Bekijk de structuur

Een andere methode die vaak wordt gebruikt om snel een overzicht van je data te krijgen, is de functie str(). De functie str() laat je de structuur van je gegevensset zien. Voor een data frame vertelt die je:

Het totale aantal observaties (bijv. 32 soorten auto's)
Het totale aantal variabelen (bijv. 11 autokenmerken)
Een volledige lijst met de variabelennamen (bijv. mpg, cyl … )
Het datatype van elke variabele (bijv. num)
De eerste observaties

De str()-functie toepassen, is vaak het eerste wat je doet wanneer je een nieuwe gegevensset of een nieuw data frame krijgt. Het is een uitstekende manier om meer inzicht te krijgen in je gegevensset voordat je met de echte analyse begint.

Deze oefening maakt deel uit van de cursus

Introductie tot R

Oefeninstructies

Onderzoek de structuur van mtcars. Controleer of je dezelfde aantallen, variabelen en datatypen ziet als hierboven genoemd.

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

# Investigate the structure of mtcars

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot R

SkillTag.level.beginnerSkillTag.label

4.8+

Begin gratis met de cursus

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: Wat is een data frame?Exercise 2: Snel even naar je gegevensset kijken Exercise 3: Bekijk de structuur

Huidige oefening

Exercise 4: Een data frame maken Exercise 5: Een data frame maken (2)Exercise 6: Selectie van data frame-elementen Exercise 7: Selectie van data frame-elementen (2)Exercise 8: Alleen planeten met ringen Exercise 9: Alleen planeten met ringen (2)Exercise 10: Alleen planeten met ringen, maar dan korter Exercise 11: Sorteren Exercise 12: Je data frame sorteren

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