Menentukan urutan dengan `parse_date_time()`

Bagaimana jika Anda memiliki sesuatu dengan urutan yang sangat aneh seperti dym_msh? Tidak ada fungsi bernama khusus untuk urutan itu, tetapi di situlah parse_date_time() berguna. parse_date_time() menerima argumen tambahan, orders, tempat Anda dapat menentukan urutan komponen dalam tanggal.

Sebagai contoh, untuk mengurai "2010 September 20th" Anda dapat menggunakan parse_date_time("2010 September 20th", orders = "ymd") dan itu setara dengan menggunakan fungsi ymd() dari latihan sebelumnya.

Salah satu keunggulan parse_date_time() adalah Anda dapat menggunakan lebih banyak karakter format. Misalnya, Anda dapat menentukan nama hari dengan A, I untuk waktu 12 jam, indikator am/pm dengan p, dan banyak lagi. Anda dapat melihat daftar lengkapnya pada halaman bantuan ?parse_date_time.

Keunggulan besar lainnya adalah Anda dapat menentukan sebuah vektor orders, yang memungkinkan penguraian tanggal ketika beberapa format mungkin digunakan.

Anda akan mencobanya dalam latihan ini.

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

Petunjuk latihan

x adalah datetime yang lebih rumit. Gunakan petunjuk dalam instruksi untuk mengurai x.
two_orders memiliki dua urutan berbeda; uraikan keduanya dengan menetapkan order menjadi c("mdy", "dmy").
Uraikan short_dates dengan orders = c("dOmY", "OmY", "Y"). Apa yang terjadi pada tanggal yang tidak memiliki bulan atau hari?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Specify an order string to parse x
x <- "Monday June 1st 2010 at 4pm"
___(x, orders = "___")

# Specify order to include both "mdy" and "dmy"
two_orders <- c("October 7, 2001", "October 13, 2002", "April 13, 2003", 
  "17 April 2005", "23 April 2017")
parse_date_time(two_orders, orders = ___)

# Specify order to include "dOmY", "OmY" and "Y"
short_dates <- c("11 December 1282", "May 1372", "1253")
parse_date_time(short_dates, orders = ___)

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: Mengurai tanggal dengan lubridate Exercise 2: Memilih fungsi parsing yang tepat Exercise 3: Menentukan urutan dengan `parse_date_time()`

Latihan Saat Ini

Exercise 4: Cuaca di Auckland Exercise 5: Impor data cuaca harian Exercise 6: Impor data cuaca per jam Exercise 7: Mengekstrak bagian dari sebuah datetime Exercise 8: Apa yang dapat Anda ekstrak?Exercise 9: Menambahkan label yang berguna Exercise 10: Ekstraksi untuk pembuatan plot Exercise 11: Mengekstrak untuk pemfilteran dan peringkasan Exercise 12: Membulatkan datetime Exercise 13: Latihan pembulatan Exercise 14: Pembulatan dengan data cuaca

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