R Programming Training Course Online Course

R Programming Training Course

All levels
English

Course Description

R programming training course exposed to fundamental programming concepts in R. After the basics, you will learn how to organize, modify and clean data frames, a useful data structure in R. Then you will learn how to create data visualizations to showcase insights in data. R is a widely used statistical programming language that’s beloved by users in academia and industry. R works well with data, making it a great language for anyone interested in data analysis, data visualization, and data science. In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
Clear understanding of Statistical programming and R environment
In-depth knowledge of basic features, functions, operators available with R
Comprehensive information about programming statistical graphics
Ways of using simulation and numerical optimization
Extract data from R objects, perform reading and writing of Data, and handle databases
Use subscripting, character manipulation, and reshaping of data
Find probability, distributions, regression and correlation
Significance of sample size and its calculation. Advance data handling techniques

Covering Topics

Lecture -1 Introduction to R

Lecture 2-10 Understanding R data structure

Lecture – 11 Importing data

Lecture 12-15 Manipulating Data

Lecture 16-17 Using functions in R

Lecture 18-19 R Programming

Lecture 20-21 Charts and Plots

Curriculum

  Lecture -1 Introduction to R
Live Lecture 
·     What is R?

·     Why R?

·     Installing R

·     R environment

·     How to get help in R

·     R Studio Overview

·     Practical Exercise

  Lecture 2-10 Understanding R data structure
Live Lecture 
·     Variables in R

·     Scalars

·     Vectors

·     Matrices

·     List

·     Data frames

·     Cbind, Rbind, attach and detach functions in R

·     Factors

·     Getting a subset of Data

·     Missing values

·     Converting between vector types

·     Practical Exercise

  Lecture – 11 Importing data
Live Lecture 
·     Reading Tabular Data files

·     Reading CSV files

·     Importing data from excel

·     Loading and storing data with a clipboard

·     Accessing database

·     Saving in R data

·     Loading R data objects

·     Writing data to file

·     Writing text and output from analyses to file

·     Practical Exercise

  Lecture 12-15 Manipulating Data
Live Lecture 
·     Selecting rows/observations

·     Rounding Number

·     Creating string from a variable

·     Search and Replace a String or Number

·     Selecting columns/fields

·     Merging data

·     Relabeling the column names

·     Data sorting

·     Data aggregation

·     Finding and removing duplicate records

·     Practical Exercise

  Lecture 16-17 Using functions in R
Live Lecture 
·     Apply Function Family

·     Commonly used Mathematical Functions

·     Commonly used Summary Functions

·     Commonly used String Functions

·     User-defined functions

·     local and global variable

·     Working with dates

·     Practical Exercise

  Lecture 18-19 R Programming
Live Lecture 
·     While loop

·     If loop

·     For loop

·     Arithmetic operations

·     Practical Exercise

  Lecture 20-21 Charts and Plots
Live Lecture 
·     Box plot

·     Histogram

·     Pie graph

·     Line chart

·     Scatterplot

·     Developing graphs

·     Cover all the current trending packages for Graphs

·     Practical Exercise

Frequently Asked Questions

Programming background like C, C++, Python will be an added advantage but not mandatory to learn R, but introductory statistics is a prerequisite.

The course offers a variety of online training options, including: Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.