Wrap-up

1. Wrap-up

Congratulations you've made it to the end of the course! I hope you've had fun mastering dates and times in R.

2. Wrapping-up

Way back in Chapter 1 you learnt about R's objects for holding dates and times: Date and the POSIX objects. It doesn't matter which of those objects you have, all the functions you've learnt from lubridate apply to both. In fact lubridate functions work with datetime objects from a whole range of contributed packages too: including the popular time series analysis packages zoo and xts. In Chapter 2 you learnt all about importing and manipulating datetimes with lubridate. With those skills you should never meet a date or time you can't get into R. You also saw how extracting parts of a datetime let's you explore the variation in something like temperature at monthly, daily, or yearly scales. In Chapter 3, you learnt about the difficulties of doing arithmetic with datetimes that stem from complexities like daylight savings time and leap years. Then you met the three objects lubridate supplies to handle the complexity: periods, durations, and intervals. In this final chapter, you learnt how to handle time zones, import dates quickly and output datetimes.

3. Next steps

Where should you go from here? If you are interested in learning about modeling and predicting data that is recorded in time, you'd love the

4. Next steps

Time Series in R Skill track. If you are more interested in mastering the packages of the tidyverse

5. Next steps

you might enjoy taking more courses on ggplot2, dplyr, or stringr, or courses that combine multiple packages to clean and explore data.

6. See you in another course!

Alright, that's all from me. Congratulations again, and I hope to see you soon in another course.

This exercise is part of the course

Working with Dates and Times in R

IntermediateSkill Level

4.9+

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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?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

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