Lionel Henry
21 packages on CRAN
2 packages on GitHub
Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With the carrier package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print at the console with their total size and can be easily tested locally before being sent to a remote.
Provides fonts licensed under the 'Bitstream Vera Fonts' license for the 'fontquiver' package.
A placeholder for the Liberation fontset intended for the `fontquiver` package. This fontset covers the 12 combinations of families (sans, serif, mono) and faces (plain, bold, italic, bold italic) supported in R graphics devices.
Provides a set of fonts with permissive licences. This is useful when you want to avoid system fonts to make sure your outputs are reproducible.
Unlike other tools that dynamically link to the 'Cairo' stack, 'freetypeharfbuzz' is statically linked to specific versions of the 'FreeType' and 'harfbuzz' libraries (2.9 and 1.7.6 respectively). This ensures deterministic computation of text box extents for situations where reproducible results are crucial (for instance unit tests of graphics).
A 'ggplot2' extension that provides flipped components: horizontal versions of 'Stats' and 'Geoms', and vertical versions of 'Positions'.
Some functions at the intersection of 'dplyr' and 'purrr' that formerly lived in 'purrr'.
A toolbox for working with base types, core R features like the condition system, and core 'Tidyverse' features like tidy evaluation.
A graphics device for R that produces 'Scalable Vector Graphics'. 'svglite' is a fork of the older 'RSvgDevice' package.
A backend for the selecting functions of the 'tidyverse'. It makes it easy to implement select-like functions in your own packages in a way that is consistent with other 'tidyverse' interfaces for selection.
An extension to the 'testthat' package that makes it easy to add graphical unit tests. It provides a Shiny application to manage the test cases.
Wrapper of .Call() that runs exit handlers to clean up C resources. Helps managing C (non-R) resources while using the R API.
A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
Provides a data.table backend for 'dplyr'. The goal of 'dtplyr' is to allow you to write 'dplyr' code that is automatically translated to the equivalent, but usually much faster, data.table code.
Extends 'ggplot2' functionality to the 'partykit' package. 'ggparty' provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class 'party'.
A wrapper of different standard estimation methods for gravity models. This package provides estimation methods for log-log models and multiplicative models.
Provides a progress bar similar to 'dplyr' that can write progress out to a variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown', and you still want to see progress of calculations in the terminal.
Provides an alternative auto-complete friendly interface to rlang that is more closely aligned with the task domain of a user 'programming with dplyr'. Facility is provided to replace friendlyeval functions with their rlang equivalents after protyping a solution.
A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
Functions for vectorised conditional recoding of variables. case_when() enables you to vectorise multiple if and else statements (like 'CASE WHEN' in 'SQL'). if_else() is a stricter and more predictable version of ifelse() in 'base' that preserves attributes. These functions are forked from 'dplyr' with all package dependencies removed and behave identically to the originals.
An evolution of 'reshape2'. It's designed specifically for data tidying (not general reshaping or aggregating) and works well with 'dplyr' data pipelines.