Example Aggregation on UK Gross Domestic Product (GDP)

examples

Files Size Format Created Updated License Source
1 4kB csv odc-pddl Office for National Statistics - GDP Time Series
This is an example Data Package to demonstrate how data transforms work. In this example, we explain how aggregation can be done before data package gets rendered in showcase page. It assumes publisher is already familiar with Data Packages and views specifications (views property in Data Package read more
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Data Files

annual  

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d)
GDP 2 number Gross Value Added at basic prices: chained volume measures: Seasonally adjusted. Millions of GBP in base period money. Base period=2009. ABMI variable in ONS source.
GDP_Change 3 number (percentage) Gross Domestic Product: Year on Year growth: CVM SA. IHYP variable in ONS source.
GDP_Index 4 number Gross domestic product index. Base period=2009. YBEZ variable in ONS source.

Read me

This is an example Data Package to demonstrate how data transforms work. In this example, we explain how aggregation can be done before data package gets rendered in showcase page. It assumes publisher is already familiar with Data Packages and views specifications (views property in Data Package specifications).

Transforming data

Data transforms are specified in resources property in views. Each resource is an object that contains following attributes:

  • "specType": "table" - this way we define the view as a table (other options are "simple" (renders a graph and accepts Plotly spec) and "vega" (renders a graph and accepts Vega spec)).
  • "resources" property is an array of objects in this case, where publishers can define data transforms they want to apply.
  • "name" - name of the resource as a reference.
  • "transform" - array of transforms. Each transform is an object, which properties vary depending on transform type. Only common property is "type" that is used to specify transform type.

Aggregating data

Under the graph on the top of this page, you can find a table that displays aggregated data. Raw data is displayed in preview section. As you can see we are aggregating “GDP” column to find its min value and “GDP_Change” column for max value. This is described in the second view object of views property:

  • "type": "aggregate" - this way we define the transform to be an aggregation.
  • "fields" - list of fields for which data aggregation will be applied.
  • "operations" - list of operation names according to list of fields. Options are: "sum", "min", "max", "count", etc. For full reference see https://vega.github.io/vega/docs/transforms/aggregate/#ops .

Descriptor for this data package

{
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    "findability": "published",
    "hash": "e37f2f7d68163a24d94d05c05ba4049b",
    "modified": "2017-08-08T17:37:41.523546",
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  "id": "examples/transform-example-gdp-uk",
  "licenses": [
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      "url": "http://opendatacommons.org/licenses/pddl/"
    }
  ],
  "name": "transform-example-gdp-uk",
  "profile": "data-package",
  "readme": "This is an example Data Package to demonstrate how data transforms work. In this example, we explain how aggregation can be done before data package gets rendered in showcase page. It assumes publisher is already familiar with Data Packages and views specifications (`views` property in Data Package specifications).\n\n### Transforming data\n\nData transforms are specified in `resources` property in `views`. Each resource is an object that contains following attributes:\n\n* `\"specType\": \"table\"` - this way we define the view as a table (other options are `\"simple\"` (renders a graph and accepts Plotly spec) and `\"vega\"` (renders a graph and accepts Vega spec)).\n* `\"resources\"` property is an array of objects in this case, where publishers can define data transforms they want to apply.\n* `\"name\"` - name of the resource as a reference.\n* `\"transform\"` - array of transforms. Each transform is an object, which properties vary depending on transform type. Only common property is `\"type\"` that is used to specify transform type.\n\n### Aggregating data\n\nUnder the graph on the top of this page, you can find a table that displays aggregated data. Raw data is displayed in preview section. As you can see we are aggregating \"GDP\" column to find its min value and \"GDP_Change\" column for max value. This is described in the second view object of `views` property:\n\n* `\"type\": \"aggregate\"` - this way we define the transform to be an aggregation.\n* `\"fields\"` - list of fields for which data aggregation will be applied.\n* `\"operations\"` - list of operation names according to list of fields. Options are: `\"sum\"`, `\"min\"`, `\"max\"`, `\"count\"`, etc. For full reference see https://vega.github.io/vega/docs/transforms/aggregate/#ops .\n\n### Descriptor for this data package\n\n{{ dp.json }}\n",
  "resources": [
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        "lineTerminator": "\r\n",
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      "encoding": "utf-8",
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      "hash": "6d997ba5020c94912df61bea69f04cee",
      "name": "annual",
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      "profile": "data-resource",
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      "schema": {
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            "format": "%Y-%m-%d",
            "name": "Year",
            "type": "date"
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            "decimalChar": ".",
            "description": "Gross Value Added at basic prices: chained volume measures: Seasonally adjusted. Millions of GBP in base period money. Base period=2009. ABMI variable in ONS source.",
            "groupChar": "",
            "name": "GDP",
            "type": "number"
          },
          {
            "decimalChar": ".",
            "description": "Gross Domestic Product: Year on Year growth: CVM SA. IHYP variable in ONS source.",
            "format": "percentage",
            "groupChar": "",
            "name": "GDP_Change",
            "type": "number"
          },
          {
            "decimalChar": ".",
            "description": "Gross domestic product index. Base period=2009. YBEZ variable in ONS source.",
            "groupChar": "",
            "name": "GDP_Index",
            "type": "number"
          }
        ]
      },
      "sources": [
        {
          "title": "unkown",
          "web": "http://www.ons.gov.uk/ons/datasets-and-tables/data-selector.html?cdid=ABMI&dataset=qna&table-id=C2"
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          "encoding": "utf-8",
          "format": "csv",
          "name": "annual",
          "path": "https://pkgstore.datahub.io/examples/transform-example-gdp-uk:non-tabular/annual.csv",
          "profile": "data-resource",
          "schema": {
            "fields": [
              {
                "format": "any",
                "name": "Year",
                "type": "date"
              },
              {
                "description": "Gross Value Added at basic prices: chained volume measures: Seasonally adjusted. Millions of GBP in base period money. Base period=2009. ABMI variable in ONS source.",
                "name": "GDP",
                "type": "number"
              },
              {
                "description": "Gross Domestic Product: Year on Year growth: CVM SA. IHYP variable in ONS source.",
                "format": "percentage",
                "name": "GDP_Change",
                "type": "number"
              },
              {
                "description": "Gross domestic product index. Base period=2009. YBEZ variable in ONS source.",
                "name": "GDP_Index",
                "type": "number"
              }
            ]
          },
          "sources": [
            {
              "title": "unkown",
              "web": "http://www.ons.gov.uk/ons/datasets-and-tables/data-selector.html?cdid=ABMI&dataset=qna&table-id=C2"
            }
          ]
        },
        {
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          "datahub": {
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            "type": "derived/json"
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          "encoding": "utf-8",
          "format": "json",
          "hash": "7130c105405e403b4b06c11d54d214b6",
          "name": "annual_json",
          "path": "https://pkgstore.datahub.io/examples/transform-example-gdp-uk:annual_json/data/annual_json.json",
          "profile": "data-resource",
          "rowcount": 65,
          "schema": {
            "fields": [
              {
                "format": "%Y-%m-%d",
                "name": "Year",
                "type": "date"
              },
              {
                "description": "Gross Value Added at basic prices: chained volume measures: Seasonally adjusted. Millions of GBP in base period money. Base period=2009. ABMI variable in ONS source.",
                "name": "GDP",
                "type": "number"
              },
              {
                "description": "Gross Domestic Product: Year on Year growth: CVM SA. IHYP variable in ONS source.",
                "format": "percentage",
                "name": "GDP_Change",
                "type": "number"
              },
              {
                "description": "Gross domestic product index. Base period=2009. YBEZ variable in ONS source.",
                "name": "GDP_Index",
                "type": "number"
              }
            ]
          },
          "sources": [
            {
              "title": "unkown",
              "web": "http://www.ons.gov.uk/ons/datasets-and-tables/data-selector.html?cdid=ABMI&dataset=qna&table-id=C2"
            }
          ]
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      ]
    }
  ],
  "sources": [
    {
      "homepage": "http://www.ons.gov.uk/ons/rel/gva/gross-domestic-product--preliminary-estimate/q4-2012/tsd---preliminary-estimate-of-gdp-q4-2012.html",
      "name": "Office for National Statistics - GDP Time Series",
      "title": "Office for National Statistics - GDP Time Series",
      "web": "http://www.ons.gov.uk/ons/datasets-and-tables/downloads/csv.csv?dataset=pgdp"
    }
  ],
  "title": "Example Aggregation on UK Gross Domestic Product (GDP)",
  "views": [
    {
      "id": "Graph",
      "state": {
        "graphType": "columns",
        "group": "Year",
        "series": [
          "GDP_Change"
        ]
      },
      "type": "Graph"
    },
    {
      "name": "table-view-aggregation",
      "resources": [
        {
          "name": "annual",
          "transform": [
            {
              "fields": [
                "GDP",
                "GDP_Change"
              ],
              "operations": [
                "min",
                "max"
              ],
              "type": "aggregate"
            }
          ]
        }
      ],
      "specType": "table"
    }
  ]
}

Import into your tool

In order to use Data Package in R follow instructions below:

install.packages("devtools")
library(devtools)
install_github("hadley/readr")
install_github("ropenscilabs/jsonvalidate")
install_github("ropenscilabs/datapkg")

#Load client
library(datapkg)

#Get Data Package
datapackage <- datapkg_read("https://pkgstore.datahub.io/examples/transform-example-gdp-uk/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"annual")

Tested with Python 3.5.2

To generate Pandas data frames based on JSON Table Schema descriptors we have to install jsontableschema-pandas plugin. To load resources from a data package as Pandas data frames use datapackage.push_datapackage function. Storage works as a container for Pandas data frames.

In order to work with Data Packages in Pandas you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-pandas

To get Data Package run following code:

import datapackage

data_url = "https://pkgstore.datahub.io/examples/transform-example-gdp-uk/latest/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# to see datasets in this package
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

In order to work with Data Packages in Python you need to install our packages:

$ pip install datapackage

To get Data Package into your Python environment, run following code:

import datapackage

dp = datapackage.DataPackage('https://pkgstore.datahub.io/examples/transform-example-gdp-uk/latest/datapackage.json')

# see metadata
print(dp.descriptor)

# get list of csv files
csvList = [dp.resources[x].descriptor['name'] for x in range(0,len(dp.resources))]
print(csvList) # ["resource name", ...]

# access csv file by the index starting 0
print(dp.resources[0].data)

To use this Data Package in JavaScript, please, follow instructions below:

Install datapackage using npm:

$ npm install [email protected]

Once the package is installed, use code snippet below


const Datapackage = require('datapackage').Datapackage

async function fetchDataPackageAndData(dataPackageIdentifier) {
  const dp = await new Datapackage(dataPackageIdentifier)
  await Promise.all(dp.resources.map(async (resource) => {
    if (resource.descriptor.format === 'geojson') {
      const baseUrl = resource._basePath.replace('/datapackage.json', '')
      const resourceUrl = `${baseUrl}/${resource._descriptor.path}`
      const response = await fetch(resourceUrl)
      resource.descriptor._values = await response.json()
    } else {
      // we assume resource is tabular for now ...
      const table = await resource.table
      // rows are simple arrays -- we can convert to objects elsewhere as needed
      const rowsAsObjects = false
      resource.descriptor._values = await table.read(rowsAsObjects)
    }
  }))

  // see the data package object
  console.dir(dp)

  // data itself is stored in Resource object, e.g. to access first resource:
  console.log(dp.resources[0]._values)

  return dp
}


fetchDataPackageAndData('https://pkgstore.datahub.io/examples/transform-example-gdp-uk/latest/datapackage.json');

Our JavaScript is written using ES6 features. We are using node.js v7.4.0 and passing --harmony option to enable ES6:

$ node --harmony index.js

In order to work with Data Packages in SQL you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-sql
$ pip install sqlalchemy

To import Data Package to your SQLite Database, run following code:

import datapackage
from sqlalchemy import create_engine

data_url = 'https://pkgstore.datahub.io/examples/transform-example-gdp-uk/latest/datapackage.json'
engine = create_engine('sqlite:///:memory:')

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'sql', engine=engine)

# to see datasets in this package
storage.buckets

# to execute sql command (assuming data is in "data" folder, name of resource is data and file name is data.csv)
storage._Storage__connection.execute('select * from data__data___data limit 1;').fetchall()

# description of the table columns
storage.describe('data__data___data')
Datapackage.json