Setting up Rcpp Armadillo in windows with Rstudio - windows

Question

I am trying to set up RcppArmadillo in my windows system with Rstudio. I have successfully installed RcppArmadillo with the command
install.packages("RcppArmadillo")
in R console.
But when I try to compile a c++ code with RcppArmadillo dependency I get a error like
g++ -m64 -I"C:/PROGRA~1/R/R-30~1.3/include" -DNDEBUG -I"C:/PROGRA~1/R/R-30~1.3/library/Rcpp/include" -I"d:/RCompile/CRANpkg/extralibs64/local/include" -O2 -Wall -mtune=core2 -c colrowStat.cpp -o colrowStat.o colrowStat.cpp:5:26: fatal error: RcppArmadillo.h: No such file or directory compilation terminated. make: *** [colrowStat.o] Error 1 Warning message: running command 'make -f "C:/PROGRA~1/R/R-30~1.3/etc/x64/Makeconf" -f "C:/PROGRA~1/R/R-30~1.3/share/make/winshlib.mk" SHLIB_LDFLAGS='$(SHLIB_CXXLDFLAGS)' SHLIB_LD='$(SHLIB_CXXLD)' SHLIB="sourceCpp_38187.dll" WIN=64 TCLBIN=64 OBJECTS="colrowStat.o"' had status 2
But the header files are available in path_to_my_documents/R/win-libraries/3.0/RcppArmadillo/Include
I think the include path for compilation dose not have this path. I don't how to add this folder to the path. I greatly appreciate any help with this problem.

Answer 1

You are doing it wrong. There are many ways to do it, and we documented several of them. What you do here is not one of them.
Try this instead and go from there:
R> library(Rcpp)
R> cppFunction("arma::mat op(arma::vec x) { return(x*x.t()); }",
+ depends="RcppArmadillo")
R> op(1:2)
[,1] [,2]
[1,] 1 2
[2,] 2 4
R>
This is one of the basic examples: take a vector, multiply it by its transpose and return the result outer product matrix.
What you ultimately want is a package, and for that you could do much worse than starting by RcppArmadillo.package.skeleton().

Answer 2

Your question is short on details, but if you are on a Windows machine and are using RStudio, then here is a fully reproducible example on how to use RcppArmadillo without using the inline package, which is not ideal except for very short functions.
As Dirk has pointed out, this advice is available elsewhere -- the Rcpp* ecosystem is bizarrely well-documented, but this might help a novice.
0. Preliminaries:
You should have the following installed:
Rtools
R package devtools
R package Rcpp
R package RcppArmadillo
1. C++ code:
The example is a simple one of computing the OLS estimator for a linear regression model. Here is what the C++ file, with one function (fnLinRegRcpp) that takes the design matrices as inputs and produces the OLS coefficient estimates and the model residuals as an Rcpp List looks like:
// LinearRegression.cpp
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
using namespace arma; // use the Armadillo library for matrix computations
using namespace Rcpp;
// [[Rcpp::export]]
List fnLinRegRcpp(vec vY, mat mX) {
// compute the OLS estimator & model residuals
vec vBeta = solve(mX.t()*mX, mX.t()*vY);
vec vResid = vY - mX * vBeta;
// construct the return object
List ret;
ret["beta"] = vBeta;
ret["resid"] = vResid;
return ret;
}
// END
Note the use of Rcpp attributes:
// [[Rcpp::depends(RcppArmadillo)]]
to indicate library dependencies on the Armadillo library.
2. R code
Here is an example of the compilation of the C++ code using the sourceCpp function, together with an example of the use of the function, and a comparison of the output to the built-in lm.fit function.
# LinearRegression.R
library(devtools)
library(Rcpp)
library(RcppArmadillo)
Rcpp::sourceCpp("code/LinearRegression.cpp",
showOutput = TRUE,
rebuild = FALSE)
# generate some sample data
iK = 4
iN = 100
mX = cbind(1, matrix(rnorm(iK*iN), iN, iK))
vBeta0 = c(2, 3.5, 0.11, 6.33, 23)
vY = rnorm(iN, mean = mX %*% vBeta0)
# test the function
linReg1 = fnLinRegRcpp(vY, mX)
linReg1$beta # coefficient estimates
# compare the results to the built-in lm.fit function
lm.fit(y = vY, x = mX)$coef # coefficient estimates
# END