Handle missing values

In chapter 3, you used na.locf() to fill missing values with the previous non-missing value. You can use interpolation when carrying the previous value forward isn't appropriate. In this exercise, you will explore two interpolation methods: linear and spline.

Linear interpolation calculates values that lie on a line between two known data points. This is a good choice for fairly linear data, like a series with a strong trend. Spline interpolation is more appropriate for series without a strong trend, because it calculates a non-linear approximation using multiple data points.

Use these two methods to interpolate the three missing values for the 10-year Treasury rate in the object DGS10. Then compare the results with the output of na.locf().

This is a part of the course

“Importing and Managing Financial Data in R”

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Exercise instructions

Complete the command to use na.approx() to fill in missing values using linear interpolation.
Complete the command to use na.spline() to fill in missing values using spline interpolation.
Merge locf, approx, and spline into one object named na_filled.
Complete the command to plot na_filled.

Hands-on interactive exercise

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

# fill NA using last observation carried forward
locf <- na.locf(DGS10)

# fill NA using linear interpolation
approx <- ___(DGS10)

# fill NA using spline interpolation
spline <- ___(DGS10)

# merge into one object


# plot combined object
___(___, col = c("black", "red", "green"))

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