Why use dates?

1. Why use dates?

As you saw in the exercises it's just a little bit of extra work to get dates recognized as dates by R, but that extra work really pays off. Once an object is a date, you can plot and do math with it and get the results you'd expect.

2. Dates act like numbers

Behind the scenes Date objects are stored as the number of days since Jan 1st 1970. That means standard mathematical comparisons work as you'd expect. You can ask if one date is greater than another, which will be true if it occurs after the other. You can add days to a date. And you can find the difference between dates.

3. Plotting with dates

It also means plotting libraries can be built to plot dates. The base R plot function will happily accept dates,

4. Plotting with dates

as will ggplot2. You'll see in the exercises if you need to set axis properties, you'll need to do it with date objects. Of course all this will be more interesting when your dates are part of a larger data set.

5. R releases

Let's take a closer look at the data you imported in the previous exercises. releases is a data set that describes the release dates of all versions of R. The first version 0 (point) 60 was released on the 4th of December 1997. R version numbers take the form: major release number, dot, minor release number, dot, patch number. Those numbers are stored in the major, minor and patch columns. When a release is a patch only the patch number increases, if it's a minor release the minor number increases and the patch number resets to zero, if it's a major release the major number increases and both the minor and patch numbers reset to zero. You can see the type of release in the type column.

6. Let's practice!

In the exercises you'll plot the release dates to explore when releases have occurred and subtract dates to see how long it's been since the last release.

This exercise is part of the course

Working with Dates and Times in R

IntermediateSkill Level

5.0+

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R doesn't know something is a date or time unless you tell it. In this chapter you'll learn about some of the ways R stores dates and times by exploring how often R versions are released, and how quickly people download them. You'll also get a sneak peek at what you'll learn in the following chapters.

Exercise 1: Introduction to dates Exercise 2: Recognizing ISO 8601 dates Exercise 3: Specifying dates Exercise 4: Automatic import Exercise 5: Why use dates?

Current Exercise

Exercise 6: Plotting Exercise 7: Arithmetic and logical operators Exercise 8: What about times?Exercise 9: Getting datetimes into R Exercise 10: Datetimes behave nicely too Exercise 11: Why lubridate?

Dates and times come in a huge assortment of formats, so your first hurdle is often to parse the format you have into an R datetime. This chapter teaches you to import dates and times with the lubridate package. You'll also learn how to extract parts of a datetime. You'll practice by exploring the weather in R's birthplace, Auckland NZ.

Exercise 1: Parsing dates with lubridate Exercise 2: Selecting the right parsing function Exercise 3: Specifying an order with `parse_date_time()`Exercise 4: Weather in Auckland Exercise 5: Import daily weather data Exercise 6: Import hourly weather data Exercise 7: Extracting parts of a datetime Exercise 8: What can you extract?Exercise 9: Adding useful labels Exercise 10: Extracting for plotting Exercise 11: Extracting for filtering and summarizing Exercise 12: Rounding datetimes Exercise 13: Practice rounding Exercise 14: Rounding with the weather data

Getting datetimes into R is just the first step. Now that you know how to parse datetimes, you need to learn how to do calculations with them. In this chapter, you'll learn the different ways of representing spans of time with lubridate and how to leverage them to do arithmetic on datetimes. By the end of the chapter, you'll have calculated how long it's been since the first man stepped on the moon, generated sequences of dates to help schedule reminders, calculated when an eclipse occurs, and explored the reigns of monarch's of England (and which ones might have seen Halley's comet!).

Exercise 1: Taking differences of datetimes Exercise 2: How long has it been?Exercise 3: How many seconds are in a day?Exercise 4: Time spans.Exercise 5: Adding or subtracting a time span to a datetime Exercise 6: Duration or Period?Exercise 7: Arithmetic with timespans Exercise 8: Generating sequences of datetimes Exercise 9: The tricky thing about months Exercise 10: Intervals Exercise 11: Examining intervals. Reigns of kings and queens Exercise 12: Comparing intervals and datetimes Exercise 13: Converting to durations and periods

You now know most of what you need to tackle data that includes dates and times, but there are a few other problems you might encounter in practice. In this final chapter you'll learn a little more about these problems by returning to some of the earlier data examples and learning how to handle time zones, deal with times when you don't care about dates, parse dates quickly, and output dates and times.

Exercise 1: Time zones Exercise 2: Setting the timezone Exercise 3: Viewing in a timezone Exercise 4: Timezones in the weather data Exercise 5: Times without dates Exercise 6: More on importing and exporting datetimes Exercise 7: Fast parsing with fasttime Exercise 8: Fast parsing with lubridate::fast_strptime Exercise 9: Outputting pretty dates and times Exercise 10: Wrap-up