Data Analysis Using R and RStudio: From Foundations to Advanced Multivariate Analysis

Master R and RStudio for professional statistical computing and data science. This comprehensive course covers the entire data pipeline—from Data Wrangling and Exploratory Data Analysis to advanced Multivariate Statistics and Machine Learning foundations.

• Core Programming: Master R syntax, data structures, and R Markdown for reproducible research.
• Statistical Mastery: Perform everything from descriptive stats to complex ANOVA and Regression models.
• Advanced Analytics: Implement PCA, Cluster Analysis, and Linear Discriminant Analysis (LDA).

Course Overview

This course is designed to transform you into a proficient data analyst using the R ecosystem. You will move beyond basic scripting to build complex statistical models and create publication-ready visualizations using industry-standard packages like tidyverse and ggplot2.

Detailed Curriculum

Phase 1: Foundation & Data Wrangling

• Introduction to R & RStudio: Environment setup, core syntax (vectors, matrices, data frames), and R Markdown for reporting.
• Data Wrangling: Importing data from CSV/Excel and mastering the tidyverse (dplyr, tidyr) for cleaning and transformation.
• Descriptive Statistics: Computing central tendency, dispersion, skewness, and frequency tables.

Phase 2: Data Visualization

• Base & Advanced Graphics: Creating histograms, box plots, and scatter plots with ggplot2.
• Interactive & Complex Plots: Developing heatmaps, violin plots, and interactive Plotly charts.

Phase 3: Statistical Inference & Hypothesis Testing

• Probability Distributions: Normal, Binomial, and Poisson distributions; assessing normality with Shapiro-Wilk.
• Hypothesis Testing: Mastering t-tests, Chi-square, Wilcoxon, and Mann-Whitney tests.
• ANOVA Mastery: One-way and Two-way ANOVA with Post-Hoc Analysis (Tukey HSD, DMRT, Bonferroni).

Phase 4: Predictive Modeling & Multivariate Analysis

• Regression Analysis: Simple and multiple linear regression, logistic regression, and model diagnostics.
• Dimensionality Reduction: Performing PCA (Principal Component Analysis) with scree plots and biplots.
• Cluster Analysis: Implementing Hierarchical and K-means clustering with silhouette validation.
• Discriminant Analysis: Introduction to Linear Discriminant Analysis (LDA).

Learning Outcomes

• Build a fully reproducible analysis environment in RStudio.
• Clean and reshape complex, real-world biological and statistical datasets.
• Apply the correct parametric or non-parametric tests for any data scenario.
• Visualize high-dimensional data for academic and professional publications.