# rlike¶

## Overview¶

The package proposes R features for a pure Python context, that is without an embedded R running.

## Containers¶

The module contains data collection-type data structures. OrdDict and TaggedList are structures with which contained items/elements can be tagged.

The module can be imported as follows:

>>> import rpy2.rlike.container as rlc


### OrdDict¶

The OrdDict proposes an implementation of what is sometimes referred to in Python as an ordered dictionnary, with a particularity: a key None means that, although an item has a rank and can be retrieved from that rank, it has no “name”.

In the hope of simplifying its usage, the API for an ordered dictionnary in PEP 372 was implemented. An example of usage is:

>>> x = (('a', 123), ('b', 456), ('c', 789))
>>> nl = rlc.OrdDict(x)

>>> nl['a']
123
>>> nl.index('a')
0


Not all elements have to be named, and specifying a key value equal to None indicates a value for which no name is associated.

>>> nl[None] = 'no name'


The Python docstring for the class is:

### TaggedList¶

A TaggedList is a Python list in which each item has an associated tag. This is similar to named vectors in R.

>>> tl = rlc.TaggedList([1,2,3])
>>> tl
[1, 2, 3]
>>> tl.tags()
(None, None, None)
>>> tl.settag(0, 'a')
>>> tl.tags()
('a', None, None)

>>> tl = rlc.TaggedList([1,2,3], tags=('a', 'b', 'c'))
>>> tl
[1, 2, 3]
>>> tl.tags()
('a', 'b', 'c')
>>> tl.settag(2, 'a')
>>> tl.tags()
('a', 'b', 'a')
>>> it = tl.iterontag('a')
>>> [x for x in it]
[1, 3]

>>> [(t, sum([i for i in tl.iterontag(t)])) for t in set(tl.itertags())]
[('a', 4), ('b', 2)]


The Python docstring for the class is:

## Tools for working with sequences¶

Tools for working with objects implementing the sequence protocol can be found here.

>>> import rpy2.rlike.functional as rlf
>>> rlf.tapply((1,2,3), ('a', 'b', 'a'), sum)
[('a', 4), ('b', 2)]


TaggedList objects can be used with their tags (although more flexibility can be achieved using their method iterontags()):

>>> import rpy2.rlike.container as rlc
>>> tl = rlc.TaggedList([1, 2, 3], tags = ('a', 'b', 'a'))
>>> rlf.tapply(tl, tl.tags(), sum)
[('a', 4), ('b', 2)]


## Indexing¶

Much of the R-style indexing can be achieved with Python’s list comprehension:

>>> l = ('a', 'b', 'c')
>>> l_i = (0, 2)
>>> [l[i] for i in l_i]
['a', 'c']


In R, negative indexes mean that values should be excluded. Again, list comprehension can be used (although this is not the most efficient way):

>>> l = ('a', 'b', 'c')
>>> l_i = (-1, -2)
>>> [x for i, x in enumerate(l) if -i not in l_i]
['a']

rpy2.rlike.indexing.order(seq, cmp = default_cmp, reverse = False)

Give the order in which to take the items in the sequence seq and have them sorted. The optional function cmp should return +1, -1, or 0.

Parameters: seq – sequence cmp – function reverse – boolean list of integers
>>> import rpy2.rlike.indexing as rli
>>> x = ('a', 'c', 'b')
>>> o = rli.order(x)
>>> o
[0, 2, 1]
>>> [x[i] for i in o]
['a', 'b', 'c']