DAC and CRS code lists

core

Files Size Format Created Updated License Source
16 1MB csv odc-pddl OECD
The DAC Secretariat maintains various code lists which are used by donors to report on their aid flows to the DAC databases. In addition, these codes are used to classify information in the DAC databases. Here you can find these codes republished in a machine readable format. They’re fetched read more
Download

Data Files

dac-members  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

multilateral-donors  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

non-dac-donors  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

private-donors  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

agencies  

Field information

Field Name Order Type (Format) Description
code 1 string
donor_name_en 2 string
donor_name_fr 3 string
agency_code 4 string
agency_name_en 5 string
agency_name_fr 6 string
acronym 7 string

nature-of-submission  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

recipients  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string
income_group 4 string
geography 5 string

channel-codes  

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
name_en 3 string
acronym_en 4 string
name_fr 5 string
acronym_fr 6 string

collaboration-types  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

flow-types  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
description_en 3 string
name_fr 4 string
description_fr 5 string

finance-types  

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
name_en 3 string
description_en 4 string
name_fr 5 string
description_fr 6 string

finance-type-categories  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

aid-types  

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
name_en 3 string
description_en 4 string
name_fr 5 string
description_fr 6 string

aid-type-categories  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
description_en 3 string
name_fr 4 string
description_fr 5 string

sectors  

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
voluntary_code 3 string
name_en 4 string
description_en 5 string
name_fr 6 string
description_fr 7 string

sector-categories  

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
description_en 3 string
name_fr 4 string
description_fr 5 string

Read me

goodtables.io

The DAC Secretariat maintains various code lists which are used by donors to report on their aid flows to the DAC databases. In addition, these codes are used to classify information in the DAC databases.

Here you can find these codes republished in a machine readable format. They’re fetched from an excel file available on the OECD website.

Preparation

You will need: python 3.x

Run the following to download and convert the data from XLS to CSV:

pip install -r requirements.txt
python scraper.py

License

This material is licensed by its maintainers under the Public Domain Dedication and License.

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/core/dac-and-crs-code-lists/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"dac-members")
View(datapackage$data$"multilateral-donors")
View(datapackage$data$"non-dac-donors")
View(datapackage$data$"private-donors")
View(datapackage$data$"agencies")
View(datapackage$data$"nature-of-submission")
View(datapackage$data$"recipients")
View(datapackage$data$"channel-codes")
View(datapackage$data$"collaboration-types")
View(datapackage$data$"flow-types")
View(datapackage$data$"finance-types")
View(datapackage$data$"finance-type-categories")
View(datapackage$data$"aid-types")
View(datapackage$data$"aid-type-categories")
View(datapackage$data$"sectors")
View(datapackage$data$"sector-categories")

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/core/dac-and-crs-code-lists/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/core/dac-and-crs-code-lists/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/core/dac-and-crs-code-lists/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/core/dac-and-crs-code-lists/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