R for the Rest of Us David Keyes

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.ioTwitter: @nrennie35LinkedIn: linkedin.com/in/nicola-rennieMastodon: @fosstodon.org/@nrennieORCID: orcid.org/0000-0003-4797-557XSubscribe to our newsletter: https://rfortherestofus.com/newsletter

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 podcastImportant resources mentioned: Get started with the {targets} R package in four minutesLearn more about Will by visiting his website and connect with him on LinkedIn and X (@wmlandau).Subscribe to our newsletter: https://rfortherestofus.com/newsletter

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 podcastImportant resources mentioned: The Humanitarian Data Exchange | ACLED | Refugee Data FinderOther R packages that Dicko has developed: refugees, rgeounbdaries, rhdx, racled, and preprLearn more about Ahmadou by visiting his website and connect with him on GitLab, LinkedIn and X.
Subscribe to our newsletter: https://rfortherestofus.com/newsletter

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)
To view all of our courses, go to https://rfortherestofus.com/

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.
To view all of our courses, go to https://rfortherestofus.com/

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
To view all of our courses, go to https://rfortherestofus.com/