Data Transform examples on Global CO2 Emissions from Fossil Fuels since 1751

examples

Files Size Format Created Updated License Source
2 63kB csv zip 2 weeks ago ODC-PDDL CDIAC GitHub repo
This is an example dataset to demonstrate how data transforms works. In this example, we explain how filtering and applying formula can be done before dataset gets rendered in showcase page. It assumes publisher is already familiar with Data Packages and views specifications (views property in Data read more
Download

Data Files

File Description Size Last changed Download Other formats
global [csv] 8kB global [csv] global [json] (39kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 10kB datapackage_zip [zip]

global  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d) Year
Total 2 number Total carbon emissions from fossil fuel consumption and cement production (million metric tons of C)
Gas Fuel 3 number Carbon emissions from gas fuel consumption
Liquid Fuel 4 number Carbon emissions from liquid fuel consumption
Solid Fuel 5 number Carbon emissions from solid fuel consumption
Cement 6 number Carbon emissions from cement production
Gas Flaring 7 number Carbon emissions from gas flaring
Per Capita 8 number Per capita carbon emissions (metric tons of carbon; after 1949 only)

datapackage_zip  

This is a preview version. There might be more data in the original version.

Read me

This is an example dataset to demonstrate how data transforms works. In this example, we explain how filtering and applying formula can be done before dataset 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 attribute of views property. Each resource is an object that contains following attributes:

  • "name" - name of the resource as a reference.
  • "transform" - array of transforms. Each transform is an object, which properties vary depending on transform type.

Filtering data

Under the graph on the top of this page, you can find a table that displays filtered data. Raw data is displayed in preview section. As you can see we are filtering by year - showing data for 2000 and onwards. This is described in the second view object of views property:

{
  "name": "table-view-from-2000",
  "specType": "table",
  "resources": [
    {
      "name": "global",
      "transform": [
        {
          "type": "filter",
          "expression": "(new Date(data['Year'])).getFullYear() >= 2000"
        }
      ]
    }
  ]
}

where transform property has:

  • "type": "filter" - this way we define the transform to be a filter.
  • "expression" - any JavaScript expression that evaluates to Boolean using some field(s) from the resource. E.g., in our example we are using "Year" field - converting it into JS date object then getting year by using getFullYear() method and filtering by larger or equal sign.

Applying formula

Under the filtered data table, there is another table that displays raw data but with column “Gas Fuels” as a percentage of total figures. JSON representation of it can be found in the third view object:

{
  "name": "table-view-in-percentage",
  "specType": "table",
  "resources": [
    {
      "name": "global",
      "transform": [
        {
          "type": "formula",
          "expressions": [
            "data['Gas Fuel'] / data['Total'] * 100 + '%'"
          ],
          "asFields": ["Gas Fuel"]
        }
      ]
    }
  ]
}

where transform property has:

  • "type": "formula" - defining transform type to be used.
  • "expressions" - list of any JavaScript expressions that will be applied to specified data field.
  • "asFields" - list of field names. This should be used according to list of expressions.

Descriptor for this data package

This is the full datapackage.json of this dataset:

{
  "license": "ODC-PDDL",
  "name": "transform-examples-on-co2-fossil-global",
  "resources": [
    {
      "name": "global",
      "path": "global.csv",
      "format": "csv",
      "schema": {
        "fields": [
          {
            "description": "Year",
            "format": "any",
            "name": "Year",
            "type": "date"
          },
          {
            "description": "Total carbon emissions from fossil fuel consumption and cement production (million metric tons of C)",
            "name": "Total",
            "type": "number"
          },
          {
            "description": "Carbon emissions from gas fuel consumption",
            "name": "Gas Fuel",
            "type": "number"
          },
          {
            "description": "Carbon emissions from liquid fuel consumption",
            "name": "Liquid Fuel",
            "type": "number"
          },
          {
            "description": "Carbon emissions from solid fuel consumption",
            "name": "Solid Fuel",
            "type": "number"
          },
          {
            "description": "Carbon emissions from cement production",
            "name": "Cement",
            "type": "number"
          },
          {
            "description": "Carbon emissions from gas flaring",
            "name": "Gas Flaring",
            "type": "number"
          },
          {
            "description": "Per capita carbon emissions (metric tons of carbon; after 1949 only)",
            "name": "Per Capita",
            "type": "number"
          }
        ]
      }
    }
  ],
  "sources": [
    {
      "name": "CDIAC",
      "web": "http://cdiac.esd.ornl.gov/ftp/ndp030/CSV-FILES/global.1751_2010.csv"
    },
    {
      "name": "GitHub repo",
      "web": "https://github.com/datapackage-examples/transform-co2-fossil-global"
    }
  ],
  "title": "Data Transform examples on Global CO2 Emissions from Fossil Fuels since 1751",
  "views": [
    {
      "id": "graph",
      "label": "Graph",
      "state": {
        "graphType": "lines-and-points",
        "group": "Year",
        "series": [
          "Total",
          "Solid Fuel"
        ]
      },
      "type": "Graph"
    },
    {
      "name": "table-view-from-2000",
      "specType": "table",
      "resources": [
        {
          "name": "global",
          "transform": [
            {
              "type": "filter",
              "expression": "(new Date(data['Year'])).getFullYear() >= 2000"
            }
          ]
        }
      ]
    },
    {
      "name": "table-view-in-percentage",
      "specType": "table",
      "resources": [
        {
          "name": "global",
          "transform": [
            {
              "type": "formula",
              "expressions": [
                "data['Gas Fuel'] / data['Total'] * 100 + '%'"
              ],
              "asFields": ["Gas Fuel"]
            }
          ]
        }
      ]
    }
  ]
}

Import into your tool

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite")
library("jsonlite")

json_file <- "http://datahub.io/examples/transform-examples-on-co2-fossil-global/datapackage.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources[[1]]$path
data <- read.csv(url(path_to_file))
print(data)

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/examples/transform-examples-on-co2-fossil-global/datapackage.json"

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

# data frames available (corresponding to data files in original dataset)
storage.buckets

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

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

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

from datapackage import Package

package = Package('http://datahub.io/examples/transform-examples-on-co2-fossil-global/datapackage.json')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

data = package.resources[0].read()
print(data)

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'http://datahub.io/examples/transform-examples-on-co2-fossil-global/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
(async () => {
  const dataset = await Dataset.load(path)

  // Get the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer
})()

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = 'http://datahub.io/examples/transform-examples-on-co2-fossil-global/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data
Datapackage.json