Welcome! | Emily Riederer

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Python Rgonomics - 2025 Update

rstats

python

Switching languages is about switching mindsets - not just syntax. New developments in python data science toolings, like polars and seaborn’s object interface, can capture the ‘feel’ that converts from R/tidyverse love while opening the door to truly pythonic workflows. (Updated from 2025 for new tools).

Role-Based Access Control for Quarto sites with Netlify Identity

quarto

rmarkdown

workflow

A quick tech note on Netlify’s managed authentication solution

Crosspost: Data discovery doesn’t belong in ad hoc queries

data

workflow

elt

crosspost

Data teams may struggle to quantify the benefits of good data documentation. But running countless ad hoc validation queries can incur both computational and cognitive cost.

Base Python Rgonomic Patterns

rstats

python

tutorial

Getting comfortable in a new language is more than the packages you use. Syntactic sugar in base python increases the efficiency, and aesthetics of python code in ways that R users may enjoy in packages like glue and purrr. This post collects a miscellaneous grab bag of tools for wrangling, formatting (f-strings), repeating (list comprehensions), faking data, and saving objects (pickle)

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

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Favorites

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.

Scaling Personalized Volunteer Emails

data

elt

An overview of the data stack used to automate over 50,000 personalized emails to voter turnout volunteers using BigQuery, dbt, Census, and MailChimp

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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