## 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!