Combined and Normalized GHEITI Data


Files Size Format Created Updated License Source
2 39kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Natural Resource Governance Institute (NRGI)

Data Files

File Description Size Last changed Download
combined-and-normalized-gheiti-data-csv 49kB csv (49kB) , json (100kB)
combined-and-normalized-gheiti-data_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 16kB zip (16kB)


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

Field information

Field Name Order Type (Format) Description
Company_Name 1 string Referes to the name of the company included in this study.
Year 2 date (%Y-%m-%d) Refers to the year when the revenue is generated.
Revenue_Commodity 3 string Referes to the specific commodity for which the revenue is generated. It includes Silver, Gold, Diamond, Bauxite, Limestone, Manganese and Oil.
Commodity_Code 4 string Indicates the Codes for Comapny/Year/Commodity combination.
Company_ID 5 string Refers to identity of different companies.
Data_File_Name 6 string Refers to the name of the data file in which the data related to several sectors is present.
Gfs_Code 7 string Indicates the revenue code by the Government Finance Statistics (GFS).
Gfs_Name 8 string Indicates the revenue name by the Government Finance Statistics (GFS)
Measurement_Value 9 number Indicates the value of the measure used to analyze data.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the almost like you use git with the github. Here are installation instructions.

data get
tree JohnSnowLabs/combined-and-normalized-gheiti-data
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

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

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

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 = ''

# 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('')

# print list of all resources:

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

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 = ''

// 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) {
  // 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
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data