MySQL to R DataFrame Converter

Transform MySQL database dumps into R DataFrame code with automatic type detection

MySQL Input

R DataFrame Output

About MySQL to R DataFrame Converter

Convert MySQL database dumps (CREATE TABLE and INSERT statements) to R data frame code. Perfect for statistical analysis, data science workflows, and R programming with MySQL data.

Key Features

  • Automatic Type Detection: Infers column types (logical, integer, numeric, character)
  • data.frame() Generation: Creates ready-to-run R code
  • Column Name Sanitization: Converts MySQL column names to valid R variable names
  • NA Handling: Properly handles missing and NULL values as NA
  • MySQL Parser: Accurately parses MySQL CREATE TABLE and INSERT syntax
  • Type Comments: Includes helpful comments about data types
  • Copy & Download: Save as .R file for RStudio

How to Use

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

Type Detection

The converter automatically detects column types based on data values:

  • logical: Values matching "TRUE" or "FALSE" (case-insensitive)
  • integer: Whole numbers without decimal points
  • numeric: Numbers with decimal points or scientific notation
  • character: Text values (default type)

Example Conversion

MySQL Input:

CREATE TABLE employees (
  id INT PRIMARY KEY,
  name VARCHAR(100),
  age INT,
  city VARCHAR(50)
);

INSERT INTO employees VALUES (1, 'John Doe', 28, 'New York');
INSERT INTO employees VALUES (2, 'Jane Smith', 34, 'London');

R DataFrame Output:

# Rows: 2, Columns: 4
# Column types: id (integer), name (character), age (integer), city (character)

df <- data.frame(
  id = c(1, 2),
  name = c("John Doe", "Jane Smith"),
  age = c(28, 34),
  city = c("New York", "London"),
  stringsAsFactors = FALSE
)

print(df)

Common Use Cases

  • Statistical Analysis: Import MySQL data for R statistical analysis
  • Data Science: Use MySQL tables in data science workflows
  • Machine Learning: Prepare MySQL data for ML models in R
  • Visualization: Create plots and charts with ggplot2
  • Research: Analyze research data from MySQL databases
  • Reporting: Generate reports with R Markdown

R DataFrame Features

  • stringsAsFactors = FALSE: Prevents automatic factor conversion
  • c() Vectors: Uses R's combine function for column data
  • NA Values: Empty cells and NULL values converted to NA
  • Type Preservation: Maintains numeric and logical types
  • Print Statement: Includes print(df) for immediate output

Column Name Sanitization

MySQL column names are automatically converted to valid R variable names:

  • Replaces special characters with underscores
  • Removes leading/trailing underscores
  • Prefixes numbers with 'X' (e.g., "1st" becomes "X1st")
  • Handles duplicate names with numeric suffixes (_2, _3, etc.)
  • Generates default names (column_1, column_2) for empty headers

Supported MySQL Data Types

  • Numeric: INT, BIGINT, DECIMAL, FLOAT, DOUBLE → integer/numeric
  • String: VARCHAR, CHAR, TEXT, MEDIUMTEXT, LONGTEXT → character
  • Boolean: BOOLEAN, TINYINT(1) → logical
  • NULL: NULL values → NA in R

Using in R

  1. Copy or download the generated R code
  2. Open RStudio or R console
  3. Paste and run the code
  4. The data frame 'df' will be created and displayed
  5. Use df in your analysis, visualizations, or models

R Packages Compatibility

The generated data frames work seamlessly with popular R packages:

  • dplyr: Data manipulation and transformation
  • ggplot2: Data visualization and plotting
  • tidyr: Data tidying and reshaping
  • caret: Machine learning and modeling
  • data.table: High-performance data operations
  • rmarkdown: Dynamic reporting

Tips for Best Results

  • Ensure MySQL data has clear column names from CREATE TABLE
  • Use consistent data types within columns
  • Avoid special characters in column names when possible
  • Test with sample data before processing large tables
  • Review column types in comments and adjust if needed
  • Use stringsAsFactors = TRUE if you need factor columns

Privacy & Security

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