---
title: "Filter observations"
output: 
  learnr::tutorial:
    progressive: true
    allow_skip: true
runtime: shiny_prerendered
description: >
  Learn how to filter observations in a data frame. Use `filter()` to extract
  observations from a data frame, and use `&`, `|`, and `!` to write logical
  tests.
---

```{r setup, include=FALSE}
library(learnr)
library(tidyverse)
library(nycflights13)
library(Lahman)

tutorial_options(exercise.timelimit = 60)
knitr::opts_chunk$set(error = TRUE)
```

## Welcome

This is a demo tutorial. Compare it to the [source code](https://github.com/rstudio/learnr/tree/master/inst/tutorials/ex-data-filter/ex-data-filter.Rmd) that made it.

###  

In this tutorial, you will learn how to:

* use `filter()` to extract observations from a data frame or tibble
* write logical tests in R
* combine logical tests with Boolean operators
* handle missing values within logical tests

The readings in this tutorial follow [_R for Data Science_](http://r4ds.had.co.nz/), section 5.2. 

### Prerequisites

To practice these skills, we will use the `flights` data set from the nycflights13 package. This data frame comes from the US [Bureau of Transportation Statistics](http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=120&Link=0) and contains all `r format(nrow(nycflights13::flights), big.mark = ",")` flights that departed from New York City in 2013. It is documented in `?flights`.

We will also use the ggplot2 package to visualize the data. 

If you are ready to begin, click on!

## Filter rows with `filter()`

### filter()

`filter()` lets you use a logical test to extract specific rows from a data frame. To use `filter()`, pass it the data frame followed by one or more logical tests. `filter()` will return every row that passes each logical test.

So for example, we can use `filter()` to select every flight in flights that departed on January 1st. Click Submit Answer to give it a try:

```{r filter1, exercise = TRUE, exercise.eval = FALSE}
filter(flights, month == 1, day == 1)
```


###  output

Like all dplyr functions, `filter()` returns a new data frame for you to save or use. It doesn't overwrite the old data frame. 

If you want to save the output of `filter()`, you'll need to use the assignment operator, `<-`. 

Rerun the command in the code chunk below, but first arrange to save the output to an object named `jan1`.

```{r filter2, exercise = TRUE, exercise.eval = FALSE}
filter(flights, month == 1, day == 1)
```

```{r filter2-solution}
jan1 <- filter(flights, month == 1, day == 1)
```

###  

Good job! You can now see the results by running the name jan1 by itself. Or you can pass `jan1` to a function that takes data frames as input.

Did you notice that this code used the double assignment operator, `==`? `==` is one of R's logical comparison operators. Comparison operators are key to using `filter()` let's take a look at them.

## Logical Comparisons

### Comparison operators

R provides a suite of comparison operators that you can use to compare values: `>`, `>=`, `<`, `<=`, `!=` (not equal), and `==` (equal). Each creates a logical test. For example, is `pi` greater than three?

```{r}
pi > 3
```

###  

When you place a logical test inside of `filter()`, filter applies the test to each row in the data frame and then returns the rows that pass, as a new data frame.

Our code above returned every row whose month value was equal to one _and_ whose day value was equal to one.

### Watch out!

When you start out with R, the easiest mistake to make is to test for equality with `=` instead of `==`. When this happens you'll get an informative error:

```{r, error = TRUE}
filter(flights, month = 1)
```

### Multiple tests

If you give `filter()` more than one logical test, `filter()` will combine the tests with an implied "and."In other words, `filter()` will return only the rows that return `TRUE` for every test. You can combine tests in other ways with Boolean operators... 

## Boolean operators

### &, |, and !

R uses boolean operators to combine multiple logical comparisons into a single logical test. These include `&` (_and_), `|` (_or_), `!` (_not_ or _negation_), and `xor()` (_exactly or_). 

Both `|` and `xor()` will return TRUE is one or the other logical comparison returns TRUE. `xor()` differs from `|` in that it will return FALSE if both logical comparisons return TRUE. The name _xor_ stands for _exactly or_.

Study the diagram below to get a feel for how these operators work.

```{r fig1, echo = FALSE, out.width = "100%", fig.cap = "In the figure above, `x` is the left-hand circle, `y` is the right-hand circle, and the shaded region show which parts each operator selects."}
knitr::include_graphics("images/transform-logical.png")
```

### Test Your Knowledge

```{r logicals, echo = FALSE}
question(" What will the following code return? `filter(flights, month == 11 | month == 12)`",
         answer("Every flight that departed in November _or_ December", correct = TRUE),
         answer("Every flight that departed in November _and_ December", message = "Technically a flight could not have departed in November _and_ December unless it departed twice."),
         answer("Every flight _except for_ those that departed in November or December"),
         answer("An error. This is an incorrect way to combine tests.", message = "The next section will say a little more about combining tests."),
         allow_retry = TRUE
)
```

### Common mistakes

In R, the order of operations doesn't work like English. You can't write `filter(flights, month == 11 | 12)`, even though you might say  "finds all flights that departed in November or December". Be sure to write oue a _complete_ test on each side of a boolean operator.

Here are four more tips to help you use logical tests and Boolean operators in R:

###  

1. A useful short-hand for this problem is `x %in% y`. This will select every row where `x` is one of the values in `y`. We could use it to rewrite the code in the question above:

    ```{r, eval = FALSE}
    nov_dec <- filter(flights, month %in% c(11, 12))
    ```

###  

2. Sometimes you can simplify complicated subsetting by remembering De Morgan's law: `!(x & y)` is the same as `!x | !y`, and `!(x | y)` is the same as `!x & !y`. For example, if you wanted to find flights that weren't delayed (on arrival or departure) by more than two hours, you could use either of the following two filters:

    ```{r, eval = FALSE}
    filter(flights, !(arr_delay > 120 | dep_delay > 120))
    filter(flights, arr_delay <= 120, dep_delay <= 120)
    ```

###  

3. As well as `&` and `|`, R also has `&&` and `||`. Don't use them with `filter()`! You'll learn when you should use them later.

###  

4. Whenever you start using complicated, multipart expressions in `filter()`, consider making them explicit variables instead. That makes it much easier to check your work. You'll learn how to create new variables shortly.

## Missing values

### NA

Missing values can make comparisons tricky in R. R uses `NA` to represent missing or unknown values. `NA`s are "contagious" because almost any operation involving an unknown value (`NA`) will also be unknown (`NA`). For example, can you determine what value these expressions that use missing values shoudl evaluate to? Make a prediction and then click "Submit Answer".

```{r nas, exercise = TRUE, evaluate.exercise = FALSE}
NA > 5
10 == NA
NA + 10
NA / 2
```

```{r nas-check}
"In every case, R does not have enough information to compute a result. Hence, each result is an unknown value, `NA`."
```

### is.na()

The most confusing result above is this one:

```{r}
NA == NA
```

It's easiest to understand why this is true with a bit more context:

```{r}
# Let x be Mary's age. We don't know how old she is.
x <- NA

# Let y be John's age. We don't know how old he is.
y <- NA

# Are John and Mary the same age?
x == y
# We don't know!
```

If you want to determine if a value is missing, use `is.na()`:

```{r}
is.na(x)
```

### filter() and NAs

`filter()` only includes rows where the condition is `TRUE`; it excludes both `FALSE` and `NA` values. If you want to preserve missing values, ask for them explicitly:

```{r}
df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)
filter(df, is.na(x) | x > 1)
```

## Exercises

### Exercise 1

Use the code chunks below to find all flights that

1. Had an arrival delay of two or more hours

    ```{r filterex1, exercise = TRUE}
    
    ```
    ```{r filterex1-solution}
    filter(flights, arr_delay >= 2)
    ```

1. Flew to Houston (`IAH` or `HOU`)

    ```{r filterex2, exercise = TRUE}
    
    ```
    ```{r filterex2-solution}
    filter(flights, dest %in% c("IAH", "HOU"))
    ```
    
    <div id="filterex2-hint">
    **Hint:** This is a good case for the `%in%` operator.
    </div>

1. Were operated by United (`UA`), American (`AA`), or Delta (`DL`)

    ```{r filterex3, exercise = TRUE}
    
    ```
    ```{r filterex3-solution}
    filter(flights, carrier %in% c("UA", "AA", "DL"))
    ```
    
    <div id="filterex3-hint">
    **Hint:** The `carrier` variable lists the airline that operated each flight. This is another good case for the `%in%` operator.
    </div>
    
1. Departed in summer (July, August, and September)
    
    ```{r filterex4, exercise = TRUE}
    
    ```
    ```{r filterex4-solution}
    filter(flights, 6 < month, month < 10)
    ```
    
    <div id="filterex4-hint">
    **Hint:** When converted to numbers, July, August, and September become 7, 8, and 9.
    </div>

1. Arrived more than two hours late, but didn't leave late

    ```{r filterex5, exercise = TRUE}
    
    ```
    ```{r filterex5-solution}
    filter(flights, arr_delay > 120, dep_delay < 0)
    ```
    
    <div id="filterex5-hint">
    **Hint:** Remember that departure and arrival delays are recorded in     _minutes_.
    </div>

1. Were delayed by at least an hour, but made up over 30 minutes in flight
    
    ```{r filterex6, exercise = TRUE}
    
    ```
    ```{r filterex6-solution}
    filter(flights, dep_delay > 60, (dep_delay - arr_delay) >= 30)
    ```

    <div id="filterex6-hint">
    **Hint:** The time a plane makes up is `dep_delay - arr_delay`.
    </div>
        
1. Departed between midnight and 6am (inclusive)

    ```{r filterex7, exercise = TRUE}
    
    ```
    ```{r filterex7-solution}
    filter(flights, dep_time <= 600 | dep_time == 2400)
    ```
    
    <div id="filterex7-hint">
    **Hint:** Don't forget flights thsat left at eactly midnight (`2400`). This is a good case for an "or" operator.
    </div>

### Exercise 2

Another useful dplyr filtering helper is `between()`. What does it do? Can you use `between()` to simplify the code needed to answer the previous challenges?

```{r filterex8, exercise = TRUE}
?between
```

### Exercise 3

How many flights have a missing `dep_time`? What other variables are missing? What might these rows represent?

```{r filterex9, exercise = TRUE}
    
```
```{r filterex9-solution}
filter(flights, is.na(dep_time))
```
    
<div id="filterex7-hint">
**Hint:** This is a good case for `is.na()`.
</div>

```{r filterex9-check}
"Good Job! these look like they might be cancelled flights."
```

### Exercise 4

Why is `NA ^ 0` not missing? Why is `NA | TRUE` not missing?
Why is `FALSE & NA` not missing? Can you figure out the general
rule?  (`NA * 0` is a tricky counterexample!)
    
```{r filterex10, exercise = TRUE}
    
```

