Helper Functions for Rmd Documents

A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.

Export R Objects to Several Markup Languages

Coerce R object to 'asciidoc', 'txt2tags', 'restructuredText', 'org', 'textile' or 'pandoc' syntax. Package comes with a set of drivers for 'Sweave'.

Data Correction and Imputation Using Deductive Methods

Attempt to repair inconsistencies and missing values in data records by using information from valid values and validation rules restricting the data.

Print Maps, Draw on Them, Scan Them Back in

Enables preparation of maps to be printed and drawn on. Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects.

Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, ).

Smoothing tools

Tools rewritten in C for various smoothing tasks

Modify Data Using Externally Defined Modification Rules

Data cleaning scripts typically contain a lot of 'if this change that' type of statements. Such statements are typically condensed expert knowledge. With this package, such 'data modifying rules' are taken out of the code and become in stead parameters to the work flow. This allows one to maintain, document, and reason about data modification rules as separate entities.

Managing Larger Data on a GitHub Repository

Because larger (> 50 MB) data files cannot easily be committed to git, a different approach is required to manage data associated with an analysis in a GitHub repository. This package provides a simple work-around by allowing larger (up to 2 GB) data files to piggyback on a repository as assets attached to individual GitHub releases. These files are not handled by git in any way, but instead are uploaded, downloaded, or edited directly by calls through the GitHub API. These data files can be versioned manually by creating different releases. This approach works equally well with public or private repositories. Data can be uploaded and downloaded programmatically from scripts. No authentication is required to download data from public repositories.

Import 'OpenStreetMap' Data as Simple Features or Spatial Objects

Download and import of 'OpenStreetMap' ('OSM') data as 'sf' or 'sp' objects. 'OSM' data are extracted from the 'Overpass' web server (< https://overpass-api.de/>) and processed with very fast 'C++' routines for return to 'R'.

Routines for Common fMRI Processing Tasks

Supports fMRI (functional magnetic resonance imaging) analysis tasks including reading in 'CIFTI', 'GIFTI' and 'NIFTI' data, temporal filtering, nuisance regression, and aCompCor (anatomical Components Correction) (Muschelli et al. (2014) ).