This repository showcases applied data analytics projects completed in R using Quarto, tidyverse, and statistical modeling workflows. The projects demonstrate practical skills in data wrangling, exploratory data analysis, statistical summaries, data visualization, correlation analysis, linear regression, and reproducible reporting.
The work in this repository focuses on using R to clean, transform, analyze, visualize, and interpret structured datasets. The projects apply tidyverse-based workflows to answer data questions, summarize patterns, evaluate relationships between variables, and communicate findings through Quarto reports.
- R programming
- Quarto report development
- tidyverse workflows
- dplyr data wrangling
- ggplot2 data visualization
- Data cleaning and transformation
- Exploratory data analysis
- Grouping and aggregation
- Summary statistics
- Correlation analysis
- Linear regression modeling
- Model interpretation
- Reproducible research documentation
- Structured analytical reporting
These projects demonstrate the use of R and tidyverse tools to filter, select, mutate, arrange, group, and summarize datasets. The workflows show how raw data can be transformed into cleaner analytical datasets for interpretation.
The repository includes exploratory analysis techniques used to understand dataset structure, variable distributions, group differences, and patterns across observations.
Several projects apply statistical methods such as summary statistics, correlation analysis, and linear regression. These methods are used to evaluate relationships between variables and support data-driven conclusions.
The projects use R visualization tools to create charts and graphical summaries that communicate trends, comparisons, distributions, and relationships in the data.
The analyses are documented using Quarto, allowing code, narrative explanation, and output to be combined in a reproducible format. This reflects a professional workflow for transparent data analysis and reporting.
- R
- RStudio
- Quarto
- tidyverse
- dplyr
- ggplot2
- openintro
- nycflights13
- lm.beta
- psych
The purpose of this repository is to showcase practical R data analytics skills through reproducible projects. It highlights the ability to move from raw data exploration to statistical analysis, visualization, interpretation, and professional reporting.
Gilbert Morgan
Data Science Graduate Student
R | Data Analytics | Statistical Analysis | Quarto | tidyverse