The RCall package is loaded via
julia> using RCallThis will initialize the R process in the background.
RCall provides multiple ways to allow R interacting with Julia.
@rput and@rget macrosR"" string macroreval,rcall,rcopy androbject etc.@rlibrary and@rimport macrosThe R REPL mode allows real time switching between the Julia prompt and R prompt. Press$ to activate the R REPL mode and the R prompt will be shown. (Pressbackspace to leave R REPL mode in case you did not know.)
julia> foo = 11R> x <- $fooR> x[1] 1The R REPL mode supports variable substitution of Julia objects via the$ symbol. It is also possible to pass Julia expressions in the REPL mode.
R> x = $(rand(10))R> sum(x)[1] 5.097083These macros transfer variables between R and Julia environments. The copied variable will have the same name as the original.
julia> z = 11julia> @rput z1R> z[1] 1R> r = 2julia> @rget r2.0julia> r2.0It is also possible to put and get multiple variables in one line.
julia> foo = 22julia> bar = 44julia> @rput foo bar4R> foo + bar[1] 6Another way to use RCall is theR"" string macro, it is especially useful in script files.
julia> R"rnorm(10)"RObject{RealSxp} [1] -0.72687855 -0.33970450 -0.73194258 1.03683473 -0.36328502 -0.72049376 [7] -0.62619973 0.09185129 1.16216142 2.17293414This evaluates the expression inside the string in R, and returns the result as anRObject, which is a Julia wrapper type around an R object.
TheR"" string macro supports variable substitution of Julia objects via the$ symbol, whenever it is not valid R syntax (i.e. when not directly following a symbol or completed expression such asaa$bb):
julia> x = randn(10)10-element Array{Float64,1}: 0.4816123619498722 0.3264937624075163 1.0541193260848456 -1.302322244295568 -0.6532385852868288 -1.9367446336772407 0.8170826524159507 0.8177315590330477 -0.42775328167862064 1.396114201915309julia> R"t.test($x)"RObject{VecSxp}One Sample t-testdata: `#JL`$xt = 0.16575, df = 9, p-value = 0.872alternative hypothesis: true mean is not equal to 095 percent confidence interval: -0.7248370 0.8394561sample estimates: mean of x0.05730951It is also possible to pass Julia expressions which are evaluated before being passed to R: these should be included in parentheses
julia> R"optim(0, $(x -> x-cos(x)), method='BFGS')"RObject{VecSxp}$par[1] -1.56343$value[1] -1.570796$countsfunction gradient 14 13$convergence[1] 0$messageNULLA large chunk of code could be quoted between triple string quotations
julia> y = 11julia> R""" f <- function(x, y) x + y ret <- f(1, $y) """RObject{RealSxp}[1] 2Thereval function evaluates any given input string as R code in the R environment. The returned result is anRObject object.
julia> jmtcars = reval("mtcars");julia> names(jmtcars)11-element Array{Symbol,1}: :mpg :cyl :disp :hp :drat :wt :qsec :vs :am :gear :carbjulia> jmtcars[:mpg]RObject{RealSxp} [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7[31] 15.0 21.4julia> typeof(jmtcars)RObject{VecSxp}Thercall function is used to construct function calls.
julia> rcall(:dim, jmtcars)RObject{IntSxp}[1] 32 11The arguments will be implicitly converted toRObject upon evaluation.
julia> rcall(:sum, Float64[1.0, 4.0, 6.0])RObject{RealSxp}[1] 11Thercopy function convertsRObjects to Julia objects. It uses a variety of heuristics to pick the most appropriate Julia type:
julia> rcopy(R"c(1)")1.0julia> rcopy(R"c(1, 2)")2-element Array{Float64,1}: 1.0 2.0julia> rcopy(R"list(1, 'zz')")2-element Array{Any,1}: 1.0 "zz"julia> rcopy(R"list(a = 1, b= 'zz')")DataStructures.OrderedDict{Symbol,Any} with 2 entries: :a => 1.0 :b => "zz"It is possible to force a specific conversion by passing the output type as the first argument:
julia> rcopy(Array{Int}, R"c(1, 2)")2-element Array{Int64,1}: 1 2Converter could also be used specifically to yield the desired type.
julia> convert(Array{Float64}, R"c(1, 2)")2-element Array{Float64,1}: 1.0 2.0Therobject function converts any julia object to an RObject.
julia> robject(1)RObject{IntSxp}[1] 1julia> robject(Dict(:a => 1, :b = 2))ERROR: syntax: invalid keyword argument name ":b"@rlibrary and@rimport macrosThis micro loads all exported functions/objects of an R package to the current module.
julia> @rlibrary bootjulia> city = rcopy(R"boot::city") # get some data10×2 DataFrames.DataFrame│ Row │ u │ x │├─────┼───────┼───────┤│ 1 │ 138.0 │ 143.0 ││ 2 │ 93.0 │ 104.0 ││ 3 │ 61.0 │ 69.0 ││ 4 │ 179.0 │ 260.0 ││ 5 │ 48.0 │ 75.0 ││ 6 │ 37.0 │ 63.0 ││ 7 │ 29.0 │ 50.0 ││ 8 │ 23.0 │ 48.0 ││ 9 │ 30.0 │ 111.0 ││ 10 │ 2.0 │ 50.0 │julia> ratio(d, w) = sum(d[:x] .* w)/sum(d[:u] .* w)ratio (generic function with 1 method)julia> b = boot(city, ratio, R = 100, stype = "w");julia> rcall(:summary, b[:t])RObject{StrSxp} V1 Min. :1.201 1st Qu.:1.400 Median :1.513 Mean :1.554 3rd Qu.:1.646 Max. :2.819Of course, it is highly inefficient, because the data are copying multiple times between R and Julia. TheR"" string macro is more recommended for efficiency.
Some R functions may have keyword arguments which contain dots. RCall provides a string macro to escape those keywords, e.g,
julia> @rimport base as rbasejulia> rbase.sum([1, 2, 3], var"rm.na" = true)RObject{IntSxp}[1] 7