This document discusses using Tableau and R integration to perform Weibull analysis. It provides an overview of Weibull reliability analysis and the bathtub curve. It then demonstrates how to set up R scripts in Tableau to calculate Weibull parameters like beta, eta, survival and failure probabilities, and confidence bands. Plots of survival data can then be created in Tableau using these R-calculated values. Links are also provided to download the necessary R packages and Tableau.

1. Weibull using Tableau + R Integration
18-JUL-17
Monica Willbrand
Tableau Professional Services

2. • 3 years Tableau Professional Services
+ Tableau Desktop Qualified Associate +
+ Tableau Server Qualified Associate +
• 8 years Semiconductor industry
Relevant Background:

3. • Talk about the tools will use
• Brief overview of Weibull analysis
• Walk through process and calculations
• If we DON’T get through it, the organizers will have this deck and
Tableau workbook
Agenda

4. What is Tableau?
• Visualization software
• On a mission to help people see and
understand data
• 2017 Gartner Magic Quadrant for BI and
Analytics: 5 years a leader

5. Tableau + R Server

6. What does Weibull look like?
• Reliability analysis or Lifetime Data Analysis
(LDA)
• Statistical analysis to determine coefficients
– Beta = shape parameter
– Eta = scale parameter
• Bathtub curve

7. Bathtub Curve
• Weibull
characteristic
failure distribution
for all three
phases of bathtub
curve
β <1 β =1 β >1

8. How does it look in other tools?
• Reliasoft (Weibull++)
• Matlab
• R
• Tableau (Kaplan Meier
method)
• Tableau + R (Weibull analysis)
e

9. R vs Tableau+ R

10. R + R Server, and a couple of packages for Weibull
• Get R @ https://www.r-project.org/
• Get Rserve package
• Get packages invoked by Rscript:
– Flexsurv
– Plyr
• Download and unzip in your R
library, e.g.
Program FilesRR-3.3.3library

11. Fire up R
• Open R
• Start Rserve

12. Point Tableau to R

13. Back to Tableau…
• Connect to data
• Build Rscripts

14. Rscript in Tableau for R(t)
• R(t) = SCRIPT_REAL('
library(flexsurv);
library(plyr)
input<-data.frame(time=.arg1);
S <- Surv(.arg1,.arg2)
fit <- flexsurvreg(S~1,dist="weibull")
s<-summary(fit, cl=0.8, tidy=TRUE, type=.arg3[1])
c<-join(input, s, by = "time");
c$est',
AVG([Time In Field]),ATTR([Failure Flag]),"survival")
arg1 = Time in Field
arg2= Failure Flag
arg3= survival

15. Confidence bands
• Create parameter in Tableau
– Data type: Float
– Allowable values: Range
– Enter range of values
• Rscript for each band

16. R(t) lower interval
SCRIPT_REAL('
library(flexsurv);
library(plyr)
input<-data.frame(time=.arg1);
S <- Surv(.arg1,.arg2)
fit <- flexsurvreg(S~1,dist="weibull")
s<-summary(fit, cl=.arg3[1], tidy=TRUE, type=.arg4[1])
c<-join(input, s, by = "time");
c$lcl',
AVG([Time In Field]),ATTR([Failure Flag]),[interval range],"survival")
arg1 = Time in Field
arg2= Failure Flag
arg3= interval range
arg4= survival

17. R(t) upper interval
SCRIPT_REAL('
library(flexsurv);
library(plyr)
input<-data.frame(time=.arg1);
S <- Surv(.arg1,.arg2)
fit <- flexsurvreg(S~1,dist="weibull")
s<-summary(fit, cl=.arg3[1], tidy=TRUE, type=.arg4[1])
c<-join(input, s, by = "time");
c$ucl',
AVG([Time In Field]),ATTR([Failure Flag]),[interval range],"survival")
arg1 = Time in Field
arg2= Failure Flag
arg3= interval range
arg4= survival

18. Calc for F(t)
• F(t)=1-R(t) = Q(t) = Unreliability over time

19. Beta calc
SCRIPT_REAL('
library(flexsurv);
S <- Surv(.arg1,.arg2)
fit <- flexsurvreg(S~1,dist="weibull")
shape <- fit$res[1];
scale <- fit$res[2];
shape',
AVG([Time In Field]),ATTR([Failure Flag]))
arg1 = Time in Field
arg2= Failure Flag

20. Eta calc
SCRIPT_REAL('
library(flexsurv);
S <- Surv(.arg1,.arg2)
fit <- flexsurvreg(S~1,dist="weibull")
shape <- fit$res[1];
scale <- fit$res[2];
(scale)',
AVG([Time In Field]),ATTR([Failure Flag]))
arg1 = Time in Field
arg2= Failure Flag

21. Plotting data points. Some calcs.
• Censored?
If [Failure Suspension Flag] = 'S' THEN 1 ELSE 0 END
• Cohort Data=
IF SUM([Censored?]) = 1 THEN NULL ELSE
([Adj Median Rank]-.3)/(TOTAL(SUM([Number of Records]))+.4)
END
• Adjusted median rank=
IF SUM([Censored?]) = 1 THEN PREVIOUS_VALUE(0)
ELSE
(([Inverse Rank] * PREVIOUS_VALUE(0))+(TOTAL(SUM([Number of Records]))+1))/([Inverse Rank]+1)
END

22. Set up marks in Tableau:

23. Survival Plot

24. Review
• Tools to perform Weibull
– Tableau
– R Integration
– Scripts
– Reference links in the slide notes

25. Links:
• Download Tableau
• Get R
• Download packages:
– https://cran.r-project.org/web/packages/Rserve/index.html
– https://cran.r-project.org/web/packages/flexsurv/index.html
– https://cran.r-project.org/web/packages/plyr/index.html
• 2017 Gartner Magic Quadrant for BI and Analytics: 5 years a leader

26. Thanks!
• Contact info:
• mwillbrand@tableau.com
• https://public.tableau.com/profile/monica.willbrand#!/