R Code Generator
Generate production-ready R code from natural language descriptions for statistical analysis, data visualization, and machine learning. AI automatically creates tidyverse-compliant code with ggplot2 visualizations, dplyr data manipulation, and statistical modeling functions. Handles linear regression, hypothesis testing, data cleaning pipelines, and publication-quality plots instantly without manual scripting.
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Issue Description
Hint: Describe what you want to build or paste requirements, select target language, and click Generate.
How It Works
- Step 1: Describe your R requirement in plain English, such as "create ggplot2 scatter plot with regression line" or "perform ANOVA test with post-hoc comparisons".
- Step 2: The AI analyzes your request and identifies R-specific patterns including tidyverse syntax with pipe operators, vectorized operations, data frame manipulation with dplyr, and statistical test selection.
- Step 3: Advanced language models generate idiomatic R code following tidyverse style guide with proper function chaining, meaningful variable names, and appropriate statistical methods for your data analysis task.
- Step 4: Download clean R code with library() calls for required packages, inline comments explaining statistical choices, and RStudio-ready syntax for immediate execution in your data science workflow.
Code Generation Examples
Example 1: Data Visualization with ggplot2
Create a scatter plot showing the relationship between car weight and fuel efficiency with a trend linelibrary(ggplot2)
library(dplyr)
# Load and prepare data
data(mtcars)
mtcars_clean <- mtcars %>%
mutate(
weight_tons = wt,
mpg_efficiency = mpg
)
# Create scatter plot with regression line
ggplot(mtcars_clean, aes(x = weight_tons, y = mpg_efficiency)) +
geom_point(aes(color = factor(cyl)), size = 3, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "blue", linetype = "dashed") +
labs(
title = "Vehicle Weight vs Fuel Efficiency",
subtitle = "Data from 1974 Motor Trend US magazine",
x = "Weight (1000 lbs)",
y = "Miles Per Gallon",
color = "Cylinders"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
legend.position = "bottom"
)
# Calculate correlation
cor_result <- cor.test(mtcars_clean$weight_tons, mtcars_clean$mpg_efficiency)
print(paste("Correlation:", round(cor_result$estimate, 3)))Key Changes:
The generated R code demonstrates tidyverse best practices with dplyr's pipe operator for data transformation and ggplot2's layered grammar of graphics. The mutate() function creates descriptive variable names improving code readability. The ggplot() call uses aesthetic mappings (aes) to bind data to visual properties, with factor(cyl) creating discrete color categories. The geom_smooth() layer adds a linear regression line with confidence interval (se = TRUE), providing statistical context. The theme_minimal() and custom theme() calls create publication-quality formatting. The cor.test() function performs Pearson correlation with hypothesis testing, returning both the coefficient and p-value. This code is production-ready for exploratory data analysis and statistical reporting in RStudio.
Example 2: Statistical Analysis with Linear Regression
Perform multiple linear regression to predict house prices based on size, bedrooms, and locationlibrary(tidyverse)
library(broom)
# Create sample dataset
set.seed(123)
housing_data <- tibble(
price = rnorm(100, mean = 300000, sd = 50000),
size_sqft = rnorm(100, mean = 2000, sd = 500),
bedrooms = sample(2:5, 100, replace = TRUE),
location_score = runif(100, min = 1, max = 10)
) %>%
mutate(
price = price + size_sqft * 100 + bedrooms * 20000 + location_score * 5000
)
# Fit multiple linear regression model
model <- lm(price ~ size_sqft + bedrooms + location_score, data = housing_data)
# Display model summary
summary(model)
# Get tidy model output
model_results <- tidy(model, conf.int = TRUE)
print(model_results)
# Calculate model performance metrics
model_metrics <- glance(model)
cat(sprintf("R-squared: %.3f\n", model_metrics$r.squared))
cat(sprintf("Adjusted R-squared: %.3f\n", model_metrics$adj.r.squared))
cat(sprintf("F-statistic: %.2f (p-value: %.4f)\n",
model_metrics$statistic, model_metrics$p.value))
# Make predictions
new_data <- tibble(
size_sqft = 2500,
bedrooms = 3,
location_score = 7.5
)
prediction <- predict(model, new_data, interval = "confidence")
cat(sprintf("\nPredicted price: $%.2f\n", prediction[1]))Key Changes:
This R code showcases advanced statistical modeling with the tidyverse ecosystem and broom package for tidy model outputs. The tibble() function creates a modern data frame with column-wise construction. The set.seed() ensures reproducibility for random number generation. The lm() function fits ordinary least squares regression with formula notation (price ~ predictors), automatically handling categorical variables and interactions if specified. The broom::tidy() function converts model output to a data frame with confidence intervals, making results easier to manipulate and visualize. The glance() function extracts model-level statistics including R-squared and F-statistic for assessing overall fit. The predict() function generates point estimates with confidence intervals for new observations. This pattern follows best practices for statistical analysis in R, providing comprehensive model diagnostics and interpretable results for data science applications.
Frequently Asked Questions
How do I generate R code?
Describe what you want to build in plain English, select R as your target language, and click "Generate Code". Our AI will create clean, functional R code based on your description.
Is there a free trial for the R code generator?
Yes! Every new user gets 5 free lifetime credits to try our tools. After that, purchase credits starting at $10 for 100 credits. Credits never expire and there are no subscriptions.
What kind of R code can I generate?
You can generate any R code including functions, classes, algorithms, API integrations, data structures, and complete applications. Just describe your requirements in detail.
Is the generated R code production-ready?
Yes, our AI generates clean, well-structured R code following best practices. However, we recommend reviewing and testing all generated code before production use.
Can I customize the generated R code style?
Yes, you can specify coding conventions, naming patterns, and style preferences in your description. The AI will adapt the generated R code accordingly.