Below is my code that 1) writes a CSV file with three columns of integer data (plus column names on the first line) and 2) reads the CSV file. I'm new to C++ and would appreciate some code review.

Also, I'm bothered by the fact that my technique requires all of the data to be integers. I'd appreciate advice on how I could generalize this code to write/read data with a mix of integers, doubles, strings, etc.

#include <string>    // std::string

#include <fstream>   // std::ofstream, std::ifstream

#include <vector>    // std::vector

#include <utility>   // std::pair

#include <stdexcept> // std::runtime_error

#include <sstream>   // std::stringstream

#include <iostream>  // std::cout, std::cin



void write_csv(std::string filename, std::vector<std::pair<std::string, std::vector<int>>> dataset){

    // Make a CSV file with one or more columns of integer values

    // Each column of data is represented by the pair <column name, column data>

    //   as std::pair<std::string, std::vector<int>>

    // The dataset is represented as a vector of these columns

    // Note that all columns should be the same size



    // Create an output filestream object

    std::ofstream myFile(filename);



    // Send column names to the stream

    for(int j = 0; j < dataset.size(); ++j)

    {

        myFile << dataset.at(j).first;

        if(j != dataset.size() - 1) myFile << ","; // No comma at end of line

    }

    myFile << "n";



    // Send data to the stream

    for(int i = 0; i < dataset.at(0).second.size(); ++i)

    {

        for(int j = 0; j < dataset.size(); ++j)

        {

            myFile << dataset.at(j).second.at(i);

            if(j != dataset.size() - 1) myFile << ","; // No comma at end of line

        }

        myFile << "n";

    }



    // Close the file

    myFile.close();

}



std::vector<std::pair<std::string, std::vector<int>>> read_csv(std::string filename){

    // Reads a CSV file into a vector of <string, vector<int>> pairs where

    // each pair represents <column name, column values>



    // Create a vector of <string, int vector> pairs to store the result

    std::vector<std::pair<std::string, std::vector<int>>> result;



    // Create an input filestream

    std::ifstream myFile(filename);



    // Make sure the file is open

    if(!myFile.is_open()) throw std::runtime_error("Could not open file");



    // Helper vars

    std::string line, colname;

    int val;



    // Read the column names

    if(myFile.good())

    {

        // Extract the first line in the file

        std::getline(myFile, line);



        // Create a stringstream from line

        std::stringstream ss(line);



        // Extract each column name

        while(std::getline(ss, colname, ',')){



            // Initialize and add <colname, int vector> pairs to result

            result.push_back({colname, std::vector<int> {}});

        }

    }



    // Read data, line by line

    while(std::getline(myFile, line))

    {

        // Create a stringstream of the current line

        std::stringstream ss(line);



        // Keep track of the current column index

        int colIdx = 0;



        // Extract each integer

        while(ss >> val){



            // Add the current integer to the 'colIdx' column's values vector

            result.at(colIdx).second.push_back(val);



            // If the next token is a comma, ignore it and move on

            if(ss.peek() == ',') ss.ignore();



            // Increment the column index

            colIdx++;

        }

    }



    // Close file

    myFile.close();



    return result;

}



int main() {

//    // Make three vectors, each of length N filled with 1s, 2s, and 3s

    int N = 1000;

    std::vector<int> vec1(N, 1);

    std::vector<int> vec2(N, 2);

    std::vector<int> vec3(N, 3);



    // Wrap into a vector

    std::vector<std::pair<std::string, std::vector<int>>> vals = {{"One", vec1}, {"Two", vec2}, {"Three", vec3}};



    // Write the vector to CSV

    write_csv("three_cols.csv", vals);



    // Read three_cols.csv

    std::vector<std::pair<std::string, std::vector<int>>> three_cols = read_csv("three_cols.csv");



    // Print row and column counts to check if this was successful

    std::cout << "Rows: " << three_cols.at(0).second.size() << ", Columns: " << three_cols.size() << std::endl;



    return 0;

}

Good documentation

Every time I wanted to say "but it behaves badly in case ...", I found specification disallowing that case. One nitpick might be that "with one or more columns" could be explicitly written as "requires at least one column". The only thing left is to pray that people will read it.

Pass by const reference for only read purposes

Copying non-scalar and heavy data structures (strings, vectors) might incur significant overhead. Prefer to pass by const reference.

Check if file is opened

The check is performed when reading, but not performed when writing.

Do not use at() unless exception-throwing version is desired

.at() incurs overhead by performing in-range check, and also throws if out of range.

Use emplace back to construct and push in place

result.push_back({colname, std::vector<int> {}});

This could be rewritten as

result.emplace_back(colname, std::vector<int> {});

From C++17, if I'm not mistaken, the two are equivalent due to copy elision, but emplace version is a bit clearer.

Improve printing algorithm

This is a general problem of string joining. This answer shows a great implementation for a simple case. One can remove templates from them if needed.

Do not explicitly close file

Destructor of std::ifstream and of it's twin automatically close the file.

Create type alias where useful

using column = std::pair<std::string, std::vector<int>>;

would save users a lot of typing.

Use locales for reading from csv

Whenever I want casual reading of csv files I just copy/paste from this asnwer and march on.

Unreliable reading algorithm

I would be a bit worried to use it as is. It tends to assume the layout to be the same as in writing, but I'm afraid edge cases slip in and hit like a truck. This is one of the rare cases when enabling exceptions on the stream might be a good idea.

Architecture for generic reading/writing

I don't think implementing reading algorithm is a viable option, because it involves extreme amounts of error checking to be useful. As for writing:

• 
  

  Upstream and downstream invariances

  
  
  

  Upstream invariance in this case is CSV format, which the final output has to obey. Downstream invariance is requirements on the result of object getting streamed (file << myObject). One will need to specify the requirements on data type very clearly and with great scrutiny. For example, if one wants to accept std::string as data type, the user has to override default streaming for the type, which pulls them into realm of undefined behavior. This functionality clearly requires a lot of thought put into it in order to be correct and robust.

  
  


• 
  

  Data layout

  
  
  

  This is one is just a speculation, but the way data is stored might be problematic in terms of performance. It would be better to create a data structure that stores headers, and then stores values row-wise. Access could be done by using header value as first subscript, and row index as second subscript. Horizontal offset can be saved for each header value to access the right cell in a row. This also would be a great learning exercise.

  
  

General design

Your dataset representation is a bit unconventional: since you need to write/read the file line by line, having the dataset oriented the other way might prove a hindrance. I understand that you might need to include / exclude columns from a particular file: but it isn't a sufficient reason to rotate your dataset: a more general, line-oriented dataset representation would allow you to do so too. In principle, you should separate concerns: handling your data on the one hand, reading/writing it from/to a csv file on the other hand.

Also, your design forces the user to create headers, which isn't required by the format. The explanation, if I understood correctly, is that you weren't able to provide a type-erasing interface, so it was either headers for everyone or for no one.

Type-flexible cell

There are lots of ways to achieve that, and none is indisputably superior. You could consider std::variant, which is a standard, type-safe union:

#include <variant>

#include <iostream>

#include <string>

#include <vector>



int main() {



    using Cell = std::variant<int, double, std::string>; // Cell can contain any of these types

    std::vector<Cell> record{"John", "Doe", 41, 1.75};

    for (const auto& cell : record) {

        std::visit((const auto& content) { std::cout << content << ' '; }, cell);

    }

}

Any column or line can now be represented by a sequence of Cells. It means that you don't need to hard-code a header, or to store your data by type-homogeneous columns. You can even have your whole dataset in one big vector: it can be a lot faster because you have better locality (in your current implementation, the processor has to fetch the cells of a given line from as many places in memory).

If you're ready to anticipate the next standard (C++20), and that you have your dataset contained in one big std::vector<Cell>, you can then have rows and columns as range::views over your dataset:

#include <variant>

#include <iostream>

#include <string>

#include <vector>

#include <range/v3/core.hpp>

#include <range/v3/view.hpp>



using namespace ranges;

using namespace ranges::view;



int main() {



    using Cell = std::variant<int, double, std::string>; 

    std::vector<Cell> records{"John", "Doe", 41, 1.75, "Jane", "Smith", 35, 1.63}; 

    const int row_sz = 4;



    auto column = [row_sz](int n) {

        // to get column n, skip the n first elements

        // then take every row-szth element

        return drop(n) | stride(row_sz);

    };



    for (auto cell : records | column(1)) {

        std::visit((const auto& content) { std::cout << content << ' '; }, cell);

    }

    std::cout << std::endl;



    auto row = [row_sz](int n) { return slice(n*row_sz, n*row_sz+row_sz); };

    for (auto cell : records | row(1)) {

        std::visit((const auto& content) { std::cout << content << ' '; }, cell);

    }

    std::cout << std::endl;



}

Performance

Your csv reader is inefficient. You read each line (one memory allocation and copy), then cut the line into pieces (as many allocations / conversions as pieces). You can build your dataset from the file without reading it into lines first: every time you read a ';' you push_back a cell, and every time you read a 'n' you push_back a last cell in the record, and then push_back the record (conceptually, because as I said I believe a flat std::vector with row-size as an additional information is better). It is a simplification of course, because you have to take care of quoted fields, eof and error-handling, but that's the general idea. You can design it as a state-machine with customization points for the cell and record handlers.

My first observation is that your object model is quite confusing; you have a vector of pairs with vectors, and it is very difficult to keep track of what is what. If I'm reading this code correctly, you should consider extracting this pair into a column class, giving you std::vector<column>.

Once you have this column class, you can add additional properties to it, such as what type of data it contains, and a void* to the data in each cell.