Handling missing data

Some of the prospective donors have missing age data. Unfortunately, R will exclude any cases with NA values when building a regression model.

One workaround is to replace, or impute, the missing values with an estimated value. After doing so, you may also create a missing data indicator to model the possibility that cases with missing data are different in some way from those without.

The data frame donors is loaded in your workspace.

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Supervised Learning in R: Classification

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Övningsinstruktioner

Use summary() on donors$age to find the average age of prospects with non-missing data.
Use ifelse() and the test is.na(donors$age) to impute the average (rounded to 2 decimal places) for cases with missing age. Be sure to also ignore NAs.
Create a binary dummy variable named missing_age indicating the presence of missing data using another ifelse() call and the same test.

Interaktiv övning med praktiskt arbete

Testa den här övningen genom att slutföra den här exempelkoden.

# Find the average age among non-missing values
summary(___)

# Impute missing age values with the mean age
donors$imputed_age <- ifelse(___)

# Create missing value indicator for age
donors$missing_age <- ___

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