Total pendapatan box office untuk seluruh saga

Seperti halnya cbind() memiliki rbind(), colSums() memiliki rowSums(). Ruang kerja R Anda sudah berisi all_wars_matrix yang Anda buat pada latihan sebelumnya, ketik all_wars_matrix untuk melihatnya kembali. Sekarang, mari hitung total pendapatan box office untuk seluruh saga.

Latihan ini merupakan bagian dari kursus

Pengantar R

Instruksi latihan

Hitung total pendapatan untuk wilayah AS dan non-AS dan simpan sebagai total_revenue_vector. Anda dapat menggunakan fungsi colSums().
Cetak total_revenue_vector untuk melihat hasilnya.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# all_wars_matrix is available in your workspace
all_wars_matrix

# Total revenue for US and non-US
total_revenue_vector <- 
  
# Print out total_revenue_vector

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Pengantar R

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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: Apa itu matriks?Exercise 2: Analisis matriks, Anda harus Exercise 3: Memberi nama pada matriks Exercise 4: Menghitung box office seluruh dunia Exercise 5: Menambahkan kolom untuk pendapatan box office dari Seluruh Dunia Exercise 6: Menambahkan sebuah baris Exercise 7: Total pendapatan box office untuk seluruh saga

Latihan Saat Ini

Exercise 8: Seleksi elemen matriks Exercise 9: Sedikit aritmatika dengan matriks Exercise 10: Sedikit aritmatika dengan matriks (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: What's a data frame?Exercise 2: Quick, have a look at your dataset Exercise 3: Have a look at the structure Exercise 4: Creating a data frame Exercise 5: Creating a data frame (2)Exercise 6: Selection of data frame elements Exercise 7: Selection of data frame elements (2)Exercise 8: Only planets with rings Exercise 9: Only planets with rings (2)Exercise 10: Only planets with rings but shorter Exercise 11: Sorting Exercise 12: Sorting your data frame

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