Truck Occupant Safety Data

JohnSnowLabs

Files Size Format Created Updated License Source
2 80kB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs Bureau of Transportation Statistics
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Data Files

File Description Size Last changed Download Other formats
truck-occupant-safety-data-csv [csv] 4kB truck-occupant-safety-data-csv [csv] truck-occupant-safety-data-csv [json] (29kB)
truck-occupant-safety-data_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 8kB truck-occupant-safety-data_zip [zip]

truck-occupant-safety-data-csv  

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

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d) Year of Data
Total_Number_Of_Fatalities 2 integer Total number of truck fatalities
Total_Number_Of_Light_Vehicle_Fatalities 3 integer Total number of light truck fatalities
Total_Number_Of_Large_Vehicle_Fatalities 4 integer Total number of large truck fatalities
Total_Number_Of_Injured_Persons 5 integer Total number of injured persons from truck accidents
Total_Number_Of_Injured_Persons_From_Light_Vehicle_Accidents 6 integer Total number of injured persons from light truck accidents
Total_Number_Of_Injured_Persons_From_Large_Vehicle_Accidents 7 integer Total number of injured persons from large truck accidents
Total_Number_Of_Trucks_Involved_In_Crashes 8 integer Total number of trucks involved in crashes
Total_Number_Of_Light_Trucks_Involved_In_Crashes 9 integer Total number of light trucks involved in crashes
Total_Number_Of_Large_Trucks_Involved_In_Crashes 10 integer Total number of large trucks involved in crashes
Total_Vehicle_Miles_In_Millions 11 integer Total vehicle distance in million miles
Total_Light_Vehicle_Miles_In_Millions 12 integer Total light truck distance in million miles
Total_Large_Vehicle_Miles_In_Millions 13 integer Total large truck distance in million miles
Rates_Of_Fatalities_Per_100_Million_Light_Vehicle_Miles 14 number Rate of fatalities per 100 million light truck vehicle miles
Rates_Of_Fatalities_Per_100_Million_Large_Vehicle_Miles 15 number Rate of fatalities per 100 million large truck vehicle miles
Rates_Of_Injured_Persons_Per_100_Million_Light_Vehicle_Miles 16 number Rate of injured persons per 100 million light truck vehicle miles
Rates_Of_Injured_Persons_Per_100_Million_Large_Vehicle_Miles 17 number Rate of injured persons per 100 million large truck vehicle miles
Rates_Of_Trucks_Involved_Per_100_Million_Light_Vehicle_Miles 18 integer Rate of truckes involved per 100 million light truck vehicle miles
Rates_Of_Trucks_Involved_Per_100_Million_Large_Vehicle_Miles 19 integer Rate of truckes involved per 100 million light truck vehicle miles

truck-occupant-safety-data_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/truck-occupant-safety-data/datapackage.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
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/truck-occupant-safety-data/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/truck-occupant-safety-data/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/truck-occupant-safety-data/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/truck-occupant-safety-data/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