Comprehensive Data Analysis and Visualization in R: Cleaning, Preprocessing, and Statistical Insight

This project showcases a complete data analysis pipeline executed entirely in R, focusing on structured data cleaning, preprocessing, transformation, statistical exploration, and visualization. It is designed to simulate a real-world analytical workflow, commonly applied in health, business, and social science domains.

Project Objective:

To transform a raw dataset into a clean, well-structured, and analysis-ready format; to conduct meaningful descriptive and inferential statistical analysis; and to present the findings visually using reproducible R workflows.

Tools and Packages Used:

Core R

tidyverse (dplyr, tidyr, ggplot2)

lubridate for date-time formatting

janitor for cleaning column names and tables

gtsummary and gt for statistical reporting and customized summary tables

Key Activities and Methodology:

Data Cleaning & Preprocessing

Inspected structure, types, and missing values

Formatted inconsistent date and time entries

Removed duplicates, handled outliers, and imputed missing values

Recoded categorical variables and standardized measurement units

Data Manipulation

Grouped and summarized data using dplyr::group_by() and summarise()

Applied joins, filters, and reshaped the data using pivot_longer() and pivot_wider()

Created derived variables to support custom analysis (e.g., age groups, binary outcomes)

Exploratory Data Analysis (EDA)

Generated univariate and bivariate summaries

Explored distributions, correlations, and key trends using ggplot2 and summary statistics

Applied conditional filtering to isolate key segments of interest (e.g., by age, gender, location)

Statistical Analysis

Conducted hypothesis testing (t-tests, chi-square tests)

Computed confidence intervals and p-values for group comparisons

Used logistic regression or linear modeling depending on the problem

Structured results into publication-ready summary tables using gtsummary

Data Visualization

Created bar charts, histograms, box plots, scatter plots, and line plots

Designed multi-facet and theme-customized plots to highlight differences and patterns

Annotated visualizations for stakeholder clarity and interpretation

Outcome:

Delivered a fully cleaned and transformed dataset ready for advanced modeling or reporting

Identified key statistical relationships and group differences

Communicated insights through both statistical summaries and intuitive visualizations

Conclusion:

This project reflects strong proficiency in R programming for data analysis workflows. It demonstrates end-to-end capabilities from raw data ingestion to insight communication, supported by statistical rigor and clean, reproducible code. The methods used align with best practices in public health research, business analytics, and academic reporting, making the work adaptable across various domains.