Title:	Scientific Content and Citation Analysis from PDF Documents
Version:	0.2.0
Description:	Provides comprehensive tools for extracting and analyzing scientific content from PDF documents, including citation extraction, reference matching, text analysis, and bibliometric indicators. Supports multi-column PDF layouts, 'CrossRef' API https://www.crossref.org/documentation/retrieve-metadata/rest-api/ integration, and advanced citation parsing.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1.0)
Imports:	base64enc (≥ 0.1-3), dplyr (≥ 1.1.0), httr2 (≥ 0.2.0), igraph, jsonlite (≥ 2.0.0), magrittr (≥ 2.0.4), openalexR (≥ 2.0.2), pdftools (≥ 3.6.0), purrr (≥ 1.1.0), stringr (≥ 1.5.2), tibble (≥ 3.3.0), tidyr (≥ 1.3.0), tidytext (≥ 0.4.3), visNetwork (≥ 2.1.4)
Suggests:	knitr, plotly, RColorBrewer, rmarkdown, scales, stringdist, testthat (≥ 3.0.0), mockery
URL:	https://github.com/massimoaria/contentanalysis,
BugReports:	https://github.com/massimoaria/contentanalysis/issues
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-10-27 08:16:47 UTC; massimoaria
Author:	Massimo Aria [cre, aut, cph], Corrado Cuccurullo [aut]
Maintainer:	Massimo Aria <aria@unina.it>
Repository:	CRAN
Date/Publication:	2025-10-30 19:50:06 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Enhanced scientific content analysis with citation extraction

Description

Comprehensive analysis of scientific documents including citation extraction, reference matching, text analysis, and bibliometric indicators.

Usage

analyze_scientific_content(
  text,
  doi = NULL,
  mailto = NULL,
  citation_type = c("all", "numeric_superscript", "numeric_bracketed", "author_year"),
  window_size = 10,
  min_word_length = 3,
  remove_stopwords = TRUE,
  language = "en",
  custom_stopwords = NULL,
  ngram_range = c(1, 3),
  parse_multiple_citations = TRUE,
  use_sections_for_citations = "auto",
  n_segments_citations = 10
)

Arguments

Details

This function performs:

• Citation extraction (numbered, author-year, narrative, parenthetical)

• Reference parsing (from text or CrossRef API)

• Citation-reference matching

• Text analysis (word frequencies, n-grams)

• Citation context extraction

• Bibliometric indicators

The citation_type parameter filters which citation patterns to search for, reducing false positives. Narrative citations are always included as they are context-dependent.

Value

List with class "enhanced_scientific_content_analysis" containing:

• text_analytics: Basic statistics and word frequencies

• citations: All extracted citations with metadata

• citation_contexts: Citations with surrounding text

• citation_metrics: Citation type distribution, density, etc.

• citation_references_mapping: Matched citations to references

• parsed_references: Structured reference list

• word_frequencies: Word frequency table

• ngrams: N-gram frequency tables

• network_data: Citation co-occurrence data

• summary: Overall analysis summary

Examples

## Not run: 
# For documents with numeric citations
doc <- pdf2txt_auto("paper.pdf", citation_type = "numeric_bracketed")
analysis <- analyze_scientific_content(
  doc,
  citation_type = "numeric_bracketed",
  doi = "10.xxxx/xxxxx",
  mailto = "your@email.com"
)

# For documents with author-year citations
doc <- pdf2txt_auto("paper.pdf", citation_type = "author_year")
analysis <- analyze_scientific_content(
  doc,
  citation_type = "author_year"
)

summary(analysis)
head(analysis$citations)
table(analysis$citation_metrics$type_distribution)

## End(Not run)

Compare two author names with fuzzy matching

Description

Determines if two author names refer to the same person, accounting for typos, prefix variations, accent differences, and minor spelling variations

Usage

author_names_match(name1, name2, threshold = 0.8)

Arguments

Value

Logical indicating if names match

Calculate readability indices for text

Description

Calculates multiple readability indices including Flesch-Kincaid Grade Level, Flesch Reading Ease, Automated Readability Index (ARI), and Gunning Fog Index.

Usage

calculate_readability_indices(text, detailed = FALSE)

Arguments

Details

Formulas:

Flesch-Kincaid Grade Level:

0.39 \times \frac{words}{sentences} + 11.8 \times \frac{syllables}{words} - 15.59

Flesch Reading Ease:

206.835 - 1.015 \times \frac{words}{sentences} - 84.6 \times \frac{syllables}{words}

Automated Readability Index (ARI):

4.71 \times \frac{characters}{words} + 0.5 \times \frac{words}{sentences} - 21.43

Gunning Fog Index:

0.4 \times (\frac{words}{sentences} + 100 \times \frac{complex\_words}{words})

where complex words are those with 3 or more syllables.

Value

A tibble with the following columns:

• flesch_kincaid_grade: US grade level required to understand the text

• flesch_reading_ease: Score from 0-100 (higher = easier to read)

• automated_readability_index: ARI grade level

• gunning_fog_index: Grade level based on sentence length and complex words

If detailed = TRUE, also includes:

• n_sentences: Number of sentences

• n_words: Number of words

• n_syllables: Total syllables

• n_characters: Total characters

• n_complex_words: Words with 3+ syllables

• avg_sentence_length: Average words per sentence

• avg_syllables_per_word: Average syllables per word

• pct_complex_words: Percentage of complex words

Examples

## Not run: 
# Simple text
text <- "The cat sat on the mat. It was a sunny day."
readability <- calculate_readability_indices(text)

# With detailed statistics
text2 <- "Reading is fun. Books open new worlds. They teach us many things."
readability_detailed <- calculate_readability_indices(text2, detailed = TRUE)

## End(Not run)

Calculate word distribution across text segments or sections

Description

Calculates the frequency of selected words/n-grams across document sections or equal-length segments.

Usage

calculate_word_distribution(
  text,
  selected_words,
  use_sections = "auto",
  n_segments = 10,
  remove_stopwords = TRUE,
  language = "en"
)

Arguments

Details

The function:

• Automatically detects if sections are available

• Removes stopwords before creating n-grams (if requested)

• Supports unigrams, bigrams, trigrams, etc.

• Calculates both absolute and relative frequencies

Value

Tibble with columns:

• segment_id: Segment identifier

• segment_name: Section name or segment number

• segment_type: "section" or "equal_length"

• word: Word/n-gram

• count: Absolute frequency

• total_words: Total words in segment

• relative_frequency: Proportion of total words

• percentage: Percentage representation

Attributes include metadata about segmentation used.

Examples

## Not run: 
doc <- pdf2txt_auto("paper.pdf")

# Track specific words across sections
words_to_track <- c("machine learning", "neural network", "accuracy")
dist <- calculate_word_distribution(doc, words_to_track)

# Use equal-length segments instead
dist <- calculate_word_distribution(doc, words_to_track,
                                    use_sections = FALSE,
                                    n_segments = 20)

## End(Not run)

Check if author conflict is real or just normalization difference

Description

Determines whether a conflict between CrossRef and OpenAlex author names is a genuine conflict (different authors) or just a normalization issue

Usage

check_author_conflict(
  crossref_author,
  openalex_author,
  doi = "",
  verbose = TRUE
)

Arguments

Value

List with:

• is_real_conflict: Logical, TRUE if authors are different

• message: Description of the conflict type

Complete references from OpenAlex with intelligent conflict resolution

Description

Merges reference data from CrossRef and OpenAlex, using OpenAlex data when available unless there is a REAL conflict in the first author name. Only genuine author conflicts (different people) trigger CrossRef precedence. Normalization differences (prefixes, accents, typos) are NOT considered conflicts.

Usage

complete_references_from_oa(references, references_oa, verbose = TRUE)

Arguments

Value

Updated references data frame

Count syllables in a word

Description

Estimates the number of syllables in an English word using improved heuristic rules.

Usage

count_syllables(word)

Arguments

Details

Uses a rule-based approach with multiple adjustments:

• Counts vowel groups (consecutive vowels = 1 syllable)

• Adjusts for silent 'e' at end of words

• Handles common suffixes and prefixes

• Special cases for diphthongs and common patterns

• Ensures minimum of 1 syllable

Value

Integer number of syllables

Create Citation Co-occurrence Network

Description

Creates an interactive network visualization of citation co-occurrences within a document. Citations that appear close to each other are connected, with the strength of the connection based on their distance (in characters). Nodes are colored by the document section where citations primarily appear.

Usage

create_citation_network(
  citation_analysis_results,
  max_distance = 1000,
  min_connections = 1,
  show_labels = TRUE
)

Arguments

Details

The function creates a network where:

• Nodes represent unique citations

• Node size is proportional to the number of connections

• Node color indicates the primary section where the citation appears

• Node border is thicker (3px) for citations appearing in multiple sections

• Edges connect citations that co-occur within the specified distance

• Edge width decreases with distance (closer citations = thicker edges)

• Edge color indicates distance: red (<=300 chars), blue (<=600 chars), gray (>600 chars)

The network uses the Fruchterman-Reingold layout algorithm for optimal node positioning. Interactive features include zooming, panning, node dragging, and highlighting of nearest neighbors on hover.

Value

A visNetwork object representing the interactive citation network, or NULL if no valid network can be created. The returned object has an additional stats attribute containing:

• n_nodes: Number of nodes in the network

• n_edges: Number of edges in the network

• avg_distance: Average distance between connected citations

• max_distance: Maximum distance parameter used

• section_distribution: Distribution of citations across sections

• multi_section_citations: Citations appearing in multiple sections

• section_colors: Color mapping for sections

Examples

## Not run: 
# Assuming you have citation_analysis_results from a previous analysis
network <- create_citation_network(
  citation_analysis_results,
  max_distance = 800,
  min_connections = 2,
  show_labels = TRUE
)

# Display the network
network

# Access network statistics
stats <- attr(network, "stats")
print(stats$n_nodes)
print(stats$section_distribution)

## End(Not run)

Create empty readability tibble

Description

Creates an empty tibble with NA values for readability indices.

Usage

create_empty_readability_tibble(detailed = FALSE)

Arguments

Value

Tibble with NA values

Extract DOI from PDF Metadata (Legacy Function)

Description

Legacy wrapper function for backward compatibility. Use extract_pdf_metadata() for more functionality.

Usage

extract_doi_from_pdf(pdf_path, return_all = FALSE)

Arguments

Value

Character string with DOI or NA_character_.

See Also

extract_pdf_metadata

Extract DOI and Metadata from PDF

Description

This function extracts the Digital Object Identifier (DOI) and other metadata from a PDF file using pdftools::pdf_info(). It searches through all metadata fields including the XMP metadata XML.

Usage

extract_pdf_metadata(pdf_path, fields = "doi", return_all_dois = FALSE)

Arguments

Details

The function searches for DOIs in:

• All fields in the keys list (prioritizing article DOI fields)

• The XMP metadata XML field

Journal DOIs/ISSNs (containing "(ISSN)" or from journal-specific fields) are automatically filtered out to return article DOIs.

For other metadata:

• Title: extracted from Title field or dc:title in XMP metadata

• Authors: extracted from Author/Creator fields or dc:creator in XMP metadata

• Journal: extracted from Subject, prism:publicationName in XMP metadata

• Year: extracted from created/modified dates, prism:coverDate, or title

Common DOI prefixes are automatically removed. The function uses regex pattern matching to validate DOI format and extract structured data from XMP XML.

Value

If fields = "doi" (default), returns a character string with the DOI or NA_character_ if not found. If multiple fields are requested, returns a named list with the requested metadata. If return_all_dois = TRUE, the DOI element will be a character vector.

See Also

pdf_info

Examples

## Not run: 
# Extract only DOI
doi <- extract_pdf_metadata("path/to/paper.pdf")

# Extract multiple metadata fields
meta <- extract_pdf_metadata("path/to/paper.pdf",
                             fields = c("doi", "title", "journal"))

# Extract all available metadata
meta <- extract_pdf_metadata("path/to/paper.pdf", fields = "all")

## End(Not run)

Process Content with Google Gemini AI

Description

Send images and/or documents to Google Gemini AI for content analysis and generation. The function supports multiple file types including images (PNG, JPG, etc.) and documents (PDF, TXT, HTML, CSV, RTF).

Usage

gemini_content_ai(
  image = NULL,
  docs = NULL,
  prompt = "Explain these images",
  model = "2.0-flash",
  image_type = "png",
  retry_503 = 5,
  api_key = NULL,
  outputSize = "medium"
)

Arguments

Details

The function handles various error scenarios including:

• Missing or invalid files

• Invalid API keys (HTTP 400)

• Service unavailability (HTTP 503/429) with automatic retry

• File encoding errors

Supported document types: PDF, TXT, HTML, CSV, RTF

Value

Character vector containing the AI-generated response(s), or an error message string starting with "ERROR:" if the request fails.

Examples

## Not run: 
# Process an image
result <- gemini_content_ai(
  image = "path/to/image.png",
  prompt = "Describe this image in detail"
)

# Process a PDF document
result <- gemini_content_ai(
  docs = "path/to/document.pdf",
  prompt = "Summarize this document",
  outputSize = "large"
)

# Process multiple images and documents
result <- gemini_content_ai(
  image = c("img1.png", "img2.png"),
  docs = c("doc1.pdf", "doc2.txt"),
  prompt = "Compare these materials"
)

## End(Not run)

Retrieve rich metadata from the CrossRef API for a given DOI

Description

Fetches a comprehensive set of metadata for a given DOI from the CrossRef API. The function parses the JSON response to extract main article details, author information, and the full reference list.

Usage

get_crossref_references(doi, mailto = NULL, output = "references")

Arguments

Details

This function accesses the CrossRef REST API to retrieve structured metadata. It handles nested information like authors and references by parsing them into tidy data frames. Providing a mailto address in the user-agent is a best practice for API interaction.

Value

A list containing three main elements:

• main_metadata: A data frame with a single row containing key metadata about the article (e.g., DOI, title, journal, year, volume, issue).

• authors: A data frame listing all authors with their given name, family name, ORCID, and affiliation (if available).

• references: A data frame containing detailed information for each reference in the article (e.g., key, DOI, title, author, year).

Returns NULL if the DOI is not found or an error occurs.

Examples

## Not run: 
metadata <- get_crossref_references(
  "10.1007/s11192-016-1948-8",
  mailto = "your.email@example.com",
  output = 'all'
)

# View main article metadata
print(metadata$main_metadata)

# View author information
head(metadata$authors)

# View reference list
head(metadata$references)

## End(Not run)

Get path to example paper

Description

Returns the path to the example paper included in the package source (available on GitHub but not in CRAN builds).

Usage

get_example_paper(example = "example_paper.pdf")

Arguments

Value

Path to example file if it exists, otherwise downloads it from GitHub

Examples

## Not run: 
paper_path <- get_example_paper()
doc <- pdf2txt_auto(paper_path, n_columns = 2)

## End(Not run)

Map citations to document segments or sections

Description

Map citations to document segments or sections

Usage

map_citations_to_segments(
  citations_df,
  text,
  use_sections = "auto",
  n_segments = 10
)

Arguments

Value

Citations data frame with segment/section information

Match citations to references

Description

Matches in-text citations to entries in the reference list using author-year matching with multiple disambiguation strategies.

Usage

match_citations_to_references(citations_df, references_df)

Arguments

Details

Matching algorithm:

1. Filter by exact year match

2. Match first author (exact, then fuzzy)

3. Disambiguate using second author or et al. heuristics

Match confidence levels include: high (exact first author + year), high_second_author (disambiguated with second author), medium_multiple_matches, medium_fuzzy, medium_etal_heuristic (various medium confidence scenarios), no_match_year, no_match_author, no_match_missing_info (no suitable reference found).

Value

Tibble with matched citations including columns:

• citation_id: Citation identifier

• citation_text: Original citation text

• citation_text_clean: Cleaned citation text

• citation_type: Type of citation

• cite_author: Extracted first author from citation

• cite_second_author: Second author (if present)

• cite_year: Extracted year

• cite_has_etal: Logical, contains "et al."

• matched_ref_id: ID of matched reference

• ref_full_text: Full text of matched reference

• ref_authors: Authors from reference

• ref_year: Year from reference

• match_confidence: Quality of match (high, medium, low, no_match)

Examples

## Not run: 
matched <- match_citations_to_references(citations_df, references_df)
table(matched$match_confidence)

## End(Not run)

Merge Text Chunks into Named Sections

Description

Takes a list of markdown text chunks and merges them into named sections. Each section name is extracted from the markdown header (# Title).

Usage

merge_text_chunks_named(
  text_chunks,
  remove_tables = TRUE,
  remove_figure_captions = TRUE
)

Arguments

Value

A named character vector where:

• Names are section titles (without the # symbol)

• Values are complete section contents (including the title line)

Normalize author name for robust comparison

Description

Removes diacritics, converts to lowercase, handles prefixes (van, de, di, etc.), removes hyphens and extra whitespace for better matching

Usage

normalize_author_name(name)

Arguments

Value

Normalized name string

Normalize references section formatting

Description

Normalizes the References section to ensure each reference is separated by double newlines and internal line breaks are removed.

Usage

normalize_references_section(text_sections)

Arguments

Details

Detects reference start patterns (Author, Initial.) and ensures consistent formatting with \n\n separators between references.

Value

Modified text_sections list with normalized References

Examples

## Not run: 
sections <- split_into_sections(text, "paper.pdf")
sections <- normalize_references_section(sections)

## End(Not run)

Parse references section from text

Description

Parses a references section into individual entries with extracted metadata including authors, year, and title information.

Usage

parse_references_section(references_text)

Arguments

Value

Tibble with columns:

• ref_id: Unique reference identifier

• ref_full_text: Complete reference text

• ref_authors: Author string

• ref_year: Publication year

• ref_first_author: First author surname

• ref_first_author_normalized: Lowercase first author

• ref_second_author: Second author surname (if present)

• ref_second_author_normalized: Lowercase second author

• n_authors: Number of authors (99 = et al.)

Examples

## Not run: 
refs_text <- doc$References
parsed_refs <- parse_references_section(refs_text)

## End(Not run)

Import PDF with Automatic Section Detection

Description

High-level function that imports PDF files, extracts text while handling multi-column layouts, and optionally splits content into sections. Supports AI-enhanced text extraction using Google Gemini API and includes control over citation format conversion.

Usage

pdf2txt_auto(
  file,
  n_columns = NULL,
  preserve_structure = TRUE,
  sections = TRUE,
  normalize_refs = TRUE,
  citation_type = c("none", "numeric_superscript", "numeric_bracketed", "author_year"),
  enable_ai_support = FALSE,
  ai_model = "2.0-flash",
  api_key = NULL
)

Arguments

Details

The function attempts multiple extraction methods:

1. First tries multi-column extraction with pdf2txt_multicolumn_safe

2. Falls back to standard pdftools::pdf_text if the first method fails

3. Optionally applies AI-enhanced extraction if enable_ai_support = TRUE

When AI support is enabled and successful, the function:

• Processes the PDF using process_large_pdf

• Merges text chunks and converts to appropriate format

• Preserves References/Bibliography section from standard extraction

• Returns AI-processed content with improved formatting

Citation conversion is applied based on the citation_type parameter to standardize reference markers throughout the document.

Value

If sections = TRUE, returns a named list where:

• The first element Full_text contains the complete document text

• Subsequent elements contain individual sections (Introduction, Methods, etc.)

If sections = FALSE, returns a character string with the full document text. Returns NA if extraction fails.

Note

• AI support requires a valid Google Gemini API key

• AI processing may take longer but provides better text extraction quality

• The function automatically handles hyphenation and line breaks

• Multi-column layouts are detected and processed appropriately

See Also

pdf2txt_multicolumn_safe for multi-column extraction, process_large_pdf for AI-enhanced processing, split_into_sections for section detection

Examples

## Not run: 
# Basic import with automatic section detection
doc <- pdf2txt_auto("paper.pdf")

# Import with superscript citation conversion
doc <- pdf2txt_auto(
  "paper.pdf",
  citation_type = "numeric_superscript"
)

# Import with AI-enhanced extraction
doc <- pdf2txt_auto(
  "paper.pdf",
  enable_ai_support = TRUE,
  ai_model = "2.0-flash",
  api_key = Sys.getenv("GEMINI_API_KEY")
)

# Import paper with author-year citations (no conversion)
doc <- pdf2txt_auto(
  "paper.pdf",
  citation_type = "author_year"
)

# Simple text extraction without sections or citation processing
text <- pdf2txt_auto(
  "paper.pdf",
  sections = FALSE,
  citation_type = "none"
)

# Access specific sections
introduction <- doc$Introduction
methods <- doc$Methods

## End(Not run)

Extract text from multi-column PDF with structure preservation

Description

Extracts text from PDF files handling multi-column layouts, with options for structure preservation and automatic column detection. This version includes post-processing to convert superscript citation numbers based on the specified citation type.

Usage

pdf2txt_multicolumn_safe(
  file,
  n_columns = NULL,
  column_threshold = NULL,
  preserve_structure = TRUE,
  citation_type = c("none", "numeric_superscript", "numeric_bracketed", "author_year")
)

Arguments

Details

This function uses pdftools::pdf_data() for precise text extraction with spatial coordinates. It handles:

• Multi-column layouts (2+ columns)

• Section detection and paragraph preservation

• Hyphenation removal

• Title and heading identification

• Superscript citation number conversion (only if citation_type = "numeric_superscript")

If pdf_data() fails, falls back to pdftools::pdf_text().

Value

Character string with extracted text.

Examples

## Not run: 
# Extract from 2-column paper with superscript citations
text <- pdf2txt_multicolumn_safe("paper.pdf", n_columns = 2,
                                  citation_type = "numeric_superscript")

# Extract paper with author-year citations (no conversion)
text <- pdf2txt_multicolumn_safe("paper.pdf", citation_type = "author_year")

## End(Not run)

Create interactive word distribution plot

Description

Creates an interactive plotly visualization of word frequencies across document segments or sections.

Usage

plot_word_distribution(
  word_distribution_data,
  plot_type = "line",
  smooth = FALSE,
  show_points = TRUE,
  colors = NULL
)

Arguments

Details

The plot shows:

• X-axis: Document sections or segments

• Y-axis: Relative frequency (percentage)

• Each word as a separate line/area

• Hover information with exact values

Value

A plotly object with interactive visualization.

Examples

## Not run: 
dist <- calculate_word_distribution(doc, c("method", "result", "conclusion"))
plot_word_distribution(dist, plot_type = "line", show_points = TRUE)

# Area plot with custom colors
plot_word_distribution(dist, plot_type = "area",
                      colors = c("#FF6B6B", "#4ECDC4", "#45B7D1"))

## End(Not run)

Process Large PDF Documents with Google Gemini AI

Description

Split a large PDF into chunks and process each chunk with Google Gemini AI to extract and format text content. Particularly useful for PDFs that exceed the token limit of a single API request.

Usage

process_large_pdf(
  pdf_path,
  api_key,
  pages_per_chunk = 4,
  model = c("1.5-flash", "2.0-flash", "2.5-flash")
)

Arguments

Details

The function performs the following steps:

1. Validates input parameters and PDF file

2. Splits the PDF into chunks based on pages_per_chunk

3. Processes each chunk sequentially with Gemini AI

4. Extracts text while:

   • Removing repeated headers

   • Maintaining hierarchical structure

   • Preserving reference numbers in bracket notation

   • Formatting output as markdown

   • Handling sections that span multiple chunks

5. Returns a list of extracted text, one element per chunk

The function includes comprehensive error handling for:

• Invalid or missing PDF files

• Missing or invalid API keys

• PDF processing errors

• Gemini AI service errors

• File system operations

Rate limiting: The function includes a 1-second delay between chunks to respect API rate limits.

Value

List of character vectors, one element per chunk, containing the extracted and formatted text in markdown format. Returns NULL if processing fails.

Note

• Requires pdftools package for PDF manipulation

• Uses temporary files that are automatically cleaned up

• Progress messages are printed for each chunk

• All warnings are captured and reported

See Also

gemini_content_ai for the underlying AI processing function

Examples

## Not run: 
# Process a large PDF with default settings
result <- process_large_pdf(
  pdf_path = "large_document.pdf",
  api_key = Sys.getenv("GEMINI_API_KEY")
)

# Process with smaller chunks and specific model
result <- process_large_pdf(
  pdf_path = "very_large_document.pdf",
  api_key = Sys.getenv("GEMINI_API_KEY"),
  pages_per_chunk = 3,
  model = "2.5-flash"
)

# Combine all chunks into single text
if (!is.null(result)) {
  full_text <- paste(unlist(result), collapse = "\n\n")
}

## End(Not run)

Calculate readability indices for multiple texts

Description

Vectorized version that calculates readability indices for multiple texts.

Usage

readability_multiple(texts, detailed = FALSE, text_id = NULL)

Arguments

Value

A tibble with one row per text, including text_id if provided

Examples

## Not run: 
texts <- c("First text here.", "Second text is longer and more complex.")
ids <- c("doc1", "doc2")
readability_multiple(texts, text_id = ids, detailed = TRUE)

## End(Not run)

Remove All Types of Tables (Markdown and Plain Text)

Description

Remove All Types of Tables (Markdown and Plain Text)

Usage

remove_all_tables(text)

Arguments

Value

Character string with all tables and table captions removed

Remove Markdown Code Block Markers

Description

Remove Markdown Code Block Markers

Usage

remove_code_blocks(text)

Arguments

Value

Character string with ⁠markdown and ⁠ markers removed

Remove Figure Captions

Description

Remove Figure Captions

Usage

remove_figure_caps(text)

Arguments

Value

Character string with figure captions removed

Split document text into sections

Description

Splits extracted text into logical sections (Introduction, Methods, Results, etc.) using either the PDF's table of contents or common academic section patterns.

Usage

split_into_sections(text, file_path = NULL)

Arguments

Details

The function attempts to:

1. Extract section names from PDF table of contents

2. Fall back to common academic section names if TOC unavailable

3. Match section headers in text using regex patterns

4. Handle duplicate section names

Common sections searched: Abstract, Introduction, Methods, Results, Discussion, Conclusion, References, etc.

Value

Named list where each element is a section's text. Always includes "Full_text" element with complete document.

Examples

## Not run: 
text <- pdf2txt_auto("paper.pdf", sections = FALSE)
sections <- split_into_sections(text, file_path = "paper.pdf")
names(sections)

## End(Not run)