Agent skill · data analytics · personamanagmentlayer
r-expert
Expert-level R statistical computing, data analysis, and visualization. Use when the user mentions statistics, data analysis, ggplot2, or the tidyverse, or when the task involves R Fundamentals, Statistical Analysis, Data Visualization, or Code Style.
Why this skill is useful
Provides specific R commands and statistical analysis techniques that the AI wouldn't reliably generate on its own.
What it needs
About 5k tokens when loaded. Last updated 2026-03-30. 41 stars on the source repository.
What this skill does
R Statistical Computing Expert Expert guidance for R programming, statistical analysis, data visualization, and data science. Core Concepts R Fundamentals Vectors and data frames Factors and lists Functions and apply family Packages and libraries R Markdown Tidyverse ecosystem Statistical Analysis Descriptive statistics Hypothesis testing Regression analysis ANOVA Time series analysis Machine learning Data Visualization ggplot2 Base R graphics Interactive plots (plotly) Statistical charts Maps and spatial data R Basics Tidyverse ggplot2 Visualization Statistical Analysis Time Series Analysis Machine Learning R Markdown {r setup, include=FALSE} knitr::optschunk$set(echo = TRUE, message = FALSE, warning = FALSE) library(tidyverse) {r load-data} df <- read.csv("data.csv") head(df) {r plot, fig.width=8, fig.height=6} ggplot(df, aes(x = x, y = y)) + geompoint() + thememinimal() Data Import/Export Best Practices Code Style Use <- for assignment Follow tidyverse style guide Write functions for repeated code Use meaningful variable names Comment complex operations Use %>% pipe for readability Data Analysis Always explore data first Check for missing values Validate assumptions Use visualization Document your analysis Make analysis reproducible Performance Vectorize operations Use data.table for large data Avoid growing objects in loops Profile code with Rprof() Use parallel processing Cache expensive computations Anti-Patterns ❌ Growing vectors in loops ❌ Not setting random seed ❌ Ignoring NA values ❌ Using attach() ❌ Not documenting code ❌ Hardcoding file paths ❌ Not checking assumptions Resources R Documentation: https://www.r-project.org/ Tidyverse: https://www.tidyverse.org/ ggplot2: https://ggplot2.tidyverse.org/ R for Data Science (book): https://r4ds.had.co.nz/ CRAN Task Views: https://cran.r-project.org/web/views/
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills personamanagmentlayer/r-expert