This PR adds three comprehensive vignettes to demonstrate key polyglotr package functionality for multilingual workflows and localization projects:

📚 New Vignettes Added

1. Translation Table Workflow (translation-table-workflow.Rmd)

Demonstrates how to use create_translation_table() to translate vectors of phrases across multiple languages:

# Basic usage
ui_phrases <- c("Welcome", "Login", "Password", "Submit", "Cancel")
languages <- c("es", "fr", "de", "it")
translation_table <- create_translation_table(ui_phrases, languages)

Features:

• Multiple API comparison (Google Translate + MyMemory)
• Tidyverse integration for advanced workflows
• Tabular output format for easy reference
• Best practices for localization projects

2. File Translation Workflow (file-translation-workflow.Rmd)

Shows end-to-end automation for translating .txt and .csv files using translate_file():

# Translate entire files with encoding support
translate_file(
  file_path = "documentation.txt",
  target_language = "es",
  source_language = "en",
  overwrite = FALSE  # Creates new translated file
)

Features:

• Different file type handling
• Encoding considerations (UTF-8 support)
• Overwrite vs. new file options
• Batch processing workflows
• Robust error handling

3. Language Detection and Conditional Translation (language-detection-conditional-translation.Rmd)

Demonstrates smart processing of mixed-language datasets:

# Detect language and translate only non-English content
mixed_data <- tibble(
  feedback = c("Great product!", "Excelente producto!", "Produit fantastique!")
)

# Process with conditional translation
processed_data <- mixed_data %>%
  mutate(
    detected_lang = map_chr(feedback, language_detect),
    needs_translation = !str_detect(detected_lang, "en"),
    english_text = ifelse(needs_translation, 
                         google_translate(feedback), 
                         feedback)
  )

Features:

• Language detection with language_detect()
• Conditional translation (translate only non-English text)
• Tidyverse integration for data cleaning
• Quality control and error handling

🔧 Integration Details

• All vignettes have proper YAML headers with VignetteIndexEntry
• Follow existing package vignette style and format
• Will be automatically detected by pkgdown for website display under Articles
• Include comprehensive examples and best practices
• Code chunks use eval = FALSE to avoid API calls during build

📋 Use Cases

These vignettes help users with:

• Localization projects: Translating UI elements and documentation
• Data cleaning: Processing multilingual user-generated content
• Document automation: Batch translating files and exports
• Quality assurance: Comparing translation APIs for accuracy

Fixes #22.

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