Waktu tanpa tanggal

Sepanjang kursus ini, setiap kali Anda bekerja dengan waktu, selalu disertai dengan tanggal, yaitu datetime. Namun, terkadang Anda hanya memiliki waktu tanpa tanggal.

Jika Anda menemui situasi ini, paket hms menyediakan kelas objek hms untuk menyimpan waktu tanpa tanggal, dan cara terbaik untuk memulai adalah dengan as.hms().

Sebenarnya, Anda sudah melihat objek kelas hms, tetapi saya tidak menyorotkannya. Lihat kembali pada latihan ini.

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

Petunjuk latihan

Gunakan read_csv() untuk membaca "akl_weather_hourly_2016.csv". readr mengenal kelas hms, sehingga jika menemukan sesuatu yang tampak seperti waktu, paket ini akan menggunakannya.
Dalam kasus ini kolom time telah diurai sebagai waktu tanpa tanggal. Periksa struktur kolom time untuk memastikan kelasnya adalah hms.
Objek hms dicetak seperti halnya waktu seharusnya. Lihatlah dengan memeriksa head dari kolom time.
Anda juga dapat menggunakan objek hms dalam plot. Buat plot dengan time pada sumbu x, temperature pada sumbu y, dengan garis dikelompokkan berdasarkan date.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import auckland hourly data 
akl_hourly <- read_csv(___)

# Examine structure of time column
str(___)

# Examine head of time column
head(___)

# A plot using just time
ggplot(akl_hourly, aes(x = ___, y = ___)) +
  geom_line(aes(group = make_date(year, month, mday)), alpha = 0.2)

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

Latihan Saat Ini

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