Generate R code from plain English. AI creates statistical analysis, data visualization, and machine learning code for data science projects.
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R is the premier language for statistical computing, data analysis, and scientific visualization used by statisticians, data scientists, and researchers worldwide. Our R code generator uses AI to create idiomatic R code with tidyverse pipelines, ggplot2 visualizations, statistical tests, and machine learning models. Whether you need data manipulation with dplyr, beautiful plots with ggplot2, hypothesis testing, or predictive models, describe your analysis needs and get working R code ready for RStudio.
Perfect for data analysts building reports, academic researchers conducting statistical analysis, business analysts creating dashboards, or data scientists developing machine learning models. The generated code follows R conventions including vectorized operations, functional programming with purrr, and uses the extensive CRAN ecosystem of statistical packages.
Generate dplyr code with select() for columns, filter() for rows, mutate() for new variables, summarize() for aggregations, and group_by() for grouped operations. Create tidyr code for pivot_longer/pivot_wider to reshape data, separate()/unite() for column splitting, or complete() for filling gaps. Uses pipe operator %>% for readable data transformation pipelines that process data step by step.
Example tasks: "Filter dataframe for active users and calculate average age by region", "Pivot data from wide to long format and remove NA values", "Group sales by month and product, calculate total revenue". The AI creates efficient tidyverse code following modern R best practices.
Create publication-quality plots with ggplot2 using geom_point() for scatter plots, geom_bar() for bar charts, geom_line() for time series, geom_boxplot() for distributions, or facet_wrap() for small multiples. Generate customized themes, color palettes, axis labels, legends, and annotations. Produces beautiful visualizations suitable for reports, presentations, or academic publications with minimal code.
For migrating analysis code, try our R to Python converter to translate R scripts to Python for production systems.
Generate code for t-tests (t.test()), ANOVA (aov()), chi-square tests (chisq.test()), correlation analysis (cor.test()), or non-parametric tests like Wilcoxon. Implement proper hypothesis test workflows with assumption checking, test execution, and result interpretation. Perfect for research projects requiring rigorous statistical validation and p-value calculations.
Create machine learning pipelines with tidymodels for model specification, recipe creation for preprocessing, and workflow execution. Generate caret code for training models with cross-validation, hyperparameter tuning with grid search, or ensemble methods. Implement linear regression, logistic regression, random forests, or gradient boosting models with proper train/test splits and performance metrics.
Generate code using map() family functions for iteration, reduce() for combining values, keep()/discard() for filtering lists, or safely() for error handling. Create functional pipelines that process lists, dataframes, or nested data structures without explicit loops. Enables concise, readable code for complex data transformations following functional programming principles.
Example: "Use map_dbl to calculate mean of multiple columns", "Apply custom function to list of dataframes with map_dfr"
Create time series objects with ts(), decompose trends and seasonality with stl(), implement ARIMA models for forecasting, use prophet for automated forecasting, or analyze temporal patterns with zoo and xts packages. Generate code for handling date-time data, creating lag features, or detecting anomalies in time series.
Example: "Decompose sales time series into trend, seasonal, and residual components", "Forecast next 12 months using ARIMA"
Generate RMarkdown documents combining narrative text, code chunks, and visualizations for reproducible reports. Create Shiny app code with ui (user interface) and server components, reactive expressions for dynamic updates, or interactive plots with plotly. Perfect for building interactive dashboards, automated reports, or data-driven applications.
Request "use tidyverse" for modern R with dplyr/ggplot2, or "base R" for traditional syntax with [ ] subsetting and apply functions. Tidyverse is more readable for data analysis, while base R has no dependencies and works in restricted environments.
Describe your data: "dataframe with columns age, income, city", "time series with daily observations", "list of matrices". The generator will use appropriate functions for vectors, dataframes, lists, or matrices based on your data structure.
Ask for "use ggplot2", "data.table for performance", "caret for ML", or "specific statistical packages". R has 19,000+ CRAN packages - mentioning specific ones ensures the generator uses their idiomatic patterns and best practices.
Provide analysis goals: "test if means differ between groups", "check for correlation", "predict continuous outcome". The generator will choose appropriate statistical tests, check assumptions, and provide proper interpretation guidelines.
R excels at statistics, Python at production. Check related tools:
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.
Yes! Sign up for a free account and get limited attempts per day with the Trial plan. For unlimited access, upgrade to the Monthly plan ($5/month) or purchase the Lifetime plan ($50) for one-time payment.
You can generate any R code including functions, classes, algorithms, API integrations, data structures, and complete applications. Just describe your requirements in detail.
Yes, our AI generates clean, well-structured R code following best practices. However, we recommend reviewing and testing all generated code before production use.
Yes, you can specify coding conventions, naming patterns, and style preferences in your description. The AI will adapt the generated R code accordingly.
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