Introduction
R has long been a dominant language in the field of statistical computing and data analysis, praised for its expressive syntax, expansive library ecosystem, and deep statistical roots. Meanwhile, .NET 9 and 10 have brought powerful updates to the .NET ecosystem, including improved support for data processing, ML, and numerical computing. This post walks through a complex regression analysis in R, evaluates its strengths and weaknesses, and compares it to implementing the same in .NET 9/10 using C#.
The Scenario: Predicting Flight Delays
Let’s imagine we’re working with historical flight data to build a model predicting arrival delays based on the following features:
- Departure delay (minutes)
- Distance (km)
- Day of the week
- Carrier
- Weather conditions
We’ll perform a multiple linear regression, interpret results, validate the model, and compare implementations.
Part 1: Implementation in R
📦 Step 1: Load the data and libraries
library(tidyverse)
library(caret)
flights <- read.csv("flights.csv")🔍 Step 2: Preprocessing
flights_clean <- flights %>%
filter(!is.na(ArrivalDelay), !is.na(DepartureDelay), !is.na(Distance)) %>%
mutate(
DayOfWeek = as.factor(DayOfWeek),
Carrier = as.factor(Carrier),
Weather = as.factor(Weather)
)📊 Step 3: Fit the regression model
model <- lm(ArrivalDelay ~ DepartureDelay + Distance + DayOfWeek + Carrier + Weather, data = flights_clean)
summary(model)🧠 Interpretation (R handles this natively)
- Coefficients with p-values show statistical significance.
- Adjusted R² provides model fit quality.
- Residuals can be plotted easily:
plot(model)🔁 Step 4: Cross-validation
train_control <- trainControl(method = "cv", number = 10)
cv_model <- train(
ArrivalDelay ~ .,
data = flights_clean,
method = "lm",
trControl = train_control
)
print(cv_model)✅ Pros of R
- Built-in statistical summaries and diagnostics.
- Rich ecosystem (e.g.,
caret,tidymodels,ggplot2). - Concise syntax for modeling.
- Advanced statistical techniques available out-of-the-box.
❌ Cons of R
- Performance may lag with large datasets.
- Less optimal for integrating with enterprise systems.
- Learning curve can be steep for non-statisticians.
Part 2: Implementation in .NET 9/10 with C#
With .NET 9 and 10, we can use the ML.NET framework to achieve similar results.
🔧 Step 1: Load data using IDataView
var context = new MLContext();
var data = context.Data.LoadFromTextFile<FlightData>("flights.csv", hasHeader: true, separatorChar: ',');public class FlightData
{
public float ArrivalDelay;
public float DepartureDelay;
public float Distance;
public string DayOfWeek;
public string Carrier;
public string Weather;
}🧪 Step 2: Preprocess and define pipeline
var pipeline = context.Transforms.Categorical.OneHotEncoding(new[] {
new InputOutputColumnPair("DayOfWeek"),
new InputOutputColumnPair("Carrier"),
new InputOutputColumnPair("Weather")
})
.Append(context.Transforms.Concatenate("Features",
"DepartureDelay", "Distance", "DayOfWeek", "Carrier", "Weather"))
.Append(context.Regression.Trainers.Ols());🔍 Step 3: Train and evaluate
var model = pipeline.Fit(data);
var predictions = model.Transform(data);
var metrics = context.Regression.Evaluate(predictions, labelColumnName: "ArrivalDelay");
Console.WriteLine($"R²: {metrics.RSquared}, RMSE: {metrics.RootMeanSquaredError}");🔁 Cross-validation
var cvResults = context.Regression.CrossValidate(data, pipeline, numberOfFolds: 10);
foreach (var result in cvResults)
{
Console.WriteLine($"Fold R²: {result.Metrics.RSquared}");
}✅ Pros of .NET 9/10 + ML.NET
- Strong IDE tooling (Visual Studio, Rider).
- Seamless enterprise integration (APIs, Blazor, MAUI).
- High-performance runtime.
- Cross-validation and evaluation metrics are easy to retrieve.
- Native type safety and integration with C#.
❌ Cons of ML.NET
- Less mature than R for statistical diagnostics.
- Lacks out-of-the-box statistical testing (e.g., p-values).
- Preprocessing is more verbose.
- Limited modeling options compared to R’s CRAN ecosystem.
Summary: R vs .NET 9/10 for Regression
| Feature/Aspect | R | .NET 9/10 with C# |
|---|---|---|
| Statistical Rigor | ✅ Extensive (p-values, AIC, residuals) | ❌ Basic (R², RMSE, MAE only) |
| Ecosystem | ✅ Thousands of statistical packages | ⚠️ Smaller, but growing |
| Performance (large data) | ⚠️ May slow with big data | ✅ Highly performant with ML.NET |
| Integration (APIs, UI) | ❌ Requires bridges (Plumber, Shiny) | ✅ Strong (Web API, Blazor, MAUI) |
| Learning Curve | ⚠️ Moderate for devs, easy for analysts | ✅ Easier for C# developers |
| Deployment | ⚠️ Script-based, Docker preferred | ✅ Native support via .NET |
What Should You Choose?
Use R when:
- You need deep statistical insight and model diagnostics.
- You’re in a research or academic environment.
- You want to rapidly prototype models with visualization.
Use .NET 9/10 with ML.NET when:
- You’re deploying models in production-grade enterprise apps.
- You need strong type safety, scalability, and performance.
- You’re already invested in the .NET ecosystem.
Both R and .NET 9/10 are powerful tools — and increasingly, they are complementary. Many production pipelines use R for prototyping and validation, and then port the final models into a .NET-based service. Choose the right tool for the right stage of your ML lifecycle!
Views: 6
