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Rural population (% of total population)

world-bank

Files Size Format Created Updated License Source
2 3MB csv zip 1 year ago 1 year ago CC-BY-4.0

Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.

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Data Files

Download files in this dataset

File Description Size Last changed Download
data Indicator data 462kB csv (462kB) , json (1MB)
sp_rur_totl_zs_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 330kB zip (330kB)

Indicator data [data]  

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

Field information

Field Name Order Type (Format) Description
Country Name 1 string Country or Region name
Country Code 2 string ISO 3-digit ISO code extended to include regional codes e.g. EUR, ARB etc
Year 3 year Year
Value 4 number Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/world-bank/sp.rur.totl.zs
data info world-bank/sp.rur.totl.zs
tree world-bank/sp.rur.totl.zs
# Get a list of dataset's resources
curl -L -s https://datahub.io/world-bank/sp.rur.totl.zs/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/world-bank/sp.rur.totl.zs/r/0.csv

curl -L https://datahub.io/world-bank/sp.rur.totl.zs/r/1.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/world-bank/sp.rur.totl.zs/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/world-bank/sp.rur.totl.zs/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/world-bank/sp.rur.totl.zs/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/world-bank/sp.rur.totl.zs/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)
    }
  }
})()

Read me

Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.

Datapackage.json

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