switch to DataFrames 0.11

- switch to Missings.jl/CategoricalArrays.jl
- add jlvec() methods handling conversion logic
- fix conversion of RLogicalVector into Vector{Bool}
- remove DataArrays.jl dependency

Author: alyst

NEWS.md

@@ -1,3 +1,14 @@
+## RData v0.3.0 Release Notes
+
+Updated to DataFrames v0.11, switched from [DataArrays](https://github.com/JuliaData/DataArrays.jl) to [Missings](https://github.com/JuliaData/Missings.jl) and [CategoricalArrays](https://github.com/JuliaData/CategoricalArrays.jl).
+
+##### Changes
+* updated to DataFrames v0.11 [#28]
+* switched from `DataVector` to `Vector{Union{T,Null}}` for NAs [#28]
+* R factors converted into `CategoricalVector` (instead of `PooledDataArray`) [#28]
+
+[#28]: https://github.com/JuliaStats/RData.jl/issues/28
+
 ## RData v0.2.0 Release Notes
 
 Updated to Julia v0.6 (older versions not supported).

REQUIRE

@@ -1,5 +1,6 @@
 julia 0.6
-DataFrames 0.9
-DataArrays 0.4
+DataFrames 0.11
+Missings 0.2
+CategoricalArrays 0.3
 FileIO 0.1.2
 CodecZlib 0.4

src/RData.jl

@@ -2,7 +2,7 @@ __precompile__()
 
 module RData
 
-using DataFrames, DataArrays, CodecZlib, FileIO
+using DataFrames, CategoricalArrays, Missings, CodecZlib, FileIO
 import DataFrames: identifier
 import FileIO: load
 

src/convert.jl

@@ -3,83 +3,125 @@
 
 function Base.convert(::Type{Hash}, pl::RPairList)
     res = Hash()
-    for i in 1:length(pl.items)
-        setindex!(res, pl.items[i], pl.tags[i])
+    for i in eachindex(pl.items)
+        @inbounds setindex!(res, pl.items[i], pl.tags[i])
     end
     res
 end
 
 ##############################################################################
 ##
-## Conversion of intermediate R objects into DataArray and DataFrame objects
+## Conversion of intermediate R objects into Vector{T} and DataFrame objects
 ##
 ##############################################################################
 
-namask(rl::RLogicalVector) = BitArray(rl.data .== R_NA_INT32)
-namask(ri::RIntegerVector) = BitArray(ri.data .== R_NA_INT32)
-namask(rn::RNumericVector) = BitArray(map(isna_float64, reinterpret(UInt64, rn.data)))
+isna(x::Int32) = x == R_NA_INT32
+isna(x::Float64) = isna_float64(reinterpret(UInt64, x))
 # if re or im is NA, the whole complex number is NA
-# FIXME avoid temporary Vector{Bool}
-namask(rc::RComplexVector) = BitArray([isna_float64(v.re) || isna_float64(v.im) for v in reinterpret(Complex{UInt64}, rc.data)])
-namask(rv::RNullableVector) = rv.na
+isna(x::Complex128) = isna(real(x)) || isna(imag(x))
 
-DataArrays.data(rv::RVEC) = DataArray(rv.data, namask(rv))
+# convert R vector into Vector holding elements of type T
+# if force_missing is true, the result is always Vector{Union{T,Missing}},
+# otherwise it's Vector{T} if `rv` doesn't contain NAs
+function jlvec(::Type{T}, rv::RVEC, force_missing::Bool=true) where T
+    anyna = any(isna, rv.data)
+    if force_missing || anyna
+        res = convert(Vector{Union{T,Missing}}, rv.data)
+        if anyna
+            @inbounds for (i,x) in enumerate(rv.data)
+                isna(x) && (res[i] = missing)
+            end
+        end
+        return res
+    else
+        return convert(Vector{T}, rv.data)
+    end
+end
+
+# convert R nullable vector (has an explicit NA mask) into Vector{T[?]}
+function jlvec(::Type{T}, rv::RNullableVector{R}, force_missing::Bool=true) where {T, R}
+    anyna = any(rv.na)
+    if force_missing || anyna
+        res = convert(Vector{Union{T,Missing}}, rv.data)
+        anyna && @inbounds res[rv.na] = missing
+        return res
+    else
+        return convert(Vector{T}, rv.data)
+    end
+end
+
+# convert R vector into Vector of appropriate type
+jlvec(rv::RVEC, force_missing::Bool=true) = jlvec(eltype(rv.data), rv, force_missing)
 
-function DataArrays.data(ri::RIntegerVector)
-    if !isfactor(ri) return DataArray(ri.data, namask(ri)) end
-    # convert factor into PooledDataArray
-    pool = getattr(ri, "levels", emptystrvec)
-    sz = length(pool)
+# convert R logical vector (uses Int32 to store values) into Vector{Bool[?]}
+function jlvec(rl::RLogicalVector, force_missing::Bool=true)
+    anyna = any(isna, rl.data)
+    if force_missing || anyna
+        return Union{Bool,Missing}[ifelse(isna(x), missing, x != 0) for x in rl.data]
+    else
+        return Bool[x != 0 for x in rl.data]
+    end
+end
+
+# kernel method that converts Vector{Int32} into Vector{R} replacing R_NA_INT32 with 0
+# it's assumed that v fits into R
+na2zero(::Type{R}, v::Vector{Int32}) where R =
+    [ifelse(!isna(x), x % R, zero(R)) for x in v]
+
+# convert to CategoricalVector{String[?]} if `ri` is a factor,
+# or to Vector{Int32[?]} otherwise
+function jlvec(ri::RIntegerVector, force_missing::Bool=true)
+    isfactor(ri) || return jlvec(eltype(ri.data), ri, force_missing)
+
+    rlevels = getattr(ri, "levels", emptystrvec)
+    sz = length(rlevels)
     REFTYPE = sz <= typemax(UInt8)  ? UInt8 :
               sz <= typemax(UInt16) ? UInt16 :
               sz <= typemax(UInt32) ? UInt32 :
                                       UInt64
-    dd = ri.data
-    dd[namask(ri)] = 0
-    refs = convert(Vector{REFTYPE}, dd)
-    return PooledDataArray(DataArrays.RefArray(refs), pool)
+    # FIXME set ordered flag
+    refs = na2zero(REFTYPE, ri.data)
+    anyna = any(iszero, refs)
+    pool = CategoricalPool{String, REFTYPE}(rlevels)
+    if force_missing || anyna
+        return CategoricalArray{Union{String, Missing}, 1}(refs, pool)
+    else
+        return CategoricalArray{String, 1}(refs, pool)
+    end
 end
 
-# convert R logical vector (uses Int32 to store values) into DataVector{Bool}
-DataArrays.data(rl::RLogicalVector) =
-    return DataArray(Bool[x != 0 for x in rl.data], namask(rl))
-
 function sexp2julia(rex::RSEXPREC)
     warn("Conversion of $(typeof(rex)) to Julia is not implemented")
     return nothing
 end
 
 function sexp2julia(rv::RVEC)
-    # FIXME dimnames
-    # FIXME forceDataArrays option to always convert to DataArray
-    nas = namask(rv)
-    hasna = any(nas)
+    # TODO dimnames?
+    # FIXME add force_missing option to control whether always convert to Union{T, Missing}
+    jv = jlvec(rv, false)
     if hasnames(rv)
         # if data has no NA, convert to simple Vector
-        return DictoVec(hasna ? DataArray(rv.data, nas) : rv.data, names(rv))
+        return DictoVec(jv, names(rv))
     else
         hasdims = hasdim(rv)
         if !hasdims && length(rv.data)==1
             # scalar
-            # FIXME handle NAs
-            # if hasna
-            return rv.data[1]
+            return jv[1]
         elseif !hasdims
             # vectors
-            return hasna ? DataArray(rv.data, nas) : rv.data
+            return jv
         else
             # matrices and so on
-            dims = tuple(convert(Vector{Int64}, getattr(rv, "dim"))...)
-            return hasna ? DataArray(reshape(rv.data, dims), reshape(nas, dims)) :
-                         reshape(rv.data, dims)
+            dims = tuple(convert(Vector{Int}, getattr(rv, "dim"))...)
+            return reshape(jv, dims)
         end
     end
 end
 
 function sexp2julia(rl::RList)
     if isdataframe(rl)
-        # FIXME remove Any type assertion workaround
-        DataFrame(Any[data(col) for col in rl.data], map(identifier, names(rl)))
+        # FIXME add force_missing option to control whether always convert to Union{T, Missing}
+        DataFrame(Any[jlvec(col, false) for col in rl.data], identifier.(names(rl)))
     elseif hasnames(rl)
         DictoVec(Any[sexp2julia(item) for item in rl.data], names(rl))
     else

test/RDA.jl

@@ -6,53 +6,54 @@ module TestRDA
     # check for Float64 NA
     @testset "Detect R floating-point NAs" begin
         @test !RData.isna_float64(reinterpret(UInt64, 1.0))
-        @test !RData.isna_float64(reinterpret(UInt64, NaN))
-        @test !RData.isna_float64(reinterpret(UInt64, Inf))
-        @test !RData.isna_float64(reinterpret(UInt64, -Inf))
-        @test RData.isna_float64(reinterpret(UInt64, RData.R_NA_FLOAT64))
+        @test !RData.isna(1.0)
+        @test !RData.isna(NaN)
+        @test !RData.isna(Inf)
+        @test !RData.isna(-Inf)
+        @test RData.isna_float64(RData.R_NA_FLOAT64)
         # check that alternative NA is also recognized (#10)
         @test RData.isna_float64(reinterpret(UInt64, RData.R_NA_FLOAT64 | ((Base.significand_mask(Float64) + 1) >> 1)))
     end
 
     testdir = dirname(@__FILE__)
     @testset "Reading minimal RData" begin
         df = DataFrame(num = [1.1, 2.2])
-        @test isequal(sexp2julia(load("$testdir/data/minimal.rda",convert=false)["df"]), df)
-        @test isequal(load("$testdir/data/minimal.rda",convert=true)["df"], df)
-        @test isequal(load("$testdir/data/minimal_ascii.rda")["df"], df)
+        @test sexp2julia(load("$testdir/data/minimal.rda",convert=false)["df"]) == df
+        @test load("$testdir/data/minimal.rda",convert=true)["df"] == df
+        @test load("$testdir/data/minimal_ascii.rda")["df"] == df
     end
 
     @testset "Conversion to Julia types" begin
         df = DataFrame(num = [1.1, 2.2],
                        int = Int32[1, 2],
                        logi = [true, false],
                        chr = ["ab", "c"],
-                       factor = pool(["ab", "c"]),
-                       cplx = Complex128[1.1+0.5im, 1.0im])
+                       factor = categorical(["ab", "c"], true),
+                       cplx = [1.1+0.5im, 1.0im])
         rdf = sexp2julia(load("$testdir/data/types.rda",convert=false)["df"])
         @test eltypes(rdf) == eltypes(df)
-        @test isequal(rdf, df)
+        @test rdf == df
         rdf_ascii = sexp2julia(load("$testdir/data/types_ascii.rda",convert=false)["df"])
         @test eltypes(rdf_ascii) == eltypes(df)
-        @test isequal(rdf_ascii, df)
+        @test rdf_ascii == df
     end
 
     @testset "NAs conversion" begin
-        df = DataFrame(num = [1.1, 2.2],
-                       int = Int32[1, 2],
-                       logi = [true, false],
-                       chr = ["ab", "c"],
-                       factor = pool(["ab", "c"]),
-                       cplx = Complex128[1.1+0.5im, 1.0im])
+        df = DataFrame(num = Union{Float64, Missing}[1.1, 2.2],
+                       int = Union{Int32, Missing}[1, 2],
+                       logi = Union{Bool, Missing}[true, false],
+                       chr = Union{String, Missing}["ab", "c"],
+                       factor = categorical(Union{String, Missing}["ab", "c"], true),
+                       cplx = Union{Complex128, Missing}[1.1+0.5im, 1.0im])
 
-        df[2, :] = NA
+        df[2, :] = missing
         append!(df, df[2, :])
         df[3, :num] = NaN
-        df[:, :cplx] = @data [NA, Complex128(1,NaN), NaN]
+        df[:, :cplx] = [missing, Complex128(1,NaN), NaN]
         @test isequal(sexp2julia(load("$testdir/data/NAs.rda",convert=false)["df"]), df)
         # ASCII format saves NaN as NA
-        df[3, :num] = NA
-        df[:, :cplx] = @data [NA, NA, NA]
+        df[3, :num] = missing
+        df[:, :cplx] = missing
         @test isequal(sexp2julia(load("$testdir/data/NAs_ascii.rda",convert=false)["df"]), df)
     end