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
Exercise instructions
Determine the count of observations from
maunaloa_missing
that areNA
.Use linear interpolation to fill in the missing values in
maunaloa_missing
; assign this asmaunaloa_linear
.Generate a
ggplot
ofmaunaloa_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() +
___