9 Imputation Techniques in R for Handling Missing Data

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Imputation techniques in R can be classified into various categories, ranging from basic to advanced methods. Here’s a list of some common imputation techniques along with sample R code:

I. Basic Imputation Techniques:

1. Mean/Median/Mode Imputation:

• Description: Replace missing values with the mean, median, or mode of the observed values.

# Mean imputation
data$variable[is.na(data$variable)] <- mean(data$variable, na.rm = TRUE)

# Median imputation
data$variable[is.na(data$variable)] <- median(data$variable, na.rm = TRUE)

# Mode imputation (using a custom function)
mode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}
data$variable[is.na(data$variable)] <- mode(data$variable)

2. Complete Case Analysis:

• Description: Remove rows with missing values.

data <- na.omit(data)

II. Intermediate Imputation Techniques:

3. Linear Regression Imputation:

• Description: Predict missing values using a linear regression model.
• Assuming you have a dataset named data with a variable…