README.md

In decisionpatterns/r-hash: A Hash/Dictionary/Map/Associated Array for R

hash

hash/dictionary/maps in R

The hash package provides a fully-functional hash/dictionaryfor the R language. It provides richer features and finer control of hash behavior than using native R structures like list or environments and has as a user-friendly interface. Performance-wise it has similar and sometimes better performance than these structures especially for larger objects.

Installation

Latest Release:

install.packages('hash')

Development Version:

install.packages('devtools')
devtools::install_github('decisionpatterns/r-hash')

Examples

# Create a hash 
h <- hash(a=1, b=2, c=3)
h <- hash( letters[1:3], 1:3 )
h <- hash( list(a=1,b=2,c=3) )

# Keys 
keys(h)

# Values (named list)
values(h)

# Assign to single key hash
h$a <- "foo"
h[['a']] <- "bar"

# Slice
h[ c('a','c') ]

Allowable Values

KEYS must be a valid character value and may not be the empty string (""). Keys must be unique.

VALUES can be any R value, vector, object, etc.

Usage Notes

Hashes probably work about how you would expect, but since there are built from R's native environments. There are three things to Remember:

PASS-BY REFERENCE. hashes are environments, special objects in R where only one copy exists globally. When passed as an argument to a function, no local copy is made and any changes to the hash in the functions are reflected globally, i.e. in the caller's namespace.

PERFORMANCE. Hashes are designed to be exceedingly fast using R environment's internal hash table. The hash function is not without its cost. For small data structures, a named lists and vectors will out-perform a hash in nearly every case. After approximately 500+ elements, the performance of the hash becomes faster than native lists and vectors.

decisionpatterns/r-hash documentation built on Feb. 6, 2019, 10:27 p.m.