MySQL to R DataFrame Converter

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

FAQ

• Do I need both CREATE TABLE and INSERT statements? The converter can infer structure from INSERT statements alone, but CREATE TABLE helps generate better column names and type hints.
• How are NULL or empty values represented? They are converted to NA in R so that standard data-cleaning and modeling workflows work as expected.
• What if R guesses the wrong type? The generated comments show inferred types, but you can always override them with explicit casts such as as.numeric(), as.integer(), or as.logical().
• Can I change the data frame name? Yes. After you paste the code, you can rename df to any valid R symbol that fits your style.
• Is my data sent to a server? No. All parsing and generation happen in your browser; your MySQL data is never uploaded.

Privacy & Security

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