Core R programming skill for all R code, package development, and data science workflows. Use when writing R functions, building packages, using tidyverse (dplyr, ggplot2, purrr), creating Shiny apps, working with R Markdown/Quarto, or doing data analysis—e.g., "write an R function", "refactor this R code", "create a Shiny dashboard", "set up package tests", "debug R errors".
89
86%
Does it follow best practices?
Impact
92%
1.55xAverage score across 3 eval scenarios
Passed
No known issues
Tidyverse data pipeline code style
Native pipe operator
100%
100%
Package-qualified calls
0%
100%
Tidyverse for manipulation
100%
100%
snake_case naming
100%
100%
No explicit return
100%
100%
groups drop in summarise
100%
100%
na.rm in aggregations
0%
100%
show_col_types FALSE
30%
50%
readr for CSV import
100%
100%
Line length
100%
100%
readr for CSV export
100%
100%
R package directory structure
DESCRIPTION file exists
100%
100%
NAMESPACE file exists
100%
100%
R/ directory exists
100%
100%
R source files named correctly
100%
100%
tests/testthat/ structure
0%
100%
man/ directory exists
0%
0%
README.Rmd at root
100%
100%
Package-qualified calls in R code
83%
83%
Tidyverse for implementation
50%
70%
snake_case in code
100%
100%
No explicit return statements
100%
100%
Native pipe usage
0%
100%
na.rm in numeric summaries
100%
100%
groups drop
100%
100%
Safe temporary file handling with withr
withr for temp files
0%
66%
show_col_types FALSE
0%
100%
readr not base R
0%
100%
Package-qualified calls
0%
100%
Tidyverse for transformation
0%
70%
Native pipe operator
0%
100%
snake_case naming
100%
100%
No explicit return
100%
100%
na.rm in aggregations
100%
100%
groups drop in summarise
100%
100%
Line length
100%
100%
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Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.