What about times?

1. What about times?

So far we've been talking just about dates, but what about times? Yes, R also has built-in objects for handling dates with times, something we'll call datetimes

2. ISO 8601

ISO 8601 also has something to say about datetimes. Just like dates, if you have an accompanying time it should be written from largest unit to smallest unit using a fixed number of digits and optionally separating the units with a colon. When combined with a date, the time is sometimes prefixed with the character T.

3. Datetimes in R

There are two built in types of objects for datetimes in R, POSIXlt and POSIXct. POSIXlt stores a date in a list with components for each unit, whereas POSIXct stores the date as the number of seconds since the beginning of 1970. Because of its simpler structure POSIXct is more amenable to being stored in data frames and the one we'll focus on exclusively. Just like Date objects, there is a function to take a string and turn into a POSIXct object. And perhaps unsurprisingly it's called as (dot) POSIXct. And, also just like dates, as (dot) POSIXct will read in ISO 8601 datetimes, but anything else will need to be parsed specially, something you'll master in Chapter 2.

4. Timezones

ISO 8601 also allows the specification of a timezone. If no time zone is specified it is assumed to be local time. The suffix on a time of Z, denotes a time in the UTC time zone. UTC is short for Coordinated Universal Time, an international standard which doesn't observe daylight savings. Other timezones are specified in ISO 8601 as offsets from this timezone. Unfortunately as (dot) POSIXct won't recognize the ISO 8601 specification of timezones, it will assume the local timezone, unless you set another one with the tz argument. You'll learn more about timezones in Chapter 4.

5. Datetimes behave nicely too

Once you've got datetimes in R, they behave nicely, just like dates. You can compare datetimes, subtract datetimes and plot datetimes.

6. Let's practice!

You'll try all these things in the following exercises.

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?Exercise 6: Plotting Exercise 7: Arithmetic and logical operators Exercise 8: What about times?

Current Exercise

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