Data analysis project management for R
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DESCRIPTION
NAMESPACE
README.md
fridge.Rproj

README.md

fridge

fridge is a simple approach to managing data analysis projects in R.

Background

fridge assumes that data analysis projects are managed using a hierarchical folder structure. Helper functions are kept seperated from the code that specifies the final models etc., in a lib directory.

Objects that need long computation times to be created are stored as binary files (*.rda) in a cache directory and are reused if needed rather than recreated each time the analyses are run.

Installation

install.packages("devtools")
library(devtools)
install_github("cszang/fridge")

Setup

Create a lib directory inside the main project folder, and populate with auxilliary code needed for e.g. munging data from various sources, creating special plots, ...

Usage

Caching

To create cached objects, use freeze() or its infix operator %<f-% for assignment and caching in one step:

freeze("a_big_sum", sum(1:2000))
# the same as
a_big_sum %<f-% sum(1:2000)

This will assign sum(1:2000) to a new object a_big_sum, and cache it for later use. The next time the exact same call is made from the same or another script, the assignment is not evaluated, but the cached object is loaded. It will also store the checksum of the expression used to create the object and issue a warning when the expression has changed from the cached version of the object.

To "forget" an assignment including cached data, use forget().

Compare checksums during assigning read in file

To assure that the object created while reading in data is identical to previous runs of the analysis, the SHA1 sum of the object can be stored and compared to the previous version. If the SHA1 of the current object diverges from the SHA1 sum of the same object from a previous run, a warning is issued and the old SHA1 sum is retained. After adapting the code to the new version of the data, delete the SHA1 sum corresponding to the object and re-create the object.

This is probably most helpful when your data is too big for Git(Hub), Git LFS is not an option, or the same big data files are used for multiple projects (e.g., large climate data sets of several GB in size).

e %<c-% read_table("some_file.txt")

Requesting objects

request checks for the existance of an object in the current session. If the object is not defined, it will try to load it into the session from the project's cache directory.

Empty cache

All cached objects can be deleted in one go using empty_cache(). This will, however, not "forget" the objects in the current workspace.

Loading library functions

Auxilliary code from the lib directory can easily be sourced into the R session using load_lib(), which can either load all files from the lib folder (default), or only specified ones, which have to be only referenced to by their base name without extension.