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Linear interpolation

For continuous and numeric data – where the values can fall anywhere within a range of values – linear interpolation is often the best option for imputation. Data such as temperature, elevation, and per capita income are examples where linear interpolation could be used.

In this exercise, you will determine the number of missing values in the maunaloa_missing time series and use linear interpolation to impute these values.

maunaloa_missing, zoo, and ggplot2 are available to you.

This exercise is part of the course

Manipulating Time Series Data in R

View Course

Exercise instructions

  • Determine the count of observations from maunaloa_missing that are NA.

  • Use linear interpolation to fill in the missing values in maunaloa_missing; assign this as maunaloa_linear.

  • Generate a ggplot of maunaloa_linear, with a red-colored line.

Hands-on interactive exercise

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

# Count the number of missing values
___

# Fill in values with linear approximation
___

# Generate a full ggplot of maunaloa_linear
ggplot(___,
       aes(___)) + 
  scale_y_continuous() + 
  ___
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