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An Example of the DRY/DAMP Principles for Package Tests
An Example of the DRY/DAMP Principles for Package Tests
rOpenSci’s second cohort of Champions has been onboarded! Their training first started with a session on code style, was followed by three sessions on the basics of R package development, and ended with a session on advanced R package development, which consisted of a potpourri of tips with discussion, followed by time for applying these principles to the participants’ packages. Here, I want to share one of the topics covered: Package testing, and in particular, the DRY (“don’t repeat yourself”) and DAMP (“descriptive and meaningful phrases”) principles. For this topic, we used a GitHub repository, containing an R package whose different commits illustrate the two principles. In each step we’ll share a commit or diff illustrating the changes made.
·ropensci.org·
An Example of the DRY/DAMP Principles for Package Tests
S3 vectors
S3 vectors
·cran.r-project.org·
S3 vectors
Data Dictionary | BatchData
Data Dictionary | BatchData
The Data Dictionary is a collection of all of the fields, descriptions, and data types found in BatchData services. Powered by Stoplight.
·developer.batchdata.com·
Data Dictionary | BatchData
batchdata | Stoplight
batchdata | Stoplight
The API Design Management Platform powering the world's leading API first companies. Powered by Stoplight.
·developer.batchdata.com·
batchdata | Stoplight
TiTiler
TiTiler
A lightweight service for creating map tiles from Cloud-Optimized GeoTiff
·developmentseed.org·
TiTiler
Slope and Aspect (Terrain Analysis)
Slope and Aspect (Terrain Analysis)
First things first; let’s try to understand what a terrain is. A terrain represents the physical features of an area of land. It includes elevation, slope and the arrangement of the natural features such as mountains, plains and rivers. A terrain influences the natural features such as how the inhabitants of a particular area carry...
·share.google·
Slope and Aspect (Terrain Analysis)
hellmer: Batch Processing for Chat Models
hellmer: Batch Processing for Chat Models
Batch processing framework for 'ellmer' chat models. Provides both sequential and parallel processing of chat interactions with features including tool calling and structured data extraction. Enables workflow management through progress tracking and recovery and automatic retry with backoff. Additional quality-of-life features include verbosity (or echo) control and sound notifications. Parallel processing is implemented via the 'future' framework. Includes methods for retrieving progress status, chat texts, and chat objects.
·rdrr.io·
hellmer: Batch Processing for Chat Models
CRAN: Package tokenizers.bpe
CRAN: Package tokenizers.bpe
Unsupervised text tokenizer focused on computational efficiency. Wraps the 'YouTokenToMe' library which is an implementation of fast Byte Pair Encoding (BPE) .
·cran.r-project.org·
CRAN: Package tokenizers.bpe
keberwein/blscrapeR: A tool to gather, analyze and visualize data from the Bureau of Labor Statistics (BLS) API. Functions include segmentation, geographic analysis and visualization.
keberwein/blscrapeR: A tool to gather, analyze and visualize data from the Bureau of Labor Statistics (BLS) API. Functions include segmentation, geographic analysis and visualization.
A tool to gather, analyze and visualize data from the Bureau of Labor Statistics (BLS) API. Functions include segmentation, geographic analysis and visualization. - keberwein/blscrapeR
·github.com·
keberwein/blscrapeR: A tool to gather, analyze and visualize data from the Bureau of Labor Statistics (BLS) API. Functions include segmentation, geographic analysis and visualization.
RAG with Ollama and ragnar in R: A Practical Guide for R Programmers – Steve’s Data Tips and Tricks
RAG with Ollama and ragnar in R: A Practical Guide for R Programmers – Steve’s Data Tips and Tricks
Learn how to build a privacy-preserving Retrieval-Augmented Generation (RAG) workflow in R using Ollama and the ragnar package. Discover step-by-step methods for summarizing health insurance policy documents, automating compliance reporting, and leveraging local LLMs—all within your R environment.
·spsanderson.com·
RAG with Ollama and ragnar in R: A Practical Guide for R Programmers – Steve’s Data Tips and Tricks
How to create your own RAG applications in R
How to create your own RAG applications in R
See how to query documents using natural language, LLMs, and R—including dplyr-like filtering on metadata. Plus, learn how to use an LLM to extract structured data for text filtering.
·infoworld.com·
How to create your own RAG applications in R
Clean, Parse, Harmonize, Match, and Geocode Messy Real-World Addresses
Clean, Parse, Harmonize, Match, and Geocode Messy Real-World Addresses
Addresses that were not validated at the time of collection are often heterogenously formatted, making them difficult to compare or link to other sets of addresses. The addr package is designed to clean character strings of addresses, use the `usaddress` library to tag address components, and paste together select components to create a normalized address. Normalized addresses can be hashed to create hashdresses that can be used to merge with other sets of addresses.
·geomarker.io·
Clean, Parse, Harmonize, Match, and Geocode Messy Real-World Addresses
Free Online OpenAPI & Swagger Converter
Free Online OpenAPI & Swagger Converter
Easily convert OpenAPI (Swagger) specifications between YAML and JSON formats online for free. Paste your code or upload a file to get started instantly.
·openapiconverter.xyz·
Free Online OpenAPI & Swagger Converter
Swagger 2.X OpenAPI 3.1
Swagger 2.X OpenAPI 3.1
Examples and server integrations for generating the Swagger API Specification, which enables easy access to your REST API - swagger-api/swagger-core
·github.com·
Swagger 2.X OpenAPI 3.1