Getting started

The RCall package is loaded via

julia> using RCall

This will initialize the R process in the background.

Several Ways to use RCall

RCall provides multiple ways to allow R interacting with Julia.

R REPL mode
@rput and @rget macros
R"" string macro
RCall API: reval, rcall, rcopy and robject etc.
@rlibrary and @rimport macros

The 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. (Press backspace to leave R REPL mode in case you did not know.)

julia> foo = 1
1

R> x <- $foo

R> x
[1] 1

The 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] 5.097083

@rput and @rget macros

These macros transfer variables between R and Julia environments. The copied variable will have the same name as the original.

julia> z = 1
1

julia> @rput z
1

R> z
[1] 1

R> r = 2

julia> @rget r
2.0

julia> r
2.0

It is also possible to put and get multiple variables in one line.

julia> foo = 2
2

julia> bar = 4
4

julia> @rput foo bar
4

R> foo + bar
[1] 6

@R_str string macro

Another way to use RCall is the R"" string macro, it is especially useful in script files.

julia> R"rnorm(10)"RObject{RealSxp}
 [1]  0.99594944  0.45600730  0.01252619 -0.71273370 -0.61277879 -0.25059001
 [7]  0.04989719 -1.08479437 -1.15882403 -0.87206339

This evaluates the expression inside the string in R, and returns the result as an RObject, which is a Julia wrapper type around an R object.

The R"" 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 as aa$bb):

julia> x = randn(10)10-element Vector{Float64}:
  0.09624642556418413
  0.34816638966457825
 -0.17718143457758492
 -0.19481870409746335
  0.9029301759509633
  0.18440860903004028
  0.18996145869172396
 -1.778584583011317
  0.7825396714446712
  0.655859368490925
julia> R"t.test($x)"RObject{VecSxp}

	One Sample t-test

data:  `#JL`$x
t = 0.42043, df = 9, p-value = 0.684
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -0.4422358  0.6441413
sample estimates:
mean of x
0.1009527

It 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

$counts
function gradient
      14       13

$convergence
[1] 0

$message
NULL

A large chunk of code could be quoted between triple string quotations

julia> y = 11
julia> R"""
       f <- function(x, y) x + y
       ret <- f(1, $y)
       """RObject{RealSxp}
[1] 2

RCall API

The reval function evaluates any given input string as R code in the R environment. The returned result is an RObject object.

julia> jmtcars = reval("mtcars");
julia> names(jmtcars)11-element Vector{Symbol}:
 :mpg
 :cyl
 :disp
 :hp
 :drat
 :wt
 :qsec
 :vs
 :am
 :gear
 :carb
julia> jmtcars[:mpg]Ptr{RealSxp}(0x0000000014050d60)
julia> typeof(jmtcars)RObject{VecSxp}

The rcall function is used to construct function calls.

julia> rcall(:dim, jmtcars)RObject{IntSxp}
[1] 32 11

The arguments will be implicitly converted to RObject upon evaluation.

julia> rcall(:sum, Float64[1.0, 4.0, 6.0])RObject{RealSxp}
[1] 11

The rcopy function converts RObjects to Julia objects. It uses a variety of heuristics to pick the most appropriate Julia type:

julia> rcopy(R"c(1)")1.0
julia> rcopy(R"c(1, 2)")2-element Vector{Float64}:
 1.0
 2.0
julia> rcopy(R"list(1, 'zz')")2-element Vector{Any}:
 1.0
  "zz"
julia> rcopy(R"list(a = 1, b= 'zz')")OrderedCollections.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 Vector{Int64}:
 1
 2

Converter could also be used specifically to yield the desired type.

julia> convert(Array{Float64}, R"c(1, 2)")2-element Vector{Float64}:
 1.0
 2.0

The robject function converts any julia object to an RObject.

julia> robject(1)RObject{IntSxp}
[1] 1
julia> robject(Dict(:a => 1, :b = 2))ERROR: syntax: invalid keyword argument name ":b" around REPL[2]:1

`@rlibrary` and `@rimport` macros

This macro loads all exported functions/objects of an R package to the current module.

julia> @rlibrary boot
julia> city = rcopy(R"boot::city")  # get some data10×2 DataFrame
 Row │ u        x       
     │ Float64  Float64 
─────┼──────────────────
   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.   :1.183
 1st Qu.:1.402
 Median :1.505
 Mean   :1.579
 3rd Qu.:1.660
 Max.   :3.295

Of course, it is highly inefficient, because the data are copying multiple times between R and Julia. The R"" 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 rbase
julia> rbase.sum([1, 2, 3], var"rm.na" = true)RObject{IntSxp}
[1] 7