Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
shiny
Shiny is a web application framework for building interactive web apps without web development skills. It is used for data science and allows users to interact with data and analysis using R or Python.
shiny.semantic
shiny.semantic is a Fomantic (Semantic) UI wrapper for Shiny, allowing users to easily style their Shiny apps with a modern and interactive look.
shinybulma
Bulma for Shiny is a package that brings the Bulma CSS framework to Shiny applications. It includes extensions and themes for customizing the appearance of Shiny apps.
shinydashboardPlus
Add More AdminLTE2 Components to shinydashboard
shinymaterial
shinymaterial is an R package that provides material design components for creating shiny apps.
shinyWidgets
shinyWidgets is an R package that provides a set of custom input controls and widgets for Shiny applications.
sjlabelled
This package contains utility functions that are useful when working with labelled data (especially intended for people coming from ‘SPSS’, ‘SAS’ or ‘Stata’ and/or who are new to R). Basically, this package covers reading and writing data between other statistical packages (like ‘SPSS’) and R, based on the haven and foreign packages; hence, this package also includes functions to make working with labelled data easier. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.
Solving iteration problems with purrr
Video tutorial from the useR! International R User 2017 Conference about solving iteration problems with purrr.
Some good practices for research with R
The article discusses best practices for conducting research with R, covering data validation with the {validate} package, ensuring reproducibility with {renv}, and using {here} for reproducible paths. It explains how to validate data by defining rules and confronting datasets with expectations, providing examples with code snippets. Moreover, it emphasizes the importance of package management for reproducibility, cautioning against the risks of evolving or unsupported packages and offering solutions like package snapshots with {renv}. It is aimed at R users conducting research who seek to avoid errors and ensure consistent results over time.