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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 is a part of the course

“Introduction to R”

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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

This exercise is part of the course

Introduction to R

BeginnerSkill Level
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Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

Chapter 1: Intro to basics

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 worksExercise 2: Arithmetic with RExercise 3: Variable assignmentExercise 4: Variable assignment (2)Exercise 5: Variable assignment (3)Exercise 6: Apples and orangesExercise 7: Basic data types in RExercise 8: What's that data type?

Chapter 2: Vectors

Chapter 3: Matrices

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 shallExercise 3: Naming a matrixExercise 4: Calculating the worldwide box officeExercise 5: Adding a column for the Worldwide box officeExercise 6: Adding a rowExercise 7: The total box office revenue for the entire sagaExercise 8: Selection of matrix elementsExercise 9: A little arithmetic with matricesExercise 10: A little arithmetic with matrices (2)

Chapter 4: Factors

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 levelsExercise 5: Summarizing a factor
Exercise 6: Battle of the sexesExercise 7: Ordered factorsExercise 8: Ordered factors (2)Exercise 9: Comparing ordered factors

Chapter 5: Data frames

Chapter 6: Lists

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 listExercise 4: Creating a named listExercise 5: Creating a named list (2)Exercise 6: Selecting elements from a listExercise 7: Creating a new list for another movie

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