What lessons from R can be applied to digital analytics workflows? Things I wish I knew at the start of my career working on data

1. #RSTATS LESSONS
FOR #MEASURE

2. 01 Introduction of R and Measure
Communities
02 Overview of the Tidyverse
03 Environments and version
control
04 Tidy data
05 Data activation

3. Mark’s #measure #rstats timeline
2006
2007
2010
2012
2015
2016
2018
SEO reporting
Pulling Google
Analytics and Search
Console into Excel
dashboards
Move to Denmark
Move into Google
Analytics consultancy,
start using BigQuery
and Google Cloud
#measure
Open source
Start creating open-source
R packages, blogging at
code.markedmondson.me
Python
Start using Python to
create SEO tools
R
Start using R to create
attribution and
forecasting models on
Google Analytics data
#rstats
Google Developer
Expert
Publish Shiny
applications, become a
GDE for Google
Analytics/Cloud.
Develop R for Google
Cloud resources
including dartistics.com
and googleAnalyticsR
Data Engineering
Creating data pipelines
for machine learning
using Google Cloud, R,
Python

4. #RSTATS COMMUNITY
•R has been working on the process of
data analysis for since 1993
•2 million users estimated in 2012 (5
million users now?)
•10,000+ packages
•@rstudio has 60k followers
•1984 R jobs at www.indeed.nl

5. #MEASURE COMMUNITY
•Since ???? (1993 WebTrends?)
•MeasureCamps all over the world
•@googleanalytics has 1.05M followers on
Twitter
•1770 Google/Adobe Analytics jobs at
indeed.nl

6. #RSTATS AND #MEASURE TRENDS

7. WHAT LESSONS FROM
R
CAN APPLY TO
MEASURE ?

8. www.tidyverse.org

9. THE PIT OF SUCCESS
Q:What is the vision behind the “tidyverse”?
A: My long term goal is to create a pit of success where the default path
leads to a great result.
> HadleyWickham, Quora Q&A, 2016
Where “flow” and magic happens

10. Environment
Version Control
Sources
Collection
Activation
Tidy
Dashboards
APIs
Process
TIDYVERSE DATA FLOWS

11. 1. REPRODUCIBILITY

12. Environment
HAPPY DATA ANALYSIS PEOPLE
A good work culture
Working on important
problems
Allowing failure,
freedom to experiment

13. Environment
CONSISTENT SOFTWARE
Consistent software
versions
Documentation
Docker

14. Environment
Version Control
DEDICATEDVERSION CONTROL
Have a system!
Shared drive links, not
email attachments
Git/SVN
Continuous Integration

15. GITHUB
FREESYOU
“Infinite Undo/Redo”
Delete code at will - free yourself from cognitive load of keeping legacy code

16. CONTINUOUS DEPLOYMENT

18. Environment Version Control
REPRODUCIBILITY
Freedom to experiment
Correction of mistakes
Trust in results

19. 2.TIDY DATA

20. Environment
Version Control
Sources
Collection
TidyProcess
TIDY IS KEY PART OF DATA WORKFLOW
Gets rid of cognitive load for analysts
Easy to check data errors
Format already suitable for data ingestion
Can be carried out by less expensive resources

21. Happy families are all alike; every unhappy family is
unhappy in its own way
— LeoTolstoy
Tidy datasets are all alike but every messy dataset is
messy in its own way
— HadleyWickham
Tidy Data
https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html

22. http://garrettgman.github.io/tidying/
Tidy datasets are easy to manipulate, model and
visualize, and have a specific structure: each variable
is a column, each observation is a row, and each
type of observational unit is a table.

23. UNTIDY DATA - FAMILY A
http://garrettgman.github.io/tidying/

24. UNTIDY DATA - FAMILY B
Cases
Population
http://garrettgman.github.io/tidying/

25. TIDY DATA
http://garrettgman.github.io/tidying/

26. https://xkcd.com/1179/
TIDY
DATES!

27. TIDY RULES
• Each variable is a column
• Each row is an observation
• One table per dataset
• No totals included in columns
• No data as formatting (e.g. colour of cells…)
• Data types are defined e.g. string, numeric,
float, boolean, date
• Missing values are defined e.g. NA, NULL or 0

28. Data Storage
• Easy access to whoever needs it
• Able to cope with your data volume and velocity
• GDPR compliant
• Can handle different types of data such as video, images
For us on Google Cloud Platform that means: Cloud Storage and BigQuery

29. REPRODUCIBILITY Tidy
Data

30. HAPPY ANALYSTS
Mimimise data preparation
Construct consistent workflows
Easy to throw into visualisation tools

31. Where “flow” and magic happens
Happy Analysts
MAXIMISE THE FLOW

32. 3. DATA ACTIVATION

33. I’ve spent 100,000 EUR on my
analytics infrastructure.
Where are my insights?

34. Data Activation
Time to action
Business
Impact
Bad Dashboards
Data APIs
Good Dashboards
Analysis
Report
Business
Alerts

35. R visualisation options
Base R plotting RMarkdown Shiny
Quick data exploration HTML,Word, PDF etc. Interactive R code

36. Time to Learn Customisation Reproducability Cost
Data Studio Easy Good Poor Free
PowerBI Medium OK Poor Minimal
Tableau Medium OK Poor Expensive
RMarkdown Medium Excellent High Free
Shiny Hard Excellent High Minimal
Excel Medium Good Terrible Free

37. Data APIs
OpenCPU
Scale R (or any other
language) to be
consumed by any
application

38. WHAT LESSONS FROM
R
CAN APPLY TO
MEASURE ?

39. Environment
Version Control
Sources
Collection
Activation
Tidy
Dashboards
APIs
Process
TIDYVERSE DATA FLOWS

40. Environment
Version Control
Sources
Collection
Activation
Tidy
Dashboards
APIs
Process
TIDYVERSE DATA FLOWS
Reproducibility
Happy Analysts
Where “flow” and
magic happens

41. R TAUGHT ME…
• Stable work environment and version control = Reproducibility
• Tidy data before analysis
• Low friction data analysis encourages “flow”
• Think deeply on final format of data
• Activated data is the ultimate goal

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Copenhagen
Artillerivej 86
2300 København S
+45 70 20 29 19
Stockholm
Strandvägen 7A
114 56 Stockholm
+45 70 20 29 19
London
43A Southend Road
BR3 1SP. London
+45 70 20 29 19
Oslo
CJ Hambros plass 2
N-0164 Oslo
+45 70 20 29 19
THANKYOU
Twitter @HoloMarkeD
Blog code.markedmondson.me
Email mark@iihnordic.com