Summarizing a factor

After finishing this course, one of your favorite functions in R will be summary(). This will give you a quick overview of the contents of a variable:

summary(my_var)

Going back to our survey, you would like to know how many "Male" responses you have in your study, and how many "Female" responses. The summary function gives you the answer to this question.

This exercise is part of the course

Introduction to R

Exercise instructions

Ask a summary() of the survey_vector and factor_survey_vector. Interpret the results of both vectors. Are they both equally useful in this case?

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Build factor_survey_vector with clean levels
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
levels(factor_survey_vector) <- c("Female", "Male")
factor_survey_vector

# Generate summary for survey_vector


# Generate summary for factor_survey_vector

Edit and Run Code

This exercise is part of the course

Introduction to R

BeginnerSkill Level

4.8+

Start Course for Free

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

Current Exercise

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