Exercise

Time series plots

The first step in any data analysis task is to plot the data. Graphs enable you to visualize many features of the data, including patterns, unusual observations, changes over time, and relationships between variables. Just as the type of data determines which forecasting method to use, it also determines which graphs are appropriate.

You can use the autoplot() function to produce a time plot of the data with or without facets, or panels that display different subsets of data:

> autoplot(usnim_2002, facets = FALSE)

The above method is one of the many taught in this course that accepts boolean arguments. Both T and TRUE mean "true", and F and FALSE mean "false", however, T and F can be overwritten in your code. Therefore, you should only rely on TRUE and FALSE to set your indicators for the remainder of the course.

You will use two more functions in this exercise, which.max() and frequency().
which.max() can be used to identify the smallest index of the maximum value

> x <- c(4, 5, 5)
> which.max(x)
[1] 2

To find the number of observations per unit time, use frequency(). Recall the usnim_2002 data from the previous exercise:

> frequency(usnim_2002)
[1] 4

Because this course involves the use of the forecast and ggplot2 packages, they have been loaded into your workspace for you, as well as myts from the previous exercise and the following three series (available in the package forecast):

  • gold containing gold prices in US dollars
  • woolyrnq containing information on the production of woollen yarn in Australia
  • gas containing Australian gas production

Instructions

100 XP
  • Plot the data you stored as myts using autoplot() with facetting.
  • Plot the same data without facetting by setting the appropriate argument to FALSE. What happens?
  • Plot the gold, woolyrnq, and gas time series in separate plots.
  • Use which.max() to spot the outlier in the gold series. Which observation was it?
  • Apply the frequency() function to each commodity to get the number of observations per unit time. This would return 52 for weekly data, for example.