The goal of tibblify is to provide an easy way of converting a nested list into a tibble.
You can install the released version of tibblify from CRAN with:
install.packages("tibblify")Or install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("mgirlich/tibblify")With tibblify() you can rectangle deeply nested lists into a tidy
tibble. These lists might come from an API in the form of JSON or from
scraping XML. The reasons to use tibblify() over other tools like
jsonlite::fromJSON() or tidyr::hoist() are:
- It can guess the output format like
jsonlite::fromJSON(). - You can also provide a specification how to rectangle.
- The specification is easy to understand.
- You can bring most inputs into the shape you want in a single step.
- Rectangling is much faster than with
jsonlite::fromJSON().
Let’s start with gh_users, which is a list containing information
about four GitHub users.
library(tibblify)
gh_users_small <- purrr::map(gh_users, ~ .x[c("followers", "login", "url", "name", "location", "email", "public_gists")])
names(gh_users_small[[1]])
#> [1] "followers" "login" "url" "name" "location"
#> [6] "email" "public_gists"Quickly rectangling gh_users_small is as easy as applying tibblify()
to it:
tibblify(gh_users_small)
#> The spec contains 1 unspecified field:
#> • email
#> # A tibble: 4 × 7
#> followers login url name location email public_gists
#> <int> <chr> <chr> <chr> <chr> <list> <int>
#> 1 780 jennybc https://api.github.co… Jenn… Vancouv… <NULL> 54
#> 2 3958 jtleek https://api.github.co… Jeff… Baltimo… <NULL> 12
#> 3 115 juliasilge https://api.github.co… Juli… Salt La… <NULL> 4
#> 4 213 leeper https://api.github.co… Thom… London,… <NULL> 46We can now look at the specification tibblify() used for rectangling
guess_tspec(gh_users_small)
#> The spec contains 1 unspecified field:
#> • email
#> tspec_df(
#> tib_int("followers"),
#> tib_chr("login"),
#> tib_chr("url"),
#> tib_chr("name"),
#> tib_chr("location"),
#> tib_unspecified("email"),
#> tib_int("public_gists"),
#> )If we are only interested in some of the fields we can easily adapt the specification
spec <- tspec_df(
login_name = tib_chr("login"),
tib_chr("name"),
tib_int("public_gists")
)
tibblify(gh_users_small, spec)
#> # A tibble: 4 × 3
#> login_name name public_gists
#> <chr> <chr> <int>
#> 1 jennybc Jennifer (Jenny) Bryan 54
#> 2 jtleek Jeff L. 12
#> 3 juliasilge Julia Silge 4
#> 4 leeper Thomas J. Leeper 46We refer to lists like gh_users_small as collection and objects
are the elements of such lists. Objects and collections are the typical
input for tibblify().
Basically, an object is simply something that can be converted to a one row tibble. This boils down to a condition on the names of the object:
- the
objectmust have names (thenamesattribute must not beNULL), - every element must be named (no name can be
NAor""), - and the names must be unique.
In other words, the names must fulfill
vec_as_names(repair = "check_unique"). The name-value pairs of an
object are the fields.
For example list(x = 1, y = "a") is an object with the fields (x, 1)
and (y, "a") but list(1, z = 3) is not an object because it is not
fully named.
A collection is basically just a list of similar objects so that the fields can become the columns in a tibble.
Providing an explicit specification has a couple of advantages:
- you can ensure type and shape stability of the resulting tibble in automated scripts.
- you can give the columns different names.
- you can restrict to parsing only the fields you need.
- you can specify what happens if a value is missing.
As seen before the specification for a collection is done with
tspec_df(). The columns of the output tibble are describe with the
tib_*() functions. They describe the path to the field to extract and
the output type of the field. There are the following five types of
functions:
tib_scalar(ptype): a length one vector with typeptypetib_vector(ptype): a vector of arbitrary length with typeptypetib_variant(): a vector of arbitrary length and type; you should barely ever need thistib_row(...): an object with the fields...tib_df(...): a collection where the objects have the fields...
For convenience there are shortcuts for tib_scalar() and
tib_vector() for the most common prototypes:
logical():tib_lgl()andtib_lgl_vec()integer():tib_int()andtib_int_vec()double():tib_dbl()andtib_dbl_vec()character():tib_chr()andtib_chr_vec()Date:tib_date()andtib_date_vec()Dateencoded as character:tib_chr_date()andtib_chr_date_vec()
Scalar elements are the most common case and result in a normal vector column
tibblify(
list(
list(id = 1, name = "Peter"),
list(id = 2, name = "Lilly")
),
tspec_df(
tib_int("id"),
tib_chr("name")
)
)
#> # A tibble: 2 × 2
#> id name
#> <int> <chr>
#> 1 1 Peter
#> 2 2 LillyWith tib_scalar() you can also provide your own prototype
Let’s say you have a list with durations
x <- list(
list(id = 1, duration = vctrs::new_duration(100)),
list(id = 2, duration = vctrs::new_duration(200))
)
x
#> [[1]]
#> [[1]]$id
#> [1] 1
#>
#> [[1]]$duration
#> Time difference of 100 secs
#>
#>
#> [[2]]
#> [[2]]$id
#> [1] 2
#>
#> [[2]]$duration
#> Time difference of 200 secsand then use it in tib_scalar()
tibblify(
x,
tspec_df(
tib_int("id"),
tib_scalar("duration", ptype = vctrs::new_duration())
)
)
#> # A tibble: 2 × 2
#> id duration
#> <int> <drtn>
#> 1 1 100 secs
#> 2 2 200 secsIf an element does not always have size one then it is a vector element.
If it still always has the same type ptype then it produces a list of
ptype column:
x <- list(
list(id = 1, children = c("Peter", "Lilly")),
list(id = 2, children = "James"),
list(id = 3, children = c("Emma", "Noah", "Charlotte"))
)
tibblify(
x,
tspec_df(
tib_int("id"),
tib_chr_vec("children")
)
)
#> # A tibble: 3 × 2
#> id children
#> <int> <list<chr>>
#> 1 1 [2]
#> 2 2 [1]
#> 3 3 [3]You can use
tidyr::unnest() or
tidyr::unnest_longer()
to flatten these columns to regular columns.
For example in gh_repos_small
gh_repos_small <- purrr::map(gh_repos, ~ .x[c("id", "name", "owner")])
gh_repos_small <- purrr::map(
gh_repos_small,
function(repo) {
repo$owner <- repo$owner[c("login", "id", "url")]
repo
}
)
gh_repos_small[[1]]
#> $id
#> [1] 61160198
#>
#> $name
#> [1] "after"
#>
#> $owner
#> $owner$login
#> [1] "gaborcsardi"
#>
#> $owner$id
#> [1] 660288
#>
#> $owner$url
#> [1] "https://api.github.com/users/gaborcsardi"the field owner is an object itself. The specification to extract it
uses tib_row()
spec <- guess_tspec(gh_repos_small)
spec
#> tspec_df(
#> tib_int("id"),
#> tib_chr("name"),
#> tib_row(
#> "owner",
#> tib_chr("login"),
#> tib_int("id"),
#> tib_chr("url"),
#> ),
#> )and results in a tibble column
tibblify(gh_repos_small, spec)
#> # A tibble: 30 × 3
#> id name owner$login $id $url
#> <int> <chr> <chr> <int> <chr>
#> 1 61160198 after gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 2 40500181 argufy gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 3 36442442 ask gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 4 34924886 baseimports gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 5 61620661 citest gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 6 33907457 clisymbols gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 7 37236467 cmaker gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 8 67959624 cmark gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 9 63152619 conditions gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> 10 24343686 crayon gaborcsardi 660288 https://api.github.com/users/gaborcs…
#> # ℹ 20 more rowsIf you don’t like the tibble column you can unpack it with
tidyr::unpack(). Alternatively, if you only want to extract some of
the fields in owner you can use a nested path
spec2 <- tspec_df(
id = tib_int("id"),
name = tib_chr("name"),
owner_id = tib_int(c("owner", "id")),
owner_login = tib_chr(c("owner", "login"))
)
spec2
#> tspec_df(
#> tib_int("id"),
#> tib_chr("name"),
#> owner_id = tib_int(c("owner", "id")),
#> owner_login = tib_chr(c("owner", "login")),
#> )
tibblify(gh_repos_small, spec2)
#> # A tibble: 30 × 4
#> id name owner_id owner_login
#> <int> <chr> <int> <chr>
#> 1 61160198 after 660288 gaborcsardi
#> 2 40500181 argufy 660288 gaborcsardi
#> 3 36442442 ask 660288 gaborcsardi
#> 4 34924886 baseimports 660288 gaborcsardi
#> 5 61620661 citest 660288 gaborcsardi
#> 6 33907457 clisymbols 660288 gaborcsardi
#> 7 37236467 cmaker 660288 gaborcsardi
#> 8 67959624 cmark 660288 gaborcsardi
#> 9 63152619 conditions 660288 gaborcsardi
#> 10 24343686 crayon 660288 gaborcsardi
#> # ℹ 20 more rowsObjects usually have some fields that always exist and some that are
optional. By default tib_*() demands that a field exists
x <- list(
list(x = 1, y = "a"),
list(x = 2)
)
spec <- tspec_df(
x = tib_int("x"),
y = tib_chr("y")
)
tibblify(x, spec)
#> Error in `tibblify()`:
#> ! Field y is required but does not exist in `x[[2]]`.
#> ℹ Use `required = FALSE` if the field is optional.You can mark a field as optional with the argument required = FALSE:
spec <- tspec_df(
x = tib_int("x"),
y = tib_chr("y", required = FALSE)
)
tibblify(x, spec)
#> # A tibble: 2 × 2
#> x y
#> <int> <chr>
#> 1 1 a
#> 2 2 <NA>You can specify the value to use with the fill argument
spec <- tspec_df(
x = tib_int("x"),
y = tib_chr("y", required = FALSE, fill = "missing")
)
tibblify(x, spec)
#> # A tibble: 2 × 2
#> x y
#> <int> <chr>
#> 1 1 a
#> 2 2 missingTo rectangle a single object you have two options: tspec_object()
which produces a list or tspec_row() which produces a tibble with one
row.
While tibbles are great for a single object it often makes more sense to convert them to a list.
For example a typical API response might be something like
api_output <- list(
status = "success",
requested_at = "2021-10-26 09:17:12",
data = list(
list(x = 1),
list(x = 2)
)
)To convert to a one row tibble
row_spec <- tspec_row(
status = tib_chr("status"),
data = tib_df(
"data",
x = tib_int("x")
)
)
api_output_df <- tibblify(api_output, row_spec)
api_output_df
#> # A tibble: 1 × 2
#> status data
#> <chr> <list<tibble[,1]>>
#> 1 success [2 × 1]it is necessary to wrap data in a list. To access data one has to
use api_output_df$data[[1]] which is not very nice.
object_spec <- tspec_object(
status = tib_chr("status"),
data = tib_df(
"data",
x = tib_int("x")
)
)
api_output_list <- tibblify(api_output, object_spec)
api_output_list
#> $status
#> [1] "success"
#>
#> $data
#> # A tibble: 2 × 1
#> x
#> <int>
#> 1 1
#> 2 2Now accessing data does not required an extra subsetting step
api_output_list$data
#> # A tibble: 2 × 1
#> x
#> <int>
#> 1 1
#> 2 2Please note that the tibblify project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.