Frictionless Data - Data Package

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.

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

Package.load(descriptor, basePath, strict=FALSE)

Constructor to instantiate Package class.

• descriptor (String/Object) - data package descriptor as local path, url or 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
• (errors.DataPackageError) - raises error if something goes wrong
• (Package) - returns data package class instance

package$valid

• (Boolean) - returns validation status. It always true in strict mode.

package$errors

• (Error[]) - returns validation errors. It always empty in strict mode.

package$profile

• (Profile) - returns an instance of Profile class (see below).

package$descriptor

• (Object) - returns data package descriptor

package$resources

• (Resource[]) - returns an list of Resource instances (see below).

package$resourceNames

• (String[]) - returns an list of resource names.

package$getResource(name)

Get data package resource by name.

• name (String) - data resource name
• (Resource/null) - returns Resource instances or null if not found

package$addResource(descriptor)

Add new resource to data package. The data package descriptor will be validated with newly added resource descriptor.

• descriptor (Object) - data resource descriptor
• (errors$DataPackageError) - raises error if something goes wrong
• (Resource/null) - returns added Resource instance or null if not added

package$removeResource(name)

Remove data package resource by name. The data package descriptor will be validated after resource descriptor removal.

• name (String) - data resource name
• (errors$DataPackageError) - raises error if something goes wrong
• (Resource/null) - returns removed Resource instances or null if not found

package$infer(pattern=FALSE)

Infer a data package metadata. If pattern is not provided only existent resources will be inferred (added metadata like encoding, profile etc). If pattern is provided new resoures with file names mathing the pattern will be added and inferred. It commits changes to data package instance.

• pattern (String) - glob pattern for new resources
• (Object) - returns data package descriptor

package$commit(strict)

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

• strict (Boolean) - alter strict mode for further work
• (errors$DataPackageError) - raises error if something goes wrong
• (Boolean) - returns true on success and false if not modified

dataPackage <- Package.load('{ 
 "name": "package",
 "resources": [{
  "name": "resource",
  "data": ["data"]
 }]
 }')

dataPackage$descriptor$name # package

## [1] "package"

dataPackage$descriptor$name <- 'renamed-package'
dataPackage$commit() # TRUE

## [1] TRUE

dataPackage$descriptor$name # renamed-package

## [1] "renamed-package"

package.save(target)

For now only descriptor will be saved.

Save data package to target destination.

• target (String) - path where to save a data package
• (errors$DataPackageError) - raises error if something goes wrong
• (Boolean) - returns true on success

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 array 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 array 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 arrays 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 onle basic introduction to the Resource class. To learn more let’s take a look on Resource class API reference.

Resource$load(descriptor, basePath, strict=FALSE)

Constructor to instantiate Resource class.

• descriptor (String/Object) - data resource descriptor as local path, url or 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
• (errors.DataPackageError) - raises error if something goes wrong
• (Resource) - returns resource class instance

resource$valid

• (Boolean) - returns validation status. It always true in strict mode.

resource$errors

• (Error[]) - returns validation errors. It always empty in strict mode.

resource$profile

• (Profile) - returns an instance of Profile class (see below).

resource$descriptor

• (Object) - returns resource descriptor

resource$name

• (String) - returns resource name

resource$inline

• (Boolean) - returns true if resource is inline

resource$local

• (Boolean) - returns true if resource is local

resource$remote

• (Boolean) - returns true if resource is remote

resource$multipart

• (Boolean) - returns true if resource is multipart

resource$tabular

• (Boolean) - returns true if resource is tabular

resource$source

• (List/String) - returns data or path property

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

resource$headers

Only for tabular resources

• (String[]) - returns data source headers

resource$schema

Only for tabular resources

It returns Schema instance to interact with data schema. Read API documentation - tableschema.Schema.

• (tableschema$Schema) - returns schema class instance

resource$iter(keyed, extended, cast=TRUE, relations=FALSE, stream=FALSE)

Only for tabular resources

Iter through the table data and emits rows cast based on table schema (async for loop). Data casting could be disabled.

• keyed (Boolean) - iter keyed rows
• extended (Boolean) - iter extended rows
• cast (Boolean) - disable data casting if false
• relations (Boolean) - if true foreign key fields will be checked and resolved to its references
• stream (Boolean) - return Node Readable Stream of table rows
• (errors.DataPackageError) - raises any error occured in this process
• (Iterator/Stream) - iterator/stream of rows:
  • [value1, value2] - base
  • {header1: value1, header2: value2} - keyed
  • [rowNumber, [header1, header2], [value1, value2]] - extended

resource$read(keyed, extended, cast=TRUE, relations=FALSE, limit)

Only for tabular resources

Read the whole table and returns as array of rows. Count of rows could be limited.

• keyed (Boolean) - flag to emit keyed rows
• extended (Boolean) - flag to emit extended rows
• cast (Boolean) - flag to disable data casting if false
• relations (Boolean) - if true foreign key fields will be checked and resolved to its references
• limit (Number) - integer limit of rows to return
• (errors.DataPackageError) - raises any error occured in this process
• (Array[]) - returns array of rows (see table.iter)

resource$checkRelations()

Only for tabular resources

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

• (errors.DataPackageError) - raises if there are integrity issues
• (Boolean) - returns True if no issues

resource$rawIter(stream = FALSE)

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

• stream (Boolean) - Node Stream will be returned
• (Iterator/Stream) - returns Iterator/Stream

resource$rawRead()

Returns resource data as bytes.

• (Buffer) - returns Buffer with resource data

resource$infer()

Infer resource metadata like name, format, mediatype, encoding, schema and profile. It commits this changes into resource instance.

• (Object) - returns resource descriptor

resource$commit(strict)

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

• strict (Boolean) - alter strict mode for further work
• (errors.DataPackageError) - raises error if something goes wrong
• (Boolean) - returns true on success and false if not modified

resource$save(target)

For now only descriptor will be saved.

Save resource to target destination.

• target (String) - path where to save a resource
• (errors.DataPackageError) - raises error if something goes wrong
• (Boolean) - returns true on success

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

Profile.load(profile)

Constuctor to instantiate Profile class.

• profile (String) - profile name in registry or URL to JSON Schema
• (errors$DataPackageError) - raises error if something goes wrong
• (Profile) - returns profile class instance

Profile$name()

• (String/null) - returns profile name if available

Profile$jsonschema()

• (Object) - returns profile JSON Schema contents

Profile$validate(descriptor)

Validate a data package descriptor against the Profile$

• descriptor (Object) - retrieved and dereferenced data package descriptor
• (Object) - returns a valid_errors object

Validate

A standalone function to validate a data package descriptor:

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

validate(descriptor)

A standalone function to validate a data package descriptor:

• descriptor (String/Object) - data package descriptor (local/remote path or object)
• (Object) - returns a valid_errors object

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": [
##           [""]
##         ]
##       }
##     }
##   ]
## }

infer(pattern, basePath)

Infer a data package descriptor.

• pattern (String) - glob file pattern
• (Object) - returns data package descriptor

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.

Errors

errors$DataPackageError

Base class for the all package errors. If there are more than one error you could get an additional information from the error object:

tryCatch({
  # some lib action
}, error = function() {
  error # you have N cast errors (see error.errors)
  if (error$multiple) {
    for ( error in error$errors) {
        error # cast error M is ...
    }
  }
})

Changelog - News

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

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