Pharmaceutical Drug Spending by countries

core

Files Size Format Created Updated License Source
2 92kB csv zip 5 days ago odc-pddl Pharmaceutical Drug Spending from Organisation for Economic Cooperation and Development
Pharmaceutical Drug Spending by countries with indicators such as a share of total health spending, in USD per capita (using economy-wide PPPs) and as a share of GDP. Plus, total spending by each countries in the specific year. Data Data comes from Organisation for Economic Cooperation and read more
Download

Data Files

File Description Size Last changed Download
data 40kB csv (40kB) , json (139kB)
pharmaceutical-drug-spending_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 58kB zip (58kB)

data  

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
TIME 2 year (default) Date in the form of %Y
PC_HEALTHXP 3 number (default) % of Health spending
PC_GDP 4 number (default) % of GDP
USD_CAP 5 number (default) in USD per capita (using economy-wide PPPs)
FLAG_CODES 6 string (default)
TOTAL_SPEND 7 number (default) Total spending in millions

pharmaceutical-drug-spending_zip  

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

Read me

Pharmaceutical Drug Spending by countries with indicators such as a share of total health spending, in USD per capita (using economy-wide PPPs) and as a share of GDP. Plus, total spending by each countries in the specific year.

Data

Data comes from Organisation for Economic Cooperation and Development on https://data.oecd.org/healthres/pharmaceutical-spending.htm

It consists of useful information about percent of health spending, percent of GDP and US dollars per capita for specific countries. Also, we added total spending by countries using their population data.

Population data comes from DataHub http://datahub.io/core/population since it is regularly updated and includes all country codes.

Preparation

There are several steps have been done to get final data.

  • We extracted separately each resource by “percent of health spending”, “percent of GDP” and “US dollars per capita”
  • We merged them into one resource and added new column “TOTAL_SPEND”

“TOTAL_SPEND” is calculated using “US dollars per capita” and “population” data. Source for original pharmacy drug spending: https://stats.oecd.org/sdmx-json/data/DP_LIVE/.PHARMAEXP.../OECD?contentType=csv&detail=code&separator=comma&csv-lang=en.

Process is recorded and automated in python script:

# to get population.csv
scripts/population.py 
# to get final data.csv
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 <- 'https://datahub.io/core/pharmaceutical-drug-spending/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    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:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/pharmaceutical-drug-spending/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

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('https://datahub.io/core/pharmaceutical-drug-spending/datapackage.json')

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

# print all tabular data(if exists any)
resources = package.resources
for resource in resources:
    if resource.tabular:
        print(resource.read())

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 = 'https://datahub.io/core/pharmaceutical-drug-spending/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 list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()
Datapackage.json