Confrontare intervalli e datetime

Un'operazione comune con gli intervalli è verificare se un certo momento cade dentro l'intervallo oppure se due intervalli si sovrappongono.

L'operatore %within% verifica se il datetime (o l'intervallo) sul lato sinistro è contenuto nell'intervallo sul lato destro. Per esempio, se y2001 è l'intervallo che copre l'anno 2001,

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

Allora ymd("2001-03-30") %within% y2001 restituirà TRUE e ymd("2002-03-30") %within% y2001 restituirà FALSE.

int_overlaps() esegue un controllo simile, ma restituisce vero se due intervalli si sovrappongono anche solo in parte.

Esercitati per scoprire quali monarchi hanno visto la cometa di Halley intorno al 1066.

Questo esercizio fa parte del corso

Lavorare con date e orari in R

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Istruzioni dell'esercizio

Abbiamo caricato halleys, un insieme di dati che descrive le apparizioni della cometa di Halley, nel tuo workspace.

Stampa halleys per esaminare le date. perihelion_date è la data in cui la cometa è più vicina al Sole. start_date ed end_date sono l'intervallo di date in cui la cometa è visibile dalla Terra.
Crea una nuova colonna, visible, che sia un intervallo da start_date a end_date.
Lavorerai con una singola apparizione: estrai la 14ª riga di halleys.
Filtra monarchs per quelli in cui halleys_1066$perihelion_date cade all'interno di reign.
Filtra monarchs per quelli in cui halleys_1066$visible si sovrappone a reign.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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)

Modifica ed esegui il codice

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: 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: Calcolare le differenze tra datetime Exercise 2: Da quanto tempo?Exercise 3: Quanti secondi ci sono in un giorno?Exercise 4: Intervalli di tempo.Exercise 5: Aggiungere o sottrarre un intervallo di tempo a una data e ora Exercise 6: Duration o Period?Exercise 7: Aritmetica con gli intervalli di tempo Exercise 8: Generare sequenze di date e orari Exercise 9: Il difficile dei mesi Exercise 10: Intervalli Exercise 11: Esaminare gli intervalli. Regni di re e regine Exercise 12: Confrontare intervalli e datetime

Esercizio in corso

Exercise 13: Conversione in duration e 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