---
title: "Data basics"
output:
  learnr::tutorial:
    progressive: true
    allow_skip: true
    df_print: default
runtime: shiny_prerendered
description: >
  Learn about the base data types in R. Explore R's data frames, and learn how
  to interact with data frames and their columns.
---

```{r setup, include=FALSE}
library(learnr)
library(tidyverse)
library(nycflights13)
tutorial_options(exercise.timelimit = 60)
```

## Welcome

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_](http://r4ds.had.co.nz/), 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 "Run Code".

```{r mpg-setup}
mpg <- as.data.frame(mpg)
```

```{r mpg, exercise = TRUE}

```

<div id="mpg-hint">
**Hint:** Type `mpg` and then click the Run Code button.
</div>

### 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 `mpg`dataset comes from and what each variable in `mpg` describes. To open the help page, type `?mpg` in the code chunk below and then click "Run Code".

```{r help, exercise = TRUE}

```

<div id="help-hint">
**Hint:** Type `?mpg` and then click the Run Code button.
</div>

### ? 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).

### Exercises

Use the code chunk below to answer the following questions.

```{r exercises1, exercise = TRUE}

```

```{r quiz1, echo = FALSE}
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("2"),
           answer("25"),
           answer("50", correct = TRUE),
           answer("100"),
           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("1"),
           answer("2", correct = TRUE),
           answer("4"),
           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
  )
)
```

## Tibbles

### 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`.

```{r flights, exercise = TRUE}

```

<div id="flights-hint">
**Hint:** Type the name of the data frame that you want to print and then click the Run Code button. I've already loaded the nycflight13 package for you.
</div>

###

Good Job. `flights` describes every flight that departed from New York City in 2013. The data comes from the [US Bureau of Transportation Statistics](http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=120&Link=0), 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

```{r flights3, echo = FALSE}
flights
```

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`:

* `int` stands for integers.

* `dbl` stands for doubles, or real numbers.

* `chr` stands for character vectors, or strings.

* `dttm` stands for date-times (a date + a time).

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

* `lgl` stands for logical, vectors that contain only `TRUE` or `FALSE`.

* `fctr` stands for factors, which R uses to represent categorical variables
  with fixed possible values.

* `date` stands for dates.

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

```{r quiz2, echo=FALSE}
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("factors"),
         answer("characters", correct = TRUE),
         correct = "Great Job!"
)
```

### Congratulations

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.

