A two-stage workflow for data science projects

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If you are a data scientist who primarily works with R, chances are you had no formal training in software development. I certainly did not pick up many skills in that direction during my statistics masters. For years my workflow was basically load a dataset and hack away on it. In the best case my R-script came to some kind of conclusion or final data set, but usually it abruptly ended. Complex projects could result in a great number of scripts and data exports. Needless to say, reproducability was typically low and stress could run high when some delivery went wrong.

Picking up software skills

Colleagues pointed me to some customs in software design, such as creating functions and classes (and documenting them), writing unit tests, and packaging-up your work. I started to read about these concepts and was soon working my way through Hadley’s R packages to see how it was done in R. I came to a adopt a rigorous system of solely writing functions, and thoroughly documenting and unit testing them. No matter the nature of the project I was working on, I treated is as a package that should be published on CRAN. This slowed me down considerably, though. I needed increasing amounts of time to answer relatively simple questions. Maybe now I was over engineering, restricting myself in such a way I was losing productivity.

Data science product

In my opinion, the tension between efficiency and rigor in data analysis, is because the data scientist’s product usually is not software, but insight. This goal is not always served best by writing code that meets the highest software standards, because this can make an insight more expensive than necessary, timewise. A software engineer’s job is much more clear cut. His product is software, and the better his software, the better he does his job. However, when a data scientist is asked if she can deliver an exploratory analysis on a new data set, should she create a full software stack for it right away? I would say not. Of course clean, well documented scripts will lead to greater reproducibility of and confidence in her results. However, writing solely functions and classes and put a load of unit tests on them is overkill in this stage.

A two-stage workflow

I came to a workflow, with which I am quite happy. It has a hard split between the exploratory stage and the product stage of a project.

Exploratory stage

Starting an analysis, I want to get to first results as quickly as possible. R markdown files are great for this, in which you can take tons of notes about the quality of the data, assumptions tested, relations found, and things that are not yet clear. Creating functions in this stage can be already worthwhile. Functions to do transformations, create specific plots or test for specific relationships. I aim for creating functions that are as generic as is feasible from the start. It is my experience that data is often refreshed or adjusted during a project, even when this was not anticipated at the start of the project. Don’t hardcode names of dataframes and columns, but rather give them as arguments to functions. Optionally with the current names as their default values. These functions can be quickly lifted to the product stage. Functions are only written though, if they facilitate the exploration. If not, I am happy to analyse the data with ordinary R code. Typically, the exploratory stage is wrapped-up by a report in which the major insights and next-steps are described. More often than not, the exploratory stage is the only part of a project as it is concluded to take no further action on the topic researched. The quicker we can get to such a conclusion, the less resources are wasted.

Product stage

A product, to me, is everything designed to share insights, model predictions, and data with others. Whenever a product gets built, whether its a Shiny app, an export of model productions, or a daily updated report, the product stage is entered. In more ignorant days I used to gradually slide from exploratory stage into product stage. In the worst cases commenting out the parts of code that were not needed for building the product. This got increasingly messy of course, yielding non reproducable results and projects that grinded to a halt. Nowadays, as soon as a product gets built I completely start over. Here the software rigor kicks in. All code written in this part only serves the final product, no explorations or asides allowed. I use R’s package structure, writing functions that work together to create the product, including documentation and unit tests. When functions are used in the exploratory stage, many components of the product are already available at the start of this stage.

Further cycles

When during product stage it becomes evident that further research is needed, switch back to the exploratory code base. I think it is crucial to be very strict about this. No mess in the product repository! The product directory only contains the code needed for the product to work. When new insight requires the product to change, maybe updating a model or add an extra tab to a dashboard, adjust the product. Again only code necessary for the product to function allowed. Legacy code no longer needed should be removed, not be commented out. This workflow prevents a large set of scripts with a great number of models, many exported data sets, several versions of a Shiny app etc. The product repository always contains the latest version and nothing more.

I hope these hard-learned lessons might be to some value for some of you. I am very curious what others designed as their system. Please share it in the comments of this blog, on Twitter, or in you own blog and notify me.

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