Total Diet Study Food Consumption Amounts 2003

JohnSnowLabs

Files Size Format Created Updated License Source
2 288kB csv zip 4 months ago John Snow Labs Standard License John Snow Labs U.S. Food & Drug Administration (FDA)
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Data Files

File Description Size Last changed Download
total-diet-study-food-consumption-amounts-2003-csv 29kB csv (29kB) , json (150kB)
total-diet-study-food-consumption-amounts-2003_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 45kB zip (45kB)

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.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

data get https://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003
data info JohnSnowLabs/total-diet-study-food-consumption-amounts-2003
tree JohnSnowLabs/total-diet-study-food-consumption-amounts-2003
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/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/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/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/JohnSnowLabs/total-diet-study-food-consumption-amounts-2003/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/total-diet-study-food-consumption-amounts-2003/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/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 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