rspatialdata
GISr-lib/producethis: What the Package Does (One Line, Title Case)
Note the use of the /exec folder for different deployable workflows
Documenting functions
The basics of roxygen2 tags and how to use them for documenting functions.
Examples
@examples provides executable R code showing how to use the function in practice. This is a very important part of the documentation because many people look at the examples before reading anything. Example code must work without errors as it is run automatically as part of R CMD check.
For the purpose of illustration, it’s often useful to include code that causes an error. You can do this by wrapping the code in try() or using \dontrun{} to exclude from the executed example code.
For finer control, you can use @examplesIf:
#' @examplesIf interactive()
#' browseURL("https://roxygen2.r-lib.org")
Instead of including examples directly in the documentation, you can put them in separate files and use @example path/relative/to/package/root to insert them into the documentation.
All functions must have examples for initial CRAN submission.
UNCHARTED DATA: Interactive Tooltip Tables
How to include tables in your {ggiraph} tooltips.
UNCHARTED DATA: Introducing the {reactablefmtr} Package
An R package created to make the styling and customization of {reactable} tables easier.
Design Patterns in R
Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. R6P provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess.
R#r-development#design-patterns#r#software-architecture#object-oriented-programming#r-package#r-package-devt#api#r-api-devt
Fast JSON, NDJSON and GeoJSON Parser and Generator
A fast JSON parser, generator and validator which converts JSON, NDJSON (Newline Delimited JSON) and GeoJSON (Geographic JSON) data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from .
Roxygen R6 Guide
mlr3: Machine Learning in R - next generation. Contribute to mlr-org/mlr3 development by creating an account on GitHub.
Quarto
R Development Guide
A guide to R development.
R#r-development#r#r-package#github#r-package-devt#development#r-project#r-devguide#contributor-guide
Have we got NEWS.md for you
When developing a package it is essential to track the changes you make to your code. This is especially vital if they are breaking changes which have implications for any code written that depends on your package, i.e. a major version bump. Although you can always look back at your version control history in git, it is also convenient to have documentation which summarises the changes. This is where the NEWS file comes in.
r-lib/pak: A fresh approach to package installation
A fresh approach to package installation. Contribute to r-lib/pak development by creating an account on GitHub.
ropensci/PackageDevelopment: Task View: PackageDevelopment
Task View: PackageDevelopment. Contribute to ropensci/PackageDevelopment development by creating an account on GitHub.
Code