In this episode, I chat with Alex Gold, who leads solution engineering and support teams at Posit. We talk about his new book ‘DevOps for Data Science’, and explore why DevOps principles are crucial for data scientists, even if you aren't working in hardcore production environments. Alex shares insights on how to think about putting your data science projects into "production"—whether it's a report for colleagues or a full-scale deployment. We discuss practical tips like using {renv} to manage package versions and ensure reproducibility, and Alex gives a live demo on setting it up in an R project. Tune in for key takeaways on making your data science work more robust, shareable, and ready for production.

Important resources mentioned:

• DevOps for Data Science: Buy the book here at 20% off using the discount code AFLY04. Check out the Github repo if interested.

Connect with Alex on:  

• X: @alexkgold
• LinkedIn: Alex Gold
• His website (alexkgold.space), where he occasionally blogs about data, management, and leadership.

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In this episode, I talk with Cédric Viddone, an Information Management Officer at UNHCR with over 15 years of experience in humanitarian data visualization. Cédric shares his journey from GIS and cartography to creating impactful infographics and embracing the R programming language. He discusses how R has revolutionized data efficiency within UNHCR, where it aids in producing faster, reproducible data products essential for crisis response. Cedric also highlights the growth of the UNHCR R community and introduces the organization's data visualization guidelines platform, which includes a custom ggplot theme that streamlines UNHCR branding. Additionally, Cédric explores the advantages of Quarto for developing UNHCR-branded templates, enabling team members to create consistent, polished reports with ease.

Important resources mentioned: 

• UNHCR data visualization platform
• UNHCR branded theme for ggplot2 and data visualization color palettes
• UNHCR Quarto revealjs extension
• UNHCR data viz GitHub repo

Connect with Cédric on:

• X: @cvidonne
• Mastodon: @[email protected]
• LinkedIn: Cédric Viddone

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In this episode, I talk with Christine Parker, the Senior GIS Analyst on the Community Broadband Networks team at the Institute for Local Self Reliance. Christine shares how she used R to clean, combine, and summarize data for a dashboard tracking enrollment in the Affordable Connectivity Program (ACP), a COVID-era initiative to help people access affordable internet. The dashboard gained wide attention. It was shared in advocacy circles, referenced in Congress, and discussed with the White House. Christine highlights R's value for performing repeatable data tasks, particularly with regularly updated datasets, and its advantages compared to manual Excel processes.

Important resources mentioned:

• The ACP dashboard 
• Code that Christine used to create the dashboard

Connect with Christine on:  

• X: @birdwrangler
• LinkedIn: Christine Parker, PhD

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In this episode, I chat with Crystal Lewis about data management and her recently published book titled ‘Data Management in Large-Scale Education Research’. Crystal, a freelance research data management consultant, shares insights on good planning and systematic implementation of practices that are key to effective data management. She discusses the importance of automated data validation, and outlines a structured approach to data cleaning. Additionally, Crystal reflects on her experience writing an open-source book with Bookdown and navigating the publishing process. 

Important resources mentioned:

• Crystal’s book Data Management in Large-Scale Education Research

Learn more about Crystal Lewis by visiting her website and connect with her on X (@Cghlewis), LinkedIn, GitHub, and Fosstodon.

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In this episode, I speak with Miles McBain, a data scientist and R package developer from Brisbane, Australia, about patterns and anti-patterns in data analysis reuse. Miles shares his journey from a generalist software developer to a data science specialist, his passion for R, and the evolution of his coding practices. We delve into the intricacies of code reuse in data analysis, discussing common pitfalls to avoid, the benefits of creating reusable code packages, the process of breaking down large codebases, and how teams can evolve their coding practices to enhance efficiency and maintainability.

Important resources mentioned:

• Patterns and anti-patterns of data analysis reuse blog post by Miles McBain
• Jenny Bryan’s talk ‘Code smells and feels’ presented during useR!2018

Connect with Miles McBain:

• Website: milesmcbain.xyz/
• GitHub: @milesmcbain
• Mastodon: https://fosstodon.org/@milesmcbain

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In this episode,  I speak with Meghan Harris, a data scientist at the Prostate Cancer Trials Consortium at the Memorial Sloan Kettering Cancer Center. Meghan is one of the people who does generative art in R. She talks about why she likes making generative art in R and how making generative art has helped her improve her R skills in other areas.

Important resources mentioned:

• Intro to Data Art Blog Post

• Art From Code Workshop by Danielle Navarro

• A talk Meghan did in September 2023 making a case for generative art

• Ijeamaka Anyene's Art Website

• Danielle Navarro's Art Website

• Ijemaka's blog Meghan referenced that help her learn/do generative art with R 

Connect with Meghan on LinkedIn, X, and Mastodon

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In this episode, I speak with Cara Thompson about color, delving into several aspects of its use in data visualization. Cara is a UK-based data visualization consultant with over 15 years of experience in transforming data insights into clear, compelling visual stories. We explore how she finds inspiration for selecting colors, her reasons for not simply using organizations' brand colors in her visualizations, and the importance of dedicating time to thoughtfully consider color choices in this context.

Important resources mentioned:

• monochromeR Shiny App: https://cararthompson.shinyapps.io/monochromeR/
• Ophelia package documentation: https://cararthompson.github.io/ophelia/
• Talk on five tips for creating and apply bespoke color palettes: https://www.cararthompson.com/talks/nhsr2022-palatable-palettes/
• In-depth talk about dataviz design systems: https://www.cararthompson.com/talks/nhsr2023-dataviz-design-system/

Connect with Cara Thompson:

• Website: https://www.cararthompson.com/
• LinkedIn: https://www.linkedin.com/in/cararthompson/
• X: @cararthompson
• Github: cararthompson

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In this episode, I talk with Nicola Rennie about making data viz for mobile devices. Nicola is a lecturer in health data science based within the Center for Health Informatics, Computing, and Statistics at Lancaster University in the UK.

She recounts her initial encounter with R and how she got deeper into data visualization in R as a means of creative expression. Amidst the plethora of programming languages available, Nicola sheds light on why she chose R specifically for data visualization. Additionally, she offers valuable advice for people wanting to get started with data visualization using R.

Important resources mentioned:

• Blog post by Nicola on Designing #TidyTuesday visualisations for mobile (with Quarto)

• Best Practices for Data Visualisation

Connect with Nicola:

• Website: nrennie.rbind.io
• Twitter: @nrennie35
• LinkedIn: linkedin.com/in/nicola-rennie
• Mastodon: @fosstodon.org/@nrennie
• ORCID: orcid.org/0000-0003-4797-557X

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In this episode, I’m joined by Will Landau, a statistician and software developer currently working with Eli Lilly and Company. Will specializes in Bayesian methods, high-performance computing, and reproducible workflows. He is the creator of the {targets} R package, a pipeline tool for reproducible computation in statistics and data science. The package became part of ROpenSci in early 2021.

Will talks about his journey into R and using it for open source projects. He gives a detailed account of {targets} - its origin and how it works as a reproducible analysis pipeline tool.

• Check out the YouTube version of this podcast
• Important resources mentioned: Get started with the {targets} R package in four minutes
• Learn more about Will by visiting his website and connect with him on LinkedIn and X (@wmlandau).

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In this episode, I talk with Ahmadou Dicko, a statistician based in Senegal working with the United Nations High Commissioner for Refugees (UNHCR). Ahmadou shares insights on utilizing data-driven approaches to address development obstacles, especially within humanitarian settings. He explores the innovative packages and strategies developed by his team using R for data management, analysis, and communication. Among these innovations is robotoolbox, an extensive R package designed for accessing and handling Kobo Toolbox data in a tidy format.

• Check out the YouTube version of this podcast
• Important resources mentioned: The Humanitarian Data Exchange | ACLED | Refugee Data Finder
• Other R packages that Dicko has developed: refugees, rgeounbdaries, rhdx, racled, and prepr
• Learn more about Ahmadou by visiting his website and connect with him on GitLab, LinkedIn and X.

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