Skip to content

Latest commit

 

History

History
59 lines (52 loc) · 2.46 KB

r-shiny-conception-to-cloud.md

File metadata and controls

59 lines (52 loc) · 2.46 KB
Raw
talk_id talk_slug talk_type talk_tags session_slug sched_url talk_title talk_title_short talk_materials_url speakers
22136
r-shiny-conception-to-cloud
regular
process
production
shiny
data-science-in-production
R Shiny - From Conception to the Cloud
R Shiny - From Conception to the Cloud
name affiliation url username photo bio
Ivonne Carrillo Dominguez
ivonne_carrillo_dominguez
/assets/img/2022Conf/_talks/22136_ivonne-carrillo-dominguez.jpeg
Ivonne is a data engineer on the Data Science team at Bixal where she has worked for 5 years now. She works on data visualization, data processing, and data analysis. She received her B.S. in computer system engineering in Mexico. Before joining Bixal, she worked as a Software Engineer for over 10 years at IBM, Toshiba, GE. This experience has given her knowledge in several parts of a system, from the design to its validation. What she likes the most about her work is seeing the positive impact that it can have in people’s lives. She lives now in Fairfax, VA with her husband and doggy Moka and likes to bake cakes on the weekends.

I will share how we published an R Shiny application to AWS, the decisions we made, and what we learned in the process.

One challenge we faced was figuring out how we could develop collaboratively. We needed to define our development workflow, including version control, dependency management, and quality assurance.

Then, we needed to define the deployment method. R Studio is great for development, but it may hide many of the aspects that break the application. We used CI/CD workflows as much as possible to make sure our code was robust before pushing the changes to production.

Lastly, our infrastructure team designed a framework that is replicable, so we are ready to deploy new R Shiny applications quickly and focus on data analysis.