Extracting for filtering and summarizing

Another reason to extract components is to help with filtering observations or creating summaries. For example, if you are only interested in observations made on weekdays (i.e. not on weekends) you could extract the weekdays then filter out weekends, e.g. wday(date) %in% 2:6.

In the last exercise you saw that January, February and March were great times to visit Auckland for warm temperatures, but will you need a raincoat?

In this exercise you'll find out! You'll use the hourly data to calculate how many days in each month there was any rain during the day.

This exercise is part of the course

Working with Dates and Times in R

Exercise instructions

Create new columns for the hour and month of the observation from datetime. Make sure you label the month.
Filter to just daytime observations, where the hour is greater than or equal to 8 and less than or equal to 22.
Group the observations first by month, then by date, and summarise by using any() on the rainy column. This results in one value per day
Summarise again by summing any_rain. This results in one value per month

Hands-on interactive exercise

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

# Create new columns hour, month and rainy
akl_hourly <- akl_hourly %>%
  mutate(
    ___ = ___(datetime),
    ___ = ___(datetime, ___),
    rainy = weather == "Precipitation"
  )

# Filter for hours between 8am and 10pm (inclusive)
akl_day <- akl_hourly %>% 
  filter(___, ___)

# Summarise for each date if there is any rain
rainy_days <- akl_day %>% 
  group_by(___, ___) %>%
  summarise(
    any_rain = ___(rainy)
  )

# Summarise for each month, the number of days with rain
rainy_days %>% 
  summarise(
    days_rainy = ___(any_rain)
  )

Edit and Run Code

This exercise is part of the course

Working with Dates and Times in R

IntermediateSkill Level

4.9+

Start Course for Free

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

Current Exercise

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