This project serves as a comprehensive demonstration of using R for data analysis, visualization, and modeling. It showcases a variety of techniques and tools to analyze and present insights from complex datasets. Developed as a final project for the Software Tools for Data Analysis course, it combines statistical rigor with the power of R for business and research-oriented analytics. Showing mpact of universal access to tertiary education on the expansion of the middle-income group
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Data Exploration and Cleaning:
- Handles missing values, outliers, and formatting inconsistencies.
- Summarizes data with statistical and graphical methods.
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Advanced Analysis:
- Performs hypothesis testing, regression analysis, and clustering.
- Applies machine learning models for predictive analytics.
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Visualization:
- Creates clear, insightful visualizations using libraries such as ggplot2 and plotly.
- Includes dashboards and interactive visualizations for enhanced usability.
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Reproducibility:
- Implements reproducible workflows with RMarkdown and tidyverse.
- Includes detailed documentation for transparency and ease of use.
- R: Core programming language for the entire project.
- RMarkdown: To generate detailed reports and documentation.
- Tidyverse Suite: For data manipulation and visualization.
- Shiny/Dashboarding: Interactive web apps or dashboards for user-friendly analysis.
- Statistical Models: Linear regression, logistic regression, clustering, and hypothesis testing.
The project demonstrates practical applications of R in solving real-world data challenges, making it ideal for academic research, business analytics, and exploratory data analysis. It is a robust example of how R can transform raw data into meaningful insights.