/// <summary>
/// Executes the program
/// </summary>
/// <param name="args">The arguments.</param>
public static void Main(string[] args)
{
using (var s = new RConnection(new System.Net.IPAddress(new byte[] { 192, 168, 37, 10 }), port: 6311, user: "******", password: "******"))
{
// Generate some example data
var x = Enumerable.Range(1, 20).ToArray();
var y = (from a in x select(0.5 * a * a) + 2).ToArray();
// Build an R data frame
var d = Sexp.MakeDataFrame();
d["x"] = Sexp.Make(x);
d["y"] = Sexp.Make(y);
s["d"] = d;
// Run a linear regression, obtain the summary, and print the result
var linearModelSummary = s["summary(lm(y ~ x, d))"];
Console.WriteLine(linearModelSummary.Count);
var coefs = linearModelSummary["coefficients"];
var rSquared = ( double )linearModelSummary["r.squared"];
Console.WriteLine("y = {0} x + {1}. R^2 = {2,4:F}%", coefs[1, 0], coefs[0, 0], rSquared * 100);
// Make a chart and transfer it to the local machine
s.VoidEval("library(ggplot2)");
s.VoidEval("pdf(\"outfile.pdf\")");
s.VoidEval("print(qplot(x,y, data=d))");
s.VoidEval("dev.off()");
using (var f = File.Create("Data Plot.pdf"))
{
s.ReadFile("outfile.pdf").CopyTo(f);
}
s.RemoveFile("outfile.pdf");
// Now let's do some linear algebra
var matA = new double[, ] {
{ 14, 9, 3 }, { 2, 11, 15 }, { 0, 12, 17 }, { 5, 2, 3 }
};
var matB = new double[, ] {
{ 12, 25 }, { 9, 10 }, { 8, 5 }
};
s["a"] = Sexp.Make(matA);
s["b"] = Sexp.Make(matB);
Console.WriteLine(s["a %*% b"].ToString());
}
Console.WriteLine("Done");
}
/// <summary>
/// Executes the program
/// </summary>
/// <param name="args">The arguments.</param>
public static void Main(string[] args)
{
using (var s = new RConnection(new System.Net.IPAddress(new byte[] { 127, 0, 0, 1 })))
{
// Generate some example data
var x = Enumerable.Range(1, 20).ToArray();
var y = (from a in x select(0.5 * a * a) + 2).ToArray();
// Build an R data frame
var d = Sexp.MakeDataFrame();
d["x"] = Sexp.Make(x);
d["y"] = Sexp.Make(y);
s["d"] = d;
// Run a linear regression, obtain the summary, and print the result
s.VoidEval("linearModelSummary = summary(lm(y ~ x, d))");
var coefs = s["linearModelSummary$coefficients"];
var rSquared = s["linearModelSummary$r.squared"].AsDouble;
Console.WriteLine("y = {0} x + {1}. R^2 = {2,4:F}%", coefs[1, 0], coefs[0, 0], rSquared * 100);
// Now let's do some linear algebra
var matA = new double[, ] {
{ 14, 9, 3 }, { 2, 11, 15 }, { 0, 12, 17 }, { 5, 2, 3 }
};
var matB = new double[, ] {
{ 12, 25 }, { 9, 10 }, { 8, 5 }
};
s["a"] = Sexp.Make(matA);
s["b"] = Sexp.Make(matB);
Console.WriteLine(s["a %*% b"].ToString());
}
Console.WriteLine("Done");
}
/// <summary>
/// Executes the program
/// </summary>
/// <param name="args">The arguments.</param>
public static void Main( string[] args )
{
using ( var s = new RConnection( new System.Net.IPAddress( new byte[] { 127 , 0 , 0 , 1 } ) ) )
{
// Generate some example data
var x = Enumerable.Range( 1 , 20 ).ToArray();
var y = ( from a in x select ( 0.5 * a * a ) + 2 ).ToArray();
// Build an R data frame
var d = Sexp.MakeDataFrame();
d[ "x" ] = Sexp.Make( x );
d[ "y" ] = Sexp.Make( y );
s[ "d" ] = d;
// Run a linear regression, obtain the summary, and print the result
s.VoidEval( "linearModelSummary = summary(lm(y ~ x, d))" );
var coefs = s[ "linearModelSummary$coefficients" ];
var rSquared = s[ "linearModelSummary$r.squared" ].AsDouble;
Console.WriteLine( "y = {0} x + {1}. R^2 = {2,4:F}%" , coefs[ 1 , 0 ] , coefs[ 0 , 0 ] , rSquared * 100 );
// Now let's do some linear algebra
var matA = new double[ , ] { { 14 , 9 , 3 } , { 2 , 11 , 15 } , { 0 , 12 , 17 } , { 5 , 2 , 3 } };
var matB = new double[ , ] { { 12 , 25 } , { 9 , 10 } , { 8 , 5 } };
s[ "a" ] = Sexp.Make( matA );
s[ "b" ] = Sexp.Make( matB );
Console.WriteLine( s[ "a %*% b" ].ToString() );
}
Console.WriteLine( "Done" );
}
/// <summary>
/// Executes the program
/// </summary>
/// <param name="args">The arguments.</param>
public static void Main( string[] args )
{
using ( var s = new RConnection( new System.Net.IPAddress( new byte[] { 192 , 168 , 37 , 10 } ) , port: 6311 , user: "******" , password: "******" ) )
{
// Generate some example data
var x = Enumerable.Range( 1 , 20 ).ToArray();
var y = ( from a in x select ( 0.5 * a * a ) + 2 ).ToArray();
// Build an R data frame
var d = Sexp.MakeDataFrame();
d[ "x" ] = Sexp.Make( x );
d[ "y" ] = Sexp.Make( y );
s[ "d" ] = d;
// Run a linear regression, obtain the summary, and print the result
var linearModelSummary = s[ "summary(lm(y ~ x, d))" ];
Console.WriteLine( linearModelSummary.Count );
var coefs = linearModelSummary[ "coefficients" ];
var rSquared = ( double )linearModelSummary[ "r.squared" ];
Console.WriteLine( "y = {0} x + {1}. R^2 = {2,4:F}%" , coefs[ 1 , 0 ] , coefs[ 0 , 0 ] , rSquared * 100 );
// Make a chart and transfer it to the local machine
s.VoidEval( "library(ggplot2)" );
s.VoidEval( "pdf(\"outfile.pdf\")" );
s.VoidEval( "print(qplot(x,y, data=d))" );
s.VoidEval( "dev.off()" );
using ( var f = File.Create( "Data Plot.pdf" ) )
{
s.ReadFile( "outfile.pdf" ).CopyTo( f );
}
s.RemoveFile( "outfile.pdf" );
// Now let's do some linear algebra
var matA = new double[ , ] { { 14 , 9 , 3 } , { 2 , 11 , 15 } , { 0 , 12 , 17 } , { 5 , 2 , 3 } };
var matB = new double[ , ] { { 12 , 25 } , { 9 , 10 } , { 8 , 5 } };
s[ "a" ] = Sexp.Make( matA );
s[ "b" ] = Sexp.Make( matB );
Console.WriteLine( s[ "a %*% b" ].ToString() );
}
Console.WriteLine( "Done" );
}