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jdouble() and jinteger().
junmber() is aliased to jdouble(), and can
still be used, however jdouble() should be preferred as it
is less ambiguous.Add tbl_json methods for join to drop the
tbl_json class early. If you need the ..JSON
column, ensure to save it with json_get_column() before
joining.
Work around an issue with dplyr 1.0.0 and
[ not subsetting properly with
transmute
Please let us know if you run into any errors of the form
The `[` method for class <tbl_json/tbl_df/tbl/data.frame> must return a data frame with 1 columnWe will need to do some more work on how we manage the
..JSON column.
attr(., "JSON"), the JSON object is now a
hidden column (..JSON). To prevent future backwards
incompatibilities of this nature, there is now an “extractor” function
to pull the raw JSON object off of the tbl_json:
json_get(). You can also use json_get_column()
to add the raw json onto your tbl_json as a dedicated
column..JSON column with
dplyr::select() will mostly ignore you for complicated
reasons. Use json_get_column() if you want to access the
raw ..JSON data.tidyjson “magic”,
tibble::as_tibble() will drop the tbl_json
class and you are back to normal!Address backwards incompatibilities in
dplyr
Address backwards incompatibilities in
vctrs
Remove tidyjson::bind_rows() in favor of
re-exporting dplyr::bind_rows()
Add a few generics to make behavior generally more consistent:
$<-.tbl_json, etc.
Add as_tbl_json as a future replacement for
as.tbl_json
Add as_tbl_json.list so that you can more easily
parse the JSON outside of tbl_json if you like. Further,
this allows tbl_json to work with any arbitrary nested
list. (#119)
bind_rows() support. Though currently not an S3
implementation, it behaves as much like the dplyr variant
as possible, preserving the attr(.,'JSON') components if
all components are tbl_json objects. (#58)“Using Multiple APIs” vignette added to show support for using tidyjson with multiple APIs (#85)
Updated README.md to better explain spread_all()
(#92)
Improve compatibility with newer dplyr and
tidyr
DROP=TRUE caused an error. Altered behavior to be
consistent with tbl_df
Fix spread_all(recursive=FALSE) bug that caused an
error (#65)
Alter spread_all() behavior to recursively check for
deduplication of names (and thus avoid an error) (#76)
Add named support for the NSE versions of dplyr
functions
(filter(),mutate(),slice(), etc.)
since the SE variants are no longer called
behind-the-scenes since dplyr 0.6.0. (#97)
Fix errors with print.tbl_json() when the JSON
attribute is missing
Fix json_structure() failure if document.id missing
by imputing the missing document.id. (#86)
json_complexity() computes the “complexity”
(recursively unlisted length) of JSON data (#5)
json_structure() recursively structures arbitrary
JSON data into a single data frame (#2)
json_schema() creates a schema for a JSON document
or collection (#12)
is_json functions for testing JSON types, such as
is_json_string(), is_json_null() or
is_json_object() (#39)
spread_all() spreads all scalar values of a JSON
object into new columns (#56)
as.character.tbl_json() converts
tbl_json objects back into JSON character strings
(#62)
gather_object() replaces gather_keys(),
with default column.name of name instead of
key (#66). This more closely matches the JSON standard, which refers to objects
as name-value pairs, and is now consistent with
gather_array().
“Using Multiple APIs” vignette added to show support for using tidyjson with multiple APIs (#85)
Updated README.md to better explain spread_all()
(#92)
“Visualizing JSON” vignette for visualizing the structure of
complex JSON data, like the companies example (#4)
Significant updates to all documentation and examples for clarity (#42)
Updated “Introduction to tidyjson” vignette to be more concise and use new functionality (#74)
enter_object and the jstring,
jnumber and jlogical functions now accept
unquoted strings to specify their path (#26)
tbl_json objects now print with a tidy character
representation of the JSON attribute (#61)
Use purrr for most list based internal operations (#1)
Use tidyr for
gather_array and gather_object functions
(#28)
Imported the magrittr %>% operator (#17)
Fixed dplyr::slice() not working correctly with
tbl_json objects (#18)
First argument to verbs is .x rather than
x to avoid name conflicts in NSE (#23)
Fixed spread_values() to not coerce types
(#24)
gather_array() and gather_object() can
be called repeatedly in the same pipeline with the same
column.name argument, and will simply append an integer
identifer to the new columns (#38)
gather_keys() -> use gather_object()
insteadThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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