Esercitati con l'arrotondamento

Come hai visto nel video, round_date() arrotonda una data al valore più vicino, floor_date() arrotonda per difetto e ceiling_date() arrotonda per eccesso.

Tutte e tre accettano un argomento unit che specifica la risoluzione dell’arrotondamento. Puoi indicare "second", "minute", "hour", "day", "week", "month", "bimonth", "quarter", "halfyear" o "year". In alternativa, puoi indicare un multiplo di queste unità, ad es. "5 years", "3 minutes" ecc.

Provale con la data e ora di rilascio di R 3.4.1.

Questo esercizio fa parte del corso

Lavorare con date e orari in R

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Istruzioni dell'esercizio

Scegli la funzione e le unità giuste per arrotondare r_3_4_1 per difetto al giorno più vicino.
Scegli la funzione e le unità giuste per arrotondare r_3_4_1 al multiplo di 5 minuti più vicino.
Scegli la funzione e le unità giuste per arrotondare r_3_4_1 per eccesso alla settimana più vicina.
Trova il tempo trascorso nel giorno del rilascio all’ora del rilascio sottraendo da r_3_4_1 il valore di r_3_4_1 arrotondato per difetto al giorno.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

r_3_4_1 <- ymd_hms("2016-05-03 07:13:28 UTC")

# Round down to day
___(r_3_4_1, unit = ___)

# Round to nearest 5 minutes
___(r_3_4_1, unit = ___)

# Round up to week 
___(r_3_4_1, unit = ___)

# Subtract r_3_4_1 rounded down to day
r_3_4_1 - ___(r_3_4_1, unit = ___)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Lavorare con date e orari in R

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4.8+

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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: 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: Analizzare le date con lubridate Exercise 2: Scegliere la funzione di parsing corretta Exercise 3: Specificare un ordine con `parse_date_time()`Exercise 4: Meteo ad Auckland Exercise 5: Importa i dati meteo giornalieri Exercise 6: Importa i dati meteo orari Exercise 7: Estrarre parti di una data-ora Exercise 8: Cosa puoi estrarre?Exercise 9: Aggiungere etichette utili Exercise 10: Estrazione per il plotting Exercise 11: Estrazione per filtrare e riassumere Exercise 12: Arrotondare i datetime Exercise 13: Esercitati con l'arrotondamento

Esercizio in corso

Exercise 14: Arrotondare con i dati meteo

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