The R(x, cgopts) calling sequence translates Maple code to R code.

- If the parameter x is an algebraic expression, then an R statement assigning the expression to a variable is generated.

- If x is a list, Maple Array, or rtable of algebraic expressions, then a sequence of R statements assigning the elements to an R Array is produced. Only the initialized elements of the rtable or Maple Array are translated.

- If x is a list of equations $\mathrm{nm}=\mathrm{expr}$, where $\mathrm{nm}$ is a name and $\mathrm{expr}$ is an algebraic expression, then this is understood as a sequence of assignment statements. In this case, the equivalent sequence of R assignment statements is generated.

- If x is a procedure, then an R class is generated containing a function equivalent to the procedure, along with any necessary import statements.

- If x is a module, then an R class is generated, as described on the RDetails help page.

- There is also limited support for the case that x is a command constructor from the Linear Algebra, Statistics or Time Series packages. For supported commands, an equivalent R command is generated. The list of supported commands for Statistics is listed on the RDetails help page.

The parameter cgopts may include one or more CodeGeneration options, as described in CodeGenerationOptions.

For more information about how the CodeGeneration package translates Maple code to other languages, see Translation Details. For more information about translation to R in particular, see RDetails.

$\mathrm{with}\left(\mathrm{CodeGeneration}\right)\:$

$R\left(x\+yz-2xz\,\mathrm{resultname}\=''w''\right)$

w <- -2 * x * z + y * z + x

$R\left(\left[\left[x\&comma;2y\right]\&comma;\left[5\&comma;z\right]\right]\&comma;\mathrm{resultname}\&equals;''w''\right)$

w <- c(c(x,2 * y),c(5,z))

$\mathrm{cs}≔\left[s\&equals;1.0\&plus;x\&comma;t\&equals;\ln \left(s\right){\&ExponentialE;}^{-x}\&comma;r\&equals;{\&ExponentialE;}^{-x}\&plus;xt\right]\&colon;$

$R\left(\mathrm{cs}\&comma;\mathrm{optimize}\right)$

s <- 0.10e1 + x
t1 <- log(s)
t2 <- exp(-x)
t <- t2 * t1
r <- x * t + t2

$s≔R\left(x\&plus;y\&plus;1\&comma;\mathrm{declare}\&equals;\left[x::\mathrm{float}\&comma;y::\mathrm{integer}\right]\&comma;\mathrm{output}\&equals;\mathrm{string}\right)$

f := proc(x, y, z) return x*y-y*z+x*z; end proc:

$R\left(f\&comma;\mathrm{defaulttype}\&equals;\mathrm{integer}\right)$

f <- function(x,y,z)
  return(y * x - y * z + x * z)

f := proc(n)
  local x, i;
  x := 0.0;
  for i to n do
    x := x + i;
  end do;
end proc:

$R\left(f\right)$

f <- function(n)
{
  x <- 0.0e0
  for(i in 1 : n)
  {
    x <- x + i
    cgret <- x
  }
  return(cgret)
}

$R\left(2\cosh \left(x\right)-7\tanh \left(x\right)\right)$

cg0 <- 2 * cosh(x) - 7 * tanh(x)

f := proc(a::integer, p::integer)
  printf("The integer remainder of %d divided by %d is: %d", a, p, irem(a, p));
end proc:

$R\left(f\right)$

f <- function(a,p)
  print(sprintf("The integer remainder of %d divided by %d is: %d",a,p,a %% p))

detHilbert := proc(M, n :: posint) uses LinearAlgebra;
   return Determinant( HilbertMatrix( n ) );
end proc:

$R\left(\mathrm{detHilbert}\right)$

require("Matrix")

detHilbert <- function(M,n)
  return(det(Hilbert(n)))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Mean}\left(\mathrm{Matrix}\left(\left[\left[2\&comma;4\&comma;8\&comma;21\right]\right]\right)\right)\&apos;\right)$

cg1 <- mean(matrix(c(2,4,8,21),nrow=1,ncol=4))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Median}\left(\mathrm{Matrix}\left(\left[\left[2\&comma;4\&comma;8\&comma;21\right]\right]\right)\right)\&apos;\right)$

cg2 <- median(matrix(c(2,4,8,21),nrow=1,ncol=4))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Variance}\left(\left[4\&comma;8\&comma;15\&comma;16\&comma;23\&comma;42\right]\right)\&apos;\right)$

cg3 <- var(c(4,8,15,16,23,42))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Scale}\left(\left[4\&comma;8\&comma;15\&comma;16\&comma;23\&comma;42\right]\&comma;\mathrm{center}\&equals;2\&comma;\mathrm{scale}\&equals;1\right)\&apos;\right)$

cg4 <- scale(c(4,8,15,16,23,42), center = 2, scale = 1)

g := proc( x ) return sprintf("Mean = %.0f\n Standard Deviation = %.0f", 'Statistics:-Mean'(x), 'Statistics:-StandardDeviation'(x) ); end proc:

$R\left(g\right)$

g <- function(x)
  return(sprintf("Mean = %.0f\n Standard Deviation = %.0f", mean(x), sd(x)))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Quartile}\left(\left[4\&comma;8\&comma;15\&comma;16\&comma;23\&comma;42\right]\&comma;3\right)\&apos;\right)$

cg5 <- quantile(c(4,8,15,16,23,42), prob = 3/4)

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Count}\left(\left[1\&comma;3\&comma;5\&comma;7\right]\right)\&apos;\right)$

cg6 <- length(c(1,3,5,7))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{CountMissing}\left(\left[1\&comma;\mathrm{Float}\left(\mathrm{undefined}\right)\&comma;3\&comma;5\&comma;7\&comma;\mathrm{Float}\left(\mathrm{undefined}\right)\right]\right)\&apos;\right)$

cg7 <- sum(is.na(c(1,NaN,3,5,7,NaN)))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{InterquartileRange}\left(\left[4\&comma;8\&comma;15\&comma;16\&comma;23\&comma;42\right]\right)\&apos;\right)$

cg8 <- IQR(c(4,8,15,16,23,42), type = 8)

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{FivePointSummary}\left(\left[1\&comma;3\&comma;5\&comma;7\right]\right)\&apos;\right)$

cg9 <- fivenum(c(1,3,5,7))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Histogram}\left(\left[1\&comma;3\&comma;5\&comma;7\&comma;8\&comma;4\&comma;4\right]\&comma;\mathrm{color}\&equals;''Red''\&comma;\mathrm{title}\&equals;''Histogram''\&comma;\mathrm{frequencyscale}\&equals;\mathrm{absolute}\&comma;\mathrm{axes}\&equals;\mathrm{none}\right)\&apos;\right)$

cg10 <- hist(c(1,3,5,7,8,4,4), axes = FALSE, col = "Red", freq = TRUE, main = "Histogram")

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{BoxPlot}\left(\left[4\&comma;8\&comma;15\&comma;16\&comma;23\&comma;42\right]\&comma;\mathrm{color}\&equals;''Orange''\&comma;\mathrm{title}\&equals;''BoxPlot''\&comma;\mathrm{notched}\&equals;\mathrm{true}\&comma;\mathrm{orientation}\&equals;\mathrm{horizontal}\right)\&apos;\right)$

cg11 <- boxplot(c(4,8,15,16,23,42), col = "Orange", notch = TRUE, horizontal = TRUE, main = "BoxPlot")

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{BarChart}\left(\left[4\&comma;8\&comma;15\&comma;16\&comma;23\&comma;42\right]\&comma;\mathrm{color}\&equals;''Orange''\&comma;\mathrm{title}\&equals;''Bar\; Chart''\&comma;\mathrm{width}\&equals;0.5\&comma;\mathrm{distance}\&equals;0.2\&comma;\mathrm{format}\&equals;\mathrm{default}\right)\&apos;\right)$

cg12 <- barplot(c(4,8,15,16,23,42), col = "Orange", space = 0.2e0, beside = TRUE, main = "Bar Chart", width = 0.5e0, horiz = TRUE)

$R\left(\&apos;\mathrm{TimeSeriesAnalysis}:-\mathrm{TimeSeriesPlot}\left(\mathrm{TimeSeriesAnalysis}:-\mathrm{TimeSeries}\left(\left[10\&comma;25\&comma;30\&comma;55\right]\right)\right)\&apos;\right)$

cg13 <- plot.ts(ts(c(10,25,30,55)))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{PDF}\left(\mathrm{LogNormal}\left(0\&comma;1\right)\&comma;1\right)\&apos;\right)$

cg14 <- dlnorm(1, meanlog = 0, sdlog = 1)

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{ProbabilityFunction}\left(\mathrm{Poisson}\left(2\right)\&comma;1\right)\&apos;\right)$

cg15 <- dpois(1,2)

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{CDF}\left(\mathrm{LogNormal}\left(0\&comma;1\right)\&comma;1\right)\&apos;\right)$

cg16 <- plnorm(1, meanlog = 0, sdlog = 1)

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Sample}\left(\mathrm{Normal}\left(0\&comma;1\right)\&comma;10\right)\&apos;\right)$

cg17 <- rnorm(10, mean = 0, sd = 1)

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{Quantile}\left(\mathrm{Weibull}\left(3\&comma;5\right)\&comma;0.5\right)\&apos;\right)$

cg18 <- qweibull(0.5e0,5, scale = 3)

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{ChiSquareIndependenceTest}\left(\left[4\&comma;8\&comma;15\&comma;16\&comma;23\&comma;42\right]\right)\&apos;\right)$

cg19 <- chisq.test(c(4,8,15,16,23,42))

$R\left(\&apos;\mathrm{Statistics}:-\mathrm{TwoSampleTTest}\left(\left[1\&comma;3\&comma;5\&comma;7\right]\&comma;\left[2\&comma;4\&comma;8\&comma;3\right]\&comma;1\&comma;\mathrm{confidence}\&equals;0.975\&comma;\mathrm{alternative}\&equals;\mathrm{twotailed}\right)\&apos;\right)$

cg20 <- t.test(c(1,3,5,7), c(2,4,8,3), mu = 1, conf.level = 0.975e0, alternative = "two.sided")

The CodeGeneration[R] command was introduced in Maple 2015.

For more information on Maple 2015 changes, see Updates in Maple 2015.