An R library for managing and documenting dplyr data pipelines - terminological/dtrackr
Track your Data Pipelines
Track and document dplyr data pipelines. As you filter, mutate, and join your way through a data set, dtrackr seamlessly keeps track of your data flow and makes publication ready documentation of a data pipeline simple.
elbersb/tidylog
Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.
Build Reproducible Analytical Pipelines with Nix
Streamlines the creation of reproducible analytical pipelines using default.nix expressions generated via the rix package for reproducibility. Define derivations in R, Python or Julia, chain them into a composition of pure functions and build the resulting pipeline using Nix as the underlying end-to-end build tool. Functions to plot the pipeline as a directed acyclic graph are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the targets package.
R for cats · and cat lovers
This is an introduction to R. I promise this will be fun. Since you have never used a programming language before, or any language for that matter, you won’t be tainted by other programming languages with different ways of doing things. This is good - we can teach you the R way of doing things.
Snakemake
With Snakemake, data analysis workflows are defined via an easy to read, adaptable, yet powerful specification language on top of Python. Steps are defined by "rules", which denote how to generate a set of output files from a set of input files (e.g. using a shell command). Wildcards (in curly braces) provide generalization. Dependencies between rules are determined automatically.
Lean Data Automation: A Principal Components Approach
Plus: A nimble, composable example with Github Actions, Hamilton, and Runhouse
Dynamic Data Science
<p>Use our Common Online Data Analysis Platform (CODAP) to explore dynamic data science activities and gain fluency in data moves to examine large datasets.</p>
DASL — Data-Addressed Structures & Links
A small set of simple, standard primitives to work on content-addressed data.
DASL — Data-Addressed Structures & Links
A small set of simple, standard primitives to work on content-addressed data.
Specification of graph translators with triple graph grammars
Data integration is a key issue for any integrated set of software tools. A typical CASE environment, for instance, offers tools for the manipulation of requirements and software design documents, and it provides more or less sophisticated assistance for keeping these documents in a consistent state. Up to now, almost all data consistency observing or preserving integration tools are hand-crafted due to the lack of generic implementation frameworks and the absence of adequate specification formalisms. Triple graph grammars are intended to fill this gap and to support the specification of interdependencies between graph-like data structures on a very high level. Furthermore, they are the fundamentals of a new machinery for the production of batch-oriented as well as incrementally working data integration tools.
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