- Learning Roadmap
- ^rstudio::conf by Year
- ~rstudio::conf 2020
- ~rstudio::conf 2019
- ~rstudio::conf 2018
- ~rstudio::conf 2017
- ~Shiny Dev Con 2016
- ^rstudio::conf by Topic
- ~Keynotes & Fireside Chats
- ~Case Studies
- ~R Markdown
- Additional Talks
- Important RStudio Sites
Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera.
Making the Shiny Contest - Mine Çetinkaya-Runde
In January 2019 RStudio launched the first-ever Shiny contest to recognize outstanding Shiny applications and to share them with the community. We received 136 submissions...
RStudio Cloud in the Classroom
Much has been written in the statistics and data science education literature about pedagogical tools and approaches to provide a practical computational foundation for students.
Data science case study an analysis in R, using a variety of packages for web scraping and processing non-tidy data into tidy data frames
An analysis in R, using a variety of packages for web scraping and processing non-tidy data into tidy data frames to be used in geospatial analysis.
General data science overview - data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication.
The courses focuses on data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, perspective.
Introduction to shiny
Shiny is an R package that makes it easy to build interactive web apps straight from R.