R Programming

R Programming

In this world of information overload and data explosion, there is a dire need to leverage this data and make sense of it all. R is rapidly becoming the leading programming language for effective data analysis and statistics. It is the tool of choice for many data science professionals in every industry. Star R Programming is an all-inclusive training program that aims at building a skill-set to tackle real-world data analysis challenges as a data engineer. It is a guide to understand how to program in R and how to use R for effective data analysis.

The program delves into intricacies of calculations, co-relations and statistical probabilities and teaches the learners the fundamental understanding of programming with R, detailing all aspects of the language such as understand and process data structures, and mine information through data analysis that can suit a wide variety of purposes, and sectors as varied as finance, defence, health, education, etc. Further, the program dives deeper into the graphical capabilities of R, and helps you create your own stunning data visualizations.

Audience

Beginner to Intermediate

R Programming Course Objectives

In this course, you will learn about:

• The basics of statistical computing and data analysis
• How to use R for analytical programming
• How to implement data structure in R
• R loop functions and debugging tools
• Object-oriented programming concepts in R
• Data visualization in R
• How to perform error handling
• Writing custom R functions

Course Outcome

After competing this course, you will be able to:

• Explain critical R programming concepts
• Demonstrate how to install and configure RStudio
• Apply OOP concepts in R programming
• Explain the use of data structure and loop functions
• Analyse data and generate reports based on the data
• Apply various concepts to write programs in R

Table Of Contents Outline

1. Exploring R Language
2. Setting Up R Environment with RStudio
3. Implementing Expressions
4. Essentials Data Structure in R
5. Implementing Strings in R
6. Performing Statistics with R
7. Visualizing and Analysing Data in R
8. Object-Oriented Programming in R
9. Implementing Data Interfaces in R
10. Errors Handling
11. Improving the Performance
12. Interacting with Other Languages
13. Executing Your Own R Functions
14. Practice Labs

Exam Details