Last observation carried forward (LOCF)

Another important imputation method is Last observation carried forward, or LOCF. This method works by filling in missing values with the most recent non-NA value that came before it.

In this exercise, you will impute a time series of a student's monthly examination scores, called monthly_test_scores. This dataset, along with the ggplot2 and zoo packages, are available to you.

This exercise is part of the course

Manipulating Time Series Data in R

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Exercise instructions

Use last observation carried forward to impute missing values in monthly_test_scores; save this as monthly_scores_locf.
Complete the first autoplot() to generate a plot of the original monthly_test_scores time series.
Autoplot monthly_scores_locf with the 'minimal' theme, and the y-axis label "Examination Score (LOCF)".
Compare the plot of monthly_scores_locf with the plot of monthly_test_scores by switching between the plots.

Hands-on interactive exercise

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

# Impute using last observation carried forward
___ <- ___

# Create a plot of the original time series 
autoplot(___) + 
  theme_minimal() + 
  labs(y = "Examination Score")

# Autoplot with theme and axis title and compare to the above plot
autoplot(___) + 
  ___

Edit and Run Code

Manipulating Time Series Data in R

IntermediateSkill Level

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You’ll learn to create “rolling” windows of a time series that move, or "roll" along with data, making it possible to summarize trends in the data across time, such as the average over success months of observations or the sum over several weeks of sales. Overall summary statistics, like mean, median, sum, maximum, and so on, do not always provide insight into how data changes over time, and rolling windows will allow you to compute statistics dynamically. In addition to rolling windows, you will also learn how to create expanding windows, which show how these summary statistics approach their final value.

Exercise 1: What is a rolling window?Exercise 2: Rolling window functions Exercise 3: Rolling windows versus aggregation Exercise 4: Applying functions to rolling windows Exercise 5: Rolling minimum Exercise 6: Rolling apply with a custom function Exercise 7: Expanding windows Exercise 8: Rolling versus expanding windows Exercise 9: Expanding sum Exercise 10: Expanding mean Exercise 11: Congratulations!