Deaths in US Cities 2015

JohnSnowLabs

Files Size Format Created Updated License Source
2 0B csv zip 6 months ago johnsnowlabs Centre for Disease Control and Protection (cdc.gov)
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Data Files

File Description Size Last changed Download
deaths-in-us-cities-2015-csv 471kB csv (471kB) , json (4MB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 571kB zip (571kB)

deaths-in-us-cities-2015-csv  

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

Field information

Field Name Order Type (Format) Description
Reporting_Area 1 string Death Reporting Area
MMWR_Year 2 integer Morbidity and Mortality Weekly Report Year
MMWR_Week 3 integer Morbidity and Mortality Weekly Report Week Number.
All_Causes_by_Age_Years_All_Ages 4 integer Identifies all death causes for all age groups.
All_Causes_by_Age_Years_All_Ages_Flag 5 string Identifies all death causes for all age groups.
All_Causes_by_Age_Years_65 6 integer Identifies all death causes for age group of 65 Years.
All_Causes_by_Age_Years_65_Flag 7 string Identifies all death causes for age group of 65 Years.
All_Causes_by_Age_Years_45_64 8 integer Identifies all death causes for age group between 45 and 64.
All_Causes_by_Age_Years_45_64_Flag 9 string Identifies all death causes for age group between 45 and 64
All_Causes_by_Age_Years_25_44 10 integer Identifies all death causes for age group between 45 and 64.
All_Causes_by_Age_Years_25_44_Flag 11 string Identifies all death causes for age group between 25 and 44.
All_Causes_by_Age_Years_1_24 12 integer Identifies all death causes for age group between 1 and 24.
All_Causes_by_Age_Years_1_24_Flag 13 string Identifies all death causes for age group between 1 and 24.
All_Causes_by_Age_Years_LT_1 14 integer Identifies all death causes for age group LT 1.
All_Causes_by_Age_Years_LT_1_Flag 15 string Identifies all death causes for age group LT 1.
P_I_Total 16 integer PI Total.
P_I_Total_Flag 17 string PI Total Flag.
Latitude 18 number Identifies the geographical location latitude where deaths occur.
Longitude 19 number Identifies the geographical location longitude where deaths occur.

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/deaths-in-us-cities-2015
data info JohnSnowLabs/deaths-in-us-cities-2015
tree JohnSnowLabs/deaths-in-us-cities-2015
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/deaths-in-us-cities-2015/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/deaths-in-us-cities-2015/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/deaths-in-us-cities-2015/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/deaths-in-us-cities-2015/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/deaths-in-us-cities-2015/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/deaths-in-us-cities-2015/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/deaths-in-us-cities-2015/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