Operatori aritmetici e logici

Dato che gli oggetti Date sono rappresentati internamente come il numero di giorni trascorsi dal 1970-01-01, puoi fare operazioni matematiche di base e confronti con le date. Puoi confrontare le date con i soliti operatori logici (<, ==, > ecc.), trovare estremi con min() e max(), e persino sottrarre due date per scoprire l'intervallo di tempo tra di esse.

In questo esercizio vedrai come funzionano queste operazioni esplorando l'ultima release di R. Nel codice troverai Sys.date(), che restituisce semplicemente la data di oggi.

Questo esercizio fa parte del corso

Lavorare con date e orari in R

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Istruzioni dell'esercizio

Trova la data della release più recente chiamando max() sulla colonna delle date in releases.
Trova le righe in releases che hanno la data più recente, specificando il confronto date == last_release_date in filter().
Stampa last_release per vedere quale release è.
Calcola da quanto tempo è stata pubblicata la release più recente sottraendo last_release_date da Sys.Date().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Find the largest date
last_release_date <- max(___)

# Filter row for last release
last_release <- filter(releases, ___)

# Print last_release
last_release

# How long since last release?
Sys.Date() - ___

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Questo esercizio fa parte del corso

Lavorare con date e orari in R

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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: Introduzione alle date Exercise 2: Riconoscere le date ISO 8601 Exercise 3: Specificare le date Exercise 4: Importazione automatica Exercise 5: Perché usare le date?Exercise 6: Creare grafici Exercise 7: Operatori aritmetici e logici

Esercizio in corso

Exercise 8: E per gli orari?Exercise 9: Importare datetime in R Exercise 10: Anche i datetime si comportano bene Exercise 11: Perché 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