Tools for functional programming in R
This package contains a collection of functions that facilitate modeling of data using a functional programming paradigm. The idea is that using tools that are more closely connected with the idioms of mathematics will make it easier to map the mathematical model to the software model.
Functional programming concepts start with functions as the foundation.
Higher-order functions provide generalized machinery for operating
on data in an element-wise manner. Lambda.tools includes idiomatic
versions of the canonical higher-order functions, such as map and fold
for data structures common in R. In most languages the semantics are
limited to single-element iterations. In R it is common to work with
panel data or sliding windows, so lambda.tools introduces block
and range semantics to support these concepts, respectively.
Hence lambda.tools defines
maprange and similar
The semantics of a block operation is that regular, contiguous chunks of
data are passed to the function. Suppose a vector
x has 12 elements.
Performing a mapblock operation with window of length 3 applies the
specified function to the following sub-vectors:
x[10:12]. This is useful for processing any vector or
list produced by a function that returns a regular length output.
Note that if the original sequence is not an integer multiple of the window length, the last sub-vector will not have the same length as the preceding sub-vectors.
While block operations use adjacent sub-vectors, range operations
use overlapping sub-vectors. This process is analogous to a
sliding window, where the index increments by one as opposed to
by the window size. For the same vector
x, a maprange operation
with window of length 3 produces the following sub-vectors as
An example of a range operation is generating n-grams from a text
document. Suppose a vector
v contains a sequence of words. Then
maprange(v, 2, function(x) paste(x, collapse=' ')) creates bigrams.
Typically map and fold operate on 1-dimensional data structures,
but in R operations can also be applied on 2-dimensional data structures.
For example, the
apply function works in this manner where the
MARGIN argument defines whether iteration operates on rows versus
lambda.tools introduces 2-dimensional versions of
these functions. For simplicity, the 2-dimensional variants of
map and fold only operate along columns.
To operate along rows requires transposing the data structure.
Consider the following code that applies multiple rotations to a collection of points.
ps <- t(matrix(c(0,0, 4,0, 2,4), nrow=2)) rt <- matrix(c(cos(pi),-sin(pi),sin(pi),cos(pi), cos(pi/2), -sin(pi/2), sin(pi/2), cos(pi/2)), nrow=2) mapblock(rt, 2, function(x) ps %*% x)
The result is a 6x2 matrix that is the union of the two rotation operations.
Other functions included are functions to manipulate sequences,
pad a sequence to a specified length,
the head and tail off a vector,
slice a sequence into
two pieces based on an expression. The
is similar, while
confine transform data to fit
The master branch of
lambda.tools builds continuously at
- From CRAN: `install.packages('lambda.tools')
- From github, use devtools:
Special thanks to Eric Cox who helped write tests for the package.