Parsing veloce con fasttime

Il pacchetto fasttime fornisce un'unica funzione, fastPOSIXct(), pensata per leggere data-ore formattate secondo ISO 8601. Poiché legge un solo formato e non deve indovinare il formato, è davvero veloce!

In questo esercizio vedrai quanto è veloce confrontando la velocità con cui legge le date dai dati meteo orari di Auckland (oltre 17.000 date) rispetto a ymd_hms() di lubridate.

Per confrontare i tempi di esecuzione userai la funzione microbenchmark() dell'omonimo pacchetto. Le passerai quanti argomenti vuoi, ciascuno essendo un'espressione da cronometrare.

Questo esercizio fa parte del corso

Lavorare con date e orari in R

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Istruzioni dell'esercizio

Abbiamo caricato le data-ore dai dati orari di Auckland come stringhe nel vettore dates.

Esamina la struttura di dates per verificare che sia una stringa e nel formato ISO 8601.
Esegui il parsing di dates con fasttime e collega in pipe a str() per verificare che fastPOSIXct le interpreti correttamente.
Ora, per confrontare i tempi, chiama microbenchmark dove il primo argomento usa ymd_hms() per eseguire il parsing di dates e il secondo usa fastPOSIXct().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

library(microbenchmark)
library(fasttime)

# Examine structure of dates
str(___)

# Use fastPOSIXct() to parse dates
fastPOSIXct(___) %>% str()

# Compare speed of fastPOSIXct() to ymd_hms()
microbenchmark(
  ymd_hms = ___(dates),
  fasttime = ___(dates),
  times = 20)

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: 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: Fusi orari Exercise 2: Impostare il fuso orario Exercise 3: Visualizzare in un fuso orario Exercise 4: Fusi orari nei dati meteo Exercise 5: Orari senza date Exercise 6: Altre informazioni su importazione ed esportazione di date-ora Exercise 7: Parsing veloce con fasttime

Esercizio in corso

Exercise 8: Parsing veloce con lubridate::fast_strptime Exercise 9: Stampare date e orari in modo gradevole Exercise 10: Riepilogo