Truck Occupant Safety Data

JohnSnowLabs

Files Size Format Created Updated License Source
2 80kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Bureau of Transportation Statistics
Download

Data Files

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

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

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the datahub.io almost like you use git with the github. Here are installation instructions.

data get https://datahub.io/JohnSnowLabs/truck-occupant-safety-data
tree JohnSnowLabs/truck-occupant-safety-data
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/truck-occupant-safety-data/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/truck-occupant-safety-data/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/truck-occupant-safety-data/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/truck-occupant-safety-data/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/truck-occupant-safety-data/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/truck-occupant-safety-data/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/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 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