R

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

Simple Shiny app to encrypt data for safe, anonymised data transfer - GitHub - msberends/hashing: Simple Shiny app to encrypt data for safe, anonymised data transfer

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.

Encrypt text, data, files, etc. Contribute to ebinabo/encryptr development by creating an account on GitHub.

Encrypt shiny app. Contribute to nasilabednego/shinysecure development by creating an account on GitHub.

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

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.

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.

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.

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In this guide, we'll learn to install R in Ubuntu. R is an open-source programming language used extensively for statistical and scientific computing. Due to

There are many community projects out there that provide binary R packages for various distributions. You may know Michael Rutter’s legendary c2d4u.team/c2d4u4.0+ PPA, but this situation has …

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How to build a CLI for R, with npm.BackgroundThis blog post was triggered by a discussion on Twitter with MartinSkarzynski,who was looking for a way to build a CLI that launches an RScript.Here’s a way to do this using npm.Please note that this blog post won’t teach you how to build the commandline tool, it will quickly go over the way to create a system-widecommand line interface, using npm.If you want to learn more about building the utility, see thisfantastic series of blogpostsby Mark Sellor.Now, the idea is to have a CLI, i.e. a way to launch your utility with:$ mytoolAnd that, system-wide.What you’ll need An R script (script.R) with in it, for example:#!/usr/bin/env Rscript --vanillacli::cat_rule("yeay")cli::cat_bullet(Sys.time()) npm, which you can get fromthere.Let’s goCreate a new folder, and go inside it.mkdir cli && cd cliCreate the R Script there.echo '#!/usr/bin/env Rscript --vanilla' script.Recho 'cli::cat_rule("yeay")' script.Recho 'cli::cat_bullet(Sys.time())' script.RTry your script to see if it works:Rscript script.RNow launch an npm project:npm init -y(You can also run it without the -y to interactively add informationto the package.json.)Now the important part: add a "bin" value in the package.json, withthe name of the bin you want to create, and the path to the script,relatively to the package file. Here is an example of a package.json(I removed some elements).{ "name": "cli", "version": "1.0.0", "description": "CLI example with npm", "bin" : { "clir" : "./script.R" }, "author": "Colin Fay", "license": "MIT"}Install it globally (need sudo rights):sudo npm linkAnd, voilà! Open your terminal, and you’re done!clirOther way to go See the {littler}implementation

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.

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.

Overview This walkthrough of the fundamentals of shell programming with Z shell (Zsh) and Bourne Again SHell (BASH) includes a comparison of similar components and features in R and RStudio. An alt…

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

This is a post I’ve been meaning to write for some time now about {capsule}. {capsule} provides alternative workflows to {renv} for establishing and working with controlled package libraries in R. It also uses an renv.lock so it is compatible with {renv} - you can switch between doing things the {capsule} way and the {renv} or vice versa at any time. Introducing an R package I wrote nearly three years ago Carefully curating a controlled package environment the {renv} way can be kind of a chore.

Here's a tutorial on getting you started deploying your RShiny application on AWS Lightsail.

Designing an outstanding interface is not just about making it look nice with HTML and CSS. How do we handle interactivity, widget creation and data flow between R and JS? This is where JavaScript...