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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 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]  0.9583381 -0.2134018  0.1058059 -0.4984809 -0.1634174 -0.4119473
 [7] -0.8863998  0.7666781 -0.2696060 -1.2110568

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}:
 -0.7942963270957478
 -0.7159475306364221
 -0.4139973872505756
 -0.9438455908495627
  1.5334303644094094
  0.6654199495816443
 -0.3249515499308118
  1.1780374918902508
 -0.7399435456593367
  0.10774327304383748

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

	One Sample t-test

data:  `#JL`$x
t = -0.16039, df = 9, p-value = 0.8761
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -0.6771748  0.5875046
sample estimates:
  mean of x
-0.04483509

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 = 1
1

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]
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 21.4

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"

@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 data
10×2 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.   :1.191
 1st Qu.:1.390
 Median :1.523
 Mean   :1.577
 3rd Qu.:1.694
 Max.   :2.625

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