Data basics

tutorial_options(exercise.timelimit = 60)


In this tutorial, you will learn how to use R to inspect the contents of a data frame or tibble. Data frames and tibbles are R's structures for storing tabular data; if you inherit a tabular dataset in R, it will almost certainly come as one of these structures.

Here, you will learn how to do three things with data frames and tibbles:

  1. Look at the contents of a data frame or tibble
  2. Open a help page that describes a data frame or tibble
  3. Identify the variables and their types in a tibble

You will also meet the mpg and flights datasets. These datasets appear frequently in R examples.

The readings in this tutorial follow R for Data Science, sections 3.2 and 5.1.

Data frames

What is a data frame?

A data frame is a rectangular collection of values, usually organized so that variables appear in the columns and observations appear in rows.

Here is an example: the mpg data frame contains observations collected by the US Environmental Protection Agency on 38 models of cars. To see the mpg data frame, type mpg in the code chunk below and then click "Submit Answer."

mpg <-

**Hint:** Type `mpg` and then click the Submit Answer button.
# checking code

A note about mpg

The code above worked because I've already loaded the ggplot2 package for you in this tutorial: mpg comes in the ggplot2 package. If you would like to look at mpg on your own computer, you will need to first load ggplot2. You can do that in two steps:

  1. Run install.packages('ggplot2') to install ggplot2 if you do not yet have it.
  2. Load ggplot2 with the library(ggplot2) command

After that, you will be able to access any object in ggplot2—including mpg—until you close R.

Did you notice how much information was inside mpg? Me too. Sometimes the contents of a data frame do not fit on a single screen, which makes them difficult to inspect. We'll look at an alternative to using and examining data frames soon. But first let's get some help...

Help pages

How to open a help page

You can learn more about mpg by opening its help page. The help page will explain where the mpgdataset comes from and what each variable in mpg describes. To open the help page, type ?mpg in the code chunk below and then click "Submit Answer".

**Hint:** Type `?mpg` and then click the Submit Answer button.
# checking code

? syntax

You can open a help page for any object that comes with R or with an R package. To open the help page, type a ? before the object's name and then run the command, as you did with ?mpg. This technique works for functions, packages, and more.

Notice that objects created by you or your colleagues will not have a help page (unless you make one).


Use the code chunk below to answer the following questions.

quiz(caption = "Quiz",
  question("What does the `drv` variable of `mpg` describe?  Read the help for `?mpg` to find out.",
           answer("Whether or not the vehicle has driver side airbags"), 
           answer("Whether a car is automatic or manual transmission"),
           answer("The number of cylinders in the car's engine"),
           answer("Something else", correct = TRUE, message = "`drv` describes the type of drivetrain in a car: front wheel drive, rear wheel drive, or four wheel drive."), 
           allow_retry = TRUE
  question("How many rows are in the data frame named `cars`?",
           answer("50", correct = TRUE),
           incorrect = "Incorrect.\nHint: R numbers the rows of a data frame when it displays the contents of a data frame. As a result, you can spot the number of rows in `cars` by examining `cars` in the code block above.", 
           allow_retry = TRUE
  question("How many columns are in the data frame named `cars`?",
           answer("2", correct = TRUE),
           answer("more than four"),
           incorrect = "Incorrect.\nHint: If you inspect the contents of `cars` in the code block above, it should be pretty easy to count the number of columns.", 
           allow_retry = TRUE


What is a tibble?

The flights data frame in the nycflights13 package is an example of a tibble. Tibbles are a data frames with some extra properties.

To see what I mean, use the code chunk below to print the contents of flights.

**Hint:** Type the name of the data frame that you want to print and then click the Submit Answer button. I've already loaded the nycflight13 package for you.

Good Job. flights describes every flight that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics, and is documented in ?flights.

The tibble display

You might notice that flights looks a little differently than mpg. flights shows only the first few rows of the data frame and only the columns that fit on one screen.

flights prints differently because it's a tibble. Tibbles are data frames that are slightly tweaked to be more user-friendly. For example, R doesn't try to show you all of a tibble at once (but it will try to show you all of a data frame that is not a tibble).

You can use as_tibble() to return a tibble version of any data frame. For example, this would return a tibble version of mpg: as_tibble(mpg).

Data types

Type codes


Did you notice that a row of three (or four) letter abbreviations appears under the column names of flights? These abbreviations describe the type of data that is stored in each column of flights:

There are three other common types of variables that aren't used in this dataset but are used in other datasets:

This row of data types is unique to tibbles and is one of the ways that tibbles try to be more user-friendly than data frames.

Test your knowledge

question("Which types of variables does `flights` contain? Check all that apply.",
         type = "multiple",
         allow_retry = TRUE,
         incorrect = "Not quite right. Look a little closer at `flights`.",
         answer("integers", correct = TRUE),
         answer("doubles", correct = TRUE),
         answer("characters", correct = TRUE),
         correct = "Great Job!"


You've met R's basic table structures—data frames and tibbles; and you have learned how to inspect their contents. When you are ready, go on to the next tutorial to begin visualizing your data.

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learnr documentation built on March 26, 2020, 7:45 p.m.