Recently I blogged about an upcoming workshop on Data Management and Git using R/RStudio. That workshop will be one part of our upcoming five-part R learning series beginning in September. In this post I want to more formally introduce the fall 2017 R learning series, which began life last spring as R we Having Fun Yet‽, or RFun. Moving toward the fall semester, we are transforming the series format by adding more hands-on exercises.
Beginning in September, DVS will host a 5-part learning series of workshops on R: Intro/Tidyverse, Reproducibility/Git, Visualization/ggplot2, Mapping, and Shiny.
Beginning In September The Data & Visualization Services (DVS) Department will kick-off our broader fall workshop series. The series offers workshops in each of our thematic service areas: Data Cleaning, Data Analysis, Research Data Management, Data Visualization, GIS and Mapping. We are also offering a connected learning series within this broader series in which we focus on the R programming language. The RFun workshops will provide demonstration, hands-on exercises, and how-to training. Our goal is to introduce the R programming language by highlighting capabilities that make R a dynamic data science tool.1
R has been called the “lingua franca of data science.”2 It’s a platform independent language that evolved out of the statistical sciences and data analysis communities. The open source language has broad community adoption, and is often touted as one of the premier data science analysis tools. The community-based popularity of R means that help, resources, and innovations are within easy reach. Furthermore, the R ecosystem is connectional and deep. R Packages, which extend R into new analysis or visualization areas, are wrapped into a common R syntax. Combining R’s broad adoption with it’s extensibility shows why R is also referred to as a “Swiss army knife” of data analysis, because R’s broad utility extends beyond the realm of data analysis. As you work through our R learning series, you will see how R is a highly functional research platform that can integrate analysis, reporting, and version control. When these steps are integrated into the R platform your daily workflow becomes practical and reproducible. UseRs who invest in R quickly discover R saves time by leveraging prior knowledge. This means the more you use R the more uses you can find for R.
Image Credit: Sebastian Niedlich
The Fall 2017 R Learning Series
Our fall 2017 RFun workshop format will change slightly. Last spring we hosted eight informal “lunches” highlighting various aspects of R. (Recordings, learning materials and slides are available.) This fall we will transform the series by adding more hands-on learning exercises. Each R workshop will take place on a Monday, beginning in mid-September. Individual workshops will last two hours. The series will extend over four consecutive weeks followed by a brief respite. The fifth and final workshop will introduce Shiny, a package for developing interactive web visualizations. The workshop line-up is intended to gently build knowledge and ensure subsequent sessions remain engaging and practicable.
- Intro to R: Data Transformations, Analysis, and Data Structures (Sept. 11, Repeated Oct. 17)
- Reproducibility: Data Management, Git, & RStudio (Sept. 18)
- Visualization in R using ggplot2 (Sept. 25)
- Mapping with R (Oct. 2)
- Developing Interactive Websites with R and Shiny (Oct. 19)
Dates and details are available through our DVS Workshops Registration page. Advanced registration with a Duke NetID is required.
Access to Workshop Materials
Importantly, advanced registration is not a conduit to receiving an announcement regarding recordings. I note this here to remind people to avoid squatting on limited seating. Squatting is unnecessary since all workshops will be recorded. Workshop materials, slide presentations, and data will be shared publicly following the actual workshops. See the DVS past workshops page for access and availability. Or see my workshops page where links to learning materials are updated frequently.