Membandingkan interval dan datetime

Tugas umum dengan interval adalah menanyakan apakah suatu waktu berada di dalam interval atau apakah interval tersebut tumpang tindih dengan interval lain.

Operator %within% menguji apakah datetime (atau interval) di sisi kiri berada dalam interval di sisi kanan. Misalnya, jika y2001 adalah interval yang mencakup tahun 2001,

y2001 <- ymd("2001-01-01") %--% ymd("2001-12-31")

Maka ymd("2001-03-30") %within% y2001 akan mengembalikan TRUE dan ymd("2002-03-30") %within% y2001 akan mengembalikan FALSE.

int_overlaps() melakukan pengujian serupa, tetapi akan mengembalikan true jika dua interval saling tumpang tindih sama sekali.

Berlatihlah untuk mencari tahu raja mana yang melihat komet Halley sekitar tahun 1066.

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

Petunjuk latihan

Kami telah menempatkan halleys, sebuah himpunan data yang menggambarkan kemunculan komet Halley, di ruang kerja Anda.

Cetak halleys untuk meninjau tanggal. perihelion_date adalah tanggal ketika komet paling dekat dengan Matahari. start_date dan end_date adalah rentang tanggal saat komet terlihat dari Bumi.
Buat kolom baru, visible, yang merupakan interval dari start_date hingga end_date.
Anda akan bekerja dengan satu kemunculan; ekstrak baris ke-14 dari halleys.
Saring monarchs untuk yang halleys_1066$perihelion_date berada di dalam reign.
Saring monarchs untuk yang halleys_1066$visible bertumpang tindih dengan reign.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Print halleys
halleys

# New column for interval from start to end date
halleys <- halleys %>% 
  mutate(visible = ___)

# The visitation of 1066
halleys_1066 <- halleys[__, ] 

# Monarchs in power on perihelion date
monarchs %>% 
  filter(___) %>%
  select(name, from, to, dominion)

# Monarchs whose reign overlaps visible time
monarchs %>% 
  filter(___) %>%
  select(name, from, to, dominion)

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: Mengambil selisih dari datetime Exercise 2: Sudah berapa lama?Exercise 3: Ada berapa detik dalam satu hari?Exercise 4: Rentang waktu.Exercise 5: Menambah atau mengurangkan rentang waktu pada datetime Exercise 6: Duration atau Period?Exercise 7: Aritmetika dengan rentang waktu Exercise 8: Membuat deret datetime Exercise 9: Hal yang rumit tentang bulan Exercise 10: Interval Exercise 11: Menguji interval. Masa kekuasaan raja dan ratu Exercise 12: Membandingkan interval dan datetime

Latihan Saat Ini

Exercise 13: Mengonversi ke duration dan period

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