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Statistical Applications for TIBCO Spotfire

( Duration: 2 Days )

In Statistical Applications for TIBCO Spotfire training course, you will learn to extend user analyses with data manipulation and data analysis tasks powered by TIBCO Enterprise Runtime for R (TERR).

  • Attend TIBCO Spotfire Analyst training or equivalent knowledge
  • Attend TIBCO Spotfire Analyst - Advanced Calculations training or equivalent knowledge

This Statistical Applications for TIBCO Spotfire workshop is ideal for Power users and data scientists who want to use R scripts in Spotfire.

COURSE AGENDA

1

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
2

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
3

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
4

Setting up 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)
5

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
6

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

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Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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