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.
| Job | \nReach for | \nNot (anymore) | \n
|---|---|---|
| Pipe | \nnative \\|> (R 4.1+) | \n%>% only when you need its placeholder/. features | \n
| Data frame | \ntibble | \ndata.frame defaults (but it's fine) | \n
| Wrangle | \ndplyr + tidyr | \nhand-rolled [, subset, aggregate | \n
| Read CSV | \nreadr::read_csv (prod), data.table::fread (speed) | \nread.csv | \n
| Excel / Parquet / DB | \nreadxl / arrow / DBI+dbplyr | \n— | \n
| Strings / dates / factors | \nstringr / lubridate / forcats | \nbase grepl/POSIXlt/factor juggling | \n
| Plot | \nggplot2 | \nbase graphics (fine for throwaway plots) | \n
| Iterate | \npurrr::map_* + across() | \nsapply (type-unstable); lapply ok in package code | \n
| Big / fast | \ndata.table (or dtplyr, arrow+duckdb) | \n— | \n
| Model | \nbase lm/glm + broom; tidymodels for CV/tuning | \ncaret | \n
| Time series | \ntsibble + fable | \nforecast::auto.arima (maintenance-only) | \n
| Reports | \nQuarto (.qmd) | \nR Markdown (still works) | \n
| Reproducibility | \nrenv + Projects + here() | \nsetwd(), saving .RData | \n
import → tidy → transform → visualize → model → communicate\nOpen the reference for the task at hand — they load on demand. For broad orientation, this file is enough.
\nlibrary(tidyverse)\n\n# The native pipe threads a value into the first argument.\ndiamonds |>\n filter(carat > 0.5) |>\n mutate(price_per_carat = price / carat) |>\n summarise(\n mean_ppc = mean(price_per_carat),\n n = n(),\n .by = cut # per-operation grouping (dplyr 1.1+)\n ) |>\n arrange(desc(mean_ppc))\n\n# across() applies one op to many columns\ndf |> summarise(across(where(is.numeric), \\(x) mean(x, na.rm = TRUE)))\n\n# map over a list/vector, type-stable; combine results\nfiles |> map(read_csv) |> list_rbind(names_to = \"source\")\n\n# ggplot: data + aesthetic mapping + layered geoms\nggplot(df, aes(x = displ, y = hwy, colour = class)) +\n geom_point() +\n geom_smooth(method = \"lm\")\nGrouping: 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.
These bite people repeatedly — full detail in the referenced files:
\nstringsAsFactors 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)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)fct_reorder for plots, fct_relevel for models. (strings-dates-factors)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)lag(k = +1) leads (future data); use k = -1. rollapply defaults to center alignment — set align = \"right\" to avoid look-ahead bias. (time-series)setwd() with an absolute path — use an RStudio Project + here::here(). Don't save/restore .RData. (workflow-tooling)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\n# named in this skill's prose, and the currency note still carries a year.\npython scripts/check-r-facts.py --offline # exit 0 consistent, 10 drift\n\n# Live (weekly freshness job, never blocks a PR): every recommended package\n# still resolves on CRAN.\npython scripts/check-r-facts.py --live # exit 10 a package is gone, 7 CRAN unreachable\nThe canonical package list lives in assets/r-packages.json; when you add or drop a recommendation, update it to match or --offline fails CI.