Description

1. Fundamentals of R Language

  • Introduction to R
  • History of R
  • Why R programming Language
  • Comparison between R and Python
  • Application of R

2. Setup of R Language

  • Local Environment setup
  • Installing R on Windows
  • Installing R on Linux
  • RStudio
  • What is RStudio?
  • Installation of RStudio
  • First Program – Hello World

3. Variables and Data Types

  • Variables in R
  • Declaration of variable
  • Variable assignment
  • Finding variable
  • Data types in R
  • Data type conversion
  • R programs for Variables and Data types in RStudio

4. Input-Output Features in R

  • scan() function
  • readline() function
  • paste() function
  • paste0() function
  • cat() function
  • R Programs for implementing these functions in RStudio

5. Operators in R

  • Arithmetic Operators
  • Relational Operators
  • Logical Operators
  • Assignment Operators
  • Miscellaneous Operators
  • R Programs to perform various operations using operators in RStudio

6. Data Structure in R (part-I)

  • What is data structure?
  • Types of data structure
  • Vector- What is a vector in R?- Creating a vector- Accessing element of vector- Some more operations on vectors- R Programs for vectors in RStudio
  • Application of Vector in R
  • List- What is a list in R?- Creating a list- Accessing element of list- Modifying element of list- Some more operations on list
  • R Programs for list in RStudio

7. Data Structure in R (part-II)

  • Matrix or Matrices- What is matrix in R?- Creating a matrix- Accessing element of matrix- Modifying element of matrix- Matrix Operations
  • R Programs for matrices in RStudio
  • Application of Matrices in R
  • Arrays- What are arrays in R?- Creating an array- Naming rows and columns- Accessing element of an array- Some more operations on arrays
  • R Programs for arrays in RStudio

8. Data Structure in R (part-III)

  • Data frame- What is a data frame in R?- Creating a data frame- Accessing element of data frame- Modifying element of data frame- Add the new element or component in data frame- Deleting element of data frame- Some more operations on data frame
  • R Programs for data frame in RStudio
  • Factors- Factors in R- Creating a factor- Accessing element of factor- Modifying element of factor
  • R Programs for Factors in RStudio
  • Application of Factors in R

9. Decision Making in R

  • Introduction to Decision making
  • Types of decision-making statements
  • Introduction, syntax, flowchart and programs for- if statement- if…else statement- if…else if…else statement- switch statement

10. Loop control in R

  • Introduction to loops in R
  • Types of loops in R- for loop- while loop- repeat loop- nested loop
  • break and next statement in R
  • Introduction, syntax, flowchart and programs for- for loop- while loop- repeat loop- nested loop

11. Functions in R

  • Introduction to function in R
  • Built-in Function
  • User-defined Function
  • Creating a Function
  • Function Components
  • Calling a Function
  • Recursive Function
  • Various programs for functions in RStudio

12. Strings in R

  • Introduction to string in R- Rules to write R Strings- Concatenate two or more strings in R- Find length of String in R- Extract Substring from a String in R- Changing the case i.e. Upper to lower case and lower to upper case
  • Various programs for String in RStudio

13. Packages in R

  • Introduction to Packages in R
  • Get the list of all the packages installed in RStudio
  • Installation of the packages
  • How to use the packages in R
  • Useful R Packages for Data Science
  • R program for package in RStudio

14. Data and File Management in R

  • Getting and Setting the Working Directory
  • Input as CSV File
  • Analysing the CSV File
  • Writing into a CSV File
  • R programs to implement CSV file

15. Plotting in R (Part-I)

  • Line graph
  • Scatterplots
  • Pie Charts
  • 3D Pie Chart

16. Plotting in R (Part-II)

  • Bar / line chart
  • Histogram
  • Box plot

Who this course is for:

  • R Developers & Data Developers
  • Data Scientists – R, Python
  • Newbies and beginners aspiring for a career in programming & statistical analysis
  • Data Engineers and Statistical Analysts
  • R & Python Programmers
  • Technical & Analytics Consultants
  • Anyone wishing to learn data science and machine learning
  • Lead R Developers
  • R Modelling Analysts
  • Data Software Developers
  • Financial and Marketing Analysts
  • Software Engineers
  • Web Application Developers
  • Business Analysts and Consultants
  • Data Science and Machine Learning enthusiasts

Requirements

  • Enthusiasm and determination to make your mark on the world!
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