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Data Package 'Frictionless Data'

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README.md
In datapackage.r: Data Package 'Frictionless Data'

Frictionless Data - Data Package

Description

R package for working with Frictionless Data Package.

Features

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/akis_/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.\

Validation errors

It always empty in strict mode.

Returns: List.\ - returns validation errors

package$profile ⇒ Profile

Profile

package$descriptor ⇒ Object

Descriptor

Returns: Object - schema descriptor

package$resources ⇒ List.\

Resources

package$resourceNames ⇒ List.\

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:

| 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:

| Param | Type | Description | |------------|--------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------| | descriptor | string \| Object | package descriptor as local path, url or object. If ththe path has a zip file extension it will be unzipped to the temp directory first. | | 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



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datapackage.r documentation built on Jan. 11, 2022, 5:07 p.m.

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