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https://github.com/ashithapallath/r-lab
This repository offers a collection of exercises, assignments, and projects designed for the R Programming course. It focuses on utilizing R for data analysis, statistical modeling, and visualization tasks.
https://github.com/ashithapallath/r-lab
data-analysis exploratory-data-analysis machine-learning r-language visualization
Last synced: 3 days ago
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This repository offers a collection of exercises, assignments, and projects designed for the R Programming course. It focuses on utilizing R for data analysis, statistical modeling, and visualization tasks.
- Host: GitHub
- URL: https://github.com/ashithapallath/r-lab
- Owner: ashithapallath
- Created: 2023-10-18T19:07:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-14T05:20:27.000Z (16 days ago)
- Last Synced: 2025-01-27T10:12:41.959Z (3 days ago)
- Topics: data-analysis, exploratory-data-analysis, machine-learning, r-language, visualization
- Language: R
- Homepage:
- Size: 120 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# R Lab
This repository contains exercises, assignments, and projects for the **R Programming** course. It focuses on using R to perform various data analysis, statistical modeling, and visualization tasks. The repository includes hands-on labs that help in building practical knowledge of R’s capabilities in data science and statistical analysis.
## Features
This repository includes:
- **Data Analysis**: Practical exercises using R for data manipulation and analysis.
- **Statistical Methods**: Application of statistical concepts such as hypothesis testing, regression analysis, and more.
- **Visualization**: Creating insightful visualizations using R packages like `ggplot2`, `plotly`, and base R.
- **Machine Learning**: Basic ML models implemented in R.
- **Exploratory Data Analysis (EDA)**: Implementing techniques like correlation analysis, feature exploration, and outlier detection.
- **R Programming Fundamentals**: Strengthening knowledge of R syntax, functions, and data structures.
## Prerequisites
Make sure you have the following installed:
- **R version 4.0+**
- **RStudio** (Recommended for a better user experience)
- **Required R packages**:
- `ggplot2`
- `dplyr`
- `tidyr`
- `plotly`
- `caret`
- `shiny`
To install the required packages, use the following command:
```R
install.packages(c("ggplot2", "dplyr", "tidyr", "plotly", "caret", "shiny"))
```
## How to Use
1. Clone the repository:
```bash
git clone https://github.com/ashithapallath/R--Lab-
cd R--Lab-
```
2. Open the R script or R Markdown file in **RStudio**.
3. Run the code in **RStudio** to execute the exercises. Each file includes comments and instructions for you to follow along.
4. Work through the individual exercises or use the provided datasets to practice your data science and statistical analysis skills.
## Example Code
### Basic Data Analysis & Visualization with ggplot2
```R
# Load required libraries
library(ggplot2)
library(dplyr)
# Load dataset
data(mtcars)
# Summarize the data
summary(mtcars)
# Create a basic scatter plot using ggplot2
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
labs(title = "Miles per Gallon vs. Weight", x = "Weight", y = "Miles per Gallon")
```
### Basic Regression Model
```R
# Linear regression model
model <- lm(mpg ~ wt + hp + qsec, data = mtcars)
# Summary of the model
summary(model)
```
## Contribution
Contributions are welcome!
- Fork the repository.
- Submit a pull request with improvements, new exercises, or fixes.
- Report any issues or bugs in the Issues section.
## License
This project is licensed under the MIT License.
## Acknowledgments
Special thanks to the R community and contributors of packages like `ggplot2`, `dplyr`, `caret`, and `shiny`, which make data analysis and visualization in R powerful and accessible.