Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Engineering Production-Grade Shiny Apps
This book is a guide to building robust Shiny applications that are ready for production use. It covers topics such as project management, technical optimization, and team collaboration. The target audience includes developers who have basic knowledge of Shiny and want to build production-grade applications.
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Enhance Quarto Project Workflows and Standards • froggeR
froggeR is an R package designed to enhance Quarto project workflows for R users. It provides a suite of functions that automate project setup tasks, enforce consistent documentation, and allow users to focus on analysis rather than configuration. The package includes features for creating Quarto projects with custom templates, managing YAML headers, applying git protection with a comprehensive .gitignore, styling documents with SCSS templates, and generating structured project documentation. It's particularly useful for R users managing multiple Quarto projects, encouraging collaboration, and standardizing project structures.
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Exploratory Data Analysis in R
This content details the process of Exploratory Data Analysis (EDA) using R. It emphasizes the importance of EDA as a crucial part of data science, particularly in understanding data and identifying biases. The article introduces several R packages that facilitate EDA, including overviewR, which is particularly focused on time series data analysis but is applicable to other data types. Key features of each package are compared, and the usage of the {palmerpenguins} dataset is illustrated. Package installation, data loading, and functions like str() and summary() are discussed, giving readers an introduction to effective data analysis in R.
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Exploring Complex Survey Data Analysis Using R
This content outlines a comprehensive guide on analyzing complex survey data using R. It begins with an introduction to survey analysis in R, prerequisites, and the datasets used, followed by detailed sections on survey design, data collection, and post-survey processing including data cleaning, weighting, and documentation. The book further delves into practical aspects like getting started with R packages, performing descriptive analyses and statistical tests, building models, and effective communication of results. Additionally, it emphasizes reproducible research with project-based workflows and version control, catering to both beginners and advanced users.
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Filenames to variables
This content describes a technique for incorporating information from the filenames of multiple CSV files into a data frame during import. The article is by Luis D. Verde Arregoitia and focuses on the scenario where related data is split across multiple files by government agencies, often with key variables only indicated in each file's name. The tutorial demonstrates using the R programming language to group a dataset by several variables, export each group to its own CSV file without the grouping variables but with the naming reflecting those variables, and then re-importing the files while adding the filename-derived information back into the data frame.
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Fix labels and understand scale functions in ggplot - YouTube
This YouTube video provides an explanation of how to fix labels and understand scale functions in ggplot.
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flair
flair is an R package that provides tools for formatting R code in knitted R Markdown files.
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flextable
The flextable package provides a framework for easily creating tables for reporting and publications in R. It allows for easy formatting and customization of tables, and supports various output formats including HTML, PDF, Word, PowerPoint, and more. The package also provides functions for tabular reporting of statistical models and the creation of complex cross tabulations.
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Flowcharts made easy with the package {flowchart}
The {flowchart} package in R facilitates the creation of flowcharts, particularly useful in health research to show participant flow in studies. It integrates with the tidyverse workflow, offering customizable functions that work with pipe operators. Unlike other packages, it adapts flowcharts automatically to the data, enhancing reproducibility. The post explains installation, initialization, and drawing processes using the SAFO clinical trial dataset. It's easy to produce complex flowcharts without manual parameter setting thanks to the package's tidyverse-centric design.
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Flowcharts with ggplot2 • ggflowchart
The {ggflowchart} package for R is a specialized tool designed to create flowcharts using the {ggplot2} framework with simplicity and minimal effort. You can install either the stable CRAN release or the development version from GitHub. Users can generate basic flowcharts by defining start and end points of edges within a data frame, and then invoking the ggflowchart() function. Additional customization is possible through aesthetic mappings such as fill, text color, and future updates may introduce further options. The package also encourages contributions, setting guidelines for pull requests including passing checks and following best practices in development.
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Focus and feedback in the tidyverse
This content features Tracy Teal interviewing Hadley Wickham for Open Source Stories, discussing the tidyverse's history and its influences. Hadley reflects on his parents' influence and his role as Posit's Chief Scientist in making data science more accessible. Themes include early computing exposure, relational databases, tidy data principles, and the balance between assisting and imposing solutions. Personal anecdotes highlight how Hadley's upbringing shaped the development of tidyverse tools aimed at simplifying and tidying data in R.
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