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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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.

• Connect with Chris on Twitter (@vizowl)

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.

• Learn more about Garrick and his work at garrickadenbuie.com and connect with him on Twitter (@grrrck).
• Connect with Travis on Twitter (@travisgerke) and LinkedIn.
• In my book R Without Statistics, I have a draft chapter on how to bundle your functions together in your own R package. You can check it out here.
• Other resources: R Packages by Hadley Wickham and Jenny Bryan.

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.

• Learn more about Kyle and his work at WALKER DATA. He’s also on Twitter (@kyle_e_walker)
• Kyle’s new book: Analyzing US Census Data: Methods, Maps, and Models in R 
• The chapter in my book that this conversation led to: Access Up To Date Census Data with the tidycensus Package
  
  

In this episode, I speak with Meghan Harris, a data integration specialist at the Primary Care Research Institute, University of Buffalo. There, she brings together data from multiple sources to create insights that benefit people affected by opioid use disorder. Meghan talks about how she uses R to pull data directly from Google Sheets, and highlights the advantages of this workflow as opposed to working on a manually downloaded Google Sheets file.

Fun fact:  When Meghan is not creating data pipelines, she makes cool art using R and posts them on her Twitter timeline.

• Connect with Meghan on LinkedIn (Meghan Harris, MPH) and Twitter (@meghansharris).
• Check out draft chapter 11 in my book where I discuss how to Access Data from Google Sheets Using R.

In this episode, I chat with Matt Herman about building websites in R. Matt shares lessons from his experience building a self-updating Covid-19 tracking site for Westchester County.

Matt is a Data Scientist at the Council of State Governments (CSG) Justice Center, where he focuses on research and policy analysis. Matt has created automated and reproducible workflows to generate outcome measures and performance indicators for several projects within the justice system.

• Check out the Westchester County Covid-19 tracking site that Matt built
• Connect with Matt: Twitter (@buddyherms); Website: mattherman.info
• Learn more about making websites to share results online in Chapter 9 of my book ‘R Without Statistics’