Zona waktu dalam data cuaca

Pernahkah Anda menyadari bahwa pada data cuaca Auckland per jam ada kolom datetime lain, date_utc? Coba perhatikan:

tibble::glimpse(akl_hourly)

Kolom datetime yang Anda buat merepresentasikan waktu lokal di Auckland, NZ. Saya menduga kolom tambahan date_utc merepresentasikan waktu pengamatan dalam UTC (namanya cukup memberi petunjuk). Namun benarkah demikian?

Gunakan keterampilan zona waktu yang baru Anda pelajari untuk mencari tahu.

Latihan ini merupakan bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

Instruksi latihan

Data tersedia dalam data frame akl_hourly.

Zona waktu apa yang saat ini digunakan oleh datetime dan date_utc? Periksa bagian awal (head) dari kolom datetime dan date_utc untuk mengetahuinya.
Perbaiki datetime agar memiliki zona waktu "Pacific/Auckland".
Periksa kembali bagian awal kolom datetime untuk memastikan waktunya tetap sama pada jamnya (clocktime), tetapi kini berada di zona waktu yang benar.
Sekarang buat tabulasi selisih antara kolom datetime dan date_utc. Seharusnya nol jika hipotesis kita benar.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Examine datetime and date_utc columns
head(___)
head(___)
  
# Force datetime to Pacific/Auckland
akl_hourly <- akl_hourly %>%
  mutate(
    datetime = ___(datetime, tzone = ___))

# Reexamine datetime
head(___)
  
# Are datetime and date_utc the same moments
table(___ - ___)

Edit dan Jalankan Kode

Latihan ini merupakan 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

Latihan Saat Ini

Exercise 5: Waktu tanpa tanggal Exercise 6: Lebih lanjut tentang mengimpor dan mengekspor tanggal-waktu Exercise 7: Parsing cepat dengan fasttime Exercise 8: Parsing cepat dengan lubridate::fast_strptime Exercise 9: Menghasilkan keluaran tanggal dan waktu yang rapi Exercise 10: Ringkasan