readme.md
In albertbuchard/r-pipeline: R-Pipeline for data analysis

Pipeline

Data analysis pipeline for the R language.

Since the repo is private for now git fork are the easiest way to contribute.

TBA - see the man/ folder - Accessible through R studio

TBA - see the examples/ folder

Contribute !

If you want to contribute to the project you can find us on : Our forum Twitter * Ask for a Slack invite ! https://r-pipeline.slack.com

The pipeline should be written following the google style guide for R : https://google.github.io/styleguide/Rguide.xml

A lot of refactoring as to be done to comply with the google style guide
camelCase for variables PascalCase for functions and objects
Comments must follow the Roxygen (https://github.com/klutometis/roxygen) syntax and the code must ortherwise be adequatly commented for anyone to understand how the data is processed and how results are generated.
Use long and overly clear names like : CenterOnMeanOfMean(), correlatedVariableNamesArray etc.

It is essential that every addition should be testable. https://cran.r-project.org/web/packages/RUnit/vignettes/RUnit.pdf

    # Testing a function that convert celsius to fahrenheit
    c2f <- function(c) return(9/5 * c + 32)

    # A corresponding test function could look like this:
    test.c2f <- function() {
    checkEquals(c2f(0), 32)
    checkEquals(c2f(10), 50)
    checkException(c2f("xx"))
    }

Please see the docs () for a complete description of the functions.

Discuss methodology with other teams
Research the open source projects already available to determine which functions are redundant, and what function to import/modify for integration in the package
Refactor the code
Discuss and build robust data-analysis workflows around functions in the package as well as outside the package
Preprocessing : Decide on the best practice and how to implement it
Time series analysis tools
Build an object wrapper (S4 ? S6?) to help in data management / versioning
Build I/O functions
Export functions to XLS, CSV, JSON
(Future: Discuss possibilities of making the package self-suficient ? (no external dependency) - Optimize with compiled lib )
Add all the R-snippets into functions (https://github.com/jvcasill/R-snippets)