Introduction to R Course Overview

Introduction to R Course Overview

The Introduction to R course is designed to equip learners with the basics of R programming for beginners, providing a strong foundation in the essentials of this powerful statistical programming language. The course begins with Module 1, orienting students to the R environment, including installation, using RStudio, managing packages, and scripting basics. It ensures that learners are comfortable with R's interface and understand how to navigate and utilize its tools.

Progressing to Module 2, the course transitions into using R as a basic calculator, familiarizing students with arithmetic operations, operators, and scalar types. This gives learners the opportunity to start performing simple calculations and understand the syntax of R.

Module 3 delves deeper into R's data structures, covering atomic vectors, matrices, lists, and data frames, which are crucial for data analysis and manipulation. Upon completion, learners will have a solid grasp of R's capabilities and be well-prepared to tackle more complex tasks. This introduction serves as the best course to learn R programming for those seeking a comprehensive and practical start in data science and statistical computing.

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  • Live Online Training (Duration : 8 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure you can successfully participate in our Introduction to R course, we recommend the following prerequisites:


  • Basic understanding of programming concepts (variables, functions, loops, etc.).
  • Familiarity with statistical concepts is helpful but not necessary.
  • Ability to navigate and operate a computer effectively.
  • Comfort with installing software on your personal computer.
  • Eagerness to learn and problem-solve.
  • No prior experience with R or any other specific programming language is required.

These prerequisites are designed to ensure that you have a smooth learning experience and can fully engage with the course content. If you're new to programming or statistics, don't worry—our course is structured to help beginners get up to speed and feel confident in their abilities.


Target Audience for Introduction to R

Introduction to R is a comprehensive course for individuals aiming to master data analysis, statistical computing, and graphics using R programming.


Target Audience for the Introduction to R Course:


  • Data Analysts
  • Statisticians
  • Data Scientists
  • Academic Researchers
  • Students in Computer Science, Statistics, or Data Science
  • Software Developers interested in data analysis or statistical computing
  • Business Analysts who require statistical analysis skills
  • Bioinformaticians and Biostatisticians
  • Market Researchers
  • Economists
  • Quality Control Analysts
  • Actuaries


Learning Objectives - What you will Learn in this Introduction to R?

  1. Introduction to the course's learning outcomes and concepts covered: This course offers foundational knowledge in R, covering essential skills like installation, package management, and basic data structures, enabling students to perform calculations and data manipulation effectively in R.

  2. Learning objectives and outcomes:

  • Understand the basics of R, including its history and applications in data analysis.
  • Successfully install R and set up a working environment with RStudio.
  • Navigate R's Comprehensive R Archive Network (CRAN) and access R documentation for help.
  • Utilize the R console and RStudio interface for executing R scripts.
  • Identify and install popular R packages and libraries to extend functionality.
  • Manage packages by learning how to install, load, and update them as needed.
  • Set and get the working directory for organizing R projects and scripts.
  • Create, run, and save R scripts, as well as exit R while preserving work.
  • Use comments and understand reserved words within the R programming language.
  • Perform basic arithmetic operations in R using various operators and understand scalar data types.
  • Manipulate atomic vectors, matrices, lists, and data frames for effective data structure management.