Data Tests

Abstract

Data science projects in commercial companies often experience a challenge arising from evolving data sources. As the project progresses, new signals and information sources are added incrementally. In practice, when the data source changes, it creates a need to change the application source code.

With no design up front, some application modules, such as a dashboard or a machine learning model, unwittingly become dependent on the data source. In this case, accommodating the evolving data source is not simply a matter of changing the code related to the data source. Rather, preserving the rest of the existing application in a working condition involves further code changes in distant elements of the application.

An alternative way of dealing with evolving data sources is to introduce a small design up front. Such design lets programmers manage the source code dependencies throughout the project life-cycle.

This talk suggests a design that (1) separates data sources from analytic applications and (2) restricts analytic applications from knowing about the data sources.

While the evolving data sources challenge is programming language agnostic, this talk demonstrates an implementation of the suggested design in R.

The takeaway of this talk is a design of a system, where data sources are plugins to the system’s analytic modules.

---

This presentation was given at satRday Auckland, February 2020.
See event details at https://auckland2020.satrdays.org/#portfolioModal7

Transcript

1. Data Tests
   Separating between Data Sources and the Rest
   satRday, February 2020
   Harel Lustiger
   Data Scientist, Harmonic Analytics
   

   View Slide

2. What are we doing?
   We are separating between the data source and the rest
   

   View Slide

3. Insert 500 hundred lines here
   ggplot(Tog3[-c(1,28),], aes(Date,Rec2All, colour = isChina))+
   geom_line(size = 2.2, alpha = 0.5)+
   data data data$isChina "China"),"China", "Not China")
   data$Date Insert 100 hundred lines here
   # main.R
   Dividing the analytic application into modules
   analytic module
   data source
   

   View Slide

4. Making the data source existence dependent on what matters
   Analytic
   Module
   Depends On
   Database
   Database
   Access
   Has a
   Data Source Data Tests
   

   View Slide

5. What’s the Story?
   We are helping our clients by fitting a solution to their needs
   

   View Slide

6. Getting to know our
   client
   Euel Cheatam the
   Mercedes dealership
   manager
   

   View Slide

7. The proposed
   solution is a weekly
   fact sheet with
   popular Q&A
   

   View Slide

8. The data scientists
   encounter real-world
   impediments
   

   View Slide

9. Iteration Zero
   developing a data-driven app without real data
   

   View Slide

10. Generate dataset assumption triplet
    01
    

    View Slide

11. The analytic application expects tidy-data1 as its input
    1 https://r4ds.had.co.nz/tidy-data.html
    

    View Slide

12. Observation Unique
    Identifier (UID)
    Target variable
    Salient features
    We formulate a tidy dataset by making three assumptions
    

    View Slide

13. Convert assumptions to assertions with data-tests
    02
    

    View Slide

14. ## 1. Check if the dataset exists
    stopifnot(exist("cars_data"), is.data.frame(cars_data))
    # data-tests.R
    ## 2. Check if the necessary columns exist
    expected_cols stopifnot(all(expected_cols %in% colnames(cars_data)))
    ## 3. Check if the records are unique
    is.distinct stopifnot(is.distinct(cars_data$car_model))
    

    View Slide

15. Implement a data source plugin
    03
    

    View Slide

16. Quick glimpse over dataset::mtcars
    Observation
    Unique Identifier
    Salient Feature
    Salient Feature
    

    View Slide

17. # data-source.R
    get_cars_data ## 1. Generate records
    data(mtcars, package = "datasets")
    cars_data % tibble::rownames_to_column("car_model")
    ## 2. Generate price
    set.seed(2020)
    price cars_data % tibble::add_column(price = price)
    ## Run data-tests
    source("data-tests.R")
    return(cars_data)
    }
    ## 1. Generate records
    data(mtcars, package = "datasets")
    cars_data % tibble::rownames_to_column("car_model")
    ## 2. Generate price
    set.seed(2020)
    price cars_data % tibble::add_column(price = price)
    

    View Slide

18. Develop an analytic module
    04
    

    View Slide

19. # app.R
    ## 1. Get the data
    source("data-source.R")
    cars_data ## 2. Render booklet
    print(cars_data %>% dplyr::select(car_model, mpg, gear, price))
    boxplot(price ~ gear, cars_data)
    lm(price ~ mpg + gear, cars_data) %>% summary()
    

    View Slide

20. Conclusion
    Why and how we separate the data source from the rest
    

    View Slide

21. Recall why we have separated the data source from the rest
    Database
    Database
    Access
    Analytic
    Module
    Depends On
    Has a
    Data Source
    

    View Slide

22. Recall why we have separated the data source from the rest
    Database
    Database
    Access
    Data
    Tests
    Analytic
    Module
    Depends On Dictates
    Has a
    Data Source
    

    View Slide

23. Recall how we have separated the data source from the rest
    Harel Lustiger
    harel.lustiger@gmail.com
    assumption triplet data-tests.R data-source.R app.R
    

    View Slide