knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(tibblify)
tibblify()
The idea of tibblify()
is to make it easier and more robust to convert lists
of lists into tibbles. This is a typical task after receiving API responses in
JSON format.
The following provides an overview which kind of R objects are supported and
the JSON they correspond to.
There are 4 basic types of scalars coming from JSON: boolean, integer, float, string. In R there are not really scalars but only vectors of length 1.
:::: {style="display: grid; grid-template-columns: 1fr 1fr; grid-column-gap: 10px; width: 100%;"}
::: {}
true 1 1.5 "a"
:::
::: {}
TRUE 1 1.5 "a"
:::
::::
Other R vectors without JSON equivalent are also supported as long as they:
vctrs::vec_size(x)
is 1.Examples are Date
or POSIXct
.
In general a scalar can be parsed with tib_scalar()
. There are some special
functions for common types:
tib_lgl()
tib_int()
tib_dbl()
tib_chr()
tib_date()
tib_chr_date()
to parse dates encoded as string.A homogeneous JSON array is an array of scalar where each scalar has the same type.
In R they correspond to a logical()
, integer()
, double()
or character()
vector:
:::: {style="display: grid; grid-template-columns: 1fr 1fr; grid-column-gap: 10px; width: 100%;"}
::: {}
[true, null, false] [1, null, 3] [1.5, null, 3.5] ["a", null, "c"]
:::
::: {}
c(TRUE, NA, FALSE) c(1L, NA, 2L) c(1.5, NA, 2.5) c("a", NA, "c")
:::
::::
As for scalars other types are also supported as long as they are a vector in the vctrs definition.
They can be parsed with tib_vector()
. As for scalars there are shortcuts for
some common types, e.g. tib_lgl_vec()
.
A special case are empty lists list()
. They might appear when parsing an empty
JSON array:
x_json <- '[ {"a": [1, 2]}, {"a": []} ]' x <- jsonlite::fromJSON(x_json, simplifyDataFrame = FALSE) str(x)
By default they are not supported but produce an error:
tibblify(x, tspec_df(tib_int_vec("a")))
Use vector_allows_empty_list = TRUE
in tspec_*()
so that they are converted
to an empty vector instead:
tibblify(x, tspec_df(tib_int_vec("a"), vector_allows_empty_list = TRUE))$a
When using jsonlite::fromJSON(simplifyVector = FALSE)
to parse JSON to an R
object one does not get R vectors but homogeneous lists of scalars:
x_json <- '[ {"a": [1, 2]}, {"a": [1, 2, 3]} ]' x <- jsonlite::fromJSON(x_json, simplifyVector = FALSE) str(x)
By default they cannot be parsed with tib_vector()
tibblify(x, tspec_df(tib_int_vec("a")))
Use input_form = "scalar_list"
in tib_vector()
to parse them:
tibblify(x, tspec_df(tib_int_vec("a", input_form = "scalar_list")))$a
Sometimes vectors are encoded as objects in JSON:
x_json <- '[ {"a": {"x": 1, "y": 2}}, {"a": {"a": 1, "b": 2, "b": 3}} ]' x <- jsonlite::fromJSON(x_json, simplifyVector = FALSE) str(x)
Use input_form = "object"
in tib_vector()
to parse them. To actually store the names use the names_to
and values_to
argument:
spec <- tspec_df( tib_int_vec( "a", input_form = "object", names_to = "name", values_to = "value" ) ) tibblify(x, spec)$a
Lists where elements do not have a common type but vary. For example:
:::: {style="display: grid; grid-template-columns: 1fr 1fr; grid-column-gap: 10px; width: 100%;"}
::: {}
[1, "a", true]
:::
::: {}
list(1, "a", TRUE)
:::
::::
can be parsed with tib_variant()
.
The R equivalent to a JSON object is a named list where the names fulfill the
requirements of vctrs::vec_as_names(repair = "check_unique")
.
:::: {style="display: grid; grid-template-columns: 1fr 1fr; grid-column-gap: 10px; width: 100%;"}
::: {}
{ "a": 1, "b": true }
:::
::: {}
x <- list( a = 1, b = TRUE )
:::
::::
They can be parsed with tib_row()
. For example
x <- list( list(row = list(a = 1, b = TRUE)), list(row = list(a = 2, b = FALSE)) ) spec <- tspec_df( tib_row( "row", tib_int("a"), tib_lgl("b") ) ) tibblify(x, spec)
List of objects
:::: {style="display: grid; grid-template-columns: 1fr 1fr; grid-column-gap: 10px; width: 100%;"}
::: {}
[ {"a": 1, "b": true}, {"b": 2, "b": false} ]
:::
::: {}
x <- list( list(a = 1, b = TRUE), list(a = 2, b = FALSE) )
:::
::::
They can be parsed with tib_df()
.
A special form are named lists of object. In JSON they are represented as objects where each element is an object.
:::: {style="display: grid; grid-template-columns: 1fr 1fr; grid-column-gap: 10px; width: 100%;"}
::: {}
{ "object1": {"a": 1, "b": true}, "object2": {"b": 2, "b": false} }
:::
::: {}
x <- list( object1 = list(a = 1, b = TRUE), object2 = list(a = 2, b = FALSE) )
:::
::::
They are also parsed with tib_df()
but you can parse the names into an extra
column via the .names_to
argument:
x_json <- '[ { "df": { "object1": {"a": 1, "b": true}, "object2": {"a": 2, "b": false} } }]' x <- jsonlite::fromJSON(x_json, simplifyDataFrame = FALSE) spec <- tspec_df( tib_df( "df", tib_int("a"), tib_lgl("b"), .names_to = "name" ) ) tibblify(x, spec)$df
The column major format is also supported
:::: {style="display: grid; grid-template-columns: 1fr 1fr; grid-column-gap: 10px; width: 100%;"}
::: {}
{ "a": [1, 2], "b": [true, false] }
:::
::: {}
x <- list( a = c(1, 2), b = c(TRUE, FALSE) )
:::
::::
via .input_form = "colmajor"
in tspec_*()
:
df_spec <- tspec_df( tib_int("a"), tib_lgl("b"), .input_form = "colmajor" ) tibblify(x, df_spec)
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