NYC Services Volunteer Opportunities

JohnSnowLabs

Files Size Format Created Updated License Source
2 1MB csv zip 1 month ago John Snow Labs Standard License John Snow Labs City of New York
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Data Files

File Description Size Last changed Download
nyc-services-volunteer-opportunities-csv 296kB csv (296kB) , json (548kB)
nyc-services-volunteer-opportunities_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 295kB zip (295kB)

nyc-services-volunteer-opportunities-csv  

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

Field information

Field Name Order Type (Format) Description
Opportunity_Id 1 integer Opportunity Id
Content_Id 2 integer Content Id
Vol_Requests 3 integer Number of Requests
Event_Time 4 number Event Time
Title 5 string Title of Event
Hits 6 integer Number of Hits
Summary 7 string Summary of Event
Is_Priority 8 boolean Priority
Category_Id 9 integer Category Id
Category_Desc 10 string Category Description
Org_Title 11 string Organization Title
Org_Content_Id 12 string Race Ethnicity
Addresses_Count 13 integer Total Addresses
Locality 14 string Location
Region 15 string Region
Postal_Code 16 integer Postal Code
URL 17 string URL Address
Recurrence_Type 18 string Recurrence Type
Hours 19 integer Time in Hours
Created_Date 20 date (%Y-%m-%d) Date Created
Last_Modified_Date 21 date (%Y-%m-%d) Modified Date
Start_Date 22 date (%Y-%m-%d) Date Started
End_Date 23 date (%Y-%m-%d) Date Ended
Status 24 string Status of the case

nyc-services-volunteer-opportunities_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/nyc-services-volunteer-opportunities/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/nyc-services-volunteer-opportunities/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/nyc-services-volunteer-opportunities/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/nyc-services-volunteer-opportunities/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