rush

rush brings R to the command line. Run an R expression, wrangle a CSV, query a Parquet file with SQL, or draw a plot right in your terminal, all without opening an R session or writing a script.

rush run 'penguins |> dplyr::filter(body_mass_g > 5000) |> head()'

R has a wonderful ecosystem for working with data, but reaching for it usually means launching R, loading packages, and reading files by hand. rush collapses that into a single command you can pipe, redirect, and drop into any shell pipeline. R becomes just another Unix tool, at home next to grep, awk, and jq.

Highlights

• Quick one-liners. Evaluate any R expression straight from the shell.
• Scaffold scripts. Use --dry-run to generate self-contained R scripts you can save, edit, and share.
• Pipeline-native. Reads from standard input and writes to standard output.
• Reads what you have. CSV, Parquet, JSON, Excel, Arrow, DuckDB, SPSS/Stata/SAS, SQLite, YAML, TOML, XML, and more.
• Converts between formats. The convert command handles it in one step.
• Query with SQL. Run DuckDB queries directly against your files.
• Plots in your terminal. Render ggplot2 graphics as ASCII art, or save to PNG/PDF.
• Zero dependency management. Packages are resolved on demand by ir.

Installation

First install ir. Then install rush as an ir tool:

ir tool install github::jeroenjanssens/rush

Quick tour

Evaluate an expression:

$ rush run '6 * 7'
#> 42

Read a file into df and filter it:

$ rush run 'df |> dplyr::filter(body_mass_g > 5000)' penguins.csv
#> species,island,bill_length_mm,...
#> Gentoo,Biscoe,49.2,...

Pipe into SQL:

$ rush run 'head(df, 100)' penguins.csv | \
    rush sql "SELECT species, COUNT(*) AS n FROM stdin GROUP BY species" -
#> species,n
#> Adelie,44
#> Gentoo,34
#> Chinstrap,22

Convert between formats:

$ rush convert -o penguins.parquet penguins.csv

Related tools

rush is, to our knowledge, the only CLI tool that combines R expression evaluation, automatic package resolution, multi-format I/O, SQL queries, and plotting in a single command. It also doubles as a script scaffolder: every invocation can emit a self-contained R script via --dry-run, giving you a starting point you can save, tweak, and version-control. That said, several excellent tools cover parts of this space, often faster or with fewer dependencies:

• DuckDB CLI: An embedded SQL engine that queries CSV, Parquet, and JSON files directly. Faster for pure SQL workloads; no R expressions or plotting.
• Miller (mlr): A streaming processor for CSV, TSV, and JSON with its own verb language. Handles files larger than RAM; no Parquet, SQL, or plotting.
• qsv: A fast Rust-based CSV toolkit with 90+ subcommands, Polars SQL, and Luau scripting. CSV-focused; no R ecosystem access.
• csvkit: A Python suite for converting, slicing, and querying CSV files. Mature and widely used; slower and CSV-only for output.
• jq: The standard CLI JSON processor. Powerful for reshaping JSON in pipelines; JSON-only.
• yq: Like jq but for YAML, TOML, XML, and JSON. Aimed at config-file editing rather than data analysis.
• Nushell: A shell where every command outputs structured tables. Requires adopting a new shell; limited analytical depth.
• VisiData: An interactive terminal spreadsheet for exploring tabular data. Designed for interactive use, not scripted pipelines.
• littler: A lightweight front-end for running R one-liners from the shell. No format I/O helpers, no dependency resolution, no SQL or plotting, but a useful building block for R scripting.

Learn more

Full documentation is available at https://rush.jeroenjanssens.com.

Code of Conduct

Please note that the rush project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.