Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/frictionlessdata/datapackage-r
An R package for working with Data Package.
https://github.com/frictionlessdata/datapackage-r
Last synced: 3 months ago
JSON representation
An R package for working with Data Package.
- Host: GitHub
- URL: https://github.com/frictionlessdata/datapackage-r
- Owner: frictionlessdata
- License: other
- Created: 2017-05-15T11:08:18.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-12-22T11:47:11.000Z (almost 3 years ago)
- Last Synced: 2024-05-21T02:10:55.398Z (6 months ago)
- Language: R
- Homepage: https://frictionlessdata.github.io/datapackage-r/
- Size: 961 KB
- Stars: 43
- Watchers: 7
- Forks: 7
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - frictionlessdata/datapackage-r - An R package for working with Data Package. (R)
README
---
title: 
rictionless Data -
Data Package
output:
github_document:
html_preview: no
number_sections: yes
---
[](https://cran.r-project.org/package=datapackage.r)
[](https://github.com/frictionlessdata/datapackage-r/actions)
[](https://coveralls.io/github/frictionlessdata/datapackage-r?branch=master)
[](http://www.repostatus.org/#active)
[](http://www.rdocumentation.org/packages/datapackage.r)
[](http://cran.rstudio.com/web/packages/datapackage.r/index.html)
[](https://opensource.org/licenses/MIT)
[](https://discordapp.com/invite/Sewv6av)
# Description
R package for working with [Frictionless Data Package][datapackage].
## 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][Rs] to your computer you have to select one of the mirror sites of the [Comprehensive R Archive Network][R], 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][Rman].
To install [RStudio][Rstudio], you can download [RStudio Desktop][Rstudiodown] with Open Source License and follow the wizard instructions:
1. Go to [RStudio](https://www.rstudio.com/products/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](https://cran.r-project.org/package=devtools) to make installation of github packages available.
```{r, eval=FALSE, include=TRUE}
# Install devtools package if not already
install.packages("devtools")
```
Install `datapackage.r`
```{r, eval=FALSE, include=T}
# And then install the development version from github
devtools::install_github("frictionlessdata/datapackage-r")
```
## Load package
```{r, echo=TRUE}
# 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](https://github.com/frictionlessdata/datapackage-r/tree/master/vignettes) in vignettes directory.
```{r eval=TRUE, include=TRUE, warning=FALSE}
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
```
```{r eval=TRUE, include=TRUE, warning=FALSE}
resource <- dataPackage$getResource('example')
# convert to json and add indentation with jsonlite prettify function
jsonlite::prettify(helpers.from.list.to.json(resource$read()))
```
# Documentation
Json objects are not included in R base data types. [Jsonlite package](https://CRAN.R-project.org/package=jsonlite) 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](https://CRAN.R-project.org/package=jsonlite).
## 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
```csv
city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,"41.89,12.51"
```
> inst/extdata/readme_example/population.csv
```csv
city,year,population
london,2017,8780000
paris,2017,2240000
rome,2017,2860000
```
```{r "setup", include=FALSE}
require("knitr")
opts_knit$set(root.dir = "inst/extdata/readme_example/")
```
First we create a blank data package:
```{r eval=TRUE, include=TRUE, echo=TRUE}
dataPackage <- Package.load()
```
Now we're ready to infer a data package descriptor based on data files we have. Because we have two csv files we use glob pattern `csv`:
```{r eval=TRUE, include=TRUE, warning=FALSE, message=FALSE}
jsonlite::toJSON(dataPackage$infer('csv'), pretty = TRUE)
```
```{r eval=TRUE, include=TRUE, warning=FALSE, message=FALSE}
jsonlite::toJSON(dataPackage$descriptor, pretty = TRUE)
```
An `infer` method has found all our files and inspected it to extract useful metadata like profile, encoding, format, Table Schema etc. Let's tweak it a little bit:
```{r eval=TRUE, include=TRUE}
dataPackage$descriptor$resources[[2]]$schema$fields[[2]]$type <- 'year'
dataPackage$commit()
dataPackage$valid
```
Because our resources are tabular we could read it as a tabular data:
```{r eval=TRUE, include=TRUE}
jsonlite::toJSON(dataPackage$getResource("population")$read(keyed = TRUE),auto_unbox = FALSE,pretty = TRUE)
```
Let's save our descriptor on the disk. After it we could update our `datapackage.json` as we want, make some changes etc:
```{r eval=FALSE, include=TRUE}
dataPackage.save('datapackage.json')
```
To continue the work with the data package we just load it again but this time using local `datapackage.json`:
```{r eval=FALSE, include=TRUE}
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:
```csv
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$load` method instantiate a resource. Because resource is tabular we could use `resourceread` method with a `keyed` option to get an list of keyed rows:
```{r eval=TRUE, include=TRUE}
resource <- Resource.load('{"path": "cities.csv"}')
resource$tabular
```
```{r eval=TRUE, include=TRUE}
jsonlite::toJSON(resource$read(keyed = TRUE), pretty = TRUE)
```
As we could see our locations are just a strings. But it should be geopoints. Also Rome's location is not available but it's also just a `N/A` string instead of `null`. First we have to infer resource metadata:
```{r eval=TRUE, include=TRUE}
jsonlite::toJSON(resource$infer(), pretty = TRUE)
```
```{r eval=TRUE, include=TRUE}
jsonlite::toJSON(resource$descriptor, pretty = TRUE)
# resource$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()`:
```{r eval=TRUE, include=TRUE}
resource$descriptor$schema$missingValues <- 'N/A'
resource$commit()
```
```{r eval=TRUE, include=TRUE}
resource$valid # FALSE
resource$errors
```
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:
```{r eval=TRUE, include=TRUE}
resource$descriptor$schema[['missingValues']] <- list('', 'N/A')
resource$commit()
resource$valid # TRUE
```
All good. It looks like we're ready to read our data again:
```{r eval=TRUE, include=TRUE}
jsonlite::toJSON(resource$read( keyed = TRUE ), pretty = TRUE)
```
Now we see that:
- locations are lists with numeric lattide and longitude
- Rome's location is a native JavaScript `null`
And because there are no errors on data reading we could be sure that our data is valid againt our schema. Let's save our resource descriptor:
```{r eval=FALSE, include=TRUE}
resource$save('dataresource.json')
```
Let's check newly-crated `dataresource.json`. It contains path to our data file, inferred metadata and our `missingValues` tweak:
```json
{
"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:
```{r eval=FALSE, include=TRUE}
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](https://specs.frictionlessdata.io/schemas/registry.json):
```{r eval=TRUE, include=TRUE}
profile <- Profile.load('data-package')
profile$name # data-package
```
```{r eval=FALSE, include=TRUE}
profile$jsonschema # List of JSON Schema contents
```
```{r eval=TRUE, include=TRUE}
valid_errors <- profile$validate(descriptor)
valid <- valid_errors$valid # TRUE if valid descriptor
valid
```
### Working with validate
A standalone function to validate a data package descriptor:
```{r eval=FALSE, include=TRUE}
valid_errors <- validate('{"name": "Invalid Datapackage"}')
```
### Working with infer
A standalone function to infer a data package descriptor.
```{r eval=TRUE, include=TRUE}
descriptor <- infer("csv",basePath = '.')
jsonlite::toJSON(descriptor, pretty = TRUE)
```
### Working with Foreign Keys
The package supports foreign keys described in the [Table Schema](https://specs.frictionlessdata.io/table-schema/#foreign-keys) 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:
```{r eval=TRUE, include=TRUE}
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:
```{r eval=TRUE, include=TRUE, warning=FALSE}
package <- Package.load(DESCRIPTOR)
teams <- package$getResource('teams')
```
```{r eval=TRUE, include=TRUE,warning=FALSE, error=TRUE}
teams$checkRelations()
# 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:
```{r eval=TRUE, include=TRUE}
package$descriptor$resources[[2]]$data <- rlist::list.append(package$descriptor$resources[[2]]$data, list('Munich', 'Germany'))
package$commit()
teams <- package$getResource('teams')
teams$checkRelations()
# TRUE
```
Fixed! But not only a check operation is available. We could use `relations` argument for `resource$iter/read` methods to dereference a resource relations:
```{r eval=TRUE, include=TRUE}
jsonlite::toJSON(teams$read(keyed = TRUE, relations = FALSE), pretty = TRUE)
```
Instead of plain city name we've got a dictionary containing a city data. These `resource$iter/read` methods will fail with the same as `resource$check_relations` error if there is an integrity issue. But only if `relations = TRUE` flag is passed.
## API Referencer
### Package
Package representation
* [Package](#Package)
* _instance_
* [$valid](#Package+valid) ⇒ Boolean
* [$errors](#Package+errors) ⇒ List.<Error>
* [$profile](#Package+profile) ⇒ Profile
* [$descriptor](#Package+descriptor) ⇒ Object
* [$resources](#Package+resources) ⇒ List.<Resoruce>
* [$resourceNames](#Package+resourceNames) ⇒ List.<string>
* [$getResource(name)](#Package+getResource) ⇒ Resource \| null
* [$addResource(descriptor)](#Package+addResource) ⇒ Resource
* [$removeResource(name)](#Package+removeResource) ⇒ Resource \| null
* [$infer(pattern)](#Package+infer) ⇒ Object
* [$commit(strict)](#Package+commit) ⇒ Boolean
* [$save(target, raises, returns)](#Package+save)
* _static_
* [.load(descriptor, basePath, strict)](#Package.load) ⇒ [Package](#Package)
#### package$.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**
```{r eval=TRUE, include=TRUE}
dataPackage <- Package.load('{
"name": "package",
"resources": [{"name": "resource", "data": ["data"]}]
}')
dataPackage$descriptor$name # package
dataPackage$descriptor$name <- 'renamed-package'
dataPackage$descriptor$name # renamed-package
dataPackage$commit()
```
#### package$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](#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](#Package) - returns data package class instance
**Throws**:
- DataPackageError raises error if something goes wrong
| 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
* [Resource](#Resource)
* _instance_
* [$valid](#Resource+valid) ⇒ Boolean
* [$errors](#Resource+errors) ⇒ List.<Error>
* [$profile](#Resource+profile) ⇒ Profile
* [$descriptor](#Resource+descriptor) ⇒ Object
* [$name](#Resource+name) ⇒ string
* [$inline](#Resource+inline) ⇒ boolean
* [$local](#Resource+local) ⇒ boolean
* [$remote](#Resource+remote) ⇒ boolean
* [$multipart](#Resource+multipart) ⇒ boolean
* [$tabular](#Resource+tabular) ⇒ boolean
* [$source](#Resource+source) ⇒ List \| string
* [$headers](#Resource+headers) ⇒ List.<string>
* [$schema](#Resource+schema) ⇒ tableschema.Schema
* [$iter(keyed, extended, cast, forceCast, relations, stream)](#Resource+iter) ⇒ AsyncIterator \| Stream
* [$read(limit)](#Resource+read) ⇒ List.<List> \| List.<Object>
* [$checkRelations()](#Resource+checkRelations) ⇒ boolean
* [$rawIter(stream)](#Resource+rawIter) ⇒ Iterator \| Stream
* [$rawRead()](#Resource+rawRead) ⇒ Buffer
* [$infer()](#Resource+infer) ⇒ Object
* [$commit(strict)](#Resource+commit) ⇒ boolean
* [$save(target)](#Resource+save) ⇒ boolean
* _static_
* [$load(descriptor, basePath, strict)](#Resource.load) ⇒ [Resource](#Resource)
#### 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$save(target) ⇒ boolean
Save resource to target destination.
> For now only descriptor will be saved.
**Returns**: boolean - returns true on success
**Throws**:
- DataPackageError raises error if something goes wrong
| Param | Type | Description |
| --- | --- | --- |
| target | string | path where to save a resource |
#### Resource.load(descriptor, basePath, strict) ⇒ [Resource](#Resource)
Factory method to instantiate `Resource` class.
This method is async and it should be used with await keyword or as a `Promise`.
**Returns**: [Resource](#Resource) - returns resource class instance
**Throws**:
- DataPackageError raises error if something goes wrong
| Param | Type | Description |
| --- | --- | --- |
| descriptor | string \| Object | resource descriptor as local path, url or object |
| basePath | string | base path for all relative paths |
| strict | boolean | strict flag to alter validation behavior. Setting it to `true` leads to throwing errors on any operation with invalid descriptor |
### Profile
Profile representation
* [Profile](#Profile)
* _instance_
* [$name](#Profile+name) ⇒ string
* [$jsonschema](#Profile+jsonschema) ⇒ Object
* [$validate(descriptor)](#Profile+validate) ⇒ Object
* _static_
* [$load(profile)](#Profile.load) ⇒ [Profile](#Profile)
#### profile$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](#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](#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 | data package descriptor (local/remote path or 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][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:
```{r, eval=FALSE, include=T}
devtools::install_github("frictionlessdata/datapackage-r", dependencies=TRUE)
```
To make test:
```{r, eval=FALSE, include=TRUE}
test_that(description, {
expect_equal(test, expected result)
})
```
To run tests:
```{r, eval=FALSE, include=TRUE, warning=FALSE}
devtools::test()
```
more detailed information about how to create and run tests you can find in [testthat package](https://github.com/hadley/testthat)
## Changelog - News
In [NEWS.md][news] described only breaking and the most important changes. The full changelog could be found in nicely formatted [commit][commits] history.
[Rs]: https://www.r-project.org/
[R]: https://cran.r-project.org//
[Rman]: https://cran.r-project.org/doc/manuals/R-admin.html
[Rstudio]: https://www.rstudio.com/
[Rstudiodown]: https://www.rstudio.com/products/rstudio/download/
[coding_standards]: https://github.com/okfn/coding-standards
[datapackage]: https://specs.frictionlessdata.io/data-package/
[news]: https://github.com/frictionlessdata/datapackage-r/blob/master/NEWS.md
[commits]: https://github.com/frictionlessdata/datapackage-r/commits/master