Total Diet Study Food Consumption Amounts 2003

JohnSnowLabs

Files Size Format Created Updated License Source
1 455kB csv 1 month ago John Snow Labs Standard License johnsnowlabs U.S. Food & Drug Administration (FDA)
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Data Files

total-diet-study-food-consumption-amounts-2003-csv  

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

Field information

Field Name Order Type (Format) Description
TDS_Food 1 number Food Codes of Total Diet Study.
Food_Description 2 string Description of Food Codes.
Total_US_Consumption_Amounts 3 number Food Consumption Amounts of Total U.S.
Male_And_Female_6_To_11_Months 4 number Food Consumption amounts of Males and Females of 6 to 11 months age.
Male_And_Female_2_Years 5 number Food Consumption amounts of Males and Females of 2 years age.
Male_And_Female_6_Years 6 number Food Consumption amounts of Males and Females of 6 years age.
Male_And_Female_10_Years 7 number Food Consumption amounts of Males and Females of 10 years age.
Female_14_To_16_Years 8 number Food Consumption amounts of Females of 14 to 16 years age.
Male_14_To_16_Years 9 number Food Consumption amounts of Males of 14 to 16 years age.
Female_25_To_30_Years 10 number Food Consumption amounts of Females of 25 to 30 years age.
Male_25_To_30_Years 11 number Food Consumption amounts of Males of 25 to 30 years age.
Female_40_To_45_Years 12 number Food Consumption amounts of Females of 40 to 45 years age.
Male_40_To_45_Years 13 number Food Consumption amounts of Males of 40 to 45 years age.
Female_60_To_65_Years 14 number Food Consumption amounts of Females of 60 to 65 years age.
Male_60_To_65_Years 15 number Food Consumption amounts of Males of 60 to 65 years age.
Female_70_Years 16 number Food Consumption amounts of Females of 70 years age.
Male_70_Years 17 number Food Consumption amounts of Males of 70 years age.

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")
library("jsonlite")

json_file <- "http://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/datapackage.json"
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))
print(data)

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 = "http://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/datapackage.json"

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

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

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

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('http://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/datapackage.json')

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

data = package.resources[0].read()
print(data)

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 = 'http://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // 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 = 'http://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

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