Healthy Eating Index 2001-2002


Files Size Format Created Updated License Source
1 8MB csv 1 month ago John Snow Labs Standard License johnsnowlabs United States Department of Agriculture (USDA)

Data Files


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

Read me

Import into your tool

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


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

# access csv file by the index starting from 1
path_to_file = json_data$resources[[1]]$path
data <- read.csv(url(path_to_file))

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = ""

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)

# you can access datasets inside storage, e.g. the first one:

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('')

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

data = package.resources[0].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 = ''

// We're using self-invoking function here as we want to use async-await syntax:
(async () => {
  const dataset = await Dataset.load(path)

  // Get the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = ''

package =
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data =
puts data