R Programming

Course Code: RPROG

Duration: 4 days


R Programming Course Overview

This R Programming course aims to give delegates the knowledge to use effective programming principles and to write code in R. R is a powerful statistical language. However, all too often practitioners even with years of experience are restrained from leveraging this powerful tool for lack of programming knowledge and skills. This course is designed to address this critical need.

From a base with little or no prior programming experience, R Programming will build your knowledge of programming concepts and constructs and train you to implement solutions confidently in R. Beginning with an introduction to the language and to RStudio, we cover most of R's data types and structures, conditional statements, loops and functions. Delegates also learn how to import and save data files, implement exceptions and error handling for robust code and, produce some common visualization charts. Finally, there is a demonstrative example of how to carry out Linear regression and two univariate data hypothesis tests, a test for a hypothesized proportion and a mean.

Please note, this is not a course in statistics but in programming. The illustrative examples are designed as demonstrations and do not discuss underlying statistics. We have a follow-up Statistics course for this.


We believe in learning-by-doing and take a hands-on approach to training. Delegates are provided with all required resources, including data, and are expected to code along with the instructor.

Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered.

After every major topic, practical exercises are used to consolidate understanding of concepts on which we build gradually. Example solutions will be available for the delegates to take away at the end of the course.

Course Objectives

This course aims to provide the delegate with the knowledge to be able to:

  • Write scripts and run them
  • Use R's inbuilt functions
  • Write their own functions
  • Use programming principles and constructs to craft solutions in R
  • Produce Charts
  • Exploit existing knowledge in statistics, data science and machine learning to leverage R's powerful libraries in these domains.

Who will the Course Benefit?

This course will benefit anyone who works or intends to work with R for statistics, machine learning or data science by increasing their programming proficiency in R.

Skills Gained

The delegate will learn and acquire skills as follows:

  • Describe R and comparing it with other programming languages
  • Access and use the REPL
  • Run R in scripts and interactive mode
  • Initialize and use atomic vectors
  • Initialize and use arrays, lists, data frames, factors and strings
  • Construct conditional statements
  • Construct and use loops
  • Use inbuilt functions
  • Define and use functions
  • Read and write data from and to files
  • Write robust code
  • plot basic charts
  • Use RStudio

R Programming Training Course

Course Introduction

  • Administration and Course Materials
  • Course Structure and Agenda
  • Delegate and Trainer Introductions


  • About R
  • Installing R
  • CRAN and R Documentation
  • Console
  • R Studio
  • Popular R Packages/Libraries
  • Installing, Loading and updating Packages
  • Working Directory
  • Scripts
  • Saving Work and Exiting R
  • Comments and reserved words
  • Statements and Statement Grouping
  • Resources
  • Help


  • Arithmetic with R
  • Operators
    • Assignment
    • Arithmetic
    • Comparison
    • Logical
  • R's 'Scalar' types'
  • R's Data Structures and their lineage

Session 3: DATA STRUCTURES - Part 1

  • Atomic Vectors
    • What are atomic vectors?
    • Creating vectors
    • Vector arithmetic
    • Calling functions on vectors
    • Named vectors
    • Accessing elements of a vector
    • Sub-setting a vector

R Programming Training Course

Session 4: DATA STRUCTURES - Part 2

  • Matrices
    • Creating matrices
    • Filling direction
    • Adding columns
    • Matrix attributes
    • Subsetting
    • Matrix Arithmetic and Inversion
  • Arrays
    • Defining arrays
    • Creating arrays
    • Subsetting arrays

Session 5: DATA STRUCTURES - Part 3

  • Lists
    • Defining lists
    • Creating lists
    • Named lists
    • Accessing elements
  • Data Frames
    • Defining data frames
    • Creating data frames
    • Accessing data
    • Attributes
    • Adding Columns
    • Combining data frames

Session 6: DATA STRUCTURES - Part 4

  • Factors
    • What are factors?
    • Creating factors
    • Ordered levels
  • Strings
    • Creating a String
    • Concatenating
    • Some special characters
    • Substrings and pattern matching

R Programming Training Course


  • Conditional statements
    • if clause
    • else
    • else if
    • switch
  • Loops
    • for
    • while
    • repeat and break
    • next

Session 8: FUNCTIONS

  • Inbuilt functions
  • Function definition
  • Arguments and Return values
  • Named arguments
  • Default values
  • Functions as arguments
  • Anonymous functions


  • The save() and load() functions
  • Delimited text files
  • CSV and Excel files

R Programming Training Course


  • Warnings
  • Errors
  • The try statement

Session 11: CHARTS

  • Bar chart
  • Pie charts
  • Box and Whiskers plot
  • Histograms
  • Scatter plot
  • Line plot


  • Linear Regression with lm()
  • Hypothesis tests
    • Single proportion test
    • Single mean test
  • Course technical content is subject to change without notice.
  • Course content is structured as sessions, this does not strictly map to course timings. Concepts, content and practicals often span sessions.


Basic computer literacy and computing skills: navigate file systems, edit and save a file. No programming experience is required.

Follow-On Courses

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Duration: 4 days

Price: £1,900.00 exc. VAT 

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