Difficulty using Rcpp to speed up data processing in R - segmentation fault issues
I've searched everywhere and can't find a clear answer... I'm trying to leverage Rcpp to speed up a data processing function in R. The function I'm attempting to optimize reads a large CSV file into a data frame, processes the data, and then returns the results. However, whenever I call the function, I experience a segmentation fault. I've implemented the following code, using Rcpp version 1.0.6: ```cpp #include <Rcpp.h> using namespace Rcpp; // [[Rcpp::export]] NumericVector processData(NumericVector x) { int n = x.size(); NumericVector result(n); for (int i = 0; i < n; i++) { result[i] = x[i] * 2; // Sample processing step } return result; } ``` In R, I'm calling it like this: ```r library(Rcpp) sourceCpp("my_cpp_file.cpp") data <- read.csv("large_data.csv") result <- processData(data$column_of_interest) ``` I’ve ensured that `data$column_of_interest` is indeed a numeric vector. However, when I run the last line, I get a segmentation fault. I've tried running `Rcpp::sourceCpp` in a clean R session and also using smaller datasets for testing, but the behavior continues. I also checked the size of the vector before passing it to the C++ function: ```r print(length(data$column_of_interest)) ``` This outputs `1000000`, which should be manageable. What could be causing this segmentation fault? Is there a better way to handle large data sets in Rcpp, or should I check for specific memory issues? Any insights or troubleshooting steps would be appreciated. Is there a better approach? This is part of a larger API I'm building. I'm working on a application that needs to handle this. Am I missing something obvious?