Why would you want to use R capabilities within FME. It allows you to bring together the strengths of both applications. FME is capable of reading and writing to a wide variety of formats. FME has powerful data preparation and data clean-up transformations. R offers a wide range of wide variety of statistical tools including classical statistical a wide range data analysis, analytics and plotting tools. We’ll show you how to get the best of both worlds.

1. Ins and outs of
using R in FME
Liz Sanderson
Mark Stoakes

2. Topics We’ll
Cover
Overview of FME & R
Simple example
Orphan Wells Demo and Qlik
Heatmap and Powerpoint
Biodiversity Estimators
Some Resources
Wrap-up & Q&A
1
2
5
6
4
Questions?
Chat them into the GoToWebinar
control panel.
Sam Walker
Natalie Wlock
Matt Smillie
3
7

3. About R
R is a free software environment for statistical
computing and graphics
● R provides a wide variety of statistical tools including:
○ classical statistical analysis
○ linear and nonlinear modelling
○ classification
○ clustering
○ time-series analysis
○ and more...
● Output includes well-designed publication-quality plots

4. Why FME & R?
Combine strengths of two applications in one:
● FME’s format support and data preparation
capabilities
● R’s data enrichment and analytics functionality
○ Leverage the extensive experience of the open
source R community
○ Traditional statistical uses for R
○ More advanced functionality with R packages
○ Plotting tools
● Automation with FME Server

5. What is FME?
Enables digital
transformation
Integrate virtually any
system
Powerful data
transformation
No coding required
safe.com/how-it-works

6. Why Bother with
Data Prep?
Data won’t always be clean
… or consistent
… or in the right data format
Automate the process to avoid mistakes

7. Getting Started with R in FME
● Install R
● Add R Packages
○ install.packages("sqldf") - required by RCaller
● Configure R in FME Options
○ R Interpreter Executable setting
● Load packages in
RCaller
○ Example:
library(igraph)
FME Documantation - RCaller

8. Simple Example
● Linear Regression
○ Read the data
○ Connect input data to
RCaller
○ Create the R script
○ Output results to FME
Output to FME is via a data frame

9. Simple Example

10. Orphan Wells
● Graphical scatter plot with 3 lines of R
● Data clean up with R
● Convert Township and Range to LL84
● Export to Qlik
Data from: AltaLIS & Alberta Oil and Gas Orphan Abandonment Well and Reclamation Association

11. Heatmaps
● Create a heatmap with R
● Use of advanced R package
○ ggmap
● Reprojection of data to LL84
● Parameter to change reporting topic
● Export to Powerpoint
Data from: City of Vancouver

12. Biodiversity
● Advanced species estimations in R
○ Jacknife & Chao estimators
● Spatial overlay with FME
● Simple statistics done with the
StatisticsCalculator
● 3D extrusion
Register for the FME World Tour 2018 for more
information
Data from: Sigogne & INSPIRE EU
@sigogne_fc

13. Minimum
Spanning Tree
A minimum spanning tree of a network is the tree
where the sum of the edge weights are the minimal

14. Other Common Data Validation
and Cleanup Tools in FME

15. ● Blogs
○ The Power of the RCaller for Estimating Biodiversity
○ FME and the Minimum Spanning Tree (a folk-tale
with a happy ending)
● Knowledge Base
○ Tutorial: Getting Started with the RCaller
○ RCaller: Orphan Well Scenario
○ Create Heatmaps and Hotspots Using R (coming soon)
● R
○ A (very) short introduction to R
○ DataCamp: Introduction to R
Resources

16. FME is great for data preparation
R has a wide range of specialist analytics
packages
The whole is greater than the sum of its parts
Recap

17. Questions?
Get a free trial of FME or live chat us at www.safe.com
Thank you!