Veri kümenize hızlıca bakma

Vay be, ne kadar çok araba var!

Büyük veri kümeleriyle çalışmak veri analizinde alışılmadık bir durum değildir. (Aşırı) büyük veri kümeleri ve veri çerçeveleri ile çalışırken veri analisti olarak ilk göreviniz, yapısını ve ana elemanlarını net bir şekilde anlamaktır. Bu nedenle, tüm veri kümesinin yalnızca küçük bir bölümünü göstermek genellikle yararlıdır.

Peki R’de bu nasıl yapılır? head() fonksiyonu, bir veri çerçevesinin ilk gözlemlerini göstermenizi sağlar. Benzer şekilde tail() fonksiyonu ise veri kümenizdeki son gözlemleri yazdırır.

Hem head() hem de tail(), veri kümenizdeki farklı değişkenlerin adlarını içeren “başlık” adlı bir üst satır yazdırır.

Bu egzersiz, kursun bir parçasıdır

R’ye Giriş

Kursa Göz Atın

Egzersiz talimatları

Başlığa ve ilk gözlemlere bakmak için mtcars veri kümesinde head() fonksiyonunu çağırın.

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# Call head() on mtcars

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

R’ye Giriş

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başla

Take your first steps with R. In this chapter, you will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let's get started.

Exercise 1: How it works Exercise 2: Arithmetic with R Exercise 3: Variable assignment Exercise 4: Variable assignment (2)Exercise 5: Variable assignment (3)Exercise 6: Apples and oranges Exercise 7: Basic data types in R Exercise 8: What's that data type?

We take you on a trip to Vegas, where you will learn how to analyze your gambling results using vectors in R. After completing this chapter, you will be able to create vectors in R, name them, select elements from them, and compare different vectors.

Exercise 1: Create a vector Exercise 2: Create a vector (2)Exercise 3: Create a vector (3)Exercise 4: Naming a vector Exercise 5: Naming a vector (2)Exercise 6: Calculating total winnings Exercise 7: Calculating total winnings (2)Exercise 8: Calculating total winnings (3)Exercise 9: Comparing total winnings Exercise 10: Vector selection: the good times Exercise 11: Vector selection: the good times (2)Exercise 12: Vector selection: the good times (3)Exercise 13: Vector selection: the good times (4)Exercise 14: Selection by comparison - Step 1 Exercise 15: Selection by comparison - Step 2 Exercise 16: Advanced selection

In this chapter, you will learn how to work with matrices in R. By the end of the chapter, you will be able to create matrices and understand how to do basic computations with them. You will analyze the box office numbers of the Star Wars movies and learn how to use matrices in R. May the force be with you!

Exercise 1: What's a matrix?Exercise 2: Analyze matrices, you shall Exercise 3: Naming a matrix Exercise 4: Calculating the worldwide box office Exercise 5: Adding a column for the Worldwide box office Exercise 6: Adding a row Exercise 7: The total box office revenue for the entire saga Exercise 8: Selection of matrix elements Exercise 9: A little arithmetic with matrices Exercise 10: A little arithmetic with matrices (2)

Data often falls into a limited number of categories. For example, human hair color can be categorized as black, brown, blond, red, grey, or white—and perhaps a few more options for people who color their hair. In R, categorical data is stored in factors. Factors are very important in data analysis, so start learning how to create, subset, and compare them now.

Exercise 1: What's a factor and why would you use it?Exercise 2: What's a factor and why would you use it? (2)Exercise 3: What's a factor and why would you use it? (3)Exercise 4: Factor levels Exercise 5: Summarizing a factor Exercise 6: Battle of the sexes Exercise 7: Ordered factors Exercise 8: Ordered factors (2)Exercise 9: Comparing ordered factors

Most datasets you will be working with will be stored as data frames. By the end of this chapter, you will be able to create a data frame, select interesting parts of a data frame, and order a data frame according to certain variables.

Exercise 1: Veri çerçevesi nedir?Exercise 2: Veri kümenize hızlıca bakma

Geçerli egzersiz

Exercise 3: Yapıya göz atma Exercise 4: Veri çerçevesi oluşturma Exercise 5: Veri çerçevesi oluşturma (2)Exercise 6: Veri çerçevesi elemanlarını seçme Exercise 7: Veri çerçevesi elemanlarını seçme (2)Exercise 8: Sadece halkalı gezegenler Exercise 9: Sadece halkalı gezegenler (2)Exercise 10: Sadece halkalı gezegenler ama kestirme yol Exercise 11: Sıralama Exercise 12: Veri çerçevenizi sıralama

As opposed to vectors, lists can hold components of different types, just as your to-do lists can contain different categories of tasks. This chapter will teach you how to create, name, and subset these lists.

Exercise 1: Lists, why would you need them?Exercise 2: Lists, why would you need them? (2)Exercise 3: Creating a list Exercise 4: Creating a named list Exercise 5: Creating a named list (2)Exercise 6: Selecting elements from a list Exercise 7: Creating a new list for another movie