Make grouping a first-class citizen in data quality checks
Which of these numbers doesn’t belong? -1, 0, 1, NA. You can’t judge data quality without data context, so our tools should enable as much context as possible.
Why machine learning hates vegetables
A personal encounter with ‘intelligent’ data products gone wrong
A lightweight data validation ecosystem with R, GitHub, and Slack
A right-sized solution to automated data monitoring, alerting, and reporting using R (
pointblank, projmgr), GitHub (Actions, Pages, issues), and Slack
Workflows for querying databases via R
Simple, self-contained, reproducible examples are a common part of good software documentation. However, in the spirit of brevity, these examples often do not demonstrate the most sustainable or flexible workflows for integrating software tools into large projects.
Understanding the data (error) generating processes for data validation
A data consumer’s guide to validating data based on the failure modes data producer’s try to avoid
A Tale of Six States: Flexible data extraction with scraping and browser automation
Exploring how
Playwright's headless browser automation (and its friends) can help unite the states’ data
Embedding column-name contracts in data pipelines with dbt
dbt supercharges SQL with Jinja templating, macros, and testing – all of which can be customized to enforce controlled vocabularies and their implied contracts on a data model
Causal design patterns for data analysts
An informal primer to causal analysis designs and data structures
Resource Round-Up: Causal Inference
Free books, lectures, blogs, papers, and more for a causal inference crash course
Building a team of internal R packages
On the jobs-to-be-done and design principles for internal tools
