Handling Missing Data

Missing data is common in real-world datasets. R provides functions to detect, remove, or replace missing values.

Detecting Missing Values

# Sample data
data <- c(10, NA, 5, NA, 8)

# Check for missing values
is.na(data)

Removing Missing Values

# Remove missing values
clean_data <- na.omit(data)
clean_data

Replacing Missing Values

# Replace NA with a specific value
data[is.na(data)] <- 0
data

Proper handling of missing data ensures accurate analysis and prevents errors in calculations.

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