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  3. Manipulating Time Series Data in R

Exercise

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.

Instructions

100 XP

  • 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.