Data Pipelines

dplyr

Efficient data manipulation package for R

Status
Generally available
Last checked
Checked · Oct 8, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

dplyr is a powerful and intuitive package in R that simplifies common data manipulation tasks, making it easier to clean, transform, and analyze datasets.

dplyr stands out as the go-to package in R for efficient and consistent data manipulation, offering a streamlined API that simplifies common tasks.

When it fits — and when it doesn’t

A good fit for

  • Developers working with R who need to manipulate data efficiently

  • Data analysts looking for a consistent and powerful API for data manipulation tasks

Look elsewhere if

  • Projects requiring real-time data processing or streaming capabilities

  • Users primarily focused on machine learning model training rather than data preparation

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited performance on very large datasets

    dplyr operations can be slower compared to base R functions or data.table for extremely large datasets due to its abstraction layer.

  2. 02

    Complex setup for non-R users

    Integration with other languages and environments outside of R requires additional configuration, which can be complex for developers unfamiliar with R.

  3. 03

    Documentation lacks detailed examples for advanced use cases

    While the basic documentation is comprehensive, it often falls short in providing detailed guidance on more sophisticated data manipulation tasks.

  4. 04

    Limited support for distributed computing

    dplyr does not natively support distributed computing frameworks like Apache Spark, limiting its scalability for big data applications without additional tools or packages.

What it does well

  • Chains operations together for efficient data manipulation

  • Supports various data sources including SQL databases and data frames

  • Offers a consistent API across different data structures

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up dplyr

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →