Transform and Validate JSON and NDJSON

Introduction & installation

Use rjsoncons for querying, transforming, and searching JSON, NDJSON, or R objects using JMESpath, JSONpath, or JSONpointer. rjsoncons supports JSON patch for document editing, and JSON schema validation. Link to the package for direct access to additional features in the jsoncons C++ library.

Install the released package version from CRAN

install.packages("rjsoncons", repos = "https://CRAN.R-project.org")

Install the development version with

if (!requireNamespace("remotes", quiety = TRUE))
    install.packages("remotes", repos = "https://CRAN.R-project.org")
remotes::install_github("mtmorgan/rjsoncons")

Attach the installed package to your R session, and check the version of the C++ library in use

library(rjsoncons)
rjsoncons::version()
## [1] "0.173.4 [+57967655d]"

Query and pivot

Functions in this package work on JSON or NDJSON character vectors, file paths and URLs to JSON or NDJSON documents, and R objects that can be transformed to a JSON string.

Select, filter and transform with `j_query()`

Here is a simple JSON example document

json <- '{
  "locations": [
    {"name": "Seattle", "state": "WA"},
    {"name": "New York", "state": "NY"},
    {"name": "Bellevue", "state": "WA"},
    {"name": "Olympia", "state": "WA"}
  ]
}'

There are several common use cases. Use rjsoncons to query the JSON string using JSONpath, JMESpath or JSONpointer syntax to filter larger documents to records of interest, e.g., only cities in New York state, using ‘JMESpath’ syntax.

j_query(json, "locations[?state == 'NY']") |>
    cat("\n")
## [{"name":"New York","state":"NY"}]

Use the as = "R" argument to extract deeply nested elements as R objects, e.g., a character vector of city names in Washington state.

j_query(json, "locations[?state == 'WA'].name", as = "R")
## [1] "Seattle"  "Bellevue" "Olympia"

The JSON Pointer specification is simpler, indexing a single object in the document. JSON arrays are 0-based.

j_query(json, "/locations/0/state")
## [1] "WA"

The examples above use j_query(), which automatically infers query specification from the form of path using j_path_type(). It may be useful to indicate query specification more explicitly using jsonpointer(), jsonpath(), or jmespath(); examples illustrating features available for each query specification are on the help pages ?jsonpointer, ?jsonpath, and ?jmespath.

Array-of-objects to R data.frame with `j_pivot()`

The following transforms a nested JSON document into a format that can be incorporated directly in R as a data.frame.

path <- '{
    name: locations[].name,
    state: locations[].state
}'
j_query(json, path, as = "R") |>
    data.frame()
##       name state
## 1  Seattle    WA
## 2 New York    NY
## 3 Bellevue    WA
## 4  Olympia    WA

The transformation from JSON ‘array-of-objects’ to ‘object-of-arrays’ suitable for direct representation as a data.frame is common, and is implemented directly as j_pivot()

j_pivot(json, "locations", as = "data.frame")
##       name state
## 1  Seattle    WA
## 2 New York    NY
## 3 Bellevue    WA
## 4  Olympia    WA

j_pivot() also support as = "tibble" when the dplyr package is installed.

NDJSON support

rjsoncons supports NDJSON (new-line delimited JSON). NDJSON consists of a file or character vector where each line / element represents a JSON record. This example uses data from the GitHub Archive project recording all actions on public GitHub repositories. The data included in the package are the first 10 lines of https://data.gharchive.org/2023-02-08-0.json.gz.

ndjson_file <-
    system.file(package = "rjsoncons", "extdata", "2023-02-08-0.json")

NDJSON can be read into R (ndjson <- readLines(ndjson_file)) and used in j_query() / j_pivot(), but it is often better to leave full NDJSON files on disk. Thus the first argument to j_query() or j_pivot() is usually a (text or gz-compressed) file path or URL. Two additional options are available when working with NDJSON. n_records limits the number of records processed. Using n_records can be very useful when exploring the data. For instance, the first record of a file can be viewed interactively with

j_query(ndjson_file, n_records = 1) |>
    listviewer::jsonedit()

The option verbose = TRUE adds a progress indicator, which provides confidence that progress is being made while parsing large files. The progress bar requires the cli package.

j_query() provides a one-to-one mapping of NDJSON lines / elements to the return value, e.g., j_query(ndjson_file, "@", as = "string") on an NDJSON file with 1000 lines will return a character vector of 1000 elements, or with j_query(ndjson, "@", as = "R") an R list with length 1000.

j_query(ndjson_file, "{id: id, type: type}", n_records = 5)
## [1] "{\"id\":\"26939254345\",\"type\":\"DeleteEvent\"}"
## [2] "{\"id\":\"26939254358\",\"type\":\"PushEvent\"}"  
## [3] "{\"id\":\"26939254361\",\"type\":\"CreateEvent\"}"
## [4] "{\"id\":\"26939254365\",\"type\":\"CreateEvent\"}"
## [5] "{\"id\":\"26939254366\",\"type\":\"PushEvent\"}"

j_pivot() transforms an NDJSON file or character vector of objects into a format convenient for input in R. j_pivot() with NDJSON files and JMESpath paths work particularly well together, because JMESpath provides flexibility in creating JSON objects to be pivoted.

j_pivot(ndjson_file, "{id: id, type: type}", as = "data.frame")
##             id        type
## 1  26939254345 DeleteEvent
## 2  26939254358   PushEvent
## 3  26939254361 CreateEvent
## 4  26939254365 CreateEvent
## 5  26939254366   PushEvent
## 6  26939254367   PushEvent
## 7  26939254379   PushEvent
## 8  26939254380 IssuesEvent
## 9  26939254382   PushEvent
## 10 26939254383   PushEvent

Filtering NDJSON files can require relatively more complicated paths, e.g., to filter ‘PushEvent’ types from organizations, construct a query that acts on each NDJSON record to return an array of a single object, then apply a filter to replace uninteresting elements with 0-length arrays (using as = "tibble" often transforms the R list-of-vectors to a tibble in a more pleasing and robust manner compared to as = "data.frame").

path <-
    "[{id: id, type: type, org: org}]
         [?@.type == 'PushEvent' && @.org != null] |
             [0]"
j_pivot(ndjson_file, path, as = "data.frame")
##            id      type    org.id          org.login org.gravatar_id
## 1 26939254358 PushEvent 123667276 johnbieren-testing                
## 2 26939254382 PushEvent 123667276 johnbieren-testing                
##                                          org.url
## 1 https://api.github.com/orgs/johnbieren-testing
## 2 https://api.github.com/orgs/johnbieren-testing
##                                       org.avatar_url  org.id.1  org.login.1
## 1 https://avatars.githubusercontent.com/u/123667276? 120284018 mornystannit
## 2 https://avatars.githubusercontent.com/u/123667276? 120284018 mornystannit
##   org.gravatar_id.1                                org.url.1
## 1                   https://api.github.com/orgs/mornystannit
## 2                   https://api.github.com/orgs/mornystannit
##                                     org.avatar_url.1
## 1 https://avatars.githubusercontent.com/u/120284018?
## 2 https://avatars.githubusercontent.com/u/120284018?

A more complete example is used in the NDJSON extended vignette

R objects as input

rjsoncons can filter and transform R objects. These are converted to JSON using jsonlite::toJSON() before queries are made; toJSON() arguments like auto_unbox = TRUE can be added to the function call.

## `lst` is an *R* list
lst <- jsonlite::fromJSON(json, simplifyVector = FALSE)
j_query(lst, "locations[?state == 'WA'].name | sort(@)", auto_unbox = TRUE) |>
    cat("\n")
## ["Bellevue","Olympia","Seattle"]

Patch

JSON Patch provides a simple way to edit or transform a JSON document using JSON commands.

Applying a patch with `j_patch_apply()`

Starting with the JSON document

json <- '{
  "biscuits": [
    { "name": "Digestive" },
    { "name": "Choco Leibniz" }
  ]
}'

one can "add" another biscuit, and copy a favorite biscuit to a new locations using the following patch

patch <- '[
    {"op": "add", "path": "/biscuits/1", "value": { "name": "Ginger Nut" }},
    {"op": "copy", "from": "/biscuits/2", "path": "/best_biscuit"}
]'

The paths are specified using JSONpointer notation; remember that JSON arrays are 0-based, compared to 1-based R arrays. Applying the patch results in a new JSON document.

j_patch_apply(json, patch)
## [1] "{\"biscuits\":[{\"name\":\"Digestive\"},{\"name\":\"Ginger Nut\"},{\"name\":\"Choco Leibniz\"}],\"best_biscuit\":{\"name\":\"Choco Leibniz\"}}"

Patches can also be created from R objects with the helper function j_patch_op().

ops <- c(
    j_patch_op(
        "add", "/biscuits/1", value = list(name = "Ginger Nut"),
        auto_unbox = TRUE
    ),
    j_patch_op("copy", "/best_biscuit", from = "/biscuits/2")
)
identical(j_patch_apply(json, patch), j_patch_apply(json, ops))
## [1] TRUE

j_patch_op() takes care of unboxing op=, path=, and from=, but some care must be taken in ‘unboxing’ the value= argument for operations such as ‘add’; it may also be appropriate to unbox only specific fields, e.g.,

value <- list(name = jsonlite::unbox("Ginger Nut"))
j_patch_op("add", "/biscuits/1", value = value)
## [
##   {"op": "add", "path": "/biscuits/1", "value": {"name": "Ginger Nut"}}
## ]

From the JSON patch web site, available operations and example JSON are:

add – add elements to an existing document.

{"op": "add", "path": "/biscuits/1", "value": {"name": "Ginger Nut"}}

remove – remove elements from a document.
```
{"op": "remove", "path": "/biscuits/0"}
```

replace – replace one element with another

{
    "op": "replace", "path": "/biscuits/0/name",
    "value": "Chocolate Digestive"
}

copy – copy a path to another location.

{"op": "copy", "from": "/biscuits/0", "path": "/best_biscuit"}

move – move a path to another location.

{"op": "move", "from": "/biscuits", "path": "/cookies"}

test – test for the existence of a path; if the path does not exist, do not apply any of the patch.
```
{"op": "test", "path": "/best_biscuit/name", "value": "Choco Leibniz"}
```

Formal description of these operations is provided in Section 4 of RFC6902. A patch command is always an array, even when a single operation is involved.

Difference between documents with `j_patch_from()`

The j_patch_from() function constructs a patch from the difference between two documents

j_patch_from(j_patch_apply(json, patch), json)
## [1] "[{\"op\":\"replace\",\"path\":\"/biscuits/1/name\",\"value\":\"Choco Leibniz\"},{\"op\":\"remove\",\"path\":\"/biscuits/2\"},{\"op\":\"remove\",\"path\":\"/best_biscuit\"}]"

Schema validation

JSON schema provides structure to JSON documents. j_schema_is_valid() checks that a JSON document is valid against a specified schema, and j_schema_validate() tries to illustrate how a document deviates from the schema.

As an example consider j_patch_op(), where the operation is supposed to conform to the JSON patch schema. For convenience, a copy of this schema is available in rjsoncons.

## alternatively: schema <- "https://json.schemastore.org/json-patch"
schema <- system.file(package = "rjsoncons", "extdata", "json-patch.json")
cat(readLines(schema), sep = "\n")
## {
##   "$schema": "http://json-schema.org/draft-04/schema#",
##   "definitions": {
##     "path": {
##       "description": "A JSON Pointer path.",
##       "type": "string"
##     }
##   },
##   "id": "https://json.schemastore.org/json-patch.json",
##   "items": {
##     "oneOf": [
##       {
##         "additionalProperties": false,
##         "required": ["value", "op", "path"],
##         "properties": {
##           "path": {
##             "$ref": "#/definitions/path"
##           },
##           "op": {
##             "description": "The operation to perform.",
##             "type": "string",
##             "enum": ["add", "replace", "test"]
##           },
##           "value": {
##             "description": "The value to add, replace or test."
##           }
##         }
##       },
##       {
##         "additionalProperties": false,
##         "required": ["op", "path"],
##         "properties": {
##           "path": {
##             "$ref": "#/definitions/path"
##           },
##           "op": {
##             "description": "The operation to perform.",
##             "type": "string",
##             "enum": ["remove"]
##           }
##         }
##       },
##       {
##         "additionalProperties": false,
##         "required": ["from", "op", "path"],
##         "properties": {
##           "path": {
##             "$ref": "#/definitions/path"
##           },
##           "op": {
##             "description": "The operation to perform.",
##             "type": "string",
##             "enum": ["move", "copy"]
##           },
##           "from": {
##             "$ref": "#/definitions/path",
##             "description": "A JSON Pointer path pointing to the location to move/copy from."
##           }
##         }
##       }
##     ]
##   },
##   "title": "JSON schema for JSONPatch files",
##   "type": "array"
## }

The well-formed ‘op’ is valid, and j_schema_validate() produces no output

op <- '[{
    "op": "add", "path": "/biscuits/1",
    "value": { "name": "Ginger Nut" }
}]'
j_schema_is_valid(op, schema)
## [1] TRUE
j_schema_validate(op, schema)
## [1] "[]"

Introduce an invalid ‘op’, "op": "invalid_op", and the schema is no longer valid.

op <- '[{
    "op": "invalid_op", "path": "/biscuits/1",
    "value": { "name": "Ginger Nut" }
}]'
j_schema_is_valid(op, schema)
## [1] FALSE

The reason can be understood from (careful!) consideration of the output of j_schema_validate(), with reference to the schema itself.

j_schema_validate(op, schema, as = "tibble") |>
    tibble::glimpse()
## Rows: 1
## Columns: 6
## $ valid            <lgl> FALSE
## $ evaluationPath   <chr> "/items/oneOf"
## $ schemaLocation   <chr> "https://json.schemastore.org/json-patch.json#/items/…
## $ instanceLocation <chr> "/0"
## $ error            <chr> "No schema matched, but exactly one of them is requir…
## $ details          <list> [[FALSE, "/items/oneOf/0/properties/op/enum", "https:…

The validation indicates that the schema evaluationPath ‘/items/oneOf’ is not satisfied, because of the error ‘No schema [i.e., ’oneOf’ elements] matched, …’.

The ‘details’ column summarizes why each of the 3 elements of /items/oneOf fails the schema specification; use as = "details" to extract this directly

j_schema_validate(op, schema, as = "details") |>
    tibble::glimpse()
## Rows: 6
## Columns: 5
## $ valid            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
## $ evaluationPath   <chr> "/items/oneOf/0/properties/op/enum", "/items/oneOf/1/…
## $ schemaLocation   <chr> "https://json.schemastore.org/json-patch.json#/items/…
## $ instanceLocation <chr> "/0/op", "/0/op", "/0/value", "/0", "/0/op", "/0/valu…
## $ error            <chr> "'invalid_op' is not a valid enum value.", "'invalid_…

This indicates that the first item in the schema is rejected because ‘invalid_op’ is not a valid enum

j_query(schema, "/items/oneOf/0/properties/op/enum") |>
    noquote()
## [1] ["add","replace","test"]

Reasons for rejecting other items can be explored using similar steps.

Flatten and find

It can sometimes be helpful to explore JSON documents by ‘flattening’ the JSON to an object of path / value pairs, where the path is the JSONpointer path to the corresponding value. It is then straight-forward to search this flattened object for, e.g., the path to a known field or value. As an example, consider the object

codes <- '{
    "discards": {
        "1000": "Record does not exist",
        "1004": "Queue limit exceeded",
        "1010": "Discarding timed-out partial msg"
    },
    "warnings": {
        "0": "Phone number missing country code",
        "1": "State code missing",
        "2": "Zip code missing"
    }
}'

The ‘flat’ JSON of this can be represented as named list (using str() to provide a compact visual representation)

j_flatten(codes, as = "R") |>
    str()
## List of 6
##  $ /discards/1000: chr "Record does not exist"
##  $ /discards/1004: chr "Queue limit exceeded"
##  $ /discards/1010: chr "Discarding timed-out partial msg"
##  $ /warnings/0   : chr "Phone number missing country code"
##  $ /warnings/1   : chr "State code missing"
##  $ /warnings/2   : chr "Zip code missing"

The names of the list are JSONpointer (default) or JSONpath, so can be used in j_query() and j_pivot() as appropriate

j_query(codes, "/discards/1010")
## [1] "Discarding timed-out partial msg"

There are two ways to find known keys and values. The first is to use exact matching to one or more keys or values, e.g.,

j_find_values(
    codes, c("Record does not exist", "State code missing"),
    as = "tibble"
)
## # A tibble: 2 × 2
##   path           value                
##   <chr>          <chr>                
## 1 /discards/1000 Record does not exist
## 2 /warnings/1    State code missing
j_find_keys(codes, "warnings", as = "tibble")
## # A tibble: 3 × 2
##   path        value                            
##   <chr>       <chr>                            
## 1 /warnings/0 Phone number missing country code
## 2 /warnings/1 State code missing               
## 3 /warnings/2 Zip code missing

It is also possible to match using a regular expression.

j_find_values_grep(codes, "missing", as = "tibble")
## # A tibble: 3 × 2
##   path        value                            
##   <chr>       <chr>                            
## 1 /warnings/0 Phone number missing country code
## 2 /warnings/1 State code missing               
## 3 /warnings/2 Zip code missing
j_find_keys_grep(codes, "card.*/100", as = "tibble") # span key delimiters
## # A tibble: 2 × 2
##   path           value                
##   <chr>          <chr>                
## 1 /discards/1000 Record does not exist
## 2 /discards/1004 Queue limit exceeded

Keys are always character vectors, but values can be of different type; j_find_values() supports searches on these.

j <- '{"x":[1,[2, 3]],"y":{"a":4}}'
j_flatten(j, as = "R") |> str()
## List of 4
##  $ /x/0  : int 1
##  $ /x/1/0: int 2
##  $ /x/1/1: int 3
##  $ /y/a  : int 4
j_find_values(j, c(2, 4), as = "tibble")
## # A tibble: 2 × 2
##   path   value
##   <chr>  <int>
## 1 /x/1/0     2
## 2 /y/a       4

A common operation might be to find the path to a know value, and then to query the original JSON to find the object in which the value is contained.

j_find_values(j, 3, as = "tibble")
## # A tibble: 1 × 2
##   path   value
##   <chr>  <int>
## 1 /x/1/1     3
## path to '3' is '/x/1/1', so containing object is at '/x/1'
j_query(j, "/x/1")
## [1] "[2,3]"
j_query(j, "/x/1", as = "R")
## [1] 2 3

Both JSONpointer and JSONpath are supported; an advantage of the latter is that the path distinguishes between integer-valued (unquoted) and string-valued (quoted) keys

j_find_values(j, 3, as = "tibble", path_type = "JSONpath")
## # A tibble: 1 × 2
##   path         value
##   <chr>        <int>
## 1 $['x'][1][1]     3

The first argument to j_find_*() can be an R object, JSON or NDJSON string, file, or URL. Using j_find_values() with an R object and JSONpath path_type leads to a path that is easily converted into an R index: double the [ and ] in the path and increment each numerical index by 1:

l <- j |> as_r()
j_find_values(l, 3, auto_unbox = TRUE, path_type = "JSONpath", as = "tibble")
## # A tibble: 1 × 2
##   path         value
##   <chr>        <int>
## 1 $['x'][1][1]     3
l[['x']][[2]] # siblings
## [1] 2 3

NDJSON files are flattened into character vectors, with each element the flattened version of the corresponding NDJSON record.

The JSON parser

The package includes a JSON parser, used with the argument as = "R" or directly with as_r()

as_r('{"a": 1.0, "b": [2, 3, 4]}') |>
    str()
#> List of 2
#>  $ a: num 1
#>  $ b: int [1:3] 2 3 4

The main rules of this transformation are outlined here. JSON arrays of a single type (boolean, integer, double, string) are transformed to R vectors of the same length and corresponding type.

as_r('[true, false, true]') # boolean -> logical
## [1]  TRUE FALSE  TRUE
as_r('[1, 2, 3]')           # integer -> integer
## [1] 1 2 3
as_r('[1.0, 2.0, 3.0]')     # double  -> numeric
## [1] 1 2 3
as_r('["a", "b", "c"]')     # string  -> character
## [1] "a" "b" "c"

JSON arrays mixing integer and double values are transformed to R numeric vectors.

as_r('[1, 2.0]') |> class() # numeric
## [1] "numeric"

If a JSON integer array contains a value larger than R’s 32-bit integer representation, the array is transformed to an R numeric vector. NOTE that this results in loss of precision for JSON integer values greater than 2^53.

as_r('[1, 2147483648]') |> class()  # 64-bit integers -> numeric
## [1] "numeric"

JSON objects are transformed to R named lists.

as_r('{}')
## named list()
as_r('{"a": 1.0, "b": [2, 3, 4]}') |> str()
## List of 2
##  $ a: num 1
##  $ b: int [1:3] 2 3 4

There are several additional details. A JSON scalar and a JSON vector of length 1 are represented in the same way in R.

identical(as_r("3.14"), as_r("[3.14]"))
## [1] TRUE

JSON arrays mixing types other than integer and double are transformed to R lists

as_r('[true, 1, "a"]') |> str()
## List of 3
##  $ : logi TRUE
##  $ : int 1
##  $ : chr "a"

JSON null values are represented as R NULL values; arrays of null are transformed to lists

as_r('null')                  # NULL
## NULL
as_r('[null]') |> str()       # list(NULL)
## List of 1
##  $ : NULL
as_r('[null, null]') |> str() # list(NULL, NULL)
## List of 2
##  $ : NULL
##  $ : NULL

Ordering of object members is controlled by the object_names= argument. The default preserves names as they appear in the JSON definition; use "sort" to sort names alphabetically. This argument is applied recursively.

json <- '{"b": 1, "a": {"d": 2, "c": 3}}'
as_r(json) |> str()
## List of 2
##  $ b: int 1
##  $ a:List of 2
##   ..$ d: int 2
##   ..$ c: int 3
as_r(json, object_names = "sort") |> str()
## List of 2
##  $ a:List of 2
##   ..$ c: int 3
##   ..$ d: int 2
##  $ b: int 1

The parser corresponds approximately to jsonlite::fromJSON() with arguments simplifyVector = TRUE, simplifyDataFrame = FALSE, simplifyMatrix = FALSE). Unit tests (using the tinytest framework) providing additional details are available at

system.file(package = "rjsoncons", "tinytest", "test_as_r.R")

Using `jsonlite::fromJSON()`

The built-in parser can be replaced by alternative parsers by returning the query as a JSON string, e.g., using the fromJSON() in the jsonlite package.

json <- '{
  "locations": [
    {"name": "Seattle", "state": "WA"},
    {"name": "New York", "state": "NY"},
    {"name": "Bellevue", "state": "WA"},
    {"name": "Olympia", "state": "WA"}
  ]
}'
j_query(json, "locations[?state == 'WA']") |>
    ## `fromJSON()` simplifies list-of-objects to data.frame
    jsonlite::fromJSON()
##       name state
## 1  Seattle    WA
## 2 Bellevue    WA
## 3  Olympia    WA

The rjsoncons package is particularly useful when accessing elements that might otherwise require complicated application of nested lapply(), purrr expressions, or tidyr unnest_*() (see R for Data Science chapter ‘Hierarchical data’).

C++ library use in other packages

The package includes the complete ‘jsoncons’ C++ header-only library, available to other R packages by adding

LinkingTo: rjsoncons
SystemRequirements: C++11

to the DESCRIPTION file. Typical use in an R package would also include LinkingTo: specifications for the cpp11 or Rcpp (this package uses cpp11) packages to provide a C / C++ interface between R and the C++ ‘jsoncons’ library.

Session information

This vignette was compiled using the following software versions

sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] rjsoncons_1.3.1.9100 BiocStyle_2.32.1    
## 
## loaded via a namespace (and not attached):
##  [1] vctrs_0.6.5         cli_3.6.3           knitr_1.48         
##  [4] rlang_1.1.4         xfun_0.47           textshaping_0.4.0  
##  [7] jsonlite_1.8.8      glue_1.7.0          htmltools_0.5.8.1  
## [10] ragg_1.3.2          sass_0.4.9          fansi_1.0.6        
## [13] rmarkdown_2.28      evaluate_0.24.0     jquerylib_0.1.4    
## [16] tibble_3.2.1        fastmap_1.2.0       yaml_2.3.10        
## [19] lifecycle_1.0.4     bookdown_0.40       BiocManager_1.30.25
## [22] compiler_4.4.1      fs_1.6.4            pkgconfig_2.0.3    
## [25] systemfonts_1.1.0   digest_0.6.37       R6_2.5.1           
## [28] utf8_1.2.4          pillar_1.9.0        magrittr_2.0.3     
## [31] bslib_0.8.0         tools_4.4.1         pkgdown_2.1.0      
## [34] cachem_1.1.0        desc_1.4.3

Martin Morgan

Marcel Ramos

Introduction & installation

Query and pivot

Select, filter and transform with `j_query()`

Array-of-objects to R data.frame with `j_pivot()`

NDJSON support

R objects as input

Patch

Applying a patch with `j_patch_apply()`

Difference between documents with `j_patch_from()`

Schema validation

Flatten and find

The JSON parser

Using `jsonlite::fromJSON()`

C++ library use in other packages

Session information

Transform and Validate JSON and NDJSON

Martin Morgan

Marcel Ramos

Introduction & installation

Query and pivot

Select, filter and transform with j_query()

Array-of-objects to R data.frame with j_pivot()

NDJSON support

R objects as input

Patch

Applying a patch with j_patch_apply()

Difference between documents with j_patch_from()

Schema validation

Flatten and find

The JSON parser

Using jsonlite::fromJSON()

C++ library use in other packages

Session information

Select, filter and transform with `j_query()`

Array-of-objects to R data.frame with `j_pivot()`

Applying a patch with `j_patch_apply()`

Difference between documents with `j_patch_from()`

Using `jsonlite::fromJSON()`