Unnamed or named vectors

For creating vectors the listArray function can be used.

# unnamed vectors
v <- 1:5
l <- listArray(v)
keys(l)
#> [1] "1" "2" "3" "4" "5"
#
l <- listArray(letters[1:5])
l[1]
#> [1] "a"
# named vector
v <- 1:5
names(v) <- letters[1:5]
l <- listArray(v)
l["a"]
#> [1] 1

Matrix

For matrices or arrays:

m <- matrix(1:9, 3, 3)
l <- listArray(m)
l[2,3]  # should be 8
#> [1] 8

Names or numbers as indices

Since for listArrays l[1] and l["A"] is something different, you have to decide with named vectors, matrices or arrays if you use the names or numbers. The default is to use names if available.

m <- matrix(1:4, 2, 2)
colnames(m) <- LETTERS[1:2]
l <- listArray(m)
keys(l)
#> [1] "1, \"A\"" "2, \"A\"" "1, \"B\"" "2, \"B\""

You can force with use.names=FALSE that always numerical indices will used

m <- matrix(1:4, 2, 2)
colnames(m) <- LETTERS[1:2]
l <- listArray(m, use.names=FALSE)
keys(l)
#> [1] "1, 1" "2, 1" "1, 2" "2, 2"

Ignore entries in a vector, matrix or array

Sometimes you may not want to store certain elements of vector, matrix or array; just think in terms of sparse objects.

m <- diag(3)
l <- listArray(m, ignore=0)
keys(l)
#> [1] "1, 1" "2, 2" "3, 3"

The parameter ignore can be either a table of values to exclude or a function which returns for a vector a logical vector with TRUE (= value excluded) and FALSE (= value included).

nozeroes <- function(v) { v==0 }
#
m <- diag(3)
l <- listArray(m, ignore=nozeroes)
keys(l)
#> [1] "1, 1" "2, 2" "3, 3"

List or environment as base?

Rather than using a list it is possible to use an environment as base which might be of interest for package developers.

e <- listArray(env=TRUE)
class(e)
#> [1] "listArray"   "environment"
e[1] <- "hello world"
e[1]
#> [1] "hello world"
ls(e)
#> [1] "58,0a,00,00,00,03,00,04,06,00,00,03,05,00,00,00,00,05,55,54,46,2d,38,00,00,00,13,00,00,00,01,00,00,00,0e,00,00,00,01,3f,f0,00,00,00,00,00,00"

Extract operators

You simply use the [ operator to access listArray elements.

l <- listArray()
l[0] <- 1
l[0]
#> [1] 1
l[pi] <- pi
l[pi]
#> [1] 3.141593
anotherpi <- pi
l[anotherpi]
#> [1] 3.141593
l[1,-2] <- 3
l[1,-2]
#> [1] 3

Difference between listArray and vector

A listArray considers each index element as different. The following works for vectors:

m <- 1:5
m[1:2]
#> [1] 1 2

But l[1:2] returns NULL since the index 1:2 does not exist.

l <- listArray(m)
l[1:2]
#> NULL
keys(l)
#> [1] "1" "2" "3" "4" "5"

Difference between listArray and matrix/array

Similarly, it holds

m <- matrix(1:4, 2, 2)
m[1,]
#> [1] 1 3
l <- listArray(m)
l[1,] # will even throw an error
#> Error in `key()`:
#> ! invalid index?

Normalization and the options listArray.XXX

To achieve, e.g. that l[1:3] and l[c(1,2,3)] address the same element, as we would expect, we need some kind of normalization. Since 1:3 and c(1,2,3) a different R objects, a normalization is internally done.

identical(1:2, c(1,2)) # delivers FALSE!
#> [1] FALSE
# but
m <- matrix(1:9, 3, 3)
m[1:2,2]
#> [1] 4 5
m[c(1,2),2]
#> [1] 4 5

There are two problems

1. 1:3 is of class integer whereas c(1,2,3) is of class numeric and
2. 1:3 is a compact sequence in R whereas c(1,2,3) is a full vector

Therefore, normalization currently consists of

1. converting in indices everything from class integer to numeric and
2. expand compact sequences like 1:3 to real vectors, e.g. c(1,2,3).

The normalization steps can be switched off by setting the options listArray.expand and listArray.int2num.

l <- listArray()
l[1:3] <- 1
l[c(1,2,3)]
#> [1] 1
options(listArray.expand=FALSE) # now 1:3 != c(1,2,3)
l <- listArray()
l[1:3] <- 1
l[c(1,2,3)]
#> NULL

The default is that listArray.expand and listArray.int2num are not set which is interpreted as listArray.expand=TRUE and listArray.int2num=TRUE.