Impor otomatis

Terkadang Anda perlu memasukkan beberapa tanggal secara manual menggunakan as.Date(), tetapi jauh lebih umum memiliki sebuah kolom tanggal dalam file data.

Beberapa fungsi pembaca data akan otomatis mengenali dan mengurai tanggal dalam berbagai format. Khususnya, fungsi impor seperti read_csv() dalam paket readr akan mengenali tanggal dalam beberapa format umum.

Ada juga fungsi anytime() dalam paket anytime yang bertujuan khusus untuk secara otomatis mengurai string sebagai tanggal, apa pun formatnya.

Cobalah keduanya dalam latihan ini.

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di R

Petunjuk latihan

Gunakan read_csv() untuk membaca file CSV rversions.csv sebagai releases.
Gunakan str() untuk memeriksa struktur kolom date. Perhatikan bahwa kolom tersebut sudah berupa objek Date.
Kami telah memuat anytime dan membuat sebuah objek bernama sep_10_2009. Gunakan fungsi anytime() untuk mengurai sep_10_2009.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Load the readr package
library(readr)

# Use read_csv() to import rversions.csv
releases <- read_csv(___)

# Examine the structure of the date column
str(___)

# Load the anytime package
library(anytime)

# Various ways of writing Sep 10 2009
sep_10_2009 <- c("September 10 2009", "2009-09-10", "10 Sep 2009", "09-10-2009")

# Use anytime() to parse sep_10_2009
anytime(___)

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

Latihan Saat Ini

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 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