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Introduction to R

3 Days Classroom Session   |  
3 Days Live Online
Live Online Registration
Self-Paced Online:
Private Onsite Package

This course can be tailored to your needs for private, onsite delivery at your location.

Request a Private Onsite Price Quote

Professional Credits


ASPE is an IIBA Endorsed Education Provider of business analysis training. Select Project Delivery courses offer IIBA continuing development units (CDU) in accordance with IIBA standards.


NASBA continuing professional education credits (CPE) assist Certified Public Accountants in reaching their continuing education requirements.


Select courses offer Leadership (PDU-L), Strategic (PDU-S) and Technical PMI professional development units that vary according to certification. Technical PDUs are available in the following types: ACP, PBA, PfMP, PMP/PgMP, RMP, and SP.

This course offers:
    14.00 PMP/PgMP Technical PDUs


Develop Core R Language Skills

This Introduction to R data training course is especially designed for people who are new to R or to any software languages. The class offers students a very detailed introduction to a core R language. After the completion of the class the students will be able to create and run their own R scripts. The class offers a very large number of exercises and problems that will help build R skills. The class may optionally offer an additional material related to statistical, data mining and machine learning analysis.

Upcoming Dates and Locations
All Live Online times are listed in Eastern Time Guaranteed To Run
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Course Outline
  1. What is R?
  2. How to load R?
  3. RGUI environment.
  4. What are R packages and how to load them.
  5. Major R data (object) types.
  6. Working with R variables.
  7. Main R operations and functions to work with numerical data types.
  8. Major R operations and functions to work with strings.
  9. Main R Boolean operations.
  10. Running conditional expressions in R.
  11. Working with R loops.
  12. Introduction to R vectors and main operations on vectors.
  13. Working with R lists.
  14. R data frames and main operations on them.
  15. Working with R matrices.
  16. Reading from and writing to external files.
  17. Creating user defined functions.
  18. Running statistical analysis with R.
  19. Working with data mining problems.
Who should attend

Anyone who is new to R or to any software languages.