United States of America education budget analysis

core

Files Size Format Created Updated License Source
3 99kB csv zip 3 months ago 2 months ago Open Data Commons Public Domain Dedication and Licence (PDDL) Office of Management and Budget, President’s Budget from white house official website Country, Regional and World GDP (Gross Domestic Product), DataHub
United States of America Education budget to GDP analysis Data Data comes from Office of Management and Budget, President’s Budget from white house official website on https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/budget/fy2018/hist05z2.xls It consists of useful information about read more
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Data Files

File Description Size Last changed Download
data 1kB csv (1kB) , json (3kB)
budget 71kB csv (71kB) , json (124kB)
usa-education-budget-analysis_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 40kB zip (40kB)

data  

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

Field information

Field Name Order Type (Format) Description
YEAR 1 year (default) Date in the form of %Y
BUDGET_ON_EDUCATION 2 number (default) in millions of dollars
GDP 3 number (default) in millions of dollars
RATIO 4 number (default) education expenditure / GDP in percentage

budget  

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

Field information

Field Name Order Type (Format) Description
Name 1 string (default) Department or other unit
Year 2 year (default) Date in the form of %Y
Value 3 string (default) in millions of dollars

Read me

United States of America Education budget to GDP analysis

Data

Data comes from Office of Management and Budget, President’s Budget from white house official website on https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/budget/fy2018/hist05z2.xls

It consists of useful information about BUDGET AUTHORITY BY AGENCY in the range 1976–2022.

Gross Domestic Value(GDP) comes from DataHub http://datahub.io/core/gdp/r/gdp.csv since it is regularly updated and includes all country codes.

Note that data in data/budget.csv starting 2017, the value is estimate value

Preparation

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

  • We extracted budget and gdp data separately
  • We merged and added new column RATIO which is calculated by education expenditure / GDP

Process is recorded and automated in python script:

# to get final data.csv
scripts/process.py

License

Public Domain Dedication and License (PDDL)

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

data get https://datahub.io/core/usa-education-budget-analysis
data info core/usa-education-budget-analysis
tree core/usa-education-budget-analysis
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/usa-education-budget-analysis/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/usa-education-budget-analysis/r/0.csv

curl -L https://datahub.io/core/usa-education-budget-analysis/r/1.csv

curl -L https://datahub.io/core/usa-education-budget-analysis/r/2.zip

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

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/usa-education-budget-analysis/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)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/usa-education-budget-analysis/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/usa-education-budget-analysis/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        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/usa-education-budget-analysis/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