# Data rectangling {#sec-rectangle-data}

```{r}
#| results: "asis"
#| echo: false
source("_common.R")
status("drafting")
```

## Introduction

In this chapter, you'll learn the art of data **rectangling**, taking data that is fundamentally tree-like and converting it into a rectangular data frames made up of rows and columns.
This is important because hierarchical data is surprisingly common, especially when working with data that comes from a web API.

To learn about rectangling, you'll first learn about lists, the data structure that makes hierarchical data possible in R.
Then you'll learn about two crucial tidyr functions: `tidyr::unnest_longer()`, which converts children in rows, and `tidyr::unnest_wider()`, which converts children into columns.
We'll then show you a few case studies, applying these simple function multiple times to solve real complex problems.
We'll finish off by talking about JSON, the most frequent source of hierarchical datasets and common format for data exchange on the web.

### Prerequisites

In this chapter we'll continue using tidyr.
We'll also use repurrrsive to supply some interesting datasets to practice your rectangling skills, and we'll finish up with a little jsonlite, which we'll use to read JSON files into R lists.

```{r}
#| label: setup
#| message: false

library(tidyverse)
library(repurrrsive)
library(jsonlite)
```

## Lists

So far we've used simple vectors, like integers, numbers, characters, date-times, and factors.
These vectors are all homogeneous: every element must be the same type.
If you want to store element of different types, you need a **list**.
You can create a list with `list()`:

```{r}
x1 <- list(1:4, "a", TRUE)
x1
```

It's often convenient to name the components of a list, which you can do in the same way as naming the columns of a tibble:

```{r}
x2 <- list(a = 1:2, b = 1:3, c = 1:4)
x2
```

Even for these very simple lists, printing takes up quite a lot of space.
A very useful alternative is `str()`, short for structure, which generates a compact display of the **str**ucture, de-emphasizing the contents:

```{r}
str(x1)
str(x2)
```

`str()` display each element (or **child**) of a list on its own line.
It displays the name if present, then an abbreviation of the type, then the first few values.

### Hierarchy

Lists can contain any type of object, including other lists.
This makes them suitable for representing hierarchical or tree-like structures:

```{r}
x3 <- list(list(1, 2), list(3, 4))
str(x3)
```

This is different to `c()`, which generates a flat vector:

```{r}
c(c(1, 2), c(3, 4))
```

You can see how `str()` starts to get even more useful as the lists get more complex, and how it allows you to see the hierarchy at a glance.

```{r}
x4 <- list(1, list(2, list(3, list(4, list(5)))))
str(x4)
```

At some point, however, even `str()` starts to fail, and if you're working with deeply nested lists in RStudio, I highly recommend using `View()`.
@fig-view-collapsed shows the result of calling `View(x4)`.
The viewer starts by showing just the top level of the list, but you can interactively expand any of the components to see more, as in @fig-view-expand-1.
RStudio will also show you the code you need to access that element, as in @fig-view-expand-2.
We'll come back to how this code works in @sec-vector-subsetting.

```{r}
#| label: fig-view-collapsed
#| fig.cap: >
#|   The RStudio allows you to interactively explore a complex list.  
#|   The viewer opens showing only the top level of the list.
#| echo: false
#| out-width: NULL
knitr::include_graphics("screenshots/View-1.png", dpi = 220)
```

```{r}
#| label: fig-view-expand-1
#| fig.cap: >
#|   Clicking on the rightward facing triangle expands that component
#|   of the list so that you can also see its children.
#| echo: false
#| out-width: NULL
knitr::include_graphics("screenshots/View-2.png", dpi = 220)
```

```{r}
#| label: fig-view-expand-2
#| fig.cap: >
#|   You can repeat this operation as many times as needed to get to the 
#|   data you're interested in. Note the bottom-right corner: if you click
#|   an element of the list, RStudio will give you the subsetting code
#|   needed to access it, in this case `x4[[2]][[2]][[2]]`.
#| echo: false
#| out-width: NULL
knitr::include_graphics("screenshots/View-3.png", dpi = 220)
```

### List columns

You can put lists in the column of a tibble:

```{r}
df <- tibble(
  x = 1:2, 
  y = c("a", "b"),
  z = list(list(1, 2), list(3, 4, 5))
)
df
```

This is a powerful idea because it allows you to store arbitrarily complex objects in a data frame; even things that wouldn't typically belong there.
This idea is used a lot in tidymodels, because it allows you to store things like models or resamples in a data frame.

And those things are carried along like any other column:

```{r}
df |> 
  filter(x == 1)
```

The default print method just displays a rough summary of the contents.
The list column could be arbitrarily complex, so there's no good way to print it.
If you want to see it, you'll need to pull the list-column out and apply of the techniques that you learned above:

```{r}
df |> 
  filter(x == 1) |> 
  pull(z) |> 
  str()
```

Similarly, if you `View()` a data frame in RStudio, you'll get the standard tabular view, which doesn't allow you to selectively expand list columns.
To explore those fields you'll need to `pull()` and view, e.g.
`View(pull(df, z))`

::: callout-note
## Base R

It's possible to put a list in a column of a `data.frame`, but it's a lot fiddlier.
List-columns are implicit in the definition of the data frame: a data frame is a named list of equal length vectors.
A list is a vector, so it's always been legitimate to use a list as a column of a data frame.
However, base R doesn't make it easy to create list-columns because `data.frame()` treats a list as a list of columns:

```{r}
data.frame(x = list(1:3, 3:5))
```

You can prevent `data.frame()` from doing this with `I()`, but the result doesn't print particularly well:

```{r}
data.frame(
  x = I(list(1:3, 3:5)), 
  y = c("1, 2", "3, 4, 5")
)
```

Tibbles make it easier to work with list-columns because `tibble()` doesn't modify its inputs and the print method is designed with lists in mind.
:::

## Unnesting

Now that you've learned the basics of lists and how you can use them as a column of a data frame, lets start to see how you can turn them back into regular columns and rows so you can use them with the tidyverse functions you've already learned about.
We'll start with very simple sample data so you can get the idea of how things work, and then in the next section switch to more realistic examples.

Lists tend to come in two basic forms:

-   A named list where every row has the same number of children with the same names. Every name has the same type.
-   An unnamed list where the number of children varies from row to row, and all the types are the same.

More complicated examples just combine these in multiple ways.

The following code creates an example of each.
In `df1`, every element of list-column `y` has two elements named `a` and `b`.
If `df2`, the elements of list-column `y` are unnamed and vary in length.

```{r}
df1 <- tribble(
  ~x, ~y,
  1, list(a = 11, b = 12),
  2, list(a = 21, b = 22),
  3, list(a = 31, b = 32),
)

df2 <- tribble(
  ~x, ~y,
  1, list(11, 12, 13),
  2, list(21),
  3, list(31, 32),
)
```

These two cases correspond to two tools from tidyr: `unnest_wider()` and `unnest_longer()`.
Their suffixes have the same meaning as `pivot_wider()` and `pivot_longer()`: `_wider()` adds more columns and `_longer()` adds more rows.
If your situation isn't as clear cut as these cases, you'll still need to use one of `unnest_longer()` and `unnest_wider()`; you'll just need to do a bit more thinking and experimentation to figure out which one is best.

The main difference between these simple examples and real data is that there's only one level of nesting here.
In real-life, there will often be many, and you'll need to use multiple calls to `unnest_wider()` and `unnest_longer()` to handle it.

### `unnest_wider()`

When each row has the same number of elements with the same names, like `df1`, it's natural to put each component into its own column with `unnest_wider()`:

```{r}
df1 |> 
  unnest_wider(y)
```

By default, the names of the new columns come exclusively from the names of the list, but you can use the `names_sep` argument to request that they combine the original column with the new column.
As you'll learn in the next section, this is useful for disambiguating repeated names.

```{r}
df1 |> 
  unnest_wider(y, names_sep = "_")
```

We can also use `unnest_wider()` with unnamed list-columns, as in `df2`.
It's not as naturally well suited, because it's not clear what the columns should be named.
So `unnest_wider()` gives them numbers:

```{r}
df2 |> 
  unnest_wider(y, names_sep = "_")
```

You'll notice that `unnested_wider()`, much like `pivot_wider()`, turns implicit missing values in to explicit missing values.
Another challenge is that if you're working with live data, you won't know exactly how many columns you'll end up with.

### `unnest_longer()`

When each row contains an unnamed list, it's most natural to put each element into its own row with `unnest_longer()`:

```{r}
df2 |> 
  unnest_longer(y)
```

You can also apply the same operation to named list-columns, like `df1$y`:

```{r}
df1 |> 
  unnest_longer(y)
```

Note the new `y_id` column.
Because the elements are named, and those names might be useful data, tidyr keeps them in the result data in a new column with the `_id` suffix.
You can suppress this with `indices_include = FALSE`.

You might also use `indices_include = TRUE` if the position of the elements is important in the unnamed case:

```{r}
df2 |> 
  unnest_longer(y, indices_include = TRUE)
```

The output contains one row for each element inside the list-column.
So what happens if the list-column is empty?

```{r}
df6 <- tribble(
  ~x, ~y,
  "a", list(1, 2),
  "b", list(3),
  "c", list()
)
df6 |> unnest_longer(y)
```

The row goes away!
--- <https://github.com/tidyverse/tidyr/issues/1339>.

### Inconsistent types

What happens if you attempt to unnest a column that doesn't contain only one type of thing.
For example, what happens if we take this data set and unnest into rows?
`y` will contain two numbers, a factor, a logical, which can't normally be mixed in a single column:

```{r}
df4 <- tribble(
  ~x, ~y,
  "a", list(1, "a"),
  "b", list(TRUE, factor("a"), 5)
)
```

An important invariant for `unnest_longer()` is that the columns say the same but the number of rows change.
So what happens?
How does `unnest_longer()` produce five rows while keeping everything in `y`?

```{r}
df4 |> unnest_longer(y)
```

We still get a list-column, but every element of the list-column contains a single element.
When `unnest_longer()` can't find a common type, it keeps the original types by using a list-column.
You might wonder if this breaks the commandment that every element of a column must be the same type --- not quite, because every element is a still a list, but each component of a list can contain something different.

What happens if you find this problem in a dataset you're trying to rectangle?
I think there are two basic options.
You could try and coerce to a class that is meaningful for all the rows using the `transform` argument.
It's not particularly useful here because there's only really one class that these five class can be converted to: character.

```{r}
df4 |> unnest_longer(y, transform = as.character)
```

Another option would be to filter down to the rows that have values of a specific type:

```{r}
df4 |> 
  unnest_longer(y) |> 
  rowwise() |> 
  filter(is.numeric(y))
```

Then you can call `unnest_longer()` once more:

```{r}
df4 |> 
  unnest_longer(y) |> 
  rowwise() |> 
  filter(is.numeric(y)) |> 
  unnest_longer(y)
```

### Other functions

There are few other useful rectangling functions that we're not going to talk about here:

-   `unnest_auto()` automatically picks between `unnest_longer()` and `unnest_wider()`based on the structure of the list-column. It's a great for rapid exploration, but I think it's ultimately a bad idea because it doesn't force you to understand how your data is structured, and makes your code harder to understand.
-   `unnest()` modifies rows and columns simultaneously. It's useful when you have a list-column that contains a 2d structure like a data frame (which we often call a nested data frame), which we don't otherwise use in this book.
-   `hoist()` allows you to reach into a deeply nested list and extract just the components that you need. It's mostly equivalent to repeated invocations of `unnest_wider()` + `select()` so you should read up on it if there's just a couple of important variables that you want to pull out, embedded in a bunch of data that you don't care about.

### Exercises

1.  From time-to-time you encounter data frames with multiple list-columns with aligned values.
    For example, in the following data frame, the values of `y` and `z` are aligned (i.e. `y` and `z` will always have the same length within a row, and the first value of `y` corresponds to the first value of `z`).
    What happens if you apply two `unnest_longer()` calls to this data frame?
    How can you preserve the relationship between `x` and `y`?
    (Hint: carefully read the docs).

    ```{r}
    df4 <- tribble(
      ~x, ~y, ~z,
      "a", list("y-a-1", "y-a-2"), list("z-a-1", "z-a-2"),
      "b", list("y-b-1", "y-b-2", "y-b-3"), list("z-b-1", "z-b-2", "z-b-3")
    )
    ```

## Case studies

Now that you understand the basics of `unnest_wider()` and `unnest_longer()` lets use them to tackle some real rectangling challenges.
These challenges share the common feature that they're mostly just a sequence of multiple `unnest_wider()` and/or `unnest_longer()` calls, with a little dash of dplyr where needed.
See `vignette("rectangling", package = "tidyr")` for more.

### Very wide data

We'll start with `gh_repos` --- this is some data about GitHub repositories retrived from GitHub API. It's a very deeply nested list so it's hard for me to display in this book; you might want to explore a little on your own with `View(gh_repos)` before we continue.
To make it more manageable I'm going to put it in a tibble in a column called `json` (for reasons we'll get to later)

```{r}
repos <- tibble(json = gh_repos)
repos
```

There are row rows, and each row contains a unnamed list with either 26 or 30 rows.
Since these are unnamed, we'll start with an `unnest_longer()` to put each child in its own row:

```{r}
repos |> 
  unnest_longer(json)
```

At first glance, it might seem like we haven't improved the situation --- while we have more rows now (176 instead of 6) it seems like each element of `json` is still a list.
However, there's an important difference: now each element is a **named** list so we can use `unnamed_wider()` to put each element into its own column:

```{r}
repos |> 
  unnest_longer(json) |> 
  unnest_wider(json) 
```

This is a bit overwhelming --- there are so many columns that tibble doesn't even print all of them!
We can see them all with `names()`:

```{r}
repos |> 
  unnest_longer(json) |> 
  unnest_wider(json) |> 
  names()
```

Let's select a few that look interesting:

```{r}
repos |> 
  unnest_longer(json) |> 
  unnest_wider(json) |> 
  select(id, full_name, owner, description)
```

`owner` is another list-column, and since it contains named list, we can use `unnest_wider()` to get at the values:

```{r}
#| error: true
repos |> 
  unnest_longer(json) |> 
  unnest_wider(json) |> 
  select(id, full_name, owner, description) |> 
  unnest_wider(owner)
```

Uh oh, this list column also contains an `id` column and we can't have two `id` columns in the same data frame.
Rather than following the advice to use `names_repair` (which would also work), I'll instead use `names_sep`:

```{r}
repos |> 
  unnest_longer(json) |> 
  unnest_wider(json) |> 
  select(id, full_name, owner, description) |> 
  unnest_wider(owner, names_sep = "_")
```

### Relational data

When you get nested data, it's not uncommon for it to contain data that we'd normally spread out into multiple data frames.
Take `got_chars`

```{r}
chars <- tibble(json = got_chars)
chars
```

The `json` column contains named values, so we'll start by widening it:

```{r}
chars |> 
  unnest_wider(json)
```

And selecting a few columns just to make it easier to read:

```{r}
characters <- chars |> 
  unnest_wider(json) |> 
  select(id, name, gender, culture, born, died, alive)
characters
```

There are also many list-columns:

```{r}
chars |> 
  unnest_wider(json) |> 
  select(id, where(is.list))
```

Lets explore a couple, starting with `titles`:

```{r}
chars |> 
  unnest_wider(json) |> 
  select(id, titles) |> 
  unnest_longer(titles)
```

You might expect to see this in its own table:

```{r}
titles <- chars |> 
  unnest_wider(json) |> 
  select(id, titles) |> 
  unnest_longer(titles) |> 
  filter(titles != "") |> 
  rename(title = titles)
titles
```

Because you could then join it on as needed.
For example, we find all the characters that are captains:

```{r}
captains <- titles |> filter(str_detect(title, "Captain"))
captains

characters |> 
  semi_join(captains)
```

You could imagine creating a table like this for each of the list-columns, and then using joins to combine when needed.

### A dash of text analysis

What if we wanted to find the most common words in the title?
There are plenty of sophisticated ways to do this, but one simple way starts by breaking each element of `title` up into words by spitting on `" "`:

```{r}
titles |> 
  mutate(word = str_split(title, " "), .keep = "unused")
```

This creates a unnamed variable length list-column, so we can use `unnest_longer()`:

```{r}
titles |> 
  mutate(word = str_split(title, " "), .keep = "unused") |> 
  unnest_longer(word) 
```

And then we can count that column to find the most common:

```{r}
titles |> 
  mutate(word = str_split(title, " "), .keep = "unused") |> 
  unnest_longer(word) |> 
  count(word, sort = TRUE)
```

Breaking up text into individual fragments is a powerful idea that underlies much of text analysis.
For more, I'd recommend reading [Text Mining with R](https://www.tidytextmining.com).

### Deeply nested

We'll finish off with an that is very deeply nested and requires repeated rounds of `unnest_wider()` and `unnest_longer()` to unravel: `gmaps_cities`.
This is a two column tibble containing five cities names and the results of using Google's [geocoding API](https://developers.google.com/maps/documentation/geocoding) to determine their location:

```{r}
gmaps_cities
```

`json` is list-column with internal names, so we start with an `unnest_wider()`:

```{r}
gmaps_cities |> 
  unnest_wider(json)
```

This gives us a status column and the actual results.
We'll drop the status column since they're all `OK`.
In a real analysis, you'd also want separately capture all the rows where `status != "OK"` so you could figure out what went wrong.
`results` is an unnamed list, with either one or two elements.
We'll figure to out why shortly.

```{r}
gmaps_cities |> 
  unnest_wider(json) |> 
  select(-status) |> 
  unnest_longer(results)
```

Now results is a named list, so we'll `unnest_wider()`:

```{r}
locations <- gmaps_cities |> 
  unnest_wider(json) |> 
  select(-status) |> 
  unnest_longer(results) |> 
  unnest_wider(results)
locations
```

Now we can see why Washington and Arlington got two results: Washington matched both the state and the city (DC), and Arlington matched Arlington Virginia and Arlington Texas.

There are few different places we could go from here.
We might want to determine the exact location of the match stored in the `geometry` list-column:

```{r}
locations |> 
  select(city, formatted_address, geometry) |> 
  unnest_wider(geometry)
```

That gives us new `bounds` (which gives a rectangular region) and the midpoint in `location`, which we can unnest to get latitude (`lat`) and longitude (`lng`):

```{r}
locations |> 
  select(city, formatted_address, geometry) |> 
  unnest_wider(geometry) |> 
  unnest_wider(location)
```

Extracting the bounds requires a few more steps

```{r}
locations |> 
  select(city, formatted_address, geometry) |> 
  unnest_wider(geometry) |> 
  # focus on the variables of interest
  select(!location:viewport) |>
  unnest_wider(bounds)
```

I then rename `southwest` and `northeast` (the corners of the rectangle) so I can use `names_sep` to create short but evocative names:

```{r}
locations |> 
  select(city, formatted_address, geometry) |> 
  unnest_wider(geometry) |> 
  select(!location:viewport) |>
  unnest_wider(bounds) |> 
  rename(ne = northeast, sw = southwest) |> 
  unnest_wider(c(ne, sw), names_sep = "_") 
```

Note that I take advantage of the fact that you can unnest multiple columns at a time by supplying a vector of variable names to `unnest_wider()`.

This one place where `hoist()`, which we mentioned briefly above can be useful.
Once you've discovered the path to get to the components you're interested in, you can extract them directly using `hoist()`:

```{r}
locations |> 
  select(city, formatted_address, geometry) |> 
  hoist(
    geometry,
    ne_lat = c("bounds", "northeast", "lat"),
    sw_lat = c("bounds", "southwest", "lat"),
    ne_lng = c("bounds", "northeast", "lng"),
    sw_lng = c("bounds", "southwest", "lng"),
  )
```

### Exercises

1.  The `owner` column of `gh_repo` contains a lot of duplicated information because each owner can have many repos.
    Can you construct a `owners` data frame that contains one row for each owner?
    (Hint: does `distinct()` work with `list-cols`?)

2.  Explain the following code.
    Why is it interesting?
    Why does it work for this dataset but might not work in general?

    ```{r}
    tibble(json = got_chars) |> 
      unnest_wider(json) |> 
      select(id, where(is.list)) %>% 
      pivot_longer(where(is.list), names_to = "media", values_to = "value") %>% 
      unnest_longer(value)
    ```

## JSON

All of the case studies in the previous section came originally as JSON, one of the most common sources of hierarchical data.
In this section, you'll learn more about JSON and some common problems you might have.
JSON, short for javascript object notation, is a data format that grew out of the javascript programming language and has become an extremely common way of representing data.

``` json
{
  "name1": "value1",
  "name2": "value2"
}
```

Which in R you might represent as:

```{r}
list(
  name1 = "value1",
  name2 = "value2"
)
```

There are five types of things that JSON can represent

``` json
{
  "strings": "are surrounded by double doubles",
  "numbers": 123456,
  "boolean": [false, true],
  "arrays": [1, 2, 3, 4, 5],
  "objects": {
    "name1": "value1",
    "name2": "value2"
  },
  "null": null
}
```

You'll notice that these types don't embrace many of the types you've learned earlier in the book like factors, and date-times.
This is important: typically these data types will be encoded as string, and you'll need coerce to the correct data type.

Most of the time you won't deal with JSON directly, instead you'll use the jsonlite package, by Jeroen Oooms, to load it into R as a nested list.

### Data frames

JSON doesn't have any 2-dimension data structures, so how would you represent a data frame?

```{r}
df <- tribble(
  ~x, ~y,
  "a", 10,
  "x", 3
)
```

There are two ways: you can either make an struct of arrays, or an array of structs.

``` json
{
  "x": ["a", "x"],
  "y": [10, 3]
}
```

``` {.json .josn}
[
  {"x": "a", "y": 10},
  {"x": "x", "y": 3}
]
```

```{r}
df_col <- jsonlite::fromJSON('
  {
    "x": ["a", "x"],
    "y": [10, 3]
  }
')
tibble(json = list(df_col)) |> 
  unnest_wider(json) |> 
  unnest_longer(everything())
```

```{r}
df_row <- jsonlite::fromJSON(simplifyVector = FALSE, '
  [
    {"x": "a", "y": 10},
    {"x": "x", "y": 3}
  ]
')
tibble(json = list(df_row)) |> 
  unnest_longer(json) |> 
  unnest_wider(json)
```

Note that we have to wrap it in a `list()` because we have a single "thing" to unnest.
