Expenditure on Research and Development(R&D)

core

Files Size Format Created Updated License Source
2 1MB csv zip 1 week ago odc-pddl UNESCO institute for statistics
Expenditure on Research and Development(R&D) by countries with indicators such as source of funds, type of R%&D activity, fields of R&D(medical and health sciences) since 1996. Data Data comes from UNESCO institute for statistics http://data.uis.unesco.org It consists of useful information about read more
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Data Files

File Description Size Last changed Download Other formats
expenditure [csv] 148kB expenditure [csv] expenditure [json] (565kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 214kB datapackage_zip [zip]

expenditure  

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

Field information

Field Name Order Type (Format) Description
LOCATION 1 string (default) Country code
Country 2 string (default) Country name
TIME 3 year (default) Date in the form of %Y
Business enterprise 4 number (default) GERD - financed by Business enterprise (in '000 current PPP$)
Government 5 number (default) GERD - financed by Government (in '000 current PPP$)
Higher Education 6 number (default) GERD - financed by Higher education (in '000 current PPP$)
Private non-profit 7 number (default) GERD - financed by Private non-profit (in '000 current PPP$)
Rest of the world 8 number (default) GERD - financed by Rest of the world (abroad) (in '000 current PPP$)
Not specified source 9 string (default) GERD - financed by Not specified source (in '000 current PPP$)
Basic research 10 string (default) GERD - Basic research (in '000 current PPP$)
Applied research 11 string (default) GERD - Applied research (in '000 current PPP$)
Experimental development 12 string (default) GERD - Experimental development (in '000 current PPP$)
Not specified activities 13 string (default) GERD - Not specified activities (in '000 current PPP$)
Medical and health sciences 14 string (default) GERD - Medical and health sciences (in '000 current PPP$)

datapackage_zip  

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

Read me

Expenditure on Research and Development(R&D) by countries with indicators such as source of funds, type of R%&D activity, fields of R&D(medical and health sciences) since 1996.

Data

Data comes from UNESCO institute for statistics http://data.uis.unesco.org

It consists of useful information about how much is spent by government/the private sectors, type of activites like Basic research, Applied research, Experimental development for specific countries. Also, we added spendings for Medical and health sciences.

Preparation

The main resource is located in archive/gerd.csv There are several steps have been done to get final data.

  • Extracted separately each resource by source of funds “Business enterprise”, “Government” and “Higher Education”, “Private non-profit”, “Rest of the world”, “Not specified source”
  • Extracted separately each resource by type of activities “Basic research”, “Applied research”, “Experimental development”, “Not specified activities”
  • Extracted by field of R&D “Medical and health sciences”
  • Merged them into one resource data/medical.csv using pandas library.

Process is recorded and automated in python script:

# to get final merged data which is `data/expenditure.csv`, run the following script
scripts/process.py

License

Public Domain Dedication and License (PDDL)

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/core/expenditure-on-research-and-development/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/core/expenditure-on-research-and-development/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/core/expenditure-on-research-and-development/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/core/expenditure-on-research-and-development/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/core/expenditure-on-research-and-development/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