Snel parsen met fasttime

Het fasttime-pakket biedt één functie, fastPOSIXct(), speciaal ontworpen om datetimes in te lezen die zijn opgemaakt volgens ISO 8601. Omdat het maar één formaat inleest en geen formaat hoeft te raden, is het echt snel!

In deze oefening zie je hoe snel, door de leestijd van de datums uit de uurlijkse weersgegevens van Auckland (meer dan 17.000 datums) te vergelijken met lubridate’s ymd_hms().

Om de runtimes te vergelijken gebruik je de functie microbenchmark() uit het gelijknamige pakket. Je geeft zoveel argumenten door als je wilt, waarbij elk argument een expressie is om te timen.

Deze oefening maakt deel uit van de cursus

Werken met datums en tijden in R

Cursus bekijken

Oefeninstructies

We hebben de datetimes uit de uurlijkse Auckland-data als strings geladen in de vector dates.

Bekijk de structuur van dates om te controleren dat het een string is en in ISO 8601-formaat staat.
Parse dates met fasttime en pipe naar str() om te controleren dat fastPOSIXct ze correct parseert.
Roep nu, om de timing te vergelijken, microbenchmark aan waarbij het eerste argument ymd_hms() gebruikt om dates te parsen en het tweede fastPOSIXct() gebruikt.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

library(microbenchmark)
library(fasttime)

# Examine structure of dates
str(___)

# Use fastPOSIXct() to parse dates
fastPOSIXct(___) %>% str()

# Compare speed of fastPOSIXct() to ymd_hms()
microbenchmark(
  ymd_hms = ___(dates),
  fasttime = ___(dates),
  times = 20)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Werken met datums en tijden in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Tijdzones Exercise 2: De tijdzone instellen Exercise 3: Bekijken in een tijdzone Exercise 4: Tijdzones in de weerdata Exercise 5: Tijden zonder datums Exercise 6: Meer over datetimes importeren en exporteren Exercise 7: Snel parsen met fasttime

Huidige oefening

Exercise 8: Snel parsen met lubridate::fast_strptime Exercise 9: Mooie datums en tijden weergeven Exercise 10: Afronding