If you write more than a few R scripts a month, you should be thinking about packages. The best way to learn is by looking at concrete examples of creating and using R packages: 3 practical examples that mirror the kind of work analysts, data scientists, and researchers actually do. Instead of abstract theory, we’ll walk through real examples of packaging your own functions, documenting them, testing them, and sharing them with your team. In this guide, we’ll start with a tiny utility package, move to a data analysis helper package, and finish with an API client package that talks to a web service. Along the way, you’ll see examples of how to structure an R package, how to use modern tools like `usethis`, `devtools`, and `testthat`, and how to avoid the most common mistakes that make packages painful to maintain. If you’ve ever copy‑pasted the same R function into three different projects, this article is written for you.
If you work with R and data, you’ve probably heard that `dplyr` makes data wrangling faster and more readable. That’s true, but it only really clicks when you see concrete examples of data manipulation with dplyr: 3 practical examples that mirror what you do at work. Instead of abstract toy problems, this guide walks through real examples you’d hit in analytics, reporting, and data science projects. We’ll start from a simple data frame and gradually layer in filtering, grouping, summarizing, and reshaping. Along the way, you’ll see multiple examples of how `dplyr` chains operations together so your code reads like a sentence instead of a puzzle. Whether you’re cleaning survey data, tracking marketing performance, or analyzing public datasets, these are the best examples to copy, adapt, and reuse in your own projects. Everything runs on modern `tidyverse` tools, so you can plug it directly into your 2024–2025 R workflow.
If you work with R for data analysis, sooner or later you’ll need clear, practical examples of loops and conditional statements in R. These control structures are the backbone of automating repetitive tasks, handling messy data, and making your scripts smart enough to react to different situations. Instead of abstract theory, this guide focuses on real examples you can drop straight into your own projects. We’ll walk through examples of loops and conditional statements in R that mirror the way analysts and data scientists actually use the language in 2024 and 2025: cleaning data from APIs, flagging outliers, simulating experiments, and building simple decision logic. Along the way, you’ll see where base R still shines, where the tidyverse and vectorization beat loops, and how to write code that’s readable for your future self and your teammates. Think of this as a hands-on reference you’ll actually keep open while you code.
If you work with data in R, you live in CSV land. You import them, clean them, export them, and repeat. Having clear, practical examples of examples of reading and writing CSV files in R saves you from constantly searching old scripts or Stack Overflow threads. In this guide, I’ll walk through real examples that mirror what analysts, data scientists, and researchers actually do every day. We’ll start with a simple example of loading a CSV from disk, then move into more realistic workflows: reading large files efficiently, handling bad encoding, exporting clean analysis-ready tables, and writing CSVs directly to compressed formats. These examples include both base R and the `readr` package from the tidyverse, so you can choose whichever style fits your projects. Along the way, I’ll point out common pitfalls, performance tips for 2024-era datasets, and how to keep your CSV imports reproducible and readable for your future self (and your teammates).
If you work with data in R, you spend a lot of time talking to databases. The difference between a quick prototype and something production‑ready often comes down to how you manage those connections. In this guide, we’ll walk through real, practical examples of top examples of connecting to databases with R that you can copy, adapt, and ship today. These examples of database connections in R cover everything from local SQLite files to enterprise PostgreSQL and SQL Server, plus cloud warehouses like BigQuery and Snowflake. You’ll see how to use `DBI`, `RSQLite`, `RPostgres`, `odbc`, and `bigrquery`, along with tips on pooling connections for Shiny apps and working with environment variables instead of hard‑coding passwords. Along the way, we’ll highlight patterns that scale, common pitfalls, and how modern R workflows (2024–2025) lean heavily on the `dbplyr` and `dplyr` ecosystem. If you’ve been hunting for clear, modern examples of R database connections instead of abstract theory, you’re in the right place.