Why lubridate?

1. Why lubridate?

As you've seen R has some built-in support for storing dates and datetimes, but for the rest of the course you'll learn about a package called lubridate.

2. lubridate

lubridate is designed to make working with dates and times as easy as possible in R. It's a tidyverse package, which means it obeys some key principles, including playing nicely with R's existing datetime objects and being designed for humans, not computers. It also means it will fit nicely in your data analysis pipelines that use other tidyverse tools. In particular, in this course you'll combine lubridate with dplyr and ggplot2 to answer questions about data that includes a datetime variable. One nice aspect is that you don't need to worry about whether your datetimes are stored in Date objects, POSIXct objects or even time series objects like zoo or xts, the lubridate functions will have consistent behaviour. You only need to learn one function for any kind of datetime object.

3. Parsing a wide range of formats

You'll start by seeing how easy lubridate makes it to parse a character string into a datetime object. Although R has some built in parsing functions, lubridate's functions are simpler to use, more forgiving of different formats, and even allow parsing of many formats in one vector.

4. Manipulating datetimes

Then, you'll learn about lubridate functions for extracting and manipulating components of a datetime. You'll be able to pull out the month, day of the week or day of the year from a datetime. Combined with dplyr and ggplot that will allow you to make plots like this joyplot of the maximum daily temperature by month in Auckland.

5. Time spans

lubridate also has special objects for handling time spans - the time that passes between two time points. You'll learn how to use time spans to generate sequences of datetimes and calculate the length of time intervals like the lengths of reigns of the kings and queens of England.

6. Other lubridate features

In the final chapter you'll learn about lubridate functions for dealing with timezones, fast parsing of datetimes and outputting datetimes.

7. Let's practice!

Ready to get started? I'll see you in the next chapter.

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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?

Current Exercise

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