File Cacher

The main functions in this package are with_cache() and cached_read(). The former is a simple way to cache an R object into a file on disk, using 'cachem'. The latter is a wrapper around any standard read function, but caches both the output and the file list info. If the input file list info hasn't changed, the cache is used; otherwise, the original files are re-read. This can save time if the original operation requires reading from many files, and/or involves lots of processing.

Cross-Platform File System Operations Based on 'libuv'

A cross-platform interface to file system operations, built on top of the 'libuv' C library.

Application Directories: Determine Where to Save Data, Caches, and Logs

An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' (< https://github.com/ActiveState/appdirs>) to R.

Simple R Cache

Simple result caching in R based on R.cache. The global environment is not considered when caching results simplifying moving files between multiple instances of R. Relies on more base functions than R.cache (e.g. cached results are saved using saveRDS() and readRDS()).

Package Dependency Resolution and Downloads

Find recursive dependencies of 'R' packages from various sources. Solve the dependencies to obtain a consistent set of packages to install. Download packages, and install them. It supports packages on 'CRAN', 'Bioconductor' and other 'CRAN-like' repositories, 'GitHub', package 'URLs', and local package trees and files. It caches metadata and package files via the 'pkgcache' package, and performs all 'HTTP' requests, downloads, builds and installations in parallel. 'pkgdepends' is the workhorse of the 'pak' package.

Export Data Frames to Excel 'xlsx' Format

Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter' < https://libxlsxwriter.github.io>. Fast and no Java or Excel required.

Track R Package Downloads from RStudio's CRAN Mirror

Allows to get and cache R package download log files from RStudio's CRAN mirror for analyzing package usage.

Reproducible Data Embedding

Allows caching of raw data directly in R code. This allows R scripts and R Notebooks to be shared and re-run on a machine without access to the original data. Cached data is encoded into an ASCII string that can be pasted into R code. When the code is run, the data is automatically loaded from the cached version if the original data file is unavailable. Works best for small datasets (a few hundred observations).

Enhance Reproducibility of R Code

A collection of high-level, machine- and OS-independent tools for making reproducible and reusable content in R. The two workhorse functions are 'Cache()' and 'prepInputs()'. 'Cache()' allows for nested caching, is robust to environments and objects with environments (like functions), and deals with some classes of file-backed R objects e.g., from 'terra' and 'raster' packages. Both functions have been developed to be foundational components of data retrieval and processing in continuous workflow situations. In both functions, efforts are made to make the first and subsequent calls of functions have the same result, but faster at subsequent times by way of checksums and digesting. Several features are still under development, including cloud storage of cached objects allowing for sharing between users. Several advanced options are available, see '?reproducibleOptions()'.

A Simpler Way to Find Your Files

Constructs paths to your project's files. Declare the relative path of a file within your project with 'i_am()'. Use the 'here()' function as a drop-in replacement for 'file.path()', it will always locate the files relative to your project root.