---
title: "Compare with Tidyr's Rectangling"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Compare with Tidyr's Rectangling}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>")
```

## Introduction

As per [tidyr][] definition unnesting (or rectangling):

     ... is the art and craft of taking a deeply nested list (often sourced
     from wild caught JSON or XML) and taming it into a tidy data set of rows and
     columns. There are three functions from tidyr that are particularly useful for
     rectangling:

[tidyr][] core functions for unnesting are `unnest_longer()`, `unnest_wider()`, `hoist()`. This guide follows the steps from [tidyr][] vignette and translates them into `unnest`'s language.

With [tidyr][] you have to unnest lists in several steps by using one of the three core functions. With `unnest` you do all at once in one step. `unnest` doesn't produce intermediate list columns.

We'll use the `repurrrsive` package as the source of our nested lists:

```{r setup, message = FALSE}
library(tidyr)
library(dplyr)
library(repurrrsive)
library(unnest)
options(unnest.return.type = "tibble")
```


## GitHub repos

With [tidyr][] you start by putting a list into a data.frame column. With [unnest][] this is not necessary.

`gh_repos` is a nested list with maximal depth of 4 "user">"repo">"owner">"[xyz]".

```r
str(gh_repos[[1]][[1]][["owner"]])
```

Let's say that we want a `data.frame` with 3 columns, "name", "homepage" and "watchers_count", from level 3 of repo characteristics and one,"login", from level 4 of owner characteristics. This is how it's done with [tidyr][]:

```{r}
repos <- tibble(repo = gh_repos)
repos <- unnest_longer(repos, repo)
hoist(repos, repo,
      login = c("owner", "login"),
      name = "name",
      homepage = "homepage",
      watchers = "watchers_count") %>%
  select(-repo)
```

With [unnest][]:

```{r}
spec <- s(stack = TRUE,
          s(stack = TRUE,
            s("name"),
            s("homepage"),
            s("watchers_count", as = "watchers"),
            s("owner",
              s("login"))))
unnest(gh_repos, spec)
```

[unnest][] selectors (`s()`) apply to corresponding levels of the hierarchy and describe which elements should be selected and how. The `stack = TRUE` says that the result of the extraction should be stacked row-wise (aka `rbind`ed). `stack = FALSE`, means spread it across multiple columns (aka `cbind`ed). The `as` argument provides the name of the output. By default it's the entire path name to the selected leaf.

Now assume that you want the 3 components of "repos" and all components of the owner at once:

```r
tibble(repo = gh_repos) %>%
  unnest_longer(repo) %>%
  hoist(repo,
        name = "name",
        homepage = "homepage",
        watchers = "watchers_count") %>%
  hoist(repo, owner = "owner") %>%
  unnest_wider(owner)
```

With [unnest][]

```{r}
spec <- s(stack = TRUE,
          s(stack = TRUE,
            s("name"),
            s("homepage"),
            s("watchers_count", as = "watchers"),
            s("owner")))
unnest(gh_repos, spec) %>% tibble()
```

Note that [unnest][] produces namespaced column names, while [tidyr'[s is not. This is a good thing as you don't have to worry about conflicting names. [tidyr][] provides a "fix" for duplicated names in the form of `names_repair` argument to its functions.

## Game of Thrones characters

What do you do with non-singleton leafs? Those are normally stacked, spread or melted depending on the analysis. For example the Game of Thrones dataset contains non-singleton leafs "titles", "aliases", "books" etc.

```r
str(got_chars[[1]])
```

Let's have a look at some common scenarios.

### Stacking

Assume that we want a row for every book and TV series that the character appears in. That is, we want a long table with all combinations (aka cross product) of books and TV series.

```{r}
tibble(char = got_chars) %>%
  unnest_wider(char) %>%
  select(name, books, tvSeries) %>%
  unnest_longer(books) %>%
  unnest_longer(tvSeries)

unnest(got_chars,
       s(stack = T,
         s("name"),
         s("books,tvSeries/", stack = T)))
```

Implementation aside, [tidyr'[s intermediary steps are generally costly for two reasons. First, because intermediary data.frames are created during the processing. Second, because intermediary objects might contain columns that are not needed in the subsequent processing. In the above examples `unnest_wider()` produced man more columns than we need. A better approach would be to replace it with a bit more verbose `hoist` call.

In contrast [unnest][] doesn't produce intermediary data structures. In fact, [unnest][] follows a 0-intermediary-copy semantics. The input vectors are directly copied into the output, no matter how complex the nesting is.

Cross-product is commonly useful when only one non-singleton variable is extracted. For example, let's match title to name:

```{r}
tibble(char = got_chars) %>%
  hoist(char, name = "name", title = "titles") %>%
  select(-char) %>%
  unnest_longer(title)

unnest(got_chars,
       s(stack = T,
         s("name"),
         s("titles/", stack = T)))
```

### Id-value long tables (aka long pivoting, or melting)

A common scenario is to stack the non-scalar leafs and replicate id labels in a separate "key" column. This is called "melting" (`reshape2`) or "long pivoting" (`tidyr`).

```r
tibble(char = got_chars) %>%
  unnest_wider(char) %>%
  select(name, books, tvSeries) %>%
  pivot_longer(c(books, tvSeries), names_to = "media", values_to = "value") %>%
  unnest_longer(value)

unnest(got_chars,
       s(stack = T,
         s("name"),
         s("books,tvSeries", stack = "media", as = "value",
           s(stack = T))))

```

### Id-value wide tables

One might want to stack id vars (media) but spread the measures (books, tvSeries) horizontally such that each row would contain all measurement for each media.

```r
# There seem not to be an easy way to achieve this with tidyr

unnest(got_chars,
       s(stack = T,
         s("name"),
         s("books,tvSeries", stack = "media", as = "value")))
```

### Wide Tables (aka spreading)

This strategy is commonly used in machine learning scenarios when large sparse tables are plugged into black-box ML algorithms. This is the default behavior in [unnest][].

```r
# Currently tidyr errors on double widening due to name conflicts.
# tibble(char = got_chars) %>%
#   unnest_wider(char) %>%
#   select(name, books, tvSeries) %>%
#   unnest_wider(books) %>%
#   unnest_wider(tvSeries)

unnest(got_chars, s(stack = T, s("name, books, tvSeries")))
```

## Sharla Gelfand's discography

Finally, the most complex transformation from [tidyr'[s vignette can be achieved with unnest in a single step.

Typical entry of `disog` collection looks like this

```{r}
str(discog[[3]])
```

We want to extract `artists` metadata and `formats` into separate tables.

```{r}
tibble(disc = discog) %>%
  unnest_wider(disc) %>%
  hoist(basic_information, artist = "artists") %>%
  select(disc_id = id, artist) %>%
  unnest_longer(artist) %>%
  unnest_wider(artist)

tibble(disc = discog) %>%
  unnest_wider(disc) %>%
  hoist(basic_information, format = "formats") %>%
  select(disc_id = id, format) %>%
  unnest_longer(format) %>%
  unnest_wider(format) %>%
  unnest_longer(descriptions)
```

With [unnest][] you can achieve this in two separate passes through the list, or in a single pass with a grouped children specification. The single pass extraction returns a list of data.frames, but scans the data only once.

Separate unnest calls:

```r
unnest(discog,
       s(stack = T,
         s("id", as = "disc_id"),
         s("basic_information/artists", as = "artist",
           s(stack = T))))

unnest(discog,
       s(stack = T,
         s("id", as = "disc_id"),
         s("basic_information/formats", as = "format",
           s(stack = T,
             s(exclude = "descriptions"),
             s("descriptions/", stack = T)))))
```

Single unnest pass:

```r
unnest(discog,
       s(stack = T,
         groups =
           list(artists =
                  list(s("id", as = "disc_id"),
                       s("basic_information/artists", as = "artist",
                         s(stack = T))),
                formats =
                  list(s("id", as = "disc_id"),
                       s("basic_information/formats", as = "format",
                         s(stack = T,
                           s(exclude = "descriptions"),
                           s("descriptions/", stack = T)))))))
```

The unnest specs inside `groups` is the same as in the separate-calls case. The `groups` argument is just like `children` argument with the difference that the output of the extraction is not cross-joined, but simply returned as list.[^groups]

The benefit is grouped extraction is twofold. First, it's faster because the list is traversed only once. Second, the de-duplication works across groups. That is, when `dedupe = TRUE` (not shown in the above examples), the fields extracted by the preceding specs are not extracted by the specs that follow.

[^groups]: Currently `groups` argument works only with the top level of the unnest specification.

[tidyr]: https://tidyr.tidyverse.org/articles/rectangle.html
[unnest]: https://vspinu.github.io/unnest/
