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"
)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").
Value
A list containing:
X: A data frame of features (all columns except ID and label).y: A factor vector of class labels, with levelsnew_negative_labelandnew_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.
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)
#> 'data.frame': 50 obs. of 3 variables:
#> $ FeatureA : num -1.1896 -0.5447 0.9136 0.045 0.0218 ...
#> $ FeatureB : num 46.9 47.1 28.1 46.3 95.2 ...
#> $ CategoricalFeature: Factor w/ 3 levels "X","Y","Z": 3 1 1 1 1 2 1 3 1 3 ...
table(prepared_data$y)
#>
#> Control Case
#> 32 18
# Clean up the dummy file
unlink(temp_csv_path)
# }