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README.md





rictionless Data -
Data Package

CRAN_Status_Badge Build Status Coverage Status Github Issues Pending Pull-Requests Project Status: Active – The project has reached a stable, usable state but is no longer being actively developed; support/maintenance will be provided as time allows. minimal R version Rdoc Licence Gitter

Description

R package for working with Frictionless Data Package.

Features

  • Package class for working with data packages
  • Resource class for working with data resources
  • Profile class for working with profiles
  • validate function for validating data package descriptors
  • infer function for inferring data package descriptors

Getting started

Installation

In order to install the latest distribution of R software to your computer you have to select one of the mirror sites of the Comprehensive R Archive Network, select the appropriate link for your operating system and follow the wizard instructions.

For windows users you can:

  1. Go to CRAN
  2. Click download R for Windows
  3. Click Base (This is what you want to install R for the first time)
  4. Download the latest R version
  5. Run installation file and follow the instrustions of the installer.

(Mac) OS X and Linux users may need to follow different steps depending on their system version to install R successfully and it is recommended to read the instructions on CRAN site carefully.

Even more detailed installation instructions can be found in R Installation and Administration manual.

To install RStudio, you can download RStudio Desktop with Open Source License and follow the wizard instructions:

  1. Go to RStudio
  2. Click download on RStudio Desktop
  3. Download on RStudio Desktop free download
  4. Select the appropriate file for your system
  5. Run installation file

To install the datapackage package it is necessary to install first devtools package to make installation of github packages available.

# Install devtools package if not already
install.packages("devtools")

Install datapackage.r

# And then install the development version from github
devtools::install_github("frictionlessdata/datapackage-r")

Load package

# load the package using
library(datapackage.r)

Examples

Code examples in this readme requires R 3.3 or higher, You could see even more examples in vignettes directory.

descriptor <- '{
  "resources": [
    {
      "name": "example",
      "profile": "tabular-data-resource",
      "data": [
        ["height", "age", "name"],
        [180, 18, "Tony"],
        [192, 32, "Jacob"]
      ],
      "schema":  {
        "fields": [
          {"name": "height", "type": "integer" },
          {"name": "age", "type": "integer" },
          {"name": "name", "type": "string" }
        ]
      }
    }
  ]
}'

dataPackage <- Package.load(descriptor)
dataPackage
## <Package>
##   Public:
##     addResource: function (descriptor) 
##     clone: function (deep = FALSE) 
##     commit: function (strict = NULL) 
##     descriptor: active binding
##     errors: active binding
##     getResource: function (name) 
##     infer: function (pattern) 
##     initialize: function (descriptor = list(), basePath = NULL, strict = FALSE, 
##     profile: active binding
##     removeResource: function (name) 
##     resourceNames: active binding
##     resources: active binding
##     save: function (target, type = "json") 
##     valid: active binding
##   Private:
##     basePath_: C:/Users/kleanthis-okfngr/Documents/datapackage-r
##     build_: function () 
##     currentDescriptor_: list
##     currentDescriptor_json: NULL
##     descriptor_: NULL
##     errors_: list
##     nextDescriptor_: list
##     pattern_: NULL
##     profile_: Profile, R6
##     resources_: list
##     resources_length: NULL
##     strict_: FALSE
resource <- dataPackage$getResource('example')
# convert to json and add indentation with jsonlite prettify function
jsonlite::prettify(helpers.from.list.to.json(resource$read()))
## [
##     [
##         180,
##         18,
##         "Tony"
##     ],
##     [
##         192,
##         32,
##         "Jacob"
##     ]
## ]
## 

Documentation

Json objects are not included in R base data types. Jsonlite package is internally used to convert json data to list objects. The input parameters of functions could be json strings, files or lists and the outputs are in list format to easily further process your data in R environment and exported as desired. The examples below show how to use jsonlite package to convert the output back to json adding indentation whitespace. More details about handling json you can see jsonlite documentation or vignettes here.

Working with Package

A class for working with data packages. It provides various capabilities like loading local or remote data package, inferring a data package descriptor, saving a data package descriptor and many more.

Consider we have some local csv files in a data directory. Let’s create a data package based on this data using a Package class:

inst/extdata/readme_example/cities.csv

city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,"41.89,12.51"

inst/extdata/readme_example/population.csv

city,year,population
london,2017,8780000
paris,2017,2240000
rome,2017,2860000

First we create a blank data package:

dataPackage <- Package.load()

Now we’re ready to infer a data package descriptor based on data files we have. Because we have two csv files we use glob pattern csv:

jsonlite::toJSON(dataPackage$infer('csv'), pretty = TRUE)
## {
##   "profile": ["tabular-data-package"],
##   "resources": [
##     {
##       "path": ["cities.csv"],
##       "profile": ["tabular-data-resource"],
##       "encoding": ["utf-8"],
##       "name": ["cities"],
##       "format": ["csv"],
##       "mediatype": ["text/csv"],
##       "schema": {
##         "fields": [
##           {
##             "name": ["city"],
##             "type": ["string"],
##             "format": ["default"]
##           },
##           {
##             "name": ["location"],
##             "type": ["string"],
##             "format": ["default"]
##           }
##         ],
##         "missingValues": [
##           [""]
##         ]
##       }
##     },
##     {
##       "path": ["population.csv"],
##       "profile": ["tabular-data-resource"],
##       "encoding": ["utf-8"],
##       "name": ["population"],
##       "format": ["csv"],
##       "mediatype": ["text/csv"],
##       "schema": {
##         "fields": [
##           {
##             "name": ["city"],
##             "type": ["string"],
##             "format": ["default"]
##           },
##           {
##             "name": ["year"],
##             "type": ["integer"],
##             "format": ["default"]
##           },
##           {
##             "name": ["population"],
##             "type": ["integer"],
##             "format": ["default"]
##           }
##         ],
##         "missingValues": [
##           [""]
##         ]
##       }
##     }
##   ]
## }
jsonlite::toJSON(dataPackage$descriptor, pretty = TRUE)
## {
##   "profile": ["tabular-data-package"],
##   "resources": [
##     {
##       "path": ["cities.csv"],
##       "profile": ["tabular-data-resource"],
##       "encoding": ["utf-8"],
##       "name": ["cities"],
##       "format": ["csv"],
##       "mediatype": ["text/csv"],
##       "schema": {
##         "fields": [
##           {
##             "name": ["city"],
##             "type": ["string"],
##             "format": ["default"]
##           },
##           {
##             "name": ["location"],
##             "type": ["string"],
##             "format": ["default"]
##           }
##         ],
##         "missingValues": [
##           [""]
##         ]
##       }
##     },
##     {
##       "path": ["population.csv"],
##       "profile": ["tabular-data-resource"],
##       "encoding": ["utf-8"],
##       "name": ["population"],
##       "format": ["csv"],
##       "mediatype": ["text/csv"],
##       "schema": {
##         "fields": [
##           {
##             "name": ["city"],
##             "type": ["string"],
##             "format": ["default"]
##           },
##           {
##             "name": ["year"],
##             "type": ["integer"],
##             "format": ["default"]
##           },
##           {
##             "name": ["population"],
##             "type": ["integer"],
##             "format": ["default"]
##           }
##         ],
##         "missingValues": [
##           [""]
##         ]
##       }
##     }
##   ]
## }

An infer method has found all our files and inspected it to extract useful metadata like profile, encoding, format, Table Schema etc. Let’s tweak it a little bit:

dataPackage$descriptor$resources[[2]]$schema$fields[[2]]$type <- 'year'
dataPackage$commit()
## [1] TRUE
dataPackage$valid
## [1] TRUE

Because our resources are tabular we could read it as a tabular data:

jsonlite::toJSON(dataPackage$getResource("population")$read(keyed = TRUE),auto_unbox = FALSE,pretty = TRUE)
## [
##   {
##     "city": ["london"],
##     "year": [2017],
##     "population": [8780000]
##   },
##   {
##     "city": ["paris"],
##     "year": [2017],
##     "population": [2240000]
##   },
##   {
##     "city": ["rome"],
##     "year": [2017],
##     "population": [2860000]
##   }
## ]

Let’s save our descriptor on the disk. After it we could update our datapackage.json as we want, make some changes etc:

dataPackage.save('datapackage.json')

To continue the work with the data package we just load it again but this time using local datapackage.json:

dataPackage <- Package.load('datapackage.json')
# Continue the work

It was one basic introduction to the Package class. To learn more let’s take a look on Package class API reference.

Resource

A class for working with data resources. You can read or iterate tabular resources using the iter/read methods and all resource as bytes using rowIter/rowRead methods.

Consider we have some local csv file. It could be inline data or remote link - all supported by Resource class (except local files for in-brower usage of course). But say it’s cities.csv for now:

city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,N/A

Let’s create and read a resource. We use static Resource$load method instantiate a resource. Because resource is tabular we could use resourceread method with a keyed option to get an list of keyed rows:

resource <- Resource.load('{"path": "cities.csv"}')
resource$tabular
## [1] TRUE
jsonlite::toJSON(resource$read(keyed = TRUE), pretty = TRUE)
## [
##   {
##     "city": ["london"],
##     "location": ["\"51.50 -0.11\""]
##   },
##   {
##     "city": ["paris"],
##     "location": ["\"48.85 2.30\""]
##   },
##   {
##     "city": ["rome"],
##     "location": ["\"41.89 12.51\""]
##   }
## ]

As we could see our locations are just a strings. But it should be geopoints. Also Rome’s location is not available but it’s also just a N/A string instead of null. First we have to infer resource metadata:

jsonlite::toJSON(resource$infer(), pretty = TRUE)
## {
##   "path": ["cities.csv"],
##   "profile": ["tabular-data-resource"],
##   "encoding": ["utf-8"],
##   "name": ["cities"],
##   "format": ["csv"],
##   "mediatype": ["text/csv"],
##   "schema": {
##     "fields": [
##       {
##         "name": ["city"],
##         "type": ["string"],
##         "format": ["default"]
##       },
##       {
##         "name": ["location"],
##         "type": ["string"],
##         "format": ["default"]
##       }
##     ],
##     "missingValues": [
##       [""]
##     ]
##   }
## }
jsonlite::toJSON(resource$descriptor, pretty = TRUE)
## {
##   "path": ["cities.csv"],
##   "profile": ["tabular-data-resource"],
##   "encoding": ["utf-8"],
##   "name": ["cities"],
##   "format": ["csv"],
##   "mediatype": ["text/csv"],
##   "schema": {
##     "fields": [
##       {
##         "name": ["city"],
##         "type": ["string"],
##         "format": ["default"]
##       },
##       {
##         "name": ["location"],
##         "type": ["string"],
##         "format": ["default"]
##       }
##     ],
##     "missingValues": [
##       [""]
##     ]
##   }
## }
# resource$read( keyed = TRUE )
# # Fails with a data validation error

Let’s fix not available location. There is a missingValues property in Table Schema specification. As a first try we set missingValues to N/A in resource$descriptor.schema. Resource descriptor could be changed in-place but all changes should be commited by resource$commit():

resource$descriptor$schema$missingValues <- 'N/A'
resource$commit()
## [1] TRUE
resource$valid # FALSE
## [1] FALSE
resource$errors
## [[1]]
## [1] "Descriptor validation error:\n            data.schema.missingValues - is the wrong type"

As a good citiziens we’ve decided to check out recource descriptor validity. And it’s not valid! We should use an list for missingValues property. Also don’t forget to have an empty string as a missing value:

resource$descriptor$schema[['missingValues']] <- list('', 'N/A')
resource$commit()
## [1] TRUE
resource$valid # TRUE
## [1] TRUE

All good. It looks like we’re ready to read our data again:

jsonlite::toJSON(resource$read( keyed = TRUE ), pretty = TRUE)
## [
##   {
##     "city": ["london"],
##     "location": ["\"51.50 -0.11\""]
##   },
##   {
##     "city": ["paris"],
##     "location": ["\"48.85 2.30\""]
##   },
##   {
##     "city": ["rome"],
##     "location": ["\"41.89 12.51\""]
##   }
## ]

Now we see that: - locations are lists with numeric lattide and longitude - Rome’s location is a native JavaScript null

And because there are no errors on data reading we could be sure that our data is valid againt our schema. Let’s save our resource descriptor:

resource$save('dataresource.json')

Let’s check newly-crated dataresource.json. It contains path to our data file, inferred metadata and our missingValues tweak:

{
"path": "data.csv",
"profile": "tabular-data-resource",
"encoding": "utf-8",
"name": "data",
"format": "csv",
"mediatype": "text/csv",
"schema": {
"fields": [
{
"name": "city",
"type": "string",
"format": "default"
},
{
"name": "location",
"type": "geopoint",
"format": "default"
}
],
"missingValues": [
"",
"N/A"
]
}
}

If we decide to improve it even more we could update the dataresource.json file and then open it again using local file name:

resource <- Resource.load('dataresource.json')
# Continue the work

It was one basic introduction to the Resource class. To learn more let’s take a look on Resource class API reference.

Working with Profile

A component to represent JSON Schema profile from Profiles Registry:

profile <- Profile.load('data-package')
profile$name # data-package
## [1] "data-package"
profile$jsonschema # List of JSON Schema contents
valid_errors <- profile$validate(descriptor)
valid <- valid_errors$valid # TRUE if valid descriptor
valid
## [1] TRUE

Working with validate

A standalone function to validate a data package descriptor:

valid_errors <- validate('{"name": "Invalid Datapackage"}')

Working with infer

A standalone function to infer a data package descriptor.

descriptor <- infer("csv",basePath = '.')
jsonlite::toJSON(descriptor, pretty = TRUE)
## {
##   "profile": ["tabular-data-package"],
##   "resources": [
##     {
##       "path": ["cities.csv"],
##       "profile": ["tabular-data-resource"],
##       "encoding": ["utf-8"],
##       "name": ["cities"],
##       "format": ["csv"],
##       "mediatype": ["text/csv"],
##       "schema": {
##         "fields": [
##           {
##             "name": ["city"],
##             "type": ["string"],
##             "format": ["default"]
##           },
##           {
##             "name": ["location"],
##             "type": ["string"],
##             "format": ["default"]
##           }
##         ],
##         "missingValues": [
##           [""]
##         ]
##       }
##     },
##     {
##       "path": ["population.csv"],
##       "profile": ["tabular-data-resource"],
##       "encoding": ["utf-8"],
##       "name": ["population"],
##       "format": ["csv"],
##       "mediatype": ["text/csv"],
##       "schema": {
##         "fields": [
##           {
##             "name": ["city"],
##             "type": ["string"],
##             "format": ["default"]
##           },
##           {
##             "name": ["year"],
##             "type": ["integer"],
##             "format": ["default"]
##           },
##           {
##             "name": ["population"],
##             "type": ["integer"],
##             "format": ["default"]
##           }
##         ],
##         "missingValues": [
##           [""]
##         ]
##       }
##     }
##   ]
## }

Working with Foreign Keys

The package supports foreign keys described in the Table Schema specification. It means if your data package descriptor use resources[]$schema$foreignKeys property for some resources a data integrity will be checked on reading operations.

Consider we have a data package:

DESCRIPTOR <- '{
"resources": [
{
"name": "teams",
"data": [
["id", "name", "city"],
["1", "Arsenal", "London"],
["2", "Real", "Madrid"],
["3", "Bayern", "Munich"]
],
"schema": {
"fields": [
{"name": "id", "type": "integer"},
{"name": "name", "type": "string"},
{"name": "city", "type": "string"}
],
"foreignKeys": [
{
"fields": "city",
"reference": {"resource": "cities", "fields": "name"}
}
]
}
}, {
"name": "cities",
"data": [
["name", "country"],
["London", "England"],
["Madrid", "Spain"]
]
}
]
}'

Let’s check relations for a teams resource:

package <- Package.load(DESCRIPTOR)
teams <- package$getResource('teams')
teams$checkRelations()
## Error: Foreign key 'city' violation in row '4'
# tableschema.exceptions.RelationError: Foreign key "['city']" violation in row "4"

As we could see there is a foreign key violation. That’s because our lookup table cities doesn’t have a city of Munich but we have a team from there. We need to fix it in cities resource:

package$descriptor$resources[[2]]$data <- rlist::list.append(package$descriptor$resources[[2]]$data, list('Munich', 'Germany'))
package$commit()
## [1] TRUE
teams <- package$getResource('teams')
teams$checkRelations()
## [1] TRUE
# TRUE

Fixed! But not only a check operation is available. We could use relations argument for resource$iter/read methods to dereference a resource relations:

jsonlite::toJSON(teams$read(keyed = TRUE, relations = FALSE), pretty =  TRUE)
## [
##   {
##     "id": [1],
##     "name": ["Arsenal"],
##     "city": ["London"]
##   },
##   {
##     "id": [2],
##     "name": ["Real"],
##     "city": ["Madrid"]
##   },
##   {
##     "id": [3],
##     "name": ["Bayern"],
##     "city": ["Munich"]
##   }
## ]

Instead of plain city name we’ve got a dictionary containing a city data. These resource$iter/read methods will fail with the same as resource$check_relations error if there is an integrity issue. But only if relations = TRUE flag is passed.

API Referencer

Package

Package representation

package$.valid ⇒ Boolean

Validation status

It always true in strict mode.

Returns: Boolean - returns validation status

package$errors ⇒ List.<Error>

Validation errors

It always empty in strict mode.

Returns: List.<Error> - returns validation errors

package$profile ⇒ Profile

Profile

package$descriptor ⇒ Object

Descriptor

Returns: Object - schema descriptor

package$resources ⇒ List.<Resoruce>

Resources

package$resourceNames ⇒ List.<string>

Resource names

package$getResource(name) ⇒ Resource | null

Return a resource

Returns: Resource | null - resource instance if exists

Param Type
name string

package$addResource(descriptor) ⇒ Resource

Add a resource

Returns: Resource - added resource instance

Param Type
descriptor Object

package$removeResource(name) ⇒ Resource | null

Remove a resource

Returns: Resource | null - removed resource instance if exists

Param Type
name string

package$infer(pattern) ⇒ Object

Infer metadata

Param Type Default
pattern string false

package$commit(strict) ⇒ Boolean

Update package instance if there are in-place changes in the descriptor.

Returns: Boolean - returns true on success and false if not modified
Throws:

  • DataPackageError raises any error occurred in the process
Param Type Description
strict boolean alter strict mode for further work

Example

dataPackage <- Package.load('{
"name": "package",
"resources": [{"name": "resource", "data": ["data"]}]
}')
dataPackage$descriptor$name # package
## [1] "package"
dataPackage$descriptor$name <- 'renamed-package'
dataPackage$descriptor$name # renamed-package
## [1] "renamed-package"
dataPackage$commit()
## [1] TRUE

package$save(target, raises, returns)

Save data package to target destination.

If target path has a zip file extension the package will be zipped and saved entirely. If it has a json file extension only the descriptor will be saved.

Param Type Description
target string path where to save a data package
raises DataPackageError error if something goes wrong
returns boolean true on success

Package.load(descriptor, basePath, strict) ⇒ Package

Factory method to instantiate Package class.

This method is async and it should be used with await keyword or as a Promise.

Returns: Package - returns data package class instance
Throws:

  • DataPackageError raises error if something goes wrong
Param Type Description
descriptor string Object
basePath string base path for all relative paths
strict boolean strict flag to alter validation behavior. Setting it to true leads to throwing errors on any operation with invalid descriptor

Resource

Resource representation

resource$valid ⇒ Boolean

Validation status

It always true in strict mode.

Returns: Boolean - returns validation status

resource$errors ⇒ List.<Error>

Validation errors

It always empty in strict mode.

Returns: List.<Error> - returns validation errors

resource$profile ⇒ Profile

Profile

resource$descriptor ⇒ Object

Descriptor

Returns: Object - schema descriptor

resource$name ⇒ string

Name

resource$inline ⇒ boolean

Whether resource is inline

resource$local ⇒ boolean

Whether resource is local

resource$remote ⇒ boolean

Whether resource is remote

resource$multipart ⇒ boolean

Whether resource is multipart

resource$tabular ⇒ boolean

Whether resource is tabular

resource$source ⇒ List | string

Source

Combination of resource.source and resource.inline/local/remote/multipart provides predictable interface to work with resource data.

resource$headers ⇒ List.<string>

Headers

Only for tabular resources

Returns: List.<string> - data source headers

resource$schema ⇒ tableschema.Schema

Schema

Only for tabular resources

resource$iter(keyed, extended, cast, forceCast, relations, stream) ⇒ AsyncIterator | Stream

Iterate through the table data

Only for tabular resources

And emits rows cast based on table schema (async for loop). With a stream flag instead of async iterator a Node stream will be returned. Data casting can be disabled.

Returns: AsyncIterator | Stream - async iterator/stream of rows: - [value1, value2] - base - {header1: value1, header2: value2} - keyed - [rowNumber, [header1, header2], [value1, value2]] - extended
Throws:

  • TableSchemaError raises any error occurred in this process
Param Type Description
keyed boolean iter keyed rows
extended boolean iter extended rows
cast boolean disable data casting if false
forceCast boolean instead of raising on the first row with cast error return an error object to replace failed row. It will allow to iterate over the whole data file even if it’s not compliant to the schema. Example of output stream: [['val1', 'val2'], TableSchemaError, ['val3', 'val4'], ...]
relations boolean if true foreign key fields will be checked and resolved to its references
stream boolean return Node Readable Stream of table rows

resource$read(limit) ⇒ List.<List> | List.<Object>

Read the table data into memory

Only for tabular resources; the API is the same as resource.iter has except for:

Returns: List.<List> | List.<Object> - list of rows: - [value1, value2] - base - {header1: value1, header2: value2} - keyed - [rowNumber, [header1, header2], [value1, value2]] - extended

Param Type Description
limit integer limit of rows to read

resource$checkRelations() ⇒ boolean

It checks foreign keys and raises an exception if there are integrity issues.

Only for tabular resources

Returns: boolean - returns True if no issues
Throws:

  • DataPackageError raises if there are integrity issues

resource$rawIter(stream) ⇒ Iterator | Stream

Iterate over data chunks as bytes. If stream is true Node Stream will be returned.

Returns: Iterator | Stream - returns Iterator/Stream

Param Type Description
stream boolean Node Stream will be returned

resource$rawRead() ⇒ Buffer

Returns resource data as bytes.

Returns: Buffer - returns Buffer with resource data

resource$infer() ⇒ Object

Infer resource metadata like name, format, mediatype, encoding, schema and profile.

It commits this changes into resource instance.

Returns: Object - returns resource descriptor

resource$commit(strict) ⇒ boolean

Update resource instance if there are in-place changes in the descriptor.

Returns: boolean - returns true on success and false if not modified
Throws:

  • DataPackageError raises error if something goes wrong
Param Type Description
strict boolean alter strict mode for further work

resource$save(target) ⇒ boolean

Save resource to target destination.

For now only descriptor will be saved.

Returns: boolean - returns true on success
Throws:

  • DataPackageError raises error if something goes wrong
Param Type Description
target string path where to save a resource

Resource.load(descriptor, basePath, strict) ⇒ Resource

Factory method to instantiate Resource class.

This method is async and it should be used with await keyword or as a Promise.

Returns: Resource - returns resource class instance
Throws:

  • DataPackageError raises error if something goes wrong
Param Type Description
descriptor string Object
basePath string base path for all relative paths
strict boolean strict flag to alter validation behavior. Setting it to true leads to throwing errors on any operation with invalid descriptor

Profile

Profile representation

profile$name ⇒ string

Name

profile$jsonschema ⇒ Object

JsonSchema

profile$validate(descriptor) ⇒ Object

Validate a data package descriptor against the profile.

Returns: Object - returns a {valid, errors} object

Param Type Description
descriptor Object retrieved and dereferenced data package descriptor

Profile.load(profile) ⇒ Profile

Factory method to instantiate Profile class.

This method is async and it should be used with await keyword or as a Promise.

Returns: Profile - returns profile class instance
Throws:

  • DataPackageError raises error if something goes wrong
Param Type Description
profile string profile name in registry or URL to JSON Schema

validate(descriptor) ⇒ Object

This function is async so it has to be used with await keyword or as a Promise.

Returns: Object - returns a {valid, errors} object

Param Type Description
descriptor string Object

infer(pattern) ⇒ Object

This function is async so it has to be used with await keyword or as a Promise.

Returns: Object - returns data package descriptor

Param Type Description
pattern string glob file pattern

DataPackageError

Base class for the all DataPackage errors.

TableSchemaError

Base class for the all TableSchema errors.

Contributing

The project follows the Open Knowledge International coding standards. There are common commands to work with the project.Recommended way to get started is to create, activate and load the package environment. To install package and development dependencies into active environment:

devtools::install_github("frictionlessdata/datapackage-r", dependencies=TRUE)

To make test:

test_that(description, {
expect_equal(test, expected result)
})

To run tests:

devtools::test()

more detailed information about how to create and run tests you can find in testthat package

Changelog - News

In NEWS.md described only breaking and the most important changes. The full changelog could be found in nicely formatted commit history.

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