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”
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
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
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 factorExercise 6: Battle of the sexesExercise 7: Ordered factorsExercise 8: Ordered factors (2)Exercise 9: Comparing ordered factorsWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.