Guida e documentazione delle funzioni

Quando non sai come usare una funzione o non sai quali argomenti richiede, a chi ti rivolgi? Per fortuna, R ha una documentazione integrata. Ad esempio, per ottenere aiuto sulla funzione names(), puoi digitare una delle seguenti opzioni:

?names

?names()

help(names)

Fanno tutte la stessa cosa: ti portano direttamente alla pagina di aiuto di names()!

Nella console di DataCamp, verrai reindirizzato al sito RDocumentation per consultare l’aiuto da lì, ma le informazioni sono le stesse!

Qui sotto esplorerai la documentazione di alcune altre funzioni.

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Usa ? per consultare la documentazione di subset().
Usa ? per consultare la documentazione di Sys.time().

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# Look at the documentation for Sys.time
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Welcome! Before we go deeper into the world of R, it will be nice to have an understanding of how dates and times are created. This chapter will teach you enough to begin working with dates, but only scratches the surface of what you can do with them.

Exercise 1: An introduction to dates in R Exercise 2: What day is it?Exercise 3: From char to date Exercise 4: Many dates Exercise 5: Date formats and extractor functions Exercise 6: Date formats (1)Exercise 7: Date formats (2)Exercise 8: Subtraction of dates Exercise 9: months() and weekdays() and quarters(), oh my!

Imagine you own stock in a company. If the stock goes above a certain price, you might want to sell. If the stock drops below a certain price, you might want to buy it while it's cheap! This kind of thinking can be implemented using operators and if statements. In this chapter, you will learn all about them, and create a program that tells you to buy or sell a stock.

Exercise 1: Relational operators Exercise 2: Relational practice Exercise 3: Vectorized operations Exercise 4: Logical operators Exercise 5: And / Or Exercise 6: Not!Exercise 7: Logicals and subset()Exercise 8: All together now!Exercise 9: If statements Exercise 10: If this Exercise 11: If this, Else that Exercise 12: If this, Else If that, Else that other thing Exercise 13: Can you If inside an If?Exercise 14: ifelse()

Loops can be useful for doing the same operation to each element of your data structure. In this chapter you will learn all about repeat, while, and for loops!

Exercise 1: Repeat loops Exercise 2: Repeat, repeat, repeat Exercise 3: When to break?Exercise 4: While loops Exercise 5: While with a print Exercise 6: While with a plot Exercise 7: Break it Exercise 8: For loops Exercise 9: Loop over a vector Exercise 10: Loop over data frame rows Exercise 11: Loop over matrix elements Exercise 12: Break and next

If data structures like data frames and vectors are how you hold your data, functions are how you tell R what to do with your data. In this chapter, you will learn about using built-in functions, creating your own unique functions, and you will finish off with a brief introduction to packages.

Exercise 1: Che cosa sono le funzioni?Exercise 2: Guida e documentazione delle funzioni

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Exercise 3: Argomenti opzionali Exercise 4: Funzioni dentro funzioni Exercise 5: Scrivere funzioni Exercise 6: La tua prima funzione Exercise 7: Argomenti multipli (1)Exercise 8: Argomenti multipli (2)Exercise 9: Ambito delle funzioni (1)Exercise 10: Ambito delle funzioni (2)Exercise 11: Pacchetti Exercise 12: pacchetto tidyquant

A popular alternative to loops in R are the apply functions. These are often more readable than loops, and are incredibly useful for scaling the data science workflow to perform a complicated calculation on any number of observations. Learn about them here!

Exercise 1: Why use apply?Exercise 2: lapply() on a list Exercise 3: lapply() on a data frame Exercise 4: FUN arguments Exercise 5: sapply() - simplify it!Exercise 6: sapply() vs. lapply()Exercise 7: Failing to simplify Exercise 8: vapply() - specify your output!Exercise 9: vapply() vs. sapply()Exercise 10: More vapply()Exercise 11: Anonymous functions Exercise 12: Congratulations