A Statistician’s R Notebook

Categories

All (20)

API (2)

CBRT (1)

Causal Inference (1)

Causation (1)

Correlation (1)

Data Analysis (2)

Data Cleaning (1)

Data Preprocessing (1)

Data Science (1)

EVDS (1)

Econometrics (1)

Excel (1)

Functions (1)

Import (2)

Imputation (1)

Machine Learning (1)

Missing Data (1)

OECD (1)

Public Health (1)

R (3)

R Programming (15)

R Studio (1)

Report Automation (1)

Standard Deviation (1)

Standard Error (1)

Statistics (4)

adjusted-r-squared (1)

apply (1)

categorical data (1)

clean_names (1)

data transformation (1)

data types (6)

dataframe (1)

date handling (1)

do.call (1)

factor (1)

grep (1)

grepl (1)

gsub (1)

lapply (1)

lists (1)

lubridate (1)

map (1)

matrices (1)

model-evaluation (1)

openxlsx (1)

pivot_longer (1)

pivot_wider (1)

predictive-modeling (1)

r-squared (1)

reduce (1)

regex (1)

rsdmx (1)

sapply (1)

sub (1)

text analysis (1)

tibble (1)

tidymodels (1)

tidyr (1)

time manipulation (1)

time series (1)

vapply (1)

vectors (1)

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