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HTML to R DataFrame Converter

Transform HTML into R DataFrame code

HTML Input

R DataFrame Code

About HTML to R DataFrame Converter

Convert HTML tables and data to R DataFrame code for statistical analysis and data science. Generate ready-to-use R code that can be directly executed in R or RStudio.

Key Features

  • Table Extraction: Automatically extracts HTML tables with headers and data
  • DataFrame Creation: Generates data.frame() constructor code
  • Type Detection: Auto-detects numeric and character columns
  • Multiple Tables: Handles multiple tables in a single HTML document
  • Tidyverse Support: Optional tidyverse library import
  • Factor Conversion: Optional strings as factors conversion
  • Code Comments: Descriptive comments for documentation

How to Use

  1. Input HTML: Paste your HTML code or upload an .html file
  2. Configure Options: Set DataFrame name and conversion options
  3. Review Code: The R code updates automatically
  4. Copy to R: Copy the code and paste into R or RStudio

Generated Code Format

  • data.frame(): Standard R DataFrame constructor
  • Column Vectors: Each column as a c() vector
  • Type Inference: Numeric columns without quotes
  • Character Vectors: String columns with proper escaping
  • Comments: Helpful comments for understanding

R Integration

To use the generated code in R:

  1. Open R or RStudio
  2. Copy the generated code
  3. Paste into R console or script file
  4. Execute the code to create the DataFrame
  5. Use standard R functions for analysis (head, str, summary, etc.)
  6. Perform statistical analysis or visualization

Common Use Cases

  • Data Analysis: Import HTML tables for statistical analysis
  • Web Scraping: Convert scraped HTML data to R DataFrames
  • Report Import: Import data from HTML reports
  • Research Data: Convert research data tables to R format
  • Teaching: Create sample datasets for R tutorials
  • Data Migration: Move data from HTML to R environment

Tips for Best Results

  • Clean Tables: Use well-structured HTML tables with proper headers
  • Meaningful Names: Use descriptive DataFrame names
  • Check Types: Review column types after import
  • Factor Conversion: Enable factors for categorical data
  • Tidyverse: Include tidyverse for modern R data manipulation
  • Test Code: Always test the generated code in R

Example R Code

# Example generated code
library(tidyverse)

iris_data <- data.frame(
  Sepal_Length = c(5.1, 4.9, 7.0),
  Sepal_Width = c(3.5, 3.0, 3.2),
  Species = c("setosa", "setosa", "versicolor")
)

# View the data
head(iris_data)
str(iris_data)
summary(iris_data)

Advanced Features

  • Multiple DataFrames: Each table gets its own DataFrame
  • Name Sanitization: Automatic cleanup of column names for R compatibility
  • Content Extraction: Extract text content when no tables present
  • Type Inference: Smart detection of numeric vs character data
  • Factor Support: Optional factor conversion for categorical data
  • Tidyverse Ready: Code compatible with tidyverse workflows

R DataFrame Operations

After importing, you can use standard R operations:

  • head(): View first few rows
  • str(): View structure and data types
  • summary(): Get statistical summary
  • subset(): Filter rows
  • aggregate(): Group and summarize
  • ggplot2: Create visualizations

Privacy & Security

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