How to improve R workflows

1. thedatacollective@danwwilson #useR2018
Practical R Workflows
How I smooth the bumps

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Who am I?

3. thedatacollective@danwwilson #useR2018
Less of…

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More of..

5. thedatacollective@danwwilson #useR2018
2017 2018 2019JulySeptember
Founded

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Who should be here?

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Where to start?

13. thedatacollective@danwwilson #useR2018
It’s all about the data

14. thedatacollective@danwwilson #useR2018
Set your requirements…
…then be flexible

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Make it yours

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*
Organise things*
• Name things well http://bit.ly/Jenny_naming
• Determine your folder structure and use it consistently

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Identify points of friction

18. thedatacollective@danwwilson #useR2018
Workflows take thinking
• What work is repetitive?
• What data can you standardize across projects?
• How should you name things?
• How should you structure projects?
• What steps in a project slow you down?

19. thedatacollective@danwwilson #useR2018
Building skills

20. thedatacollective@danwwilson #useR2018
Example Project
Receive
Data
Standardise
Data
Review
Summary
Client
Thedata
collective
Analyse
Data
Review data
Zip Outputs

21. thedatacollective@danwwilson #useR2018
Re-use code

22. thedatacollective@danwwilson #useR2018
Create functions
get_recency(tx_date, current_date)
get_frequency(num_gifts)
get_value(gift_amount)
All in src/99_functions.R
get_value <- function(amount) {
amount <- suppressWarnings(as.numeric(amount))
x <- replace(amount, is.na(amount), -Inf)
cut(x, breaks = c(-Inf, 0.00001, 10, 25, 50, 100, 250, 500, 1000, Inf),
labels = c("ERROR", "<$10", "$10-$24.99", "$25-$49.99", "$50-$99.99",
"$100-$249.99", "$250-$499.99", "$500-$999.99", "$1,000+"),
include.lowest = TRUE, right = FALSE)
}

23. thedatacollective@danwwilson #useR2018
But…

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Create a package
segmentr - http://bit.ly/segmentr
• http://bit.ly/pkg_hadley
• http://bit.ly/pkg_hilary
• http://bit.ly/pkg_rstudio
• http://bit.ly/pkg_karl

25. thedatacollective@danwwilson #useR2018
Start at your level, and improve
• Start simple
• Re-usable code snippets
• Keep them together
• Build a file of commonly used functions (99_functions.R)
• Build a package
• When you can make the time, or skills are ready

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Reducing friction

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What slows you down?
• R is great even with its quirks
> paste(“Dan”, NA, “Wilson”)
# Dan NA Wilson
paste_na()
> paste_na(“Dan”, NA, “Wilson”)
# Dan Wilson

28. thedatacollective@danwwilson #useR2018
Standardised data
> data(package = "segmentr")
Data sets in package 'segmentr’:
ask_conversion Ask Conversion Table
lookup_channel Channel Code Lookup
lookup_classification Classification Code Lookup
segments Segments

29. thedatacollective@danwwilson #useR2018
Streamlined process*
• Build templates
• Start the project quickly
• Next step is client specific templates

30. thedatacollective@danwwilson #useR2018
Getting data out of R*
• Copying and pasting from the console is painful (or impossible)
• Writing to CSV and opening to copy/paste from is too many steps
• copy_clip()

31. thedatacollective@danwwilson #useR2018
Build solutions to common problems
• Limit manual intervention
• Make objects (data, functions, etc) accessible
• Reduce repetition
• Simplify integration with other tools/software

32. thedatacollective@danwwilson #useR2018
Key take outs
1. Workflows require a little bit of forethought… take the time
2. Start at your level and build your skills
3. Ease the friction

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Thanks
@danwwilson