inst/doc/language-detection-conditional-translation.R

In polyglotr: Translate Text

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----setup--------------------------------------------------------------------
#  library(polyglotr)
#  library(dplyr)
#  library(tibble)
#  library(purrr)
#  

## ----basic_detection----------------------------------------------------------
#  # Sample texts in different languages
#  sample_texts <- c(
#    "Hello, how are you today?",           # English
#    "Bonjour, comment allez-vous?",        # French
#    "Hola, ¿cómo estás hoy?",             # Spanish
#    "Guten Tag, wie geht es Ihnen?",      # German
#    "Ciao, come stai oggi?"               # Italian
#  )
#  
#  # Detect languages
#  detected_languages <- sapply(sample_texts, language_detect)
#  print("Detected languages:")
#  print(detected_languages)

## ----conditional_translation--------------------------------------------------
#  translate_if_not_english <- function(text, target_language = "en") {
#    # Detect language of the input text
#    detected_lang <- language_detect(text)
#  
#    # Check if the detected language is English
#    is_english <- grepl("en", detected_lang, ignore.case = TRUE)
#  
#    if (is_english) {
#      # Return original text if already English
#      return(list(
#        original = text,
#        translated = text,
#        was_translated = FALSE,
#        detected_language = detected_lang
#      ))
#    } else {
#      # Translate to English if not English
#      translated_text <- google_translate(text, target_language = target_language, source_language = "auto")
#      return(list(
#        original = text,
#        translated = translated_text,
#        was_translated = TRUE,
#        detected_language = detected_lang
#      ))
#    }
#  }
#  
#  # Test the function
#  test_text_fr <- "Bonjour, j'aimerais acheter un billet."
#  result <- translate_if_not_english(test_text_fr)
#  
#  print("Conditional translation result:")
#  print(paste("Original:", result$original))
#  print(paste("Translated:", result$translated))
#  print(paste("Was translated:", result$was_translated))
#  print(paste("Detected language:", result$detected_language))

## ----mixed_language_tibble----------------------------------------------------
#  # Create a dataset with mixed languages (typical of user-generated content)
#  mixed_data <- tibble(
#    id = 1:8,
#    user_feedback = c(
#      "Great product, very satisfied!",                    # English
#      "Excelente producto, muy satisfecho!",               # Spanish
#      "Produit fantastique, je le recommande!",            # French
#      "This service exceeded my expectations.",            # English
#      "Der Service war wirklich hervorragend.",            # German
#      "Servizio eccellente, davvero impressionante!",     # Italian
#      "The delivery was fast and reliable.",               # English
#      "La livraison était rapide et fiable."               # French
#    ),
#    rating = c(5, 5, 4, 5, 4, 5, 4, 4),
#    category = rep(c("product", "service"), 4)
#  )
#  
#  print("Original mixed-language dataset:")
#  print(mixed_data)

## ----detect_and_translate-----------------------------------------------------
#  # Function to process each text entry
#  process_feedback <- function(text) {
#    result <- translate_if_not_english(text)
#    return(tibble(
#      original_text = result$original,
#      english_text = result$translated,
#      was_translated = result$was_translated,
#      detected_language = result$detected_language
#    ))
#  }
#  
#  # Apply to all feedback entries
#  processed_results <- purrr::map_dfr(mixed_data$user_feedback, process_feedback)
#  
#  # Combine with original data
#  enhanced_data <- bind_cols(mixed_data, processed_results)
#  
#  print("Enhanced dataset with language detection and translation:")
#  print(enhanced_data)

## ----advanced_tidyverse-------------------------------------------------------
#  library(stringr)
#  
#  # Enhanced processing function with more details
#  enhanced_language_processing <- function(df, text_column) {
#    df %>%
#      mutate(
#        # Detect language for each text entry
#        detected_lang = map_chr(!!rlang::sym(text_column),
#                               ~ tryCatch(language_detect(.x), error = function(e) "unknown")),
#  
#        # Determine if translation is needed
#        needs_translation = !str_detect(detected_lang, "en"),
#  
#        # Translate only non-English text
#        english_text = map2_chr(!!rlang::sym(text_column), needs_translation,
#                               ~ if (.y) {
#                                 tryCatch(google_translate(.x, target_language = "en"),
#                                         error = function(e) .x)
#                               } else {
#                                 .x
#                               }),
#  
#        # Add translation confidence/status
#        translation_status = case_when(
#          detected_lang == "unknown" ~ "detection_failed",
#          !needs_translation ~ "already_english",
#          english_text != !!rlang::sym(text_column) ~ "translated",
#          TRUE ~ "translation_failed"
#        )
#      )
#  }
#  
#  # Apply enhanced processing
#  result_data <- enhanced_language_processing(mixed_data, "user_feedback")
#  
#  print("Advanced processing results:")
#  print(result_data %>% select(id, detected_lang, needs_translation, translation_status))

## ----batch_filtering----------------------------------------------------------
#  # Create larger sample dataset
#  large_dataset <- tibble(
#    id = 1:20,
#    content = c(
#      # Mix of English and non-English content
#      "Amazing service quality",                           # EN
#      "Fantástico servicio al cliente",                   # ES
#      "Service client exceptionnel",                      # FR
#      "Great user experience",                            # EN
#      "Esperienza utente eccellente",                     # IT
#      "Ausgezeichnete Benutzerführung",                  # DE
#      "Fast shipping and delivery",                       # EN
#      "Livraison rapide et efficace",                    # FR
#      "Excellent product quality",                        # EN
#      "Qualità del prodotto superiore",                  # IT
#      "Easy to use interface",                           # EN
#      "Interfaz muy fácil de usar",                      # ES
#      "Highly recommend this product",                    # EN
#      "Je recommande vivement ce produit",               # FR
#      "Outstanding customer support",                     # EN
#      "Soporte al cliente sobresaliente",                # ES
#      "Very satisfied with purchase",                     # EN
#      "Sehr zufrieden mit dem Kauf",                     # DE
#      "Will definitely buy again",                       # EN
#      "Sicuramente acquisterò di nuovo"                  # IT
#    ),
#    timestamp = Sys.time() + sample(-1000:1000, 20),
#    priority = sample(c("high", "medium", "low"), 20, replace = TRUE)
#  )
#  
#  # Efficient batch processing workflow
#  batch_process_languages <- function(df, text_col, batch_size = 5) {
#    # First, detect languages for all entries
#    df_with_detection <- df %>%
#      mutate(
#        row_id = row_number(),
#        detected_lang = map_chr(!!rlang::sym(text_col),
#                               ~ tryCatch(language_detect(.x), error = function(e) "en")),
#        is_english = str_detect(detected_lang, "en")
#      )
#  
#    # Separate English and non-English content
#    english_content <- df_with_detection %>% filter(is_english)
#    non_english_content <- df_with_detection %>% filter(!is_english)
#  
#    # Process non-English content in batches
#    if (nrow(non_english_content) > 0) {
#      non_english_content <- non_english_content %>%
#        mutate(
#          batch_id = ceiling(row_number() / batch_size),
#          english_text = map_chr(!!rlang::sym(text_col),
#                                ~ tryCatch(google_translate(.x, target_language = "en"),
#                                          error = function(e) .x))
#        )
#    } else {
#      non_english_content <- non_english_content %>%
#        mutate(batch_id = integer(0), english_text = character(0))
#    }
#  
#    # For English content, keep original text
#    english_content <- english_content %>%
#      mutate(
#        batch_id = NA_integer_,
#        english_text = !!rlang::sym(text_col)
#      )
#  
#    # Combine results
#    result <- bind_rows(english_content, non_english_content) %>%
#      arrange(row_id) %>%
#      select(-row_id)
#  
#    return(result)
#  }
#  
#  # Apply batch processing
#  processed_large <- batch_process_languages(large_dataset, "content", batch_size = 3)
#  
#  # Summary statistics
#  summary_stats <- processed_large %>%
#    summarise(
#      total_entries = n(),
#      english_entries = sum(is_english),
#      translated_entries = sum(!is_english),
#      translation_rate = mean(!is_english),
#      unique_languages = n_distinct(detected_lang)
#    )
#  
#  print("Processing summary:")
#  print(summary_stats)
#  
#  print("Sample of processed data:")
#  print(processed_large %>%
#        select(id, detected_lang, is_english, content, english_text) %>%
#        head(10))