Deaths in US Cities 2015

JohnSnowLabs

Files Size Format Created Updated License Source
2 7MB csv zip 2 weeks ago johnsnowlabs Centre for Disease Control and Protection (cdc.gov)
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Data Files

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

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.

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")
library("jsonlite")

json_file <- "http://datahub.io/JohnSnowLabs/deaths-in-us-cities-2015/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/deaths-in-us-cities-2015/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/deaths-in-us-cities-2015/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/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 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/deaths-in-us-cities-2015/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