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Overview

minioR is an R package that provides a simple, consistent, and production-ready interface for interacting with MinIO object storage from R.

The package acts as a lightweight wrapper around low-level S3-compatible APIs, focusing on:

  • Clear and explicit function naming
  • Robust error handling
  • Safe defaults for data workflows
  • Ease of use in analytical and data engineering pipelines

minioR is especially suited for data teams using MinIO as a data lake, object store, or intermediate storage layer.


Features

  • Upload and download objects to/from MinIO
  • Support for in-memory (raw) and file-based uploads
  • Explicit handling of content types
  • Multipart upload support for large objects
  • Clear and informative error messages
  • Minimal configuration with sensible defaults

Installation

CRAN

Once released on CRAN, install the package with:

Development version

You can install the development version from GitHub:

install.packages("devtools")
devtools::install_github("https://github.com/inesscc/minioR.git")

Configuration

minioR uses standard MinIO / S3 environment variables.

Make sure the following variables are defined before using the package:

Sys.setenv(
  AWS_S3_ENDPOINT = "localhost:9000",
  AWS_REGION = "us-east-1",
  AWS_SECRET_ACCESS_KEY = "minioadmin",
  AWS_ACCESS_KEY_ID = "minioadmin",
  AWS_SIGNATURE_VERSION=2
)

HTTPS can be enabled depending on your MinIO deployment.


Quick Start

List Objects

objs <- minio_list_objects(
  bucket = "data",
  prefix = "raw/",
  recursive = TRUE
)

Read a CSV directly from MinIO

df <- minio_get_csv(
  bucket = "data",
  object = "raw/example.csv"
)

Upload data from memory

minio_put_object(
  bucket = "data",
  object = "tmp/hello.txt",
  raw_obj = charToRaw("Hello MinIO"),
  content_type = "text/plain"
)

Documentation

Full documentation, tutorials, and function reference are available on the package website:

https://inesscc.github.io/minioR/

The site is built with pkgdown and includes:

  • Getting started guides
  • Format-specific readers and writers
  • Data lake patterns and best practices
  • Complete function reference

License

MIT License © 2026