Overview of the healthyR package in R
The healthyR package provides functions for simplifying common tasks in healthcare data analysis. This can be useful for those working in healthcare data analysis, providing tools to simplify tasks such as data cleaning and visualization.
Explore the healthyR package on CRAN
If you're interested in learning about the healthyR package, you can find valuable information from the Comprehensive R Archive Network (CRAN).
Explore the healthyR package in R
The pkgsearch package offers a convenient method for accessing basic information about the healthyR package in the R environment. Please follow our instructions for installing and loading the pkgsearch package. Once that package is loaded, you can use pkg_search("healthyR") to browse package details and access relevant documentation.
Basic information about the healthyR package
At R Basics, our goal is to make it easy for you to find information about the healthyR package. That’s why we’ve compiled all the information you can obtain using the CRAN website, the pkgsearch package, and the available.packages() function in one place.
Package title
The title of an R package on CRAN provides a concise representation of the package’s purpose. It serves as a brief summary, capturing the essence of what the package offers.
The title for the healthyR package on CRAN is: “Hospital Data Analysis Workflow Tools”.
Package description
The description of an R package on CRAN provides an overview of the package’s key features, allowing you to quickly determine if the package meets your requirements.
The description of the healthyR package on CRAN is: “Hospital data analysis workflow tools, modeling, and automations. This libraryprovides many useful tools to review common administrative hospital data. Someof these include average length of stay, readmission rates, average net payamounts by service lines just to name a few. The aim is to provide a simpleand consistent verb framework that takes the guesswork out of everything.”
Package author
In R programming, the author refers to the person or group of people who created the package. Authors play a crucial role in the R community because they develop packages with specific functionalities that enhance the capabilities of R. Their contributions make it easier for users to effectively solve problems.
The authors of the healthyR package are Steven Sanderson [aut, cre],Steven Sanderson [cph].
Package maintainer
Maintainers refer to the person or group of people who are responsible for managing an R package. They keep packages functional, up-to-date, and compatible with new versions of R and other dependencies. They also actively interact with users and address technical issues.
The maintainer of the healthyR package is Steven Sanderson .
Documentation for the healthyR package
R documentation provides comprehensive information about a package, including its functions, datasets, vignettes, and more. Exploring this information will help you make the most of the healthyR package’s functionalities.
Documentation sources
One easy way to access documentation for an R package is through RDocumentation.org. This website allows users to search for documentation of any R package available on CRAN or Bioconductor. Simply search for thehealthyR package to find its documentation.
In addition, R provides built-in documentation that you can access using the help() function or the ? operator. By using help(healthyR) or ?healthyR, you can explore documentation directly in R.
Reference manual
A reference manual for an R package is a comprehensive document that provides detailed explanations of each function, argument, and data structure for a package. It serves as a valuable guide when working with a package. You can access the reference manual for the healthyR package from the CRAN website. Check out the PDF file to explore the complete reference manual.
Functions
Functions play a crucial role in R packages. They allow you to perform specific tasks and computations efficiently. To identify the functions in the healthyR package, you can use the ls(“package:healthyR”) function. This approach provides a comprehensive list of functions, facilitating a deeper understanding of the package’s capabilities.
Vignettes and examples
An R vignette is a document that includes examples for using a package for a specific purpose.
You can view any available vignettes for the healthyR package in the Vignette section on its CRAN page. Please note that some packages do not have vignettes. In those cases, you can refer to the package’s documentation, user guides, and other available resources to gain a better understanding of its capabilities.
Package data
Many R packages include built-in datasets that you can access in the R environment. You can use these datasets to familiarize yourself with the functionalities and capabilities of the package. To identify any built-in datasets in the healthyR package, you can use the data(package = “healthyR”) function. To learn about the dataset, please consult the package documentation.
Package website
Some R packages go beyond their presence on the CRAN repository and have their own dedicated websites or GitHub pages. Package websites can offer detailed documentation, examples, tutorials, and other materials that can help you master the package’s functionalities. GitHub pages allow you to access the latest development version, contribute to the project, and report issues directly to the developers.
The healthyR package has a dedicated website. You can visit: https://github.com/spsanderson/healthyR.
Technical details of the healthyR package
To fully understand the healthyR package in R, you need to familiarize yourself with the technical details. In this section, we will go over everything you need to know.
Version
In R, a version refers to a specific iteration or release of the package. A version number typically consists of three parts: major version, minor version, and patch level. The major version represents updates with significant changes, the minor version indicates smaller updates or added features, and the patch level typically signifies bug fixes.
Versions are crucial for tracking changes made to a package. Specifying a version helps guarantee that your code remains reproducible, even if there are future updates to the package. This is particularly beneficial when collaborating with others.
To find the version number of the healthyR package in the R console, you can use the packageVersion(“healthyR”) function.
License
When using an R package, it’s crucial to know and understand its license. A license is a legal agreement that governs how a package can be used, modified, and distributed. It outlines the permissions and restrictions placed on the package’s code and associated resources.
The healthyR package uses the “MIT + file LICENSE” license.
We suggest you take some time to learn more about R package licenses. Remember, licenses play a vital role in open-source software, and respecting them contributes to a collaborative R community.
Dependencies
In R programming, dependencies are components that a package relies on to work correctly. They can include other packages that provide specific functions.
It’s important to note that some R packages list required dependencies, some list suggested dependencies, and some do not have any dependencies.
Required dependencies
A required dependency in the context of R packages refers to the components or other packages that are essential for the functioning of a particular package. Without these required dependencies, the package may not work at all or may not work as intended.
By listing the required dependencies, package developers can ensure that users have the necessary tools and resources to use the package effectively and help users avoid any conflicts or errors.
The healthyR package has the following required dependencies: R (>= 3.3).
Suggested dependencies
A suggested dependency is a component or package that adds extra features to the main package, but the main package can still work without it.
The healthyR package has the following suggested dependencies: knitr, rmarkdown, roxygen2, pacman, healthyR.data, broom, tidyselect.
External sources
In R programming, any external sources that need to be linked to a specific package are referred to as “LinkingTo” dependencies. These external sources are typically other packages that the main package depends on for linking at compile time, enabling seamless integration of additional functionalities.
The healthyR package does not use any external sources.
Enhancements
In R programming, enhancements extend a specific package’s functionalities. They help developers expand the capabilities of their packages, add new features, and improve performance without starting from scratch.
The healthyR package has no enhancements.
Imported packages
In R programming, external packages used within a specific package are called imported packages. Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel, saving time and effort. It also helps ensure that the required dependencies are available during runtime.
The healthyR package has the following imported packages: magrittr, rlang (>= 0.1.2), tibble, timetk, ggplot2, dplyr, lubridate, graphics, purrr, stringr, writexl, cowplot, scales, sqldf, plotly.
Compilation requirements
Some R packages include internal code that must be compiled for them to function correctly. This code ensures that the package works as intended.
The healthyR package does not have compilation requirements.
Citing the healthyR package
R packages often have citation information that you can include in your publications, research papers, or assignments. Citing R packages is important as it recognizes the effort and contribution of their developers. It also promotes reproducibility, enabling others to reference and build upon your work.
To find the citation information for the healthyR package in the R console, you can use the citation(“healthyR”) function. Please note that not all R packages come with built-in citation information.
Getting help with the healthyR package
If you are seeking comprehensive guidance on the healthyR package, please read our beginner’s guide. It provides instructions on how to install, load, and use the package. We also provide examples and useful tips to help you master the package.
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