svFlow-package: Data analysis workflow and alternate pipeline operator

In SciViews/flow: Data Analysis Work Flow and Alternate Pipeline Operator

Data analysis workflow and alternate pipeline operator

Description

Data (work)flow analysis using proto objects (see proto()) and a pipe operator that integrates non-standard evaluation and the tidyeval mechanism in a most transparent way.

Important functions

• %>.% and %>_% are two alternate pipe operators designed to supplement magrittr's \ tidyverse and elsewhere. They are provided for good reasons. %>.% requires explicit indication of the position of . in the pipeline expression all the time. The expression is not modified. As a consequence, it can never surprise you with an unexpected behavior, and all valid R expressions are usable in the pipeline. Another consequence: it is very fast. %>_% works with Flow objects that allow for encapsulation of satellite objects (data or functions) within the pipeline. It is self-contained. The pipeline can be interrupted and restarted at any time. It also allows for a class-less object-oriented approach with single inheritance (could be useful to test easily different scenarios on the same pipeline and to prototype objects that are "pipe-aware"). It also manages the tidyeval mechanism for non-standard expressions in the most transparent way: the only "rule" to remember is to suffix the name of variables that needs special treatment with an underscore (⁠_⁠) and the pipe operator manages the rest for you.

• debug_flow() provides a convenient way to debug problematic pipelines build with our own pipe operators %>.% and %>_% in a comfortable way. Everything from the step that raised a error is available: the piped data, the expression to be evaluated, and possibly, the last state of the Flow object. Everything can be inspected, modified, and the expression can be rerun as if you were still right in the middle of the pipeline evaluation.

• flow() constructs a Flow object that is pipe-aware and tidyeval-aware. This opens new horizons in your analysis workflow. You start building a simple ad hoc pipeline, then you can include satellite data or functions right inside it, perhaps also test different scenarios by using the object inheritance features of Flow (common parts are shared among the different scenarios, thus reducing the memory footprint). While your pipeline matures you gradually and naturally move towards either a functional sequence or a dedicated object. The functional sequence pathway consists in building a reusable function to recycle you pipeline in a different context. The object pathway is not fully developed yet in the present version. But in the future, the object-oriented nature of Flow will also be leveraged, so that you could automatically translate your "flow pipeline" into an S3 or R6 object with satellite data becoming object attributes, and satellite functions becoming methods. The pipeline itself would then become the default method for that object. Of course, both functions and objects derived from a "flow pipeline" will be directly compatible with the tidyeval mechanism, as they will be most tidyverse-friendly as possible per construction.

• str.Flow() compactly displays the content of a Flow object.

• as.quosure(), and unary + and - operators combined with formula objects provide an alternate way to create quosures.

• quos_underscore() automatically converts arguments whose name ends with ⁠_⁠ into quosures, and this mechanism is used by our flow pipe operator to implement the tidyeval mechanism most transparently inside "flow pipelines".

SciViews/flow documentation built on Sept. 9, 2023, 10:22 a.m.