Data manipulation tools based on lambda.r
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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. 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 introduces block and range semantics to support these concepts, respectively. Hence defines mapblock and maprange and similar functions for fold.

Block operations

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[1:3], x[4:6], x[7:9], 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.

Range operations

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 arguments: x[1:3], x[2:4], x[3:5], ..., x[10:12].

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.

Two-dimensional operations

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 columns. Hence 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, such as pad a sequence to a specified length, chomp the head and tail off a vector, slice a sequence into two pieces based on an expression. The partition function is similar, while quantize and confine transform data to fit specific ranges.

Continuous Builds

The master branch of builds continuously at Travis CI


  • From CRAN: `install.packages('')
  • From github, use devtools: install_github('','zatonovo')


Special thanks to Eric Cox who helped write tests for the package.