No Clocks

Entrata property management software incorporates property accounting, purchasing, facilities, and leasing data into a comprehensive property management software available.

A curated list of awesome JSON Schema resources, tutorials, tools, and more - sourcemeta/awesome-jsonschema

Collection of resources I've found to be helpful for data science and programming - katiejolly/data-resources

Carefully curated resource links for data science in one place - tirthajyoti/Data-science-best-resources

A curated list of awesome R packages, frameworks and software. - qinwf/awesome-R

This is a list of R developers and advocates on Github. This is to help new comers create a following list. - amrrs/awesome_R_Githubers

A curated list of amazing Bootstrap tools and themes.

A collection of resources for developers, categorized and crowdsourced. Learn programming, UI inspiration, job boards, images, icons and much more.

GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.

A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin - pditommaso/awesome-pipeline

A curated list of awesome READMEs. Contribute to matiassingers/awesome-readme development by creating an account on GitHub.

Find Useful Open Source By Browsing and Combining 7,000 Topics In 59 Categories, Spanning The Top 348,565 Projects

A curated list of awesome ETL frameworks, libraries, and software. - pawl/awesome-etl

This is a list of Awesome Tech Blogs. Add yourself.

An awesome curated list of blogs built using blogdown - sellorm/awesome-blogdown

Alleviating my ADHD headaches with Obsidian. Periodic Notes and Templater extensions save the day by reminding me of the next step towards my larger goals.

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.