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Questo esercizio fa parte del corso

Rimodellare i dati con tidyr

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You'll be introduced to the concept of tidy data which is central to this course. In the first two lessons, you'll jump straight into the action by separating messy character columns into tidy variables and observations ready for analysis. In the final lesson, you'll learn how to overwrite and remove missing values.

Exercise 1: What is tidy data?Exercise 2: Tidy data structure Exercise 3: Multiple variables per column Exercise 4: Columns with multiple values Exercise 5: International phone numbers Exercise 6: Extracting observations from values Exercise 7: Separating into columns and rows Exercise 8: Missing values Exercise 9: And the Oscar for best director goes to ... <NA>Exercise 10: Imputing sales data Exercise 11: Nuclear bombs per continent

This chapter is all about pivoting data from a wide to long format and back again using the pivot_longer() and pivot_wider() functions. You'll need these functions when variables are hidden in messy column names or when variables are stored in rows instead of columns. You'll learn about space dogs, nuclear bombs, and planet temperatures along the way.

Exercise 1: From wide to long data Exercise 2: Nuclear bombs per country Exercise 3: WHO obesity per country Exercise 4: Bond... James Bond Exercise 5: Deriving variables from column headers Exercise 6: New-Zealand's bird of the year Exercise 7: Big tech stock prices Exercise 8: Deriving variables from complex column headers Exercise 9: Soviet space dogs, the dog perspective Exercise 10: WHO obesity vs. life expectancy Exercise 11: Uncounting observations Exercise 12: From long to wide data Exercise 13: Soviet space dogs, the flight perspective Exercise 14: Planet temperature & distance to the Sun Exercise 15: Transposing planet data

Values can often be missing in your data, and sometimes entire observations are absent too. In this chapter, you'll learn how to complete your dataset with these missing observations. You'll add observations with zero values to counted data, expand time series to a full sequence of intervals, and more!

Exercise 1: Creating unique combinations of vectors Exercise 2: Letters of the genetic code Exercise 3: When did humans replace dogs in space?Exercise 4: Finding missing observations Exercise 5: Completing data with all value combinations Exercise 6: Completing the Solar System Exercise 7: Zero Olympic medals Exercise 8: Creating a sequence with full_seq()Exercise 9: The Cold War's hottest year Exercise 10: Advanced completions Exercise 11: Olympic medals per continent Exercise 12: Tracking a virus outbreak Exercise 13: Counting office occupants

In the final chapter, you'll learn how to turn nested data structures such as JSON and XML files into tidy, rectangular data. This skill will enable you to process data from web APIs. You'll also learn how nested data structures can be used to write elegant modeling pipelines that produce tidy outputs.

Exercise 1: Introduzione ai dati non rettangolari Exercise 2: File rettangolari vs. non rettangolari Exercise 3: Rendere rettangolari i film di Star Wars Exercise 4: Dai valori annidati alle osservazioni

Esercizio in corso

Exercise 5: Unnesting wide o long Exercise 6: Rettangolare i pianeti di Star Wars Exercise 7: Le lune più grandi del Sistema Solare Exercise 8: Selezionare variabili annidate Exercise 9: Promuovere i film di Star Wars Exercise 10: Estrarre le valutazioni dei film con hoist Exercise 11: Raggruppare i dati per il modeling Exercise 12: Rendi ordinati i risultati dei modelli con broom Exercise 13: Raggruppare tibbles annidati Exercise 14: Modellazione su data frame annidati Exercise 15: Congratulazioni!