SSA Percentage of Fast Track Receipts

JohnSnowLabs

Files Size Format Created Updated License Source
2 0B csv zip 3 months ago John Snow Labs Standard License johnsnowlabs Social Security Administration
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Data Files

File Description Size Last changed Download
ssa-percentage-of-fast-track-receipts-csv 16kB csv (16kB) , json (72kB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 16kB zip (16kB)

ssa-percentage-of-fast-track-receipts-csv  

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

Field information

Field Name Order Type (Format) Description
State 1 string The state the Disability Determination Services is in
State_Abbreviation 2 string The universal abbreviation code for the state.
Region 3 string Region
Total_Fast_Track_Receipts 4 integer The total number of initial disability cases identified as fast-track that were received and accepted by the DDS
Total_DDS_Electronic_Receipts 5 integer The total number of DDS electronic receipts that were received and accepted by the DDS
Percentage_Of_Fast_Track_Receipts 6 number The total number of fast-track receipts divided by the total DDS electronic receipts
Fiscal_Year 7 date (%Y-%m-%d) Year of the Report

datapackage_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/ssa-percentage-of-fast-track-receipts/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/ssa-percentage-of-fast-track-receipts/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/ssa-percentage-of-fast-track-receipts/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/ssa-percentage-of-fast-track-receipts/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