https://github.com/SimranShaikh20/R-Fundamentals

https://github.com/SimranShaikh20/R-Fundamentals

Last synced: 8 months ago
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• Host: GitHub
• URL: https://github.com/SimranShaikh20/R-Fundamentals
• Owner: SimranShaikh20
• License: mit
• Created: 2025-01-25T07:28:16.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-15T05:05:40.000Z (about 1 year ago)
• Last Synced: 2025-06-07T15:46:56.500Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 295 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# R-Fundamentals

 

## 📌 Lab Work

**R Lab Assignment**  

## 🎯 Objective  

This lab focuses on hands-on practice with **R programming**, covering arithmetic operations, data structures, subsetting, and built-in functions in **RStudio**.  

## 📖 Topics Covered  

- Arithmetic operations and variable assignments  

- Built-in functions and data types  

- Data structures: Vectors, Matrices, Lists, Data Frames  

- Advanced practice: Logarithmic, Exponential, and Trigonometric functions  

## 📖 Tasks  

1. Perform and print addition, subtraction, division, and multiplication of two numbers.  

2. Convert temperature between **Celsius** and **Fahrenheit**.  

3. Convert an amount in **rupees** to **dollars** and vice versa.  

4. Swap values of two variables using a third variable.  

5. Swap values of two variables **without** using a third variable.  

6. Calculate and display the **volume of a cube** with given dimensions.  

7. Calculate and print **simple interest**.  

---

## 📁 Folder Structure

This lab is organized into the following subfolders and files:

### 🔹 Directories

- **Basic_Syntax/**  

  Contains basic R syntax demonstrations including variables, operators, conditional statements, loops, and functions.

- **Correlation Analysis/**  

  Covers how to calculate and visualize correlation between numerical variables using functions like `cor()`, `cor.test()`, and plotting correlation matrices.

- **Curve Fitting/**  

  Demonstrates how to perform curve fitting using polynomial and non-linear regression techniques.

- **Regression Analysis/**  

  Includes examples of simple linear regression and multiple linear regression using R's `lm()` function. Also covers residual analysis and model interpretation.

- **Statistical/**  

  Contains various statistical analysis methods, including hypothesis testing (t-tests, chi-squared tests), descriptive statistics, and data distribution checks.

### 🔹 File

- **Lab_4.ipynb**  

  A Jupyter Notebook version of the above scripts combining explanations, code, and visual outputs for easier understanding and execution.

---

## 🎯 Objectives

This lab helps learners:

- Understand and implement basic R programming syntax

- Conduct correlation and regression analysis

- Perform statistical tests and hypothesis validation

- Apply data modeling and curve fitting techniques

- Gain hands-on experience with real-world data manipulation

---

## ⚙️ Requirements

Make sure you have the following installed:

### For R Scripts

- R (≥ 4.0.0)

- RStudio (optional, but preferred)

Install required packages in R:

```r

install.packages(c("ggplot2", "car", "readr", "tidyverse", "MASS"))