This course introduces the user of Spotfire to TIBCO Enterprise Runtime for R (TERR) and shows how scripts written in the R language can be used to extend Spotfire’s capabilities.
Spotfire Analyst includes TIBCO Enterprise Runtime for R (TERR). TERR gives you the power to extend Spotfire’s capabilities by using R scripts to perform data manipulation and data analysis tasks in Spotfire analyses. This course includes examples representing some of the most common applications of R scripting in Spotfire. The course also enables you with the general skills and knowledge required to apply your own R scripts in Spotfire. Spotfire version 6.5 or higher is required.
Note: This course does not include R language instruction.
Training and experience coding in the R language is recommended, but not required, before you take this course.
Students should also participate in Spotfire Analyst Essentials I and II before taking this course.
- Introduction to R and TERR
- What is R? Why use R?
- What is TERR? Why use TERR?
- Differences between TERR and open source R
- Learning the R language
- Overview of TERR in Spotfire
- Reasons for using TERR and Spotfire together
- User roles for working with TERR and Spotfire
- Ways to use TERR in Spotfire
- TERR expression functions
- TERR data functions
- Using TERR in the different Spotfire clients
- Architecture for TERR and Spotfire
- TERR embedded in Spotfire Analyst and Spotfire Desktop
- TIBCO Spotfire Statistics Services
- Publishing analyses with TERR functions on Spotfire Web Player
- Setting Up Your TERR-Spotfire Environment
- Using the TERR console
- Using TERR with RStudio
- Installing packages from CRAN on TERR
- Accessing TERR documentation and R package documentation
- Installing open source R (optional)
- TERR Expression Functions
- Using in-line TERR expressions
- Registering and using TERR expression functions
- TERR expression function inputs
- TERR expression function outputs
- How TERR expression functions respond to filtering
- Using TERR expression functions with trellised visualizations
- TERR expressions warnings and errors Example: K-means clustering
- Example: LOWESS smoothing
- Example: Regression lines with confidence bands
- TERR Data Functions
- Creating TERR data functions
- Running TERR data functions
- Modifying TERR data functions
- Data function input parameters
- Data function output parameters
- Data functions progress, warnings and errors
- Troubleshooting TERR data functions
- Other ways to insert data functions
- Refreshing data functions with action controls
- Example: K-means clustering
- Example: Regression modeling
- Example: Classification modeling