r-lib/producethis: What the Package Does (One Line, Title Case)
Note the use of the /exec folder for different deployable workflows
No Clocks
R
Deep R Programming
Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and machine learning. This textbook is a non-profit project. Its online and PDF versions are freely available at https://deepr.gagolewski.com/.
Reactive Resume - A free and open-source resume builder
A free and open-source resume builder that simplifies the process of creating, updating, and sharing your resume.
The Most Underrated R packages
A curated list of awesome libraries
The Most Underrated R packages: Part 2
A curated list of awesome and less known R libraries
Managing imports and exports
On Quosures
Exploring Non-Standard Evaluation with rlang's Quosures.
Preventive Care for R Packages
DRY R Package Development
Top Nine ETL Packages for R Programming Tools | Panoply
Discover a curated list of top ETL packages and R programming tools to enhance your data analysis workflow. Stay ahead with our expert recommendations.
Load data from a REST API | dlt Docs
How to extract data from a REST API using dlt's REST API source
Data Engineering
Home | OpenLineage
Data lineage is the foundation for a new generation of powerful, context-aware data tools and best practices. OpenLineage enables consistent collection of lineage metadata, creating a deeper understanding of how data is produced and used.
Guest Blog: Reproducible Data Pipelines In R With {targets} - ESIP
Reproducibility is a huge challenge in science, especially as datasets grow larger and workflows become more complex. Enter targets — an R package that helps
A data workflow is the series of steps that turn raw data into something meaningful — think downloading, cleaning, analyzing and visualizing. You might already do this in R with a mix of scripts and notebooks. Some steps in your data workflow may also be manual and require no coding, such as data processing in Excel or uploading model output data to OneDrive.
A data pipeline, on the other hand, is an automated version of that workflow. It ensures that every step happens in order, only the necessary steps are rerun when data changes, and guarantees the results are reproducible every time. A well-structured pipeline ensures that anyone revisiting the analysis — including your future self — can rerun, verify and build on the work without extra effort or missing pieces.
Geolocation with PostgreSQL
We have loaded Open Street Map points of interests in the article The Most
Popular Pub Names — which
compares PostgreSQL with MongoDB for simple geographical queries, and is
part of our PostgreSQL Extensions article series. In
today’s article, look at how to geolocalize an IP address and locate the
nearest pub, all within a single SQL query!
For that, we are going to use the awesome
ip4r extension from
RhodiumToad.
Pipes and Filters pattern - Azure Architecture Center
Break down a task that performs complex processing into a series of separate elements that can be reused or reordered.
A Production Example Using Plumber and Docker – mlr-org
Write a REST API using plumber and deploy it using Docker.
How to Create an R Package with Integrated Shiny Apps
In R, everything is (must be) a package! There are a lot of benefits of using an R package to manage a project. For example, it promotes code organization, reusability, and collaboration by keeping everything related to your project neatly packaged and easily shared.
Using jQuery Core | jQuery Learning Center
$( document ).ready() | jQuery Learning Center
Create and Validate Dockerfiles Programmatically
A toolkit for programmatically creating, modifying, and validating Dockerfiles in R. Provides a pipe-friendly interface for building Docker environments from R sessions, packages, and scripts, with support for templates and automatic system requirement detection.
Get Started with chk
William Michael Landau: Non-blocking Shiny apps
A convenient way to launch a Shiny app without blocking your R session.
Debugging with the RStudio IDE
Introduction
Entering debug mode (stopping)
Stopping on a line
Stopping when a function executes
Stopping when an error occurs
Using the debugger
Environment pane
Code window
Console
Debuggin...
Blueprints | Mozilla.ai
Stop Searching,Start Building. The Developer First Hub for Open-Source AI Workflows.
seascapemodels
Marine Science
Organizing tests in R packages – cynkra blog
ai-angineers-handbook/building_agents_from_scratch/deep_research_agent at main · swirl-ai/ai-angineers-handbook
Contribute to swirl-ai/ai-angineers-handbook development by creating an account on GitHub.
State of AI Coding - Engineering with Exponentials
Your value as an engineer scales directly with the amount of compute you can harness. Learn to maximize your Compute Advantage in this comprehensive guide.
Designing Empty States in Complex Applications: 3 Guidelines
Empty states provide opportunities for designers to communicate system status, increase learnability of the system, and deliver direct pathways for key tasks. This article provides guidance for designing empty-state dialogues for content-less containers.
Solutions - ScraperAPI
Get access to these core solutions with ScraperAPI, and take your web scraping efforts to the next level. Read more about each solution.