Parsing cepat dengan fasttime

Paket fasttime menyediakan satu fungsi fastPOSIXct(), yang dirancang untuk membaca datetime yang diformat sesuai ISO 8601. Karena hanya membaca satu format dan tidak perlu menebak format, fungsinya sangat cepat!

Anda akan melihat seberapa cepat dalam latihan ini dengan membandingkan kecepatan membaca tanggal dari data cuaca per jam Auckland (lebih dari 17.000 tanggal) dengan ymd_hms() dari lubridate.

Untuk membandingkan waktu eksekusi, Anda akan menggunakan fungsi microbenchmark() dari paket dengan nama yang sama. Anda dapat meneruskan sebanyak mungkin argumen, masing-masing berupa ekspresi yang akan diukur waktunya.

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

Petunjuk latihan

Kami telah memuat datetime dari data per jam Auckland sebagai string ke dalam vektor dates.

Periksa struktur dates untuk memastikan bahwa isinya berupa string dan dalam format ISO 8601.
Parse dates dengan fasttime dan teruskan ke str() untuk memastikan fastPOSIXct mem-parse dengan benar.
Sekarang untuk membandingkan waktu, panggil microbenchmark di mana argumen pertama menggunakan ymd_hms() untuk mem-parse dates dan argumen kedua menggunakan fastPOSIXct().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Zona waktu Exercise 2: Mengatur zona waktu Exercise 3: Melihat dalam suatu zona waktu Exercise 4: Zona waktu dalam data cuaca Exercise 5: Waktu tanpa tanggal Exercise 6: Lebih lanjut tentang mengimpor dan mengekspor tanggal-waktu Exercise 7: Parsing cepat dengan fasttime

Latihan Saat Ini

Exercise 8: Parsing cepat dengan lubridate::fast_strptime Exercise 9: Menghasilkan keluaran tanggal dan waktu yang rapi Exercise 10: Ringkasan