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

The R REPL mode allows real time switching between the Julia prompt and R promot. 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)"
RCall.RObject{RCall.RealSxp}
 [1]  1.77516475 -0.34133889  1.29470091  0.90010403 -0.09827062  0.12555522
 [7]  0.10019542 -1.14406076 -1.46607081  2.02941801

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 Array{Float64,1}:
  0.623381
 -0.387858
 -1.45438
 -0.0720356
 -0.890493
 -0.596855
  1.80708
 -0.493843
  0.152995
 -0.193589

julia> R"t.test($x)"
RCall.RObject{RCall.VecSxp}

    One Sample t-test

data:  `#JL`$x
t = -0.53408, df = 9, p-value = 0.6062
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -0.7882700  0.4871518
sample estimates:
 mean of x
-0.1505591

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')"
RCall.RObject{RCall.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 = 1
1

julia> R"""
       f <- function(x, y) x + y
       ret <- f(1, $y)
       """
RCall.RObject{RCall.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> size(jmtcars)
(11,)

julia> typeof(jmtcars)
RCall.RObject{RCall.VecSxp}

The rcall function is used to construct function calls.

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

The arguments will be implicitly converted to RObject upon evaluation.

julia> rcall(:sum, Float64[1.0, 4.0, 6.0])
RCall.RObject{RCall.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 Array{Float64,1}:
 1.0
 2.0

julia> rcopy(R"list(1,'zz')")
2-element Array{Any,1}:
 1.0
  "zz"

julia> rcopy(R"list(a=1,b='zz')")
Dict{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
 2