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Getting Started

Using datasetjson

datasetjson works by allowing you to take a data frame and apply the necessary attributes required for the CDISC Dataset JSON. The goal is to make this experience simple. Before you can write a Dataset JSON file to disk, you first need to build the Dataset JSON object. An example call looks like this:

ds_json <- dataset_json(iris[1:5, ], "IG.IRIS", "IRIS", "Iris", iris_items)

This is the minimum information required to provide to create a datasetjson object.

The parameters here can be described as follows:

The input data frame iris
The item_id, which can be described as the “Object of Dataset”, which is a key value is a unique identifier for the dataset, corresponding to ItemGroupDef/@OID in Define-XML.
name, which is the dataset name
label, which is the dataset label, and finally
items, which is the variable level metadata for your dataset.

The items parameter is special here, in that you provide a data frame with the necessary variable metadata. Take a look at the iris_items data frame.

iris_items
#>                  OID         name          label   type length displayFormat
#> 1 IT.IR.Sepal.Length Sepal.Length   Sepal Length  float     NA          <NA>
#> 2  IT.IR.Sepal.Width  Sepal.Width    Sepal Width  float     NA          <NA>
#> 3 IT.IR.Petal.Length Petal.Length   Petal Length  float     NA          <NA>
#> 4  IT.IR.Petal.Width  Petal.Width    Petal Width  float     NA          <NA>
#> 5      IT.IR.Species      Species Flower Species string     10          <NA>
#>   keySequence
#> 1           2
#> 2          NA
#> 3           3
#> 4          NA
#> 5           1

This data frame has 7 columns, 4 of which are strictly required. This is defined by the CDISC Dataset JSON Specification.

Attribute	Requirement	Description
OID	Required	OID of a variable (must correspond to the variable OID in the Define-XML file)
name	Required	Variable name
label	Required	Variable description
type	Required	Type of the variable. Allowed values: “string”, “integer”, “decimal”, “float”, “double”, “boolean”. See ODM types for details.
length	Optional	Variable length
displayFormat	Optional	Display format supports data visualization of numeric float and date values.Â
keySequence	Optional	Indicates that this item is a key variable in the dataset structure. It also provides an ordering for the keys.

The data within this dataframe ultimate populates the items element of the Dataset JSON file. The OID, name, label, and type columns are all required and must be populated for each variable. Note that the type column has a list of allowable values:

string
integer
float
double
decimal
boolean

This information must be provided directly by the user. Note that no type conversions of your data are performed by the datasetjson package. The displayFormat column inherently refers to display formats used within SAS.

Setting Other Data Attributes

The Dataset JSON specification has a number of other attributes available that are beyond normal ones present in an R data frame. These can be applied using a variety of setter functions directly to the dataset JSON object.

ds_updated <- ds_json |>
  set_data_type("referenceData") |>
  set_file_oid("/some/path") |>
  set_metadata_ref("some/define.xml") |>
  set_metadata_version("MDV.MSGv2.0.SDTMIG.3.3.SDTM.1.7") |>
  set_originator("Some Org") |>
  set_source_system("source system", "1.0") |>
  set_study_oid("SOMESTUDY")

In a practical setting, applying these attributes during the creation a dataset JSON file would be tedious, and present a challenge if the fields update - because the text would have to be updated in each program individually. For this reason, the datasetjson package allows you to use pre-built objects to create a datasetjson object.

file_meta <- file_metadata(
  originator = "Some Org",
  sys = "source system",
  sys_version = "1.0"
)

data_meta <- data_metadata(
  study = "SOMESTUDY",
  metadata_version = "MDV.MSGv2.0.SDTMIG.3.3.SDTM.1.7",
  metadata_ref = "some/define.xml"
)

dataset_meta <- dataset_metadata(
  item_id = "IG.IRIS",
  name = "IRIS",
  label = "Iris",
  items = iris_items
)

ds_json_from_meta <- dataset_json(
  iris,
  dataset_meta = dataset_meta,
  file_meta = file_meta,
  data_meta = data_meta
)

Or more practically, just file_meta and data_meta could be provided, and the dataset_metadata could be provided directly to dataset_json.

file_meta <- file_metadata(
  originator = "Some Org",
  sys = "source system",
  sys_version = "1.0"
)

data_meta <- data_metadata(
  study = "SOMESTUDY",
  metadata_version = "MDV.MSGv2.0.SDTMIG.3.3.SDTM.1.7",
  metadata_ref = "some/define.xml"
)


ds_json_from_meta <- dataset_json(
  iris,
  item_id = "IG.IRIS",
  name = "IRIS",
  label = "Iris",
  items = iris_items,
  file_meta = file_meta,
  data_meta = data_meta
)

Writing and Reading

The datasetjson object allows you to collect the information needed to generate a Dataset JSON file, but to write the dataset out need to use the write_dataset_json() file. Once the Dataset JSON object is available, all you need is that object name and a file path.

write_dataset_json(ds_updated, file="iris.json")

The write_dataset_json() also has the option to return the JSON output as a character string.

js <- write_dataset_json(ds_updated, pretty=TRUE)
cat(js)
#> {
#>   "creationDateTime": "2024-01-09T20:36:24",
#>   "datasetJSONVersion": "1.0.0",
#>   "fileOID": "/some/path",
#>   "originator": "Some Org",
#>   "sourceSystem": "source system",
#>   "sourceSystemVersion": "1.0",
#>   "referenceData": {
#>     "studyOID": "SOMESTUDY",
#>     "metaDataVersionOID": "MDV.MSGv2.0.SDTMIG.3.3.SDTM.1.7",
#>     "metaDataRef": "some/define.xml",
#>     "itemGroupData": {
#>       "IG.IRIS": {
#>         "records": 5,
#>         "name": "IRIS",
#>         "label": "Iris",
#>         "items": [
#>           {
#>             "OID": "ITEMGROUPDATASEQ",
#>             "name": "ITEMGROUPDATASEQ",
#>             "label": "Record Identifier",
#>             "type": "integer"
#>           },
#>           {
#>             "OID": "IT.IR.Sepal.Length",
#>             "name": "Sepal.Length",
#>             "label": "Sepal Length",
#>             "type": "float",
#>             "keySequence": 2
#>           },
#>           {
#>             "OID": "IT.IR.Sepal.Width",
#>             "name": "Sepal.Width",
#>             "label": "Sepal Width",
#>             "type": "float"
#>           },
#>           {
#>             "OID": "IT.IR.Petal.Length",
#>             "name": "Petal.Length",
#>             "label": "Petal Length",
#>             "type": "float",
#>             "keySequence": 3
#>           },
#>           {
#>             "OID": "IT.IR.Petal.Width",
#>             "name": "Petal.Width",
#>             "label": "Petal Width",
#>             "type": "float"
#>           },
#>           {
#>             "OID": "IT.IR.Species",
#>             "name": "Species",
#>             "label": "Flower Species",
#>             "type": "string",
#>             "length": 10,
#>             "keySequence": 1
#>           }
#>         ],
#>         "itemData": [
#>           [1, 5.1, 3.5, 1.4, 0.2, "setosa"],
#>           [2, 4.9, 3, 1.4, 0.2, "setosa"],
#>           [3, 4.7, 3.2, 1.3, 0.2, "setosa"],
#>           [4, 4.6, 3.1, 1.5, 0.2, "setosa"],
#>           [5, 5, 3.6, 1.4, 0.2, "setosa"]
#>         ]
#>       }
#>     }
#>   }
#> }

Similarly, to read a Dataset JSON object, you can use the function read_dataset_json(). This function will return a dataframe to you, ready to use. To read, provide a file path.

read_dataset_json("path/to/file")

You can also provide single element character vector of the JSON text already read in.

dat <- read_dataset_json(js)

The data frame that’s read in carries a number of attributes. For example, opening the dataframe within the RStudio IDE will present the variable labels. All data available within the Dataset JSON file is ultimately attached to the imported data frame.

attributes(dat)
#> $names
#> [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"     
#> 
#> $class
#> [1] "data.frame"
#> 
#> $row.names
#> [1] 1 2 3 4 5
#> 
#> $creationDateTime
#> [1] "2024-01-09T20:36:24"
#> 
#> $datasetJSONVersion
#> [1] "1.0.0"
#> 
#> $fileOID
#> [1] "/some/path"
#> 
#> $originator
#> [1] "Some Org"
#> 
#> $sourceSystem
#> [1] "source system"
#> 
#> $sourceSystemVersion
#> [1] "1.0"
#> 
#> $name
#> [1] "IRIS"
#> 
#> $records
#> [1] 5
#> 
#> $label
#> [1] "Iris"
#> 
#> $referenceData
#> $referenceData$studyOID
#> [1] "SOMESTUDY"
#> 
#> $referenceData$metaDataVersionOID
#> [1] "MDV.MSGv2.0.SDTMIG.3.3.SDTM.1.7"
#> 
#> $referenceData$metaDataRef
#> [1] "some/define.xml"
#> 
#> $referenceData$itemGroupData
#> [1] "IG.IRIS"

For variable level metadata, the attributes are applied directly to the columns.

attributes(dat$Species)
#> $label
#> [1] "Flower Species"
#> 
#> $OID
#> [1] "IT.IR.Species"
#> 
#> $length
#> [1] 10
#> 
#> $type
#> [1] "string"
#> 
#> $keySequence
#> [1] 1

These 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.