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AI Agent Coding for Admins
AI Agent Coding for Admins
Like many of you, my first real exposure to AI was when ChatGPT dropped. I spent way too much time prompting it with random stuff, used it for some PowerShell, and tried out the voice feature when that launched. Mostly, I’ve used AI for things like writing docs, double-checking my grammar and English, and making some funny pictures.
AIAI
·hardstl.github.io·
AI Agent Coding for Admins
https://www.storybench.org/using-ai-agents-and-r-to-create-map-annotations/
https://www.storybench.org/using-ai-agents-and-r-to-create-map-annotations/
Let’s start this with some confessions: I’m at best an enthusiastic amateur with AI. I know more than most, and I know nothing in the grand scheme. Example: I’m not sure I have any idea of what an AI agent is. I think I do, but there’s so much marketing hype around them that I
AIAI
·storybench.org·
https://www.storybench.org/using-ai-agents-and-r-to-create-map-annotations/
Gitingest
Gitingest
Replace 'hub' with 'ingest' in any GitHub URL for a prompt-friendly text.
AIAI
·gitingest.com·
Gitingest
A Fully Featured Logging Framework
A Fully Featured Logging Framework
A flexible, feature-rich yet light-weight logging framework based on R6 classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, JSON, (rotating) files, memory buffers. For extra appenders that support logging to databases, email and push notifications see the the package lgr.app.
RR
·s-fleck.github.io·
A Fully Featured Logging Framework
Introducing gander - Posit
Introducing gander - Posit
gander is an in-editor AI tool that describes R objects to improve coding efficiency.
RR
·posit.co·
Introducing gander - Posit
Guest Blog: Reproducible Data Pipelines In R With {targets} - ESIP
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.
RR#r
·esipfed.org·
Guest Blog: Reproducible Data Pipelines In R With {targets} - ESIP
rcrd (record) S3 class — new_rcrd
rcrd (record) S3 class — new_rcrd
The rcrd class extends vctr. A rcrd is composed of 1 or more fields, which must be vectors of the same length. Is designed specifically for classes that can naturally be decomposed into multiple vectors of the same length, like POSIXlt, but where the organisation should be considered an implementation detail invisible to the user (unlike a data.frame).
RR
·vctrs.r-lib.org·
rcrd (record) S3 class — new_rcrd
Advanced Tidyverse
Advanced Tidyverse
Use piped workflows for efficient data cleaning and visualization.
RR
·sesync-ci.github.io·
Advanced Tidyverse
Ploomber AI Editor
Ploomber AI Editor
Create custom Streamlit and Shiny R apps effortlessly with AI assistance. Design, code, and deploy data apps in minutes.
AIAI
·editor.ploomber.io·
Ploomber AI Editor
Agentic AI for Data Management and Warehousing
Agentic AI for Data Management and Warehousing
Explore how Agentic AI for data management enhances automation, governance, and decision-making by leveraging intelligent workflows, real-time insights
·xenonstack.com·
Agentic AI for Data Management and Warehousing
Sports and Fantasy Data from Fantasypros
Sports and Fantasy Data from Fantasypros
The goal of the fantasypros R package is to provide easy and reproducable access to data provided on the fantasypros website. The intital focus is on NFL and fantasy football data, but other sports are planned to be added
·jpiburn.github.io·
Sports and Fantasy Data from Fantasypros
Add Authentication and SSO to Your Shiny App
Add Authentication and SSO to Your Shiny App
Learn how to implement strong authentication and SSO in Shiny apps with Descope. This guide integrates both OIDC and SAML with Posit Connect for seamless login.
R - ShinyR - Shiny
·descope.com·
Add Authentication and SSO to Your Shiny App
Powerful Classes for HTTP Requests and Responses
Powerful Classes for HTTP Requests and Responses
In order to facilitate parsing of http requests and creating appropriate responses this package provides two classes to handle a lot of the housekeeping involved in working with http exchanges. The infrastructure builds upon the rook specification and is thus well suited to be combined with httpuv based web servers.
R - APIsR - APIs
·reqres.data-imaginist.com·
Powerful Classes for HTTP Requests and Responses
Deep R Programming
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/.
·deepr.gagolewski.com·
Deep R Programming