Course Introduction

Teaching Assistants

• Mr. RI Rosinha

  • Research Scholar at Doms Rudki
  • Graduated from University of Delhi and Postgraduated from Jamia Millia Islamia University
  • Areas of Interest: Climate Finance, Financial Markets, Energy Markets

• Miss Charu Adwa

  • Research Scholar at Doms Rudki
  • Graduation: University of Delhi, Postgraduation: Jamia Millia Islamia University
  • Areas of Interest: Climate Finance, Financial Markets, Market Efficiency, Energy Markets

• Miss Lagan Jindel

  • Research Scholar at Doms Rudki
  • 1 year of teaching experience in Financial Management and Cost Accounting
  • Graduated and Postgraduated from University of Delhi
  • Areas of Interest: Mutual Funds, Sustainable Finance, Security Analysis, Portfolio Management, Financial Markets

• Miss Manisha Yow

  • Research Scholar at Doms Rudki
  • Postgraduation from the Delhi School of Economics
  • Areas of Interest: Mutual Funds, Security Analysis, Portfolio Management, Financial Markets, Cryptocurrencies

• Mr. Mang Gupta

  • Senior Research Fellow at Department of Management Studies, IT RI
  • Specialization in Finance and Accounting from University of Delhi
  • Areas of Interest: Banking, Financial Reporting, Accounting Quality, ESG, Earnings Management, Corporate Governance, CR Risk

• Miss Disha Mal

  • Research Scholar at Department of Management Studies, IT RKI
  • Graduation and Postgraduation from University of Delhi
  • Research Areas: Banking, Risk Management in Financial Institutions, Information Incentives

R Programming Basics

Objectives of the Course

• Introduction to R programming and its uses
• Installation and setup of R and R Studio
• Familiarization with R Studio interface
• Creating and managing working directories and projects
• Installing and managing packages
• Understanding basic arithmetic and logical operators
• Working with different data types (vectors, user-defined functions)

Installation of R and R Studio

• R: Open-source statistical computing environment

  • Easily downloadable for Windows, Mac OS, Linux
  • Supports many statistical applications

• R Studio: Interactive development environment (IDE) for R

  • Also open-source and available across platforms
  • Provides tools for writing R code, conducting statistical analysis, and data visualization

Familiarizing with R Studio Interface

• Four main panes:
  1. Source Editor: For creating and editing R scripts
  2. Console: Where R evaluates the code
  3. Environment/History Panel: Shows data objects and command history
  4. Multi-purpose Panel: Access files, plots, packages, help

Working with R Scripts

• Create, save, and run scripts using the Source Editor
• Evaluate code directly in the Console
• Use Environment panel for managing data objects
• Import datasets via GUI

Setting Up Working Directory

• Set working directory to organize files
• Use setwd() command to define the directory
• Default working directory can be set in Global Options

R Variables and Data Types

• Five basic data types:

  1. Numeric
  2. Character
  3. Integer
  4. Factor
  5. Logical

• Vector Creation: Use c() for combining elements

• Vector Operations: Arithmetic operations can be applied to vectors of the same length; recycling occurs if vectors are of different lengths

Basic Operations in R

Arithmetic Operators

• Basic operations: Addition, Subtraction, Multiplication, Division
• Example: a + b, a - b, etc.

Logical Operators

• Logical and: &
• Logical or: |
• Used for Boolean operations and comparisons

Conditional Statements in R

• Use if, else, and for loops to control flow based on conditions

User-defined Functions

• Create reusable functions for common tasks
• Example of defining a mean and standard deviation function

Summary and Next Steps

• Learners will be equipped to create projects, write basic R codes, and understand data types
• Next session: Types of data science projects and their lifecycle in R.