Datetime juga berperilaku dengan baik

Sama seperti objek Date, Anda dapat membuat plot dan melakukan operasi aritmetika dengan objek POSIXct.

Sebagai contoh, dalam latihan ini Anda akan melihat seberapa cepat orang mengunduh versi R yang baru, dengan menelaah log unduhan dari cermin CRAN milik RStudio.

R 3.2.0 dirilis pada "2015-04-16 07:13:33" sehingga cran-logs_2015-04-17.csv berisi sampel acak unduhan pada tanggal 16, 17, dan 18.

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

Petunjuk latihan

Gunakan read_csv() untuk mengimpor cran-logs_2015-04-17.csv.
Cetak logs untuk melihat informasi yang kita miliki tentang setiap unduhan.
Simpan waktu rilis R 3.2.0 sebagai objek POSIXct.
Cari kapan permintaan pertama untuk 3.2.0 dilakukan dengan memfilter nilai pada kolom datetime yang lebih besar daripada release_time.
Terakhir, lihat bagaimana unduhan meningkat dengan membuat histogram waktu unduhan untuk 3.2.0 dan versi sebelumnya 3.1.3. Kami telah menyediakan sebagian besar kodenya; Anda hanya perlu menentukan estetika x sebagai kolom datetime.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import "cran-logs_2015-04-17.csv" with read_csv()
logs <- read_csv(___)

# Print logs
___

# Store the release time as a POSIXct object
release_time <- ___("2015-04-16 07:13:33", tz = "UTC")

# When is the first download of 3.2.0?
logs %>% 
  filter(___,
    r_version == "3.2.0")

# Examine histograms of downloads by version
ggplot(logs, aes(x = ___)) +
  geom_histogram() +
  geom_vline(aes(xintercept = as.numeric(release_time)))+
  facet_wrap(~ r_version, ncol = 1)

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: Pengantar tanggal Exercise 2: Mengenali tanggal ISO 8601 Exercise 3: Menentukan tanggal Exercise 4: Impor otomatis Exercise 5: Mengapa menggunakan tanggal?Exercise 6: Memplot Exercise 7: Operator aritmetika dan logika Exercise 8: Bagaimana dengan waktu?Exercise 9: Memasukkan datetime ke dalam R Exercise 10: Datetime juga berperilaku dengan baik

Latihan Saat Ini

Exercise 11: Mengapa 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: 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