📖 R Language Projects – Learning Statistics with R

As part of my Finance Data Scientist roadmap, R is where I’m building a deep statistical foundation. While Python powers my machine learning, SQL handles my databases, and Power BI delivers my dashboards, R fills the gap by sharpening my statistical thinking — ensuring I can interpret models, validate assumptions, and explain uncertainty with confidence.

💡 Why this matters?

Finance is full of risk, variance, and uncertainty. R is the best environment to explore probability, hypothesis testing, and time-series forecasting — tools that make financial analysis not just descriptive, but statistically robust and predictive.

My Learning Tracks (GitHub Repos)

• 📘 BooK_Learn_R_For_Applied_Statisitcs

Applied stats in practice — hypothesis testing, ANOVA, regression, bootstrap resampling.

• 📝 R_Note_R_Bootcamp

Bootcamp notes covering syntax, tidyverse basics, and statistical utilities for finance.

• 📚 R_for_Data_Science_Hadley

Structured learning of the tidyverse workflow (import → tidy → model → visualise).

How R Complements My Roadmap

• 🧮 R + Statistics → solid foundations in distributions, inference, and forecasting.
• 🐍 Python + ML → applied machine learning (classification, regression, deployment).
• 🗄️ SQL + Data Engineering → query optimisation, data pipelines, financial integration.
• 📊 Power BI + Reporting → dashboards, KPIs, and executive insights.

Together, these tools create a full-stack finance analytics pipeline:

• SQL brings the data → R tests hypotheses & statistical models → Python builds predictive ML → Power BIcommunicates insights.

Focus Areas in R Learning

• 📊 Statistical Modelling & Inference — regression, GLMs, ANOVA, significance testing.
• ⏱️ Time-Series Forecasting — ARIMA, ETS, seasonality, anomaly detection.
• 💼 Financial Applications — risk-return analysis, factor models, variance decomposition.
• 🎨 Visualisation — ggplot2 dashboards and clean, layered storytelling.
• 🔗 Hybrid Workflows — Excel/SQL integration, reticulate for Python bridges.

What I’m Building

• Statistical mindset — learning how assumptions, tests, and uncertainty guide real finance decisions.
• Reusable R templates for forecasting, variance analysis, and reporting.
• R Markdown/Quarto reports that explain code, results, and insight in one narrative.
• Case-driven projects — applying stats to financial data, not just textbook datasets.

🛠️ Tools & Packages

• 🧰 Core: tidyverse (dplyr, tidyr, purrr, readr, stringr)
• 🎨 Visualisation: ggplot2, plotly
• 📈 Time-series: forecast, fable, tsibble, prophet
• 📚 Stats: broom, infer, boot, effectsize, car
• ♻️ Workflow: targets, renv, quarto

Next Steps

• Apply R statistical methods to real finance projects (e.g., stock returns decomposition, liquidity stress tests, forecasting cash flows).
• Build an internal R package of helper functions for finance KPIs and plotting themes.
• Use R + Python together (reticulate) for hybrid statistical + ML projects.
• Share progress through GitHub updates, making my repos a learning-in-public portfolio.

📌 R is my “statistics lab” within the Finance Data Scientist roadmap. By combining R with Python, SQL, and Power BI, I’m building a complete skillset — from raw data to statistical validation, predictive modelling, and business storytelling.

Explore the full Project on Github