Healthy Eating Index 2001-2002

JohnSnowLabs

Files Size Format Created Updated License Source
2 6MB csv zip 1 month ago John Snow Labs Standard License John Snow Labs United States Department of Agriculture (USDA)
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Data Files

File Description Size Last changed Download
healthy-eating-index-2001-2002-csv 748kB csv (748kB) , json (3MB)
healthy-eating-index-2001-2002_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 1MB zip (1MB)

healthy-eating-index-2001-2002-csv  

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

Field information

Field Name Order Type (Format) Description
SEQN 1 integer Respondent sequence number
Fat_Score 2 number Score of fats
Saturated_Fatty_Acids_Score 3 string Score of saturated fatty acids
Sodium_Score 4 number Score of sodium
Cholestrol_Score 5 number Score of Cholestrol
Grain_Score 6 number Score of Grain
Fruit_Score 7 number Score of fruit
Vegetable_Score 8 number Score of vegetable
Meat_Score 9 number Score of meat
Dairy_Score 10 number Score of dairy
Vitamin_Score 11 integer Score of Vitamin
Healthy_Eating_Index 12 number It is measurement of a quality of diet

healthy-eating-index-2001-2002_zip  

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

Read me

Import into your tool

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/JohnSnowLabs/healthy-eating-index-2001-2002/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

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/JohnSnowLabs/healthy-eating-index-2001-2002/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/JohnSnowLabs/healthy-eating-index-2001-2002/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/JohnSnowLabs/healthy-eating-index-2001-2002/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