6 Time series

In the last chapter, we visualized the relationship between per capita GDP and life expectancy. You might have wondered how time fits into that association. In this chapter, we’ll explore life expectancy and GDP over time.

The following ggplot2 cheat sheet sections will be helpful for this chapter:

  • Geoms
    • geom_path()
  • Scales

The lubridate package is a helpful tool for working with dates. We’ll use some lubridate functions throughout the chapter. Take a look at the lubridate cheat sheet if you’re not already familiar with the package.

Not all time series are alike. In some situations, you’ll be interested in a long-term trend, but in others you’ll want to highlight short-term changes or even just individual values. In this chapter, we’ll cover various strategies for dealing with these different scenarios.

First, we’ll talk about the mechanics of date scales, which are useful for time series.

6.1 Mechanics

6.1.1 Date/time scales

Sometimes, your time series data will include detailed date or time information stored as a date, time, or date-time. For example, the nycflights13::flights variable time_hour is a date-time.

When you map time_hour to an aesthetic, ggplot2 uses scale_*_datetime(), the scale function for date-times. There is also scale_*_date() for dates and scale_*_time() for times. The date- and time-specific scale functions are useful because they create meaningful breaks and labels.

flights_0101_0102 contains data on the number of flights per hour on January 1st and January 2nd, 2013.

Just like with the other scale functions, you can change the breaks using the breaks argument. scale_*_date() and scale_*_datetime() also include a date_breaks argument that allows you to supply the breaks in date-time units, like “1 month”, “6 years”, or “2 hours.”

Similarly, you can change the labels using the labels argument, but scale_*_date() and scale_*_datetime() also include a date_labels function made for working with dates. date_labels takes the same formatting strings as functions like ymd() and as_datetime(). You can see a list of all formatting strings at ?strptime.

We’ll use date_labels to format time_hour so that it doesn’t take up as much space.

6.3 Short-term fluctuations

In the mechanics section of this chapter, you saw the following plot.

You might wonder why we used geom_col() to represent a time series. Here’s the same plot using geom_line() and geom_point().

From both plots, you can see that most flights occur in the early morning and around 4pm, but notice that we’re actually treating time like a discrete variable in this situation. We’ve counted the number of flights for each hour, and so it’s useful to be able to connect a number of flights with a specific hour. Columns make it easier to connect numbers of flights to specific hours.

Vertical segment plots using geom_segment() can also be helpful for some time series data. Say we want to understand what the first week in January looked like.

geom_point() and geom_line() produce the following plot.

You can see that each day is shaped similarly. However, you can’t tell that there are actually no flights for a couple hours each night.

geom_segment() does a better job of showing the gaps between days. Segments also make it easier to perceive each day as a group to compare against the others. Another advantage of geom_segment() is that we can use color to encode a categorical variable.

In this case, there’s no long-term trend we’re interested in. Instead, we want to understand short-term fluctuations, and we care about individual values. In these situations, geom_col() and geom_segment() are good options.

6.4 Individual values

Sometimes, you’ll want to display time on the x-axis like a time series, but you won’t actually care about displaying any kind of trend.

The famines dataset from dcldata tracks major famines across time.

There’s no obvious relationship between time and deaths due to famines.

Even though there’s no trend, this data is still interesting if you’re curious about individual famines.

The above plot only uses the start date, but we also have the length of the famines. We can treat the x-axis as representing year generally and encode the length of a line as the length of the famine.