The v8() function (formerly called new_context) creates a
new V8 context. A context provides an execution environment that allows
separate, unrelated, JavaScript code to run in a single instance of V8, like a
tab in a browser.
v8(global = "global", console = TRUE, ...)
engine_info()A V8 context cannot be saved or duplicated, but creating a new context and sourcing code is very cheap. You can run as many parallel v8 contexts as you want. R packages that use V8 can use a separate V8 context for each object or function call.
The name of the global object (i.e. global in node and window
in browsers) can be set with the global argument. A context always have a global
scope, even when no name is set. When a context is initiated with global = NULL,
the global environment can be reached by evaluating this in the global scope,
for example: ct$eval("Object.keys(this)").
## ctx <- v8()
<V8 engine 11.9.169.7>
$assign(name, value, auto_unbox = TRUE, ...)
$call(fun, ..., auto_unbox = TRUE, await = FALSE)
$console()
$eval(src, serialize = FALSE, await = FALSE)
$get(name, ..., await = FALSE)
$reset()
$source(file)
$validate(src)
The ct$eval method evaluates a string of JavaScript code in the same way
as eval in JavaScript. By default eval() returns a string with
console output; but when the serialize parameter is set to TRUE it
serializes the JavaScript return object to a JSON string or a raw buffer.
The ct$get, ct$assign and ct$call functions automatically
convert arguments and return value between R and JavaScript (using JSON). To pass
literal JavaScript arguments that should not be converted to JSON, wrap them in
JS(), see examples.
If a call to ct$eval(),ct$get(), or ct$call() returns a JavaScript promise,
you can set await = TRUE to wait for the promise to be resolved. It will then
return the result of the promise, or an error in case the promise is rejected.
The ct$validate function is used to test
if a piece of code is valid JavaScript syntax within the context, and always
returns TRUE or FALSE.
In an interactive R session you can use ct$console() to switch to an
interactive JavaScript console. Here you can use console.log to print
objects, and there is some support for JS tab-completion. This is mostly for
testing and debugging, it may not work perfectly in every IDE or R-frontend.
JSON is used for data interchange between R and JavaScript. Therefore you can
(and should) only exchange data types that have a sensible JSON representation.
One exception is raw vectors which are converted to/from Uint8Array buffers, see
below. All other arguments and objects are automatically converted according to the mapping
described in Ooms (2014), and implemented
by the jsonlite package in jsonlite::fromJSON() and jsonlite::toJSON().
As for version 3.0 of this R package, Raw vectors are converted to Uint8Array
typed arrays, and vice versa. This makes it possible to efficiently copy large chunks
binary data between R and JavaScript, which is useful for running wasm
or emscripten.
This R package can be compiled against modern (V8 version 6+) libv8 API, or the legacy
libv8 API (V8 version 3.15 and below). You can check V8::engine_info() to see the version
that is running. The legacy version does not support modern JS (ES6) or WASM, but it is
still the default on older versions of Ubuntu and CentOS. The latest versions of all major
Linux distributions now provide a modern version of V8. For Ubuntu 16.04 and 18.04
we provide backports of libv8 (via libnode-dev), see the
readme for details.
A Mapping Between JSON Data and R Objects (Ooms, 2014): https://arxiv.org/abs/1403.2805
# Create a new context
ctx <- v8();
# Evaluate some code
ctx$eval("var foo = 123")
ctx$eval("var bar = 456")
ctx$eval("foo+bar")
#> [1] "579"
# Functions and closures
ctx$eval("JSON.stringify({x:Math.random()})")
#> [1] "{\"x\":0.3016656510868325}"
ctx$eval("(function(x){return x+1;})(123)")
#> [1] "124"
# Objects (via JSON only)
ctx$assign("mydata", mtcars)
ctx$get("mydata")
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
outlist <- ctx$get("mydata", simplifyVector = FALSE)
outlist[1]
#> [[1]]
#> [[1]]$mpg
#> [1] 21
#>
#> [[1]]$cyl
#> [1] 6
#>
#> [[1]]$disp
#> [1] 160
#>
#> [[1]]$hp
#> [1] 110
#>
#> [[1]]$drat
#> [1] 3.9
#>
#> [[1]]$wt
#> [1] 2.62
#>
#> [[1]]$qsec
#> [1] 16.46
#>
#> [[1]]$vs
#> [1] 0
#>
#> [[1]]$am
#> [1] 1
#>
#> [[1]]$gear
#> [1] 4
#>
#> [[1]]$carb
#> [1] 4
#>
#> [[1]]$`_row`
#> [1] "Mazda RX4"
#>
#>
# Assign JavaScript
ctx$assign("foo", JS("function(x){return x*x}"))
ctx$assign("bar", JS("foo(9)"))
ctx$get("bar")
#> [1] 81
# Validate script without evaluating
ctx$validate("function foo(x){2*x}") #TRUE
#> [1] TRUE
ctx$validate("foo = function(x){2*x}") #TRUE
#> [1] TRUE
ctx$validate("function(x){2*x}") #FALSE
#> [1] FALSE
# Use a JavaScript library
ctx$source("https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.13.6/underscore-min.js")
#> [1] "true"
ctx$call("_.filter", mtcars, JS("function(x){return x.mpg < 15}"))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
#> Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
#> Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
# Example from underscore manual
ctx$eval("_.templateSettings = {interpolate: /\\{\\{(.+?)\\}\\}/g}")
#> [1] "[object Object]"
ctx$eval("var template = _.template('Hello {{ name }}!')")
ctx$call("template", list(name = "Mustache"))
#> [1] "Hello Mustache!"
# Call anonymous function
ctx$call("function(x, y){return x * y}", 123, 3)
#> [1] 369
if (FALSE) { # \dontrun{
#CoffeeScript
ct2 <- v8()
ct2$source("http://coffeescript.org/v1/browser-compiler/coffee-script.js")
jscode <- ct2$call("CoffeeScript.compile", "square = (x) -> x * x", list(bare = TRUE))
ct2$eval(jscode)
ct2$call("square", 9)
# Interactive console
ct3 <- v8()
ct3$console()
# //this is JavaScript
# var test = [1,2,3]
# JSON.stringify(test)
# exit
} # }