E2E (version 0.1.2)

load_and_prepare_data_dia: Load and Prepare Data for Diagnostic Models

Description

Loads a CSV file containing patient data, extracts features, and converts the label column into a factor suitable for classification models. Handles basic data cleaning like trimming whitespace and type conversion.

Usage

load_and_prepare_data_dia(
  data_path,
  label_col_name,
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Positive",
  new_negative_label = "Negative"
)

Value

A list containing:

• X: A data frame of features (all columns except ID and label).

• y: A factor vector of class labels, with levels new_negative_label and new_positive_label.

• sample_ids: A vector of sample IDs (the first column of the input data).

• pos_class_label: The character string used for the positive class factor level.

• neg_class_label: The character string used for the negative class factor level.

• y_original_numeric: The original numeric/character vector of labels.

Arguments

data_path

A character string, the file path to the input CSV data. The first column is assumed to be a sample ID.

label_col_name

A character string, the name of the column containing the class labels.

positive_label_value

A numeric or character value that represents the positive class in the raw data.

negative_label_value

A numeric or character value that represents the negative class in the raw data.

new_positive_label

A character string, the desired factor level name for the positive class (e.g., "Positive").

new_negative_label

A character string, the desired factor level name for the negative class (e.g., "Negative").

Examples

# \donttest{
# Create a dummy CSV file in a temporary directory for demonstration
temp_csv_path <- tempfile(fileext = ".csv")
dummy_data <- data.frame(
  ID = paste0("Patient", 1:50),
  Disease_Status = sample(c(0, 1), 50, replace = TRUE),
  FeatureA = rnorm(50),
  FeatureB = runif(50, 0, 100),
  CategoricalFeature = sample(c("X", "Y", "Z"), 50, replace = TRUE)
)
write.csv(dummy_data, temp_csv_path, row.names = FALSE)

# Load and prepare data from the temporary file
prepared_data <- load_and_prepare_data_dia(
  data_path = temp_csv_path,
  label_col_name = "Disease_Status",
  positive_label_value = 1,
  negative_label_value = 0,
  new_positive_label = "Case",
  new_negative_label = "Control"
)

# Check prepared data structure
str(prepared_data$X)
table(prepared_data$y)

# Clean up the dummy file
unlink(temp_csv_path)
# }