What can you extract?

As you saw in the video, components of a datetime can be extracted by lubridate functions with the same name like year(), month(), day(), hour(), minute() and second(). They all work the same way just pass in a datetime or vector of datetimes.

There are also a few useful functions that return other aspects of a datetime like if it occurs in the morning am(), during daylight savings dst(), in a leap_year(), or which quarter() or semester() it occurs in.

Try them out by exploring the release times of R versions using the data from Chapter 1.

This exercise is part of the course

Working with Dates and Times in R

Exercise instructions

We've put release_time, the datetime column of the releases dataset from Chapter 1, in your workspace.

Examine the head() of release_time to verify this is a vector of datetimes.
Extract the month from release_time and examine the first few with head().
To see which months have most releases, extract the month then pipe to table().
Repeat, to see which years have the most releases.
Do releases happen in the morning (UTC)? Find out if the hour of a release is less than 12 and summarise with mean().
Alternatively use am() to find out how often releases happen in the morning.

Hands-on interactive exercise

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

# Examine the head() of release_time
head(___)

# Examine the head() of the months of release_time
head(___(release_time))

# Extract the month of releases 
___(release_time) %>% table()

# Extract the year of releases
___(release_time) %>% table()

# How often is the hour before 12 (noon)?
mean(___(release_time) < ___)

# How often is the release in am?
mean(___(release_time))

Edit and Run Code

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?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?

Current Exercise

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