Replace missing values

In the last exercise, you noticed that for six observations, the value of RelationalNeighborSecond was missing. In this exercise, you will replace those missing values with 0.

This exercise is part of the course

Predictive Analytics using Networked Data in R

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Exercise instructions

Use summary() to inspect the RelationalNeighborSecond feature.
Find the indices of the observations that are missing using which() and assign to the variable toReplace.
Use the toReplace vector to replace the missing values instudentnetworkdata$RelationalNeighborSecond with a zero.
Inspect RelationalNeighborSecond again to make sure there are no longer any missing value.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Inspect the feature
___(studentnetworkdata$RelationalNeighborSecond)

# Find the indices of the missing values
toReplace <- ___(is.na(studentnetworkdata$___))

# Replace the missing values with 0
studentnetworkdata$RelationalNeighborSecond[___] <- ___

# Inspect the feature again
___(___$___)

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