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Integrating R in Julia

Julia offers a seamless interface to the R language.

  • The RCall.jl package enables interaction with R functions in Julia.
  • The RData.jl package allows interfacing with R data in Julia.

Reading BUGS data and init from R like lists

Warning: The data layout in BUGS assumes that the data is stored in row-major order, while R uses column-major order. This discrepancy can lead to issues. Stan developers have transformed the data and initializations of example BUGS models for R, which can be found here.

Reading the list data structure from R

The data for Rats is available here.

In Julia, we can read this data into a Julia dictionary using the RCall.jl package.

julia> using RCall

julia> data = R"
list(
    x = c(8.0, 15.0, 22.0, 29.0, 36.0), xbar = 22, N = 30, T = 5,
    Y = structure(
        .Data = c(
            151, 199, 246, 283, 320,
            145, 199, 249, 293, 354,
            147, 214, 263, 312, 328,
            155, 200, 237, 272, 297,
            135, 188, 230, 280, 323,
            159, 210, 252, 298, 331,
            141, 189, 231, 275, 305,
            159, 201, 248, 297, 338,
            177, 236, 285, 350, 376,
            134, 182, 220, 260, 296,
            160, 208, 261, 313, 352,
            143, 188, 220, 273, 314,
            154, 200, 244, 289, 325,
            171, 221, 270, 326, 358,
            163, 216, 242, 281, 312,
            160, 207, 248, 288, 324,
            142, 187, 234, 280, 316,
            156, 203, 243, 283, 317,
            157, 212, 259, 307, 336,
            152, 203, 246, 286, 321,
            154, 205, 253, 298, 334,
            139, 190, 225, 267, 302,
            146, 191, 229, 272, 302,
            157, 211, 250, 285, 323,
            132, 185, 237, 286, 331,
            160, 207, 257, 303, 345,
            169, 216, 261, 295, 333,
            157, 205, 248, 289, 316,
            137, 180, 219, 258, 291,
            153, 200, 244, 286, 324
        ),
        .Dim = c(30, 5)
    )
)
"
RObject{VecSxp}
$x
[1]  8 15 22 29 36

$xbar
[1] 22

$N
[1] 30

$T
[1] 5

$Y
      [,1] [,2] [,3] [,4] [,5]
 [1,]  151  141  154  157  132
 [2,]  199  189  200  212  185
 [3,]  246  231  244  259  237
 [4,]  283  275  289  307  286
 [5,]  320  305  325  336  331
 [6,]  145  159  171  152  160
 [7,]  199  201  221  203  207
 [8,]  249  248  270  246  257
 [9,]  293  297  326  286  303
[10,]  354  338  358  321  345
[11,]  147  177  163  154  169
[12,]  214  236  216  205  216
[13,]  263  285  242  253  261
[14,]  312  350  281  298  295
[15,]  328  376  312  334  333
[16,]  155  134  160  139  157
[17,]  200  182  207  190  205
[18,]  237  220  248  225  248
[19,]  272  260  288  267  289
[20,]  297  296  324  302  316
[21,]  135  160  142  146  137
[22,]  188  208  187  191  180
[23,]  230  261  234  229  219
[24,]  280  313  280  272  258
[25,]  323  352  316  302  291
[26,]  159  143  156  157  153
[27,]  210  188  203  211  200
[28,]  252  220  243  250  244
[29,]  298  273  283  285  286
[30,]  331  314  317  323  324

alternatively, reval(s::String) will produce the same result in this case.

If the data is stores in a file, user can use function (may require customizing the function to fit specific needs)

function read_rlist_to_dictionary(filepath::String)
    r_data = open(filepath) do f
        s = read(f, String)
        reval(s)
    end
    return rcopy(r_data)
end

, and save the result to a Julia variable and access the data as a Julia dictionary

julia> rcopy(data)
OrderedDict{Symbol, Any} with 5 entries:
  :x    => [8.0, 15.0, 22.0, 29.0, 36.0]
  :xbar => 22.0
  :N    => 30.0
  :T    => 5.0
  :Y    => [151.0 141.0 … 157.0 132.0; 199.0 189.0 … 212.0 185.0; … ; 298.0 273.0 … 285.0 286.0; 331.0 314.0 … 323.0 324.0]

It is worth noting that rcopy will automatically convert data names contains . to _ in Julia. E.g.

julia> rcopy(R"list(a.b = 1)")
OrderedDict{Symbol, Any} with 1 entry:
  :a_b => 1.0

Transform Dta read from R to Julia convention

If you want to load data using the R interface, but the data source is in the same layout as BUGS, you can process the data in Julia, for instance

# define a row-major reshape function, because Julia's `reshape` is column-major
julia> function rreshape(v::Vector, dim)
           return permutedims(reshape(v, reverse(dim)), length(dim):-1:1)
       end   
rreshape (generic function with 1 method)

julia> rreshape(vcat(data[:Y]...), (30, 5))
30×5 Matrix{Float64}:
 151.0  199.0  246.0  283.0  320.0
 145.0  199.0  249.0  293.0  354.0
 147.0  214.0  263.0  312.0  328.0
 155.0  200.0  237.0  272.0  297.0
 135.0  188.0  230.0  280.0  323.0
 159.0  210.0  252.0  298.0  331.0
 141.0  189.0  231.0  275.0  305.0
 159.0  201.0  248.0  297.0  338.0
   ⋮                         
 146.0  191.0  229.0  272.0  302.0
 157.0  211.0  250.0  285.0  323.0
 132.0  185.0  237.0  286.0  331.0
 160.0  207.0  257.0  303.0  345.0
 169.0  216.0  261.0  295.0  333.0
 157.0  205.0  248.0  289.0  316.0
 137.0  180.0  219.0  258.0  291.0
 153.0  200.0  244.0  286.0  324.0

Please always verify the data before using.