process_missing_value: Process Missing Values in a Data Frame

In ggpca: Publication-Ready PCA, t-SNE, and UMAP Plots

Process Missing Values in a Data Frame

Description

This function filters columns in a data frame based on a specified threshold for missing values and performs imputation on remaining non-metadata columns using half of the minimum value found in each column. Metadata columns are specified by the user and are exempt from filtering and imputation.

Usage

process_missing_value(data, missing_threshold = 25, metadata_cols = NULL)

Arguments

data	A data frame containing the data to be processed.
missing_threshold	A numeric value representing the percentage threshold of missing values which should lead to the removal of a column. Default is 25.
metadata_cols	A vector of either column names or indices that should be treated as metadata and thus exempt from missing value filtering and imputation. If NULL, no columns are treated as metadata.

Value

A data frame with filtered and imputed columns as necessary.

Examples

data <- data.frame(
  A = c(1, 2, NA, 4),
  B = c(NA, NA, NA, 4),
  C = c(1, 2, 3, 4)
)
# Process missing values while ignoring column 'C' as metadata
processed_data <- process_missing_value(data, missing_threshold = 50, metadata_cols = "C")

ggpca documentation built on April 3, 2025, 10:28 p.m.