RProgramming Study Notes

The apply Family of Functions

How R's apply family — lapply, sapply, apply, vapply, and mapply — replaces explicit loops with predictable, functional-style iteration.

Building a Data Analysis Report in R

A practical walkthrough of producing a reproducible data analysis report in R, from R Markdown/Quarto setup through structuring analysis, visualizations, and r…

Conditionals in R

How R evaluates TRUE/FALSE logic to branch code with if/else, the vectorized ifelse(), and the multi-case switch() function.

Data Frames in R

Understand R's core tabular data structure — how data.frame combines vectors of equal length into rows and columns for real-world datasets.

Data Import and Export in R

Master reading and writing common file formats in R — CSV, Excel, and RDS — and know which function fits which situation.

dplyr Basics

Learn the core dplyr verbs—filter, select, mutate, arrange, summarise, and group_by—for fast, readable data manipulation in R.

Factors in R

Learn how R represents categorical data with factors — encoding fixed levels efficiently and controlling ordinal comparisons.

Functions in R

How R functions are defined, scoped, and evaluated — covering default arguments, lexical scoping, return values, and the ... variadic argument.

ggplot2 Basics

Learn ggplot2's grammar of graphics—aesthetics, geoms, facets, and themes—to build layered, publication-quality charts in R.

Handling Missing Data in R

Understand how R represents missing values with NA, how to detect and quantify missingness, and strategies for removal versus imputation.

Installing R and RStudio

How to install R and RStudio Desktop, understand the difference between them, and navigate the RStudio IDE for the first time.

Lists and Matrices in R

Compare R's two other core structures — the flexible, heterogeneous list and the strict, two-dimensional matrix — and learn when to use each.

Loops in R

How R's for, while, and repeat loops work, including break/next control flow and the classic vector-preallocation performance fix.

R and Machine Learning

Explore how to train, evaluate, and tune machine learning models in R using caret, tidymodels, and core algorithms like random forests and logistic regression.

R Best Practices

Practical conventions for writing maintainable, reproducible R code: project structure, style, vectorization, and testing.

R Interview Questions

A curated set of commonly asked R programming interview questions and concepts, spanning core language fundamentals, data structures, tidyverse wrangling, and…

R Markdown Basics

Learn how to combine R code, output, and narrative text into reproducible documents, reports, and slides using R Markdown.

R Operators

A tour of R's arithmetic, relational, logical, and special operators, including vectorized behavior and common pitfalls like && vs &.

R Quick Reference

A condensed cheat sheet of essential R syntax: data types and structures, dplyr verbs and piping, string/date/regex helpers, and common base R and statistics f…

R Variables and Data Types

How to create variables in R with the assignment operator, the core atomic data types, special values like NA and NULL, and how type coercion works.

R vs Python for Data Science

A practical comparison of R and Python for data science work — language design, data wrangling syntax, statistical modeling, visualization, and how teams choos…

Statistical Analysis in R

Learn how to compute descriptive statistics, run hypothesis tests, and fit regression models using R's base stats package.

String Manipulation in R

Learn to detect, extract, split, join, and clean text in R using base functions and the consistent stringr package.

Testing R Code with testthat

Learn how to write, organize, and run automated unit tests for R functions and packages using the testthat framework.