Latest Guides
View all →Interactive ggplot with ggiraph
Add tooltips, hover effects, and click selection to ggplot2 plots with ggiraph, an htmlwidget that makes static R graphics interactive.
Rcpp and C++ Integration in R
Learn how to integrate C++ code into R using Rcpp for performance gains. This beginner guide covers installation, your first C++ function, and benchmarking.
Advanced Table Formatting with gt
Learn how to create presentation-ready display tables in R using the gt package. Covers formatting, styling, row groups, and custom themes.
Calling Python from R with reticulate
Learn how to use Python libraries directly inside R using the reticulate package, with clear examples and common pitfalls explained.
Benchmarking R Code
Learn to measure and compare R code performance with the microbenchmark package. Includes practical examples comparing base R, dplyr, and data.table.
Reference
View all →dplyr::distinct
Remove duplicate rows from an R data frame, keeping only unique combinations of specified columns.
dplyr::arrange
Sort rows of a data frame by column values with dplyr::arrange(), dplyr's row-ordering verb.
dplyr::group_by
Group a data frame by one or more columns for per-group operations with summarise() and mutate().
dplyr::select
Select specific columns from a data frame using flexible tidyselect helpers and syntax.
dplyr::filter()
Subset rows of a data frame or tibble using logical conditions with dplyr's filter() function.
dplyr::mutate
Add or modify columns in a tibble or data frame with mutate, dplyr's column-wise transformation function.
Tutorial Series
View all →Latest Articles
View all →ggplot2 vs plotly: When to Use Each in R
ggplot2 and plotly are the two most popular plotting libraries in R. Here is how to choose the right one for your data visualization project in 2026.
R for Machine Learning in 2026: A Complete Guide
From tidymodels to xgboost, R has become a powerhouse for ML. Here is what you need to know about the machine learning ecosystem in R in 2026.
Data Visualization Best Practices in R
Create effective, informative data visualizations in R. This guide covers ggplot2, color theory, chart selection, and common pitfalls to avoid.