SQL to R DataFrame Converter

About SQL to R DataFrame Converter

Convert SQL database dumps (CREATE TABLE and INSERT statements) to R data frame code with automatic type detection. Perfect for statistical analysis, data science, and machine learning workflows in R and RStudio.

Key Features

• Automatic Type Detection: Detects logical, integer, numeric, and character types
• data.frame() Generation: Creates proper R data frame code
• Column Name Sanitization: Converts to valid R variable names
• NA Handling: Properly handles missing values with NA
• Type Comments: Includes comments showing detected types
• Usage Examples: Includes print() and summary() statements
• stringsAsFactors: Uses modern R practices (FALSE by default)
• File Download: Save as .R file for RStudio

How to Use

1. Input SQL Data: Paste your SQL CREATE TABLE and INSERT statements or upload a .sql file
2. Review Output: The R code generates automatically with type detection
3. Copy or Download: Use the Copy or Download button to save your R code
4. Run in R: Execute the code in R, RStudio, or R Markdown

Type Detection

The converter automatically determines the appropriate R type:

• logical: For TRUE/FALSE values
• integer: For whole numbers
• numeric: For decimal/floating-point values
• character: For text values (default)
• NA: For NULL or empty values

Example Conversion

SQL Input:

CREATE TABLE products (
  id INT,
  name VARCHAR(100),
  price DECIMAL(10,2),
  inStock BOOLEAN
);

INSERT INTO products VALUES (1, 'Laptop', 999.99, true);
INSERT INTO products VALUES (2, 'Mouse', 24.99, true);

R Output:

# R data frame generated from SQL table: products

products <- data.frame(
  id = c(1, 2)  # integer
  ,name = c("Laptop", "Mouse")  # character
  ,price = c(999.99, 24.99)  # numeric
  ,inStock = c(TRUE, TRUE)  # logical
  ,
  stringsAsFactors = FALSE
)

# View the data frame
print(products)

Common Use Cases

• Statistical Analysis: Import database data for statistical tests
• Data Science: Prepare data for machine learning models
• Visualization: Create plots with ggplot2 from database data
• Research: Analyze research data from databases
• Reporting: Generate R Markdown reports from SQL data
• Data Exploration: Explore database tables in RStudio
• Teaching: Create datasets for R programming courses

R Data Frame Features

• Column-based Structure: Each column is a vector of the same type
• Named Columns: Access columns by name (df$column)
• Row Names: Automatic row numbering
• Type Safety: Each column maintains its data type
• Subsetting: Easy filtering and selection
• Compatible: Works with tidyverse, dplyr, ggplot2

Variable Name Sanitization

• Special Characters: Replaced with underscores
• Numbers: Prefixed with 'X' if starting with digit
• Spaces: Converted to underscores
• Reserved Words: Handled to avoid R keyword conflicts

Modern R Practices

• stringsAsFactors = FALSE: Keeps strings as character vectors
• c() Vectors: Explicit vector creation for clarity
• Type Comments: Documents the intended data type
• Print Statement: Includes code to view the data frame

Compatible R Packages

The generated data frames work seamlessly with:

• tidyverse: dplyr, tidyr, ggplot2, readr
• data.table: High-performance data manipulation
• caret: Machine learning workflows
• shiny: Interactive web applications
• rmarkdown: Dynamic documents and reports

Supported SQL Syntax

• CREATE TABLE: Extracts column names for data frame columns
• INSERT INTO: Parses data values for type detection
• Data Types: Handles all SQL data types (VARCHAR, INT, DECIMAL, BOOLEAN, etc.)
• Quoted Strings: Handles single and double quotes with proper escaping
• NULL Values: Converts to R's NA

Privacy & Security

All conversions happen locally in your browser. Your SQL data is never uploaded to any server, ensuring complete privacy and security.