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tidyr ile Veriyi Şekillendirme

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tidyr ile Veriyi Şekillendirme

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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: Dikdörtgensel olmayan verilere giriş Exercise 2: Dikdörtgen ve dikdörtgen olmayan dosyalar Exercise 3: Star Wars filmlerini dikdörtgenleştirme Exercise 4: İç içe değerlerden gözlemlere Exercise 5: Geniş mi uzun mu açılacak?Exercise 6: Star Wars gezegenlerini dikdörtgenleştirme Exercise 7: Güneş Sistemi'nin en büyük uyduları Exercise 8: İç içe değişkenleri seçme Exercise 9: Star Wars filmlerini hoist etmek Exercise 10: Film puanlarını hoist etme Exercise 11: Modelleme için veriyi iç içe yerleştirme

Geçerli Egzersiz

Exercise 12: broom ile model çıktılarını düzenle Exercise 13: Tibble’ları iç içe yerleştirme Exercise 14: İç içe geçmiş veri çerçevelerinde modelleme Exercise 15: Tebrikler!