Know, know, know your rows
Understanding data models - plaguing junior analysts since forever, saving data jobs from automation since 2023
Oh, I'm sure it's probably nothing
How we do (or don’t) think about null values and why the polyglot push makes it all the more important
Update: grouped data quality check PR merged to dbt-utils
After a prior post on the merits of grouped data quality checks, I demo my newly merged implementation for dbt
Using databases with Shiny
Key issues when adding persistent storage to a Shiny application, featuring {golem} app development and Digital Ocean serving
How to Make R Markdown Snow
Much like ice sculpting, applying powertools to absolutely frivolous pursuits
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
Update: column-name contracts with dbtplyr
Following up on ‘Embedding Column-Name Contracts… with dbt’ to demo my new dbtplyr package to further streamline the process
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.
