Read Data, Access R Packages, Write Functions, Make Graphs, Analyze Data & More

TTDS6680

Introductory

3 Days

Course Overview

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. 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's the perfect tool for when the analyst has a statistical, numerical, or probabilities-based problem based on real data and they've pushed Excel past its limits.

Introduction to R Programming is a hands-on course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (http://www.r-project.org) both on a command line and in a graphical user interface (GUI).

Course Objectives

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools.  Working in a hands-on learning environment, led by our expert practitioner, students will learn R and its ecosystem, and where it’s a better a tool than Excel.

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom.  Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:

  • R Language and Mathematics
  • How to work with R Vectors
  • How to read and write data from files, and how to categorize data in factors
  • How to work with Dates and perform Date math
  • How to work with multiple dimensions and DataFrame essentials
  • Essential Data Science and how to use R with it
  • Visualization in R
  • How R can be used in Spark (Optional / Overview)

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional R Programming, Python, data science, AI / machine learning / deep learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

Attendees for this course should have prior practical hands-on experience with another programming language.  Prior exposure to working with statistics and probability, as well as hands-on working knowledge of Excel would also be helpful but is not required.  We will collaborate with you to design the best solution to ensure your needs are met, whether we customize the material, or devise a different educational path to help your team best prepare for this training. 

Our core R and Python programming, data science, analytics, AI and machine learning training courses provide students with a solid foundation for continued learning based on role, goals, or their areas of specialty. 

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses or Follow On training options. Our team will be happy to help you with recommendations for next steps in your Learning Journey

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

  1. Getting Started with R
  • 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
  1. 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
  1. 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
  1. Ordered and unordered factors
  • A specific example
  • The function tapply() and ragged arrays
  • Ordered factors
  1. Arrays and matrices
  • Arrays
  • Array indexing. Subsections of an array
  • Index matrices
  • The array() function
  • Mixed vector and array arithmetic. The recycling rule
  • The outer product of two arrays
  • Generalized transpose of an array
  • Matrix facilities
  • Matrix multiplication
  • Linear equations and inversion
  • Eigenvalues and eigenvectors
  • Singular value decomposition and determinants
  • Least squares fitting and the QR decomposition
  • Forming partitioned matrices, cbind() and rbind()
  • The concatenation function, (), with arrays
  • Frequency tables from factors
  1. Lists and data frames
  • Lists
  • Constructing and modifying lists
  • Concatenating lists
  • Data frames
  • Making data frames
  • attach() and detach()
  • Working with data frames
  • Attaching arbitrary lists
  • Managing the search path
  1. Reading data from files
  • The read.table()function
  • The scan() function
  • Accessing builtin datasets
  • Loading data from other R packages
  • Editing data
  1. Probability distributions
  • R as a set of statistical tables
  • Examining the distribution of a set of data
  • One- and two-sample tests
  1. Grouping, loops and conditional execution
  • Grouped expressions
  • Control statements
  • Conditional execution: if statements
  • Repetitive execution: for loops, repeat and while
  1. Writing your own functions
  • Simple examples
  • Defining new binary operators
  • Named arguments and defaults
  • The '...' argument
  • Assignments within functions
  • More advanced examples
  • Efficiency factors in block designs
  • Dropping all names in a printed array
  • Recursive numerical integration
  • Scope
  • Customizing the environment
  • Classes, generic functions and object orientation
  1. Statistical models in R
  • Defining statistical models; formulae
  • Contrasts
  • Linear models
  • Generic functions for extracting model information
  • Analysis of variance and model comparison
  • ANOVA tables
  • Updating fitted models
  • Generalized linear models
  • Nonlinear least squares and maximum likelihood models
  • Least squares
  • Maximum likelihood
  • Some non-standard models
  1. Graphical procedures
  • High-level plotting commands
  • The plot() function
  • Displaying multivariate data
  • Display graphics
  • Arguments to high-level plotting functions
  • Low-level plotting commands
  • Mathematical annotation
  • Hershey vector fonts
  • Interacting with graphics
  • Using graphics parameters
  • Permanent changes: The par() function
  • Temporary changes: Arguments to graphics functions
  • Graphics parameters list
  • Graphical elements
  • Axes and tick marks
  • Figure margins
  • Multiple figure environment
  • Device drivers
  • PostScript diagrams for typeset documents
  • Multiple graphics devices
  • Dynamic graphics
  1. Packages
  • Standard packages
  • Contributed packages and CRAN
  • Namespaces

Course Materials

Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. Please inquire for details and options.

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Please see the current upcoming available open enrollment course dates posted below. Please feel free to Register Online below, or call 844-475-4559 toll free to connect with our Registrar for assistance. If you need additional date options, please contact us for scheduling.

Course Title Days Date Time Price
Introduction to R Programming | R Programming JumpStart 3 Days Jun 7 to Jun 9 10:00 AM to 06:00 PM EST $2,195.00 Enroll
Introduction to R Programming | R Programming JumpStart 3 Days Aug 2 to Aug 4 10:00 AM to 06:00 PM EST $2,195.00 Enroll
Introduction to R Programming | R Programming JumpStart 3 Days Oct 4 to Oct 6 10:00 AM to 06:00 PM EST $2,195.00 Enroll
Introduction to R Programming | R Programming JumpStart 3 Days Nov 29 to Dec 1 10:00 AM to 06:00 PM EST $2,195.00 Enroll

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