r-ops

name	r-ops
description	Modern R for data analysis and statistics — tidyverse-first (dplyr, tidyr, ggplot2, native |> pipe), with base R and data.table as alternatives. Triggers on: R, Rstats, tidyverse, dplyr, tidyr, ggplot2, pivot/join/group, data.table, purrr map, broom, renv, Quarto.
when_to_use	Use for any R question — wrangling, visualization, modeling, time series — or when reviewing/modernizing R code to current (2024+) idioms.
license	MIT
compatibility	R >= 4.1 (native |> pipe); tidyverse 2.0; Quarto
allowed-tools	Read Write Bash
metadata	{"author":"claude-mods","related-skills":"sql-ops, postgres-ops, python-database-ops"}

Modern R Operations

A tidyverse-first, current-best-practice reference for working in R (2024+): data analysis, statistics, visualization, and reproducible workflow. Opinionated where the community has converged, with base R and data.table flagged as the right tool when they are.

The modern R stack at a glance

Job	Reach for	Not (anymore)
Pipe	native |> (R 4.1+)	%>% only when you need its placeholder/. features
Data frame	tibble	data.frame defaults (but it's fine)
Wrangle	dplyr + tidyr	hand-rolled [, subset, aggregate
Read CSV	readr::read_csv (prod), data.table::fread (speed)	read.csv
Excel / Parquet / DB	readxl / arrow / DBI+dbplyr	—
Strings / dates / factors	stringr / lubridate / forcats	base grepl/POSIXlt/factor juggling
Plot	ggplot2	base graphics (fine for throwaway plots)
Iterate	purrr::map_* + across()	sapply (type-unstable); lapply ok in package code
Big / fast	data.table (or dtplyr, arrow+duckdb)	—
Model	base lm/glm + broom; tidymodels for CV/tuning	caret
Time series	tsibble + fable	forecast::auto.arima (maintenance-only)
Reports	Quarto (.qmd)	R Markdown (still works)
Reproducibility

The analysis workflow (and where each reference lives)

import → tidy → transform → visualize → model → communicate

1. Import — get data in: import-io.md
2. Tidy & transform — the dplyr/tidyr core: tidyverse-core.md
3. Clean types — strings, dates, factors: strings-dates-factors.md
4. Iterate — map over many things, list-columns: iteration-functional.md
5. Visualize — ggplot2 + EDA: visualization.md
6. Model — tests, lm/glm, broom, tidymodels: modeling-stats.md
7. Scale up — when dplyr is too slow: data-table.md
8. Time series — tsibble/fable, xts: time-series.md
9. Ship it — projects, renv, Quarto, testing: workflow-tooling.md

Open the reference for the task at hand — they load on demand. For broad orientation, this file is enough.

Core idioms (internalize these)

library(tidyverse)

# The native pipe threads a value into the first argument.
diamonds |>
  filter(carat > 0.5) |>
  mutate(price_per_carat = price / carat) |>
  summarise(
    mean_ppc = mean(price_per_carat),
    n = n(),
    .by = cut                      # per-operation grouping (dplyr 1.1+)
  ) |>
  arrange(desc(mean_ppc))

# across() applies one op to many columns
df |> summarise(across(where(is.numeric), \(x) mean(x, na.rm = TRUE)))

# map over a list/vector, type-stable; combine results
files |> map(read_csv)  list_rbindnames_to  


ggplotdf aesx  displ y  hwy colour   
  geom_point 
  geom_smoothmethod  

Decision shortcuts

Grouping: prefer per-operation .by = over group_by() |> ... |> ungroup() — it avoids sticky-group bugs.

Joins: always write join_by(...) explicitly. Natural joins on shared names are almost always wrong on real data.

Which CSV reader? read_csv (readable, good defaults, production) · fread (fastest, big files) · vroom (many files, column subset).

dplyr or data.table? dplyr for readability and teams; data.table (or dtplyr) when profiling says dplyr is the bottleneck or data is large. arrow+duckdb for larger-than-memory.

lm or tidymodels? Base lm/glm is the right default — reach for tidymodels only when you need cross-validation, tuning, or uniform multi-model comparison.

base R or tidyverse? Tidyverse for analysis, readability, teams. Base R (or data.table) for package development, minimal-dependency scripts, and performance-critical inner loops. The |> pipe is base and dependency-free — use it everywhere.

High-value gotchas

These bite people repeatedly — full detail in the referenced files:

• stringsAsFactors is FALSE since R 4.0 (2020). Old advice warning about automatic factor conversion on import is stale and sometimes backwards. (import-io)
• predict(glm_model, type = "response") for probabilities — the default returns link-scale (log-odds). (modeling-stats)
• cor.test(), not cor() when you care whether a correlation is real. (modeling-stats)
• sapply is type-unstable — never in function bodies; use a typed map_*. (iteration-functional)
• map_dfr/map_dfc are superseded → map() |> list_rbind() / list_cbind(). (iteration-functional)
• ggplot mapping vs setting: aes(colour = class) maps a variable; colour = "blue" sets a constant. Putting a constant inside aes() is the #1 ggplot mistake. (visualization)
• coord_cartesian(ylim=) zooms; scale_y_continuous(limits=) drops data — the latter silently corrupts smooths/boxplots. (visualization)
• Factor order is not cosmetic — it sets ggplot axis/legend order and regression reference levels. fct_reorder for plots, fct_relevel for models. (strings-dates-factors)
• lubridate periods vs durations: months(1) (calendar) vs dmonths(1) (fixed seconds); use %m+% for safe month-end arithmetic. (strings-dates-factors)
• data.table := mutates in place — DT2 <- DT is not a copy; use copy(DT). (data-table)
• xts lag(k = +1) leads (future data); use k = -1. rollapply defaults to center alignment — set align = "right" to avoid look-ahead bias. (time-series)
• Never setwd() with an absolute path — use an RStudio Project + here::here(). Don't save/restore .RData. (workflow-tooling)

Currency note

Reflects the R ecosystem as of 2024–2026: R ≥ 4.3, tidyverse 2.0, native |>, dplyr .by=, the \(x) lambda, list_rbind/list_cbind, the tidyverts (tsibble/fable) time-series stack, and Quarto. Where a once-standard approach has been superseded (base apply → purrr, forecast → fable, R Markdown → Quarto, map_dfr → list_rbind), the modern form leads and the older one is noted for when you encounter it in the wild.

This currency is verified, not asserted — scripts/check-r-facts.py guards it against silent drift:

# Structural (PR CI, no network): every CRAN package in the catalog is still
# named in this skill's prose, and the currency note still carries a year.
python scripts/check-r-facts.py --offline        # exit 0 consistent, 10 drift

# Live (weekly freshness job, never blocks a PR): every recommended package
# still resolves on CRAN.
python scripts/check-r-facts.py --live            # exit 10 a package is gone, 7 CRAN unreachable

The canonical package list lives in assets/r-packages.json; when you add or drop a recommendation, update it to match or --offline fails CI.