RMarkdown

mlr3: Machine Learning in R - next generation. Contribute to mlr-org/mlr3 development by creating an account on GitHub.

Contribute to jameslairdsmith/bankstatements development by creating an account on GitHub.

300+ Free R programming books

Welcome to WikiOD - Wiki Online Documentation WikiOD is a collaborative writing project to build the highest quality online documentation of all programming languages, education-related subjects and concepts.

Easily automate the following tasks to describe data frames: Summarise the distributions, and labelled missings of variables graphically and using descriptive statistics. For surveys, compute and summarise reliabilities (internal consistencies, retest, multilevel) for psychological scales. Combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on rmarkdown partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so that search engines can find your data and index the metadata. The metadata are also available at your fingertips via RStudio Addins.

Yet another post with a focus on package documentation! This time, we’ll cover vignettes a.k.a “long-form package documentation”, both basics around vignette building and infrastructure, and some tips for more maintainer- and user- friendliness. What is a vignette? Where does it live? In this section we shall go over basics of package vignettes. Vignette 101 In the “R packages” book by Hadley Wickham and Jenny Bryan, the vignettes chapter starts with “A vignette is a long-form guide to your package.

Turn your analyses into high quality documents, reports, presentations and dashboards with R Markdown. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. Use multiple languages including R, Python, and SQL. R Markdown supports a reproducible workflow for dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.

This work is intended to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting.

Tidy your JSON data in R with tidyjson.

The R graph gallery displays hundreds of charts made with R, always providing the reproducible code.

Allows one to fully create an R package in a single .Rmd file. Includes functionality and .Rmd templates for a literate programming approach to R package development.

An R package to quickly obtain clean and tidy college football play by play data - sportsdataverse/cfbfastR

Build CLI applications in R.

R - JSON Files - JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.

Best practice and tips for testing packages interfacing web resources.

It's turtles all the way down...

“Can you explain me what are environments in R?”The beginning of a series of blogpost about R concepts, explained to mydaughter. Side note: no, my daughter is not five, and she’s not named Alice. Andshe doesn’t speak english either ¯\(ツ)/¯.“Daddy, I’ve seen you reading this book with a weird chapter named‘Environments in R’. What does it mean?”“Alice darling, just sit down for a minute.Let’s say the world is a big computer, and everyone living in it is apiece of information we call ‘data’. Right now, we are at home, and homeis a small piece from the whole world. In R, these smaller places arecalled environments, and they are used just as our home: they cancontain data, and we can refer to these data with names which arespecific to the environment.For example, when we are at home, there are five pieces of data: you,me, mommy, and the two cats. At home, I can say ‘Darling’, and as we arein this small subset of the whole world where ‘darling’ refers to you,I’m pretty sure I will find you. But if I go in another home, that isto say in another environment with other data, another dad is callinghis daughter ‘Darling’. In this small other environment, different fromours, ‘Darling’ does not refer to the same piece of data. And the samegoes for “Mommy” and “Daddy”: in another home, they refer to otherpersons.If I go out in the wild world and try to use the word ‘Mommy’, thiswon’t specifically refer to your mum, as there are not one single‘Mommy’ in this world, and because this word refers to someonespecific to the home we are using it in. In the wild world, if I want torefer to your mum, I’ll need to specify from which home the ‘Mommy’ I’mlooking for is coming from.”“So why don’t we use the full name every time then? It seems simpler.”“Environments allow us to use the same word to refer to different data,depending on where we are using the word. It also allows to giveinformation about a piece of data: it’s quite normal to think that afather uses ‘Darling’ to refer to someone he loves very very much. Evenif, strictly speaking, nothing prevents the contrary from happening.Also, it wouldn’t be fair to only allow only one ‘Darling’ in the wholeworld. Thanks to environment, there won’t be any problem if every fatherin the world use this word, as it refers, in each home, to a specificlittle girl.”“Ok, thanks dadddy!”“You’re welcome, Darling”The R code behindAbout environments# Creating two houseshome

An introduction to using github actions with R.

A workflow for doing data science in the R language, using Agile principles.

This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)

This book showcases a basic example of how to create an R package based on S3 classes.

A simple R package development best-practices-example · GitHub

This is a script that you can use to encrypt data using Rscript command line tool. It would be impossibly difficult to decrypt the data if you had not seen the code in this file. - GitHub - hacidur...

R code that allows you to encrypt and decrypt large sensitive files - GitHub - phildias/cryptR: R code that allows you to encrypt and decrypt large sensitive files

🔒 Encrypt/decrypt files of R packages. Contribute to gipso/encryptrpak development by creating an account on GitHub.

Small, opinionated package to manage encrypted credentials in R - encryptedCredentials/crypt.R at master · dirkschumacher/encryptedCredentials

13 votes and 12 comments so far on Reddit

How to write a well-structured and understandable R program

Illustrating with a Principal Component Analysis

Comprehensive set of tools for scaffolding R interfaces to modules, classes, functions, and documentations written in other programming languages, such as Python.