This function provides a complete text mining workflow that follows the same sequence as the Shiny application: file processing → text uniting → preprocessing → DFM creation → analysis. It serves as a convenience function for users who want to execute the entire pipeline programmatically.
Usage
run_text_workflow(
dataset_choice,
file_info = NULL,
text_input = NULL,
listed_vars,
min_char = 2,
remove_punct = TRUE,
remove_symbols = TRUE,
remove_numbers = TRUE,
remove_url = TRUE,
detect_compounds = FALSE,
compound_size = 2:3,
compound_min_count = 2,
verbose = TRUE
)Arguments
- dataset_choice
A character string indicating the dataset choice: "Upload an Example Dataset", "Upload Your File", "Copy and Paste Text".
- file_info
A data frame containing file information (for file upload).
- text_input
A character string containing text input (for copy-paste).
- listed_vars
A character vector of column names to unite into text.
- min_char
The minimum number of characters for tokens (default: 2).
- remove_punct
Logical; remove punctuation (default: TRUE).
- remove_symbols
Logical; remove symbols (default: TRUE).
- remove_numbers
Logical; remove numbers (default: TRUE).
- remove_url
Logical; remove URLs (default: TRUE).
- detect_compounds
Logical; detect multi-word expressions (default: FALSE).
- compound_size
Size range for compound detection (default: 2:3).
- compound_min_count
Minimum count for compounds (default: 2).
- verbose
Logical; print progress messages (default: TRUE).
Examples
if (interactive()) {
# Using example dataset
workflow_result <- TextAnalysisR::run_text_workflow(
dataset_choice = "Upload an Example Dataset",
listed_vars = c("title", "keyword", "abstract")
)
# Using file upload
file_info <- data.frame(filepath = "path/to/your/file.xlsx")
workflow_result <- TextAnalysisR::run_text_workflow(
dataset_choice = "Upload Your File",
file_info = file_info,
listed_vars = c("column1", "column2")
)
# Using copy-paste text
workflow_result <- TextAnalysisR::run_text_workflow(
dataset_choice = "Copy and Paste Text",
text_input = "Your text content here",
listed_vars = "text"
)
}