Welcome! | Emily Riederer

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Python Rgonomics: User-defined functions in polars

rstats

python

tutorial

Polars provides a consistent API for conducting transformations against a DataFrame. But what do you do when you need to apply a user-defined function beyond the native API? This post surveys the patterns for transformation and aggregation with UDFs, including vector / object output, expression expansion, and when to break the abstraction with partitions and list comprehensions

MLOrbs?: MLOps in the database with `orbital` and `dbt`

rstats

python

dbt

sql

data

ml

Playing with the potential, perils, and design principles of deploying ML models into the analytical database using orbital’s sklearn-to-sql translation, sqlglot, and dbt

How Quarto `embed` fixes data science storytelling

workflow

rstats

python

quarto

rmarkdown

Literate programming excels at capturing our stream of conscience. Our stream of conscience does not excel at explaining the impact of our work. Notebooks enable some of data scientists’ worst tendencies in technical communication, but Quarto’s embed feature bridges the gap between reproducible research and resonate story-telling.

In my orbit: hacking orbital’s ML-to-SQL for xgboost

python

ml

The orbital package offers an interface for translating a fitted SciKitLearn pipeline to pure SQL for scaling predictions. In this tech note, I explore how this framework can (mostly) be used for xgboost models, as well, with a bit of wrangling (and a few limitations).

Building a team of internal R packages

rstats

pkgdev

jtbd

On the jobs-to-be-done and design principles for internal tools

Causal design patterns for data analysts

causal

data

tutorial

An informal primer to causal analysis designs and data structures

Column Names as Contracts

data

workflow

rstats

Using controlled dictionaries for low-touch documentation, validation, and usability of tabular data

RMarkdown Driven Development (RmdDD)

pkgdev

rmarkdown

rstats

workflow

A workflow for refactoring one-time analyses to sustainable data products

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Talks

Recent
Favorites

Casual Inference Pod - Optimizing Data Workflows with Emily Riederer (Season 6, Episode 8)

causal

data

Casual Inference is a podcast on all things epidemiology, statistics, data science, causal inference, and public health. Sponsored by the American Journal of Epidemiology. As a guest on this episode, I discuss data science communication, the different challenges of causal analysis in industry versus academia, and much more.

A different type of DAG - data pipelines for epidemiology

workflow

data

A tour of data pipeline techniques and tools for use in academia

Python Rgonomics

workflow

python

rstats

A survey of modern python tooling that “feels good” to R users

Data Downtime Horror Stories Panel

data

Panel discussion with Chad Sanderson and Joe Reis, hosted by Monte Carlo Data, on our thorniest brushes with data downtime, leading data teams to tackle data quality at scale with testing, contracts, observability and monitoring, and more.

Operationalizing Column-Name Contracts with dbtplyr

workflow

rmarkdown

rstats

An exploration of how data producers and consumers can use column names as interfaces, configuations, and code to improve data quality and discoverability. The second half of the talk demonstrates how to implement these ideas with my dbtplyr dbt package.

Causal Design Patterns

causal

An overview of basic research design patterns in causal inference, modern extensions, and data management strategies to set up a causal inference initiative for success

oRganization: Design patterns for internal packages

workflow

pkgdev

rstats

An overview of the unique design challenges and opportunities when building R packages for use inside of a single organization versus open-source. By using the jobs-to-be-done framework, this talk explores how internal packages can be better teammates by following specific design patterns for API design, testing, documentaiton, and more.

RMarkdown Driven Development

workflow

rmarkdown

rstats

How and why to refactor one time analyses in RMarkdown into sustainable data products

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Publications

Causal and Associational Language in Observational Health Research: A Systematic Evaluation

causal

We screened over 1,000 articles from 18 high-profile journals published from 2010-2019 to review the use of causal language to describe results in experimental and observational data.

97 Things Every Data Engineer Should Know: Collective Wisdom from the Experts

data

With this in-depth book, data engineers will learn powerful, real-world best practices for managing data—both big and small. Contributors from companies including Google, Microsoft, IBM, Facebook, Databricks, and GitHub share their experiences and lessons learned on cleaning, prepping, wrangling, and storing data. I contributed 6 of the 97 essays.

R Markdown Cookbook

rmarkdown

rstats

This book is designed to provide a range of examples of how to extend the functionality of your R Markdown documents. As a cookbook, this guide is recommended to new or intermediate R Markdown users who desire to enhance the efficiency of using R Markdown and also explore the power of R Markdown. Read online at https://bookdown.org/yihui/rmarkdown-cookbook/

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