A Statistician’s R Notebook

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Introducing tuiksdmx: A Metadata-Driven R Client for Turkish Official Statistics

Official Statistics

An introduction to tuiksdmx, an open-source R package for authenticated access to the Turkish Statistical Institute’s SDMX API, with metadata discovery, validated filters…

M. Fatih Tüzen

Why ACF Is More Than Just a Plot

Statistical Thinking

An intuitive guide to lags and the autocorrelation function, using reproducible simulations and U.S. industrial production data to separate genuine persistence from…

M. Fatih Tüzen

Differencing: A Transformation or a Trap?

Statistical Thinking

Differencing is one of the most common transformations in time series analysis.

M. Fatih Tüzen

Publishing a Quarto Blog: What I Learned Moving from Netlify to GitHub Pages

Quarto makes it surprisingly easy to build a blog.

M. Fatih Tüzen

Why Most Time Series Models Fail Before They Start

Statistical Thinking

Many time series models fail before they even begin. Not because the software crashes. Not because the code is wrong. But because the data entering the model violate one of…

M. Fatih Tüzen

Data Leakage in R: Why Correct Evaluation Matters Even When Metrics Do Not Change

Data Preprocessing

Model Evaluation

Statistical Learning

Machine Learning Pitfalls

Reproducible Research

A model that performs exceptionally well on a test set is not necessarily a good model; in many cases, it is a warning sign. High accuracy or low error metrics are…

M. Fatih Tüzen

Data Normalization in R: When, Why, and How to Scale Your Data Correctly

Data Preprocessing

Machine Learning

This article is part of a broader series on data preprocessing in R. In earlier posts, we focused on two problems that quietly ruin analyses long before modeling begins: miss…

M. Fatih Tüzen

Understanding Data Import and Export in R: Working with CSV and Excel Files

When learning R, most people focus on functions, models, and visualizations. However, many real-world problems start much earlier — at the data import stage — and end much…

M. Fatih Tüzen

Outliers in Data Analysis: Detecting Extreme Values Before Modeling in R with İstanbul Airbnb Data

Data Preprocessing

Inter Quartile Range

Data preprocessing is often presented as a sequence of technical steps. However, each preprocessing decision implicitly embeds a statistical assumption.

M. Fatih Tüzen

Handling Missing Data in R: A Comprehensive Guide

Data Preprocessing

Data preprocessing is a cornerstone of any data analysis or machine learning pipeline. Raw data rarely comes in a form ready for direct analysis — it often requires…

M. Fatih Tüzen

Standard Deviation vs. Standard Error: Meaning, Misuse, and the Math Behind the Confusion

Standard Deviation

In the world of data analysis and statistics, standard deviation (SD) and standard error (SE) are two concepts that are often misunderstood or—worse—used interchangeably.…

M. Fatih Tüzen

Correlation vs Causation: Understanding the Difference

Causal Inference

“Correlation is not causation” – it’s a refrain we hear often, yet the distinction between these concepts is deceptively easy to overlook. Correlation refers to a…

M. Fatih Tüzen

Explained vs. Predictive Power: R², Adjusted R², and Beyond

Machine Learning

adjusted-r-squared

predictive-modeling

model-evaluation

You trust R². Should you?
You proudly present a model with R² = 0.95. Everyone applauds.
But what if your model fails miserably on the next new data?

M. Fatih Tüzen

Underrated Gems in R: Must-Know Functions You’re Probably Missing Out On

R is packed with powerhouse tools—think dplyr for data wrangling, ggplot2 for stunning visuals, or tidyr for tidying up messes. But beyond the headliners, there’s a lineup…

M. Fatih Tüzen

Unlocking CBRT Data in R: A Guide to the CBRT R Package

The Central Bank of the Republic of Turkey (CBRT) provides a wealth of economic data crucial for researchers, analysts, and policymakers. Through the Electronic Data…

M. Fatih Tüzen

Extracting Data from OECD Databases in R: Using the oecd and rsdmx Packages

The OECD (Organisation for Economic Co-operation and Development) provides extensive databases for economic, social, and environmental indicators. Accessing these…

M. Fatih Tüzen

Creating Professional Excel Reports with R: A Comprehensive Guide to openxlsx Package

Report Automation

The ability to generate professional Excel reports programmatically is a crucial skill in data analysis and business reporting. In this comprehensive guide, we’ll explore…

M. Fatih Tüzen

Mastering Date and Time Data in R with lubridate

time manipulation

lubridate is a powerful and widely-used package in the tidyverse ecosystem, specifically designed for making date-time manipulation in R both easier and more intuitive. It…

M. Fatih Tüzen

Mastering Data Transformation in R with pivot_longer and pivot_wider

data transformation

Data analysis requires a deep understanding of how to structure data effectively. Often, datasets are not in the format most suitable for analysis or visualization. That’s…

M. Fatih Tüzen

Text Data Analysis in R: Understanding grep, grepl, sub and gsub

In text data analysis, being able to search for patterns, validate their existence, and perform substitutions is crucial. R provides powerful base functions like grep, grepl, …

M. Fatih Tüzen

Exploring apply, sapply, lapply, and map Functions in R

In R programming, Apply functions (apply(), sapply(), lapply()) and the map() function from the purrr package are powerful tools for data manipulation and analysis. In this…

M. Fatih Tüzen

R Function Writing 101:A Journey Through Syntax, Best Practices, and More

R is a powerful and versatile programming language widely used in data analysis, statistics, and visualization. One of the key features that make R so flexible is its…

M. Fatih Tüzen

Cracking the Code of Categorical Data: A Guide to Factors in R

categorical data

R programming is a versatile language known for its powerful statistical and data manipulation capabilities. One often-overlooked feature that plays a crucial role in…

M. Fatih Tüzen

Unraveling DataFrames in R: A Comprehensive Guide

In R, a data frame is a fundamental data structure used for storing data in a tabular format, similar to a spreadsheet or a database table. It’s a collection of vectors of…

M. Fatih Tüzen

Understanding Lists in R Programming

R, a powerful statistical programming language, offers various data structures, and among them, lists stand out for their versatility and flexibility. Lists are collections…

M. Fatih Tüzen

Understanding Matrices in R Programming

Matrices are an essential data structure in R programming that allows for the manipulation and analysis of data in a two-dimensional format. Understanding their creation…

M. Fatih Tüzen

Exploring Vectors in R Programming: The Fundamental Building Blocks

In the realm of R programming, vectors serve as the fundamental building blocks that underpin virtually every data analysis and manipulation task. Much like atoms are the…

M. Fatih Tüzen

Understanding Data Types in R

Learning R programming is akin to constructing a sturdy building. You need a powerful foundation to support the structure. Just as a building’s foundation dictates its…

M. Fatih Tüzen

A Gentle Introduction to R Programming

Hello everyone! For my first post on my blog, I would like to make an introduction about R. Before we start coding with R, it’s not a bad idea to know a little about this…

M. Fatih Tüzen