dplyrs

Dplyrs is a data manipulation library designed to simplify the process of wrangling tabular data. It provides a cohesive set of operations, or verbs, that let users filter, select, rearrange, mutate, and summarize data in a readable, fluent style. The library is designed to work with both in-memory data frames and database-backed sources through pluggable backends.

Core verbs and joins cover the common data-wrangling needs. Typical operations include select for choosing columns, filter for row filtering, mutate for creating or transforming variables, arrange for sorting, and summarise for aggregation, often within groups defined by group_by. The library also supports a range of join operations such as inner_join, left_join, right_join, full_join, anti_join, and semi_join to combine datasets. A pipe-style operator is used to chain operations, enabling readable, linear workflows like data %>% filter(A > 0) %>% group_by(B) %>% summarise(mean_A = mean(A)).

Design and ecosystem emphasize readability, composability, and performance. By abstracting common data-wrangling steps into named verbs, dplyrs aims to make data pipelines easy to reason about and maintain. Backends can translate operations to optimized expressions for databases or large datasets, while keeping a consistent syntax across sources. The project typically includes robust documentation, example datasets, and compatibility layers with similar tidy-data tooling.

History and usage scenarios: dplyrs emerged in the open-source community in the early 2020s as an approachable alternative and complement to existing data-wrangling libraries. It is used in analytics workflows, reporting pipelines, and exploratory data analysis, often as part of a larger ecosystem of data tools. See also: dplyr, tidyverse, data manipulation libraries.