The Ultimate Guide to the dplyr Package in R
Explore valuable documentation and insights to make the most of the dplyr package in R. Get ready to unlock the full potential of the dplyr package!
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What is the dplyr package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
Dplyr is a package that provides functions for manipulating data frames in R. It includes tools for selecting data, filtering data, arranging data, summarizing data, and performing other tasks related to data manipulation.
- Title: A Grammar of Data Manipulation
- Description: A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
- Author: Hadley Wickham [aut, cre] (), Romain François [aut] (), Lionel Henry [aut], Kirill Müller [aut] (), Davis Vaughan [aut] (), Posit Software, PBC [cph, fnd]
- Maintainer: Hadley Wickham
How to install the dplyr package?
In this section, we’ll walk you through the process of installing and loading the dplyr package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.
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What package information should you know?
In this section, we’ll go over the technical aspects of the dplyr package.
Key features
- Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the dplyr package, visit our database of R datasets.
- Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the dplyr package, you can visit our visit our database of R vignettes.
- Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the dplyr package, visit our database of R package citations.
Technical details
- License type: MIT + file LICENSE. For license details, visit the Open Source Initiative website.
- Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The dplyr package has compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The dplyr package has the following required dependencies: R (>= 3.5.0).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The dplyr package has the following suggested dependencies: bench, broom, callr, covr, DBI, dbplyr (>= 2.2.1), ggplot2, knitr, Lahman, lobstr, microbenchmark, nycflights13, purrr, rmarkdown, RMySQL, RPostgreSQL, RSQLite, stringi (>= 1.7.6), testthat (>= 3.1.5), tidyr (>= 1.3.0), withr.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The dplyr package does not use any external sources.
- Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The dplyr package has the following imported packages: cli (>= 3.4.0), generics, glue (>= 1.3.2), lifecycle (>= 1.0.3), magrittr (>= 1.5), methods, pillar (>= 1.9.0), R6, rlang (>= 1.1.0), tibble (>= 3.2.0), tidyselect (>= 1.2.0), utils, vctrs (>= 0.6.4).
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The dplyr package has no enhancements.
How to get help with the dplyr package?
In this section, we’ll discuss a variety of available resources for getting help with the dplyr package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("dplyr") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
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Package website: The dplyr package has a dedicated website. You can visit: https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.
- Developer support: You can email Hadley Wickham . For contact information, visit our R community directory.
Additional resources
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- Online courses: Try our handpicked collection of R programming courses designed to boost your proficiency in R programming.
- Books: Explore our curated selection of R programming books tailored to help you master R programming.
- Discussion forums: Online forums are excellent platforms to ask questions, share knowledge, and troubleshoot issues. The most popular forums for R programmers are StackOverflow and Posit Community.
Other package guides
Explore our comprehensive guides for other R packages. These guides are valuable resources for accessing a wide range of information, making it easier to navigate R documentation in one place.
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