Comparing intervals and datetimes

A common task with intervals is to ask if a certain time is inside the interval or whether it overlaps with another interval.

The operator %within% tests if the datetime (or interval) on the left hand side is within the interval of the right hand side. For example, if y2001 is the interval covering the year 2001,

y2001 <- ymd("2001-01-01") %--% ymd("2001-12-31")

Then ymd("2001-03-30") %within% y2001 will return TRUE and ymd("2002-03-30") %within% y2001 will return FALSE.

int_overlaps() performs a similar test, but will return true if two intervals overlap at all.

Practice to find out which monarchs saw Halley's comet around 1066.

This exercise is part of the course

Working with Dates and Times in R

Exercise instructions

We've put halleys a data set describing appearances of Halley's comet in your workspace.

Print halleys to examine the date. perihelion_date is the date the Comet is closest to the Sun. start_date and end_date are the range of dates the comet is visible from Earth.
Create a new column, visible, that is an interval from start_date to end_date.
You'll work with one appearance, extract the 14th row of halleys.
Filter monarchs to those where halleys_1066$perihelion_date is within reign.
Filter monarchs to those where halleys_1066$visible overlaps reign.

Hands-on interactive exercise

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

# Print halleys
halleys

# New column for interval from start to end date
halleys <- halleys %>% 
  mutate(visible = ___)

# The visitation of 1066
halleys_1066 <- halleys[__, ] 

# Monarchs in power on perihelion date
monarchs %>% 
  filter(___) %>%
  select(name, from, to, dominion)

# Monarchs whose reign overlaps visible time
monarchs %>% 
  filter(___) %>%
  select(name, from, to, dominion)

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?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

Current Exercise

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