Replace collect with (Vector|Matrix|Array) in base/.

Author: Sacha0

base/abstractarray.jl

@@ -1727,7 +1727,7 @@ for all `i` and `j`.
 
 # Examples
 ```jldoctest
-julia> a = reshape(collect(1:16),(2,2,2,2))
+julia> a = reshape(Vector(1:16),(2,2,2,2))
 2×2×2×2 Array{Int64,4}:
 [:, :, 1, 1] =
  1  3

base/abstractarraymath.jl

@@ -51,7 +51,7 @@ Elements of `dims` must be unique and within the range `1:ndims(A)`.
 
 # Examples
 ```jldoctest
-julia> a = reshape(collect(1:4),(2,2,1,1))
+julia> a = reshape(Vector(1:4),(2,2,1,1))
 2×2×1×1 Array{Int64,4}:
 [:, :, 1, 1] =
  1  3
@@ -186,7 +186,7 @@ first dimension.
 
 # Examples
 ```jldoctest
-julia> b = reshape(collect(1:16), (4,4))
+julia> b = reshape(Vector(1:16), (4,4))
 4×4 Array{Int64,2}:
  1  5   9  13
  2  6  10  14

base/array.jl

@@ -1334,7 +1334,7 @@ for reverse-order iteration without making a copy.
 
 # Examples
 ```jldoctest
-julia> A = collect(1:5)
+julia> A = Vector(1:5)
 5-element Array{Int64,1}:
  1
  2
@@ -1392,7 +1392,7 @@ In-place version of [`reverse`](@ref).
 
 # Examples
 ```jldoctest
-julia> A = collect(1:5)
+julia> A = Vector(1:5)
 5-element Array{Int64,1}:
  1
  2
@@ -2117,7 +2117,7 @@ The function `f` is passed one argument.
 
 # Examples
 ```jldoctest
-julia> filter!(isodd, collect(1:10))
+julia> filter!(isodd, Vector(1:10))
 5-element Array{Int64,1}:
  1
  3

base/linalg/qr.jl

@@ -161,7 +161,7 @@ end
 
 function qrfactPivotedUnblocked!(A::StridedMatrix)
     m, n = size(A)
-    piv = collect(UnitRange{BlasInt}(1,n))
+    piv = Vector(UnitRange{BlasInt}(1,n))
     τ = Vector{eltype(A)}(uninitialized, min(m,n))
     for j = 1:min(m,n)
 

base/multidimensional.jl

@@ -31,7 +31,7 @@ module IteratorsMD
 
     # Examples
     ```jldoctest
-    julia> A = reshape(collect(1:16), (2, 2, 2, 2))
+    julia> A = reshape(Vector(1:16), (2, 2, 2, 2))
     2×2×2×2 Array{Int64,4}:
     [:, :, 1, 1] =
      1  3
@@ -1306,7 +1306,7 @@ arrays have overlapping indices, then on the domain of the overlap
 
 # Examples
 ```julia-repl
-julia> src = reshape(collect(1:16), (4,4))
+julia> src = reshape(Vector(1:16), (4,4))
 4×4 Array{Int64,2}:
  1  5   9  13
  2  6  10  14
@@ -1706,7 +1706,7 @@ Return unique regions of `A` along dimension `dim`.
 
 # Examples
 ```jldoctest
-julia> A = map(isodd, reshape(collect(1:8), (2,2,2)))
+julia> A = map(isodd, reshape(Vector(1:8), (2,2,2)))
 2×2×2 Array{Bool,3}:
 [:, :, 1] =
   true   true
@@ -1816,7 +1816,7 @@ Compute the minimum and maximum elements of an array over the given dimensions.
 
 # Examples
 ```jldoctest
-julia> A = reshape(collect(1:2:16), (2,2,2))
+julia> A = reshape(Vector(1:2:16), (2,2,2))
 2×2×2 Array{Int64,3}:
 [:, :, 1] =
  1  5

base/permuteddimsarray.jl

@@ -87,7 +87,7 @@ See also: [`PermutedDimsArray`](@ref).
 
 # Examples
 ```jldoctest
-julia> A = reshape(collect(1:8), (2,2,2))
+julia> A = reshape(Vector(1:8), (2,2,2))
 2×2×2 Array{Int64,3}:
 [:, :, 1] =
  1  3

base/random/misc.jl

@@ -158,7 +158,7 @@ optionally supplying the random-number generator `rng`.
 ```jldoctest
 julia> rng = MersenneTwister(1234);
 
-julia> shuffle!(rng, collect(1:16))
+julia> shuffle!(rng, Vector(1:16))
 16-element Array{Int64,1}:
   2
  15
@@ -204,7 +204,7 @@ indices, see [`randperm`](@ref).
 ```jldoctest
 julia> rng = MersenneTwister(1234);
 
-julia> shuffle(rng, collect(1:10))
+julia> shuffle(rng, Vector(1:10))
 10-element Array{Int64,1}:
   6
   1

base/reducedim.jl

@@ -255,7 +255,7 @@ faster because the intermediate array is avoided.
 
 # Examples
 ```jldoctest
-julia> a = reshape(collect(1:16), (4,4))
+julia> a = reshape(Vector(1:16), (4,4))
 4×4 Array{Int64,2}:
  1  5   9  13
  2  6  10  14
@@ -289,7 +289,7 @@ associativity, e.g. left-to-right, you should write your own loop. See documenta
 
 # Examples
 ```jldoctest
-julia> a = reshape(collect(1:16), (4,4))
+julia> a = reshape(Vector(1:16), (4,4))
 4×4 Array{Int64,2}:
  1  5   9  13
  2  6  10  14

base/reshapedarray.jl

@@ -52,7 +52,7 @@ the specified dimensions is equal to the length of the original array
 `A`. The total number of elements must not change.
 
 ```jldoctest
-julia> A = collect(1:16)
+julia> A = Vector(1:16)
 16-element Array{Int64,1}:
   1
   2

base/sparse/sparsematrix.jl

@@ -729,7 +729,7 @@ end
 
 function sparse(D::Diagonal{T}) where T
     m = length(D.diag)
-    return SparseMatrixCSC(m, m, collect(1:(m+1)), collect(1:m), Vector{T}(D.diag))
+    return SparseMatrixCSC(m, m, Vector(1:(m+1)), Vector(1:m), Vector{T}(D.diag))
 end
 
 ## Transposition and permutation methods

base/sparse/sparsevector.jl

@@ -188,7 +188,7 @@ function sparsevec(I::AbstractVector{<:Integer}, V::AbstractVector, combine::Fun
             len = i
         end
     end
-    _sparsevector!(collect(I), collect(V), len, combine)
+    _sparsevector!(Vector(I), Vector(V), len, combine)
 end
 
 function sparsevec(I::AbstractVector{<:Integer}, V::AbstractVector, len::Integer, combine::Function)
@@ -197,7 +197,7 @@ function sparsevec(I::AbstractVector{<:Integer}, V::AbstractVector, len::Integer
     for i in I
         1 <= i <= len || throw(ArgumentError("An index is out of bound."))
     end
-    _sparsevector!(collect(I), collect(V), len, combine)
+    _sparsevector!(Vector(I), Vector(V), len, combine)
 end
 
 sparsevec(I::AbstractVector, V::Union{Number, AbstractVector}, args...) =
@@ -849,8 +849,8 @@ function SparseMatrixCSC{Tv,Ti}(x::AbstractSparseVector) where {Tv,Ti}
     colptr = Ti[1, m+1]
     # Note that this *cannot* share data like normal array conversions, since
     # modifying one would put the other in an inconsistent state
-    rowval = collect(Ti, xnzind)
-    nzval = collect(Tv, xnzval)
+    rowval = Vector{Ti}(xnzind)
+    nzval = Vector{Tv}(xnzval)
     SparseMatrixCSC(n, 1, colptr, rowval, nzval)
 end
 
@@ -1934,7 +1934,7 @@ function sort(x::SparseVector{Tv,Ti}; kws...) where {Tv,Ti}
     sinds = sortperm(allvals;kws...)
     n,k = length(x),length(allvals)
     z = findfirst(equalto(k),sinds)
-    newnzind = collect(Ti,1:k-1)
+    newnzind = Vector{Ti}(1:k-1)
     newnzind[z:end] .+= n-k+1
     newnzvals = allvals[deleteat!(sinds[1:k],z)]
     SparseVector(n,newnzind,newnzvals)