```{css echo=FALSE} img { border: 0px !important; margin: 2em 2em 2em 2em !important; }
```r library(knitr) opts_chunk$set( cache = FALSE, echo = TRUE, warning = FALSE, error = FALSE, message = FALSE, out.width = 700, fig.width = 12, fig.height = 8, dpi = 84, concordance = TRUE, collapse = TRUE, comment = "#>" ) library(narray)
This package provides consistent utility functions for array programming with arbitrary dimensions (summary below).
We recommend to load this package in its own namespace to not shadow base R
functions using box
or
import
.
# example referencing the package namespace # do not load the package with 'library(...)' here narray::stack(...)
stack()
is like cbind
/rbind
, but along arbitrary axes, and taking care of (1) names
along each dimension and (2) padding partial matching arrays.
A = matrix(1:4, nrow=2, ncol=2, dimnames=list(c('a','b'),c('x','y'))) B = matrix(5:6, nrow=2, ncol=1, dimnames=list(c('b','a'),'z')) C = stack(A, B, along=2) C D = stack(m=A, n=C, along=3) # we can also introduce new dimensions D
split()
splits an array along a given axis; can do each element or defined subsets.
split(C, along=2, subsets=c('s1','s1','s2'))
Like apply
, but not reordering array dimensions and allowing to specify
subsets that the function should be applied on. The function must either return
a vector of the same length as the input (returns matrix of same dimension) or
of length 1 (drops current dimension or returns subsets).
map(C, along=2, function(x) x*2) # return same length vector map(C, along=2, mean, subsets=c('s1', 's1', 's2')) # summarize each subset to scalar
We can also index multiple arrays using the lambda
function. If the result
is a scalar we will get back an array, and an index with result column otherwise.
dot = function(x, y) sum(x * y) lambda(~ dot(A, B), along=c(A=1, B=2)) lambda(~ dot(A, B), along=c(A=1, B=2), simplify=FALSE)
Takes a number of arrays, intersects their names along a given dimension,
and returns sub-arrays that match in their names; intersect_list
takes
a list of arrays and returns a list of subsets.
E = matrix(1:6, nrow=3, dimnames=list(c('a','b','d'), c('x','y'))) F = matrix(7:9, nrow=3, dimnames=list(c('b','a','c'), 'z')) intersect(E, F, along=1) E F
data.frame
sconstruct()
takes a data frame and a formula specifying dependent (values) and independent
(axes) of the resulting array.
DF = data.frame(k1=base::rep(letters[1:3],2), k2=base::rep(letters[24:25],3), v=1:6)[-6,] construct(v ~ k1 + k2, data=DF)
Takes either a factor or a list of vectors and creates a binary matrix specifying whether each element is present.
G = list(a='e1', b=c('e1','e2'), c='e2') mask(G)
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