By David Keyes
The podcast currently has 19 episodes available.
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:
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:
Connect with Miles McBain:
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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:
Connect with Cara Thompson:
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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:
Best Practices for Data Visualisation
Connect with Nicola:
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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.
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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.
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In this episode, I speak with Chris Knox, who is currently the Head of Data Journalism at the New Zealand Herald. Prior to that, he worked at the New Zealand ministry of health, where he led an analytics team focused on New Zealand's COVID response.
During our conversation, Chris highlights why he considers R as the optimal tool for data analysis and reporting, especially when dealing with frequently changing data sources and parameters. He also emphasizes the benefits of using R in a collaborative environment, where junior analysts can be quickly integrated into the data analytics and reporting process and assume significant responsibilities, thanks to the reproducibility of R code.
In this episode, Travis Gerke and Garrick Aden-Buie join me to demystify the process behind developing custom packages in R. Travis is the Director of Data Science at The Prostate Cancer Clinical Trials Consortium (PCCTC), and Garrick is a Data Science Educator and R developer at R Studio.
During the discussion, Travis and Garrick highlight the numerous benefits of having a custom package, including making it easier to access data, automation & documentation of functions, and enhanced learning opportunities for R users seeking to upskill. They also delve into their own experiences working together at Moffitt Cancer Center, discussing how their set of custom R packages helped alleviate data reporting pain points within the organization.
In this episode, I speak with Kyle Walker, Associate Professor of Geography and Director of the Center for Urban Studies at Texas Christian University. Kyle has developed several packages, but the one we talk about in this chat is called tidycensus. tidycensus allows R users to return Census and ACS data as tidyverse-ready data frames. Kyle had a rough start with R programming and he didn’t want anything to do with it for 3 years. What made him come back to R and become one of its renowned champions? We chat about that as well.
The podcast currently has 19 episodes available.
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