Jinhwan Kim
Shiny developer, Zarathu Co., Ltd.
Seoul, South Korea
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Jinhwan is R / Shiny developer with background in bioinformatics. He has dedicated his career to crafting data products using R and Shiny across diverse industries as a Data Scientist.
Currently, He is a key contributor at Zarathu, where he specializes in developing R packages and Shiny applications for medical research. His role also extends to educating individuals on optimizing their data utilization and crafting informative articles.
His expertise extends to cutting-edge topics, as he has recently delivered presentations on the innovative use of web assembly and github for Shiny applications.
Area of Expertise
R4CR: R Education for Clinical Researchers via Quarto
Clinical research is one of the fastest growing fields in the world, and R is becoming increasingly important as a way to handle data, especially as more and more studies are conducted with small numbers of patients, or in collaboration with multiple institutions to collect data and conduct research.
Rather than using R to analyze data, clinical researchers have typically focused on study design, data collection, and validation, while coding has been done by professional developers, but now more and more clinical researchers are trying to use R themselves, including data management.
To this end, we have been providing R training for clinical researchers, but there is a lot of room for improvement compared to professional training services, such as reflecting the latest R-related technology trends and making the training experience better.
In this session, I will share how we decided to use Quarto, what we considered in order to provide R training for clinical researchers, how we actually used Quarto, the advantages and disadvantages of using Quarto, our achievements, and our future plans.
Shiny in clinical research: from SAS to Shiny
Clinical medical research uses something called a TFL as a way to represent statistics based on key characteristics of the subjects in a study.
Traditionally, SAS and MS Office (Word) have been utilized to create these, but more recently (and perhaps after FDA approval) R and Shiny are being considered.
In this session, I'd like to share some of the architecture, some of the features, some of the challenges and things we've tried along the way, and finally some of the lessons learned in creating TFL Builder.
More specifically, I'd like to share three special experiences I had while creating the application.
1. creating a Shiny application and using "shiny.fluent" for UI.
2. I utilized "reactable" to implement CRUD.
3. I used "temporary file" to add dynamic bookmark feature.
After completing the project, in retrospect, I wish I had prepared shiny beforehand.
1. Consider the user's scenario
2. Actively utilize shiny test
3. Keep researching on the technical things
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