Motor Vehicle Fatal Crashes by Posted Speed Limit

JohnSnowLabs

Files Size Format Created Updated License Source
2 59kB 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
motor-vehicle-fatal-crashes-by-posted-speed-limit-csv 3kB csv (3kB) , json (18kB)
motor-vehicle-fatal-crashes-by-posted-speed-limit_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 7kB zip (7kB)

motor-vehicle-fatal-crashes-by-posted-speed-limit-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_Fatal_Crashes 2 integer Total number of fatal accidents
Fatal_Crashes_Under_Speed_Limit_Under_55_Mph 3 integer Number of crashes when speed limit of vehicle is under 55mph
Fatal_Crashes_Under_Speed_No_Statutory_Limit 4 integer Number of crashes when speed limit of vehicle is 5,10,15,20 or 25mph
Fatal_Crashes_Under_Speed_Limit_30_35_Mph 5 integer Number of crashes when speed limit of vehicle is 30-35mph
Fatal_Crashes_Under_Speed_Limit_40_45_Mph 6 integer Number of crashes when speed limit of vehicle is 40-45mph
Fatal_Crashes_Under_Speed_Limit_50_Mph 7 integer Number of crashes when speed limit of vehicle is 50mph
Fatal_Crashes_Under_Speed_Limit_55_Mph_Above 8 integer Number of crashes when speed limit of vehicle is above 50mph
Fatal_Crashes_Under_Speed_Limit_55_Mph 9 integer Number of crashes when speed limit of vehicle is 55mph
Fatal_Crashes_Under_Speed_Limit_60_Mph 10 integer Number of crashes when speed limit of vehicle is 60mph
Fatal_Crashes_Under_Speed_Limit_65_Mph 11 integer Number of crashes when speed limit of vehicle is 65mph
Fatal_Crashes_Under_Speed_Limit_70_Mph 12 integer Number of crashes when speed limit of vehicle is 70mph
Fatal_Crashes_Under_Speed_Over_70_Mph 13 integer Number of crashes when speed limit of vehicle is over 70mph
Fatal_Crashes_Under_Speed_Limit_Unknown 14 integer Number of crashes when speed limit of vehicle is unknown

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/motor-vehicle-fatal-crashes-by-posted-speed-limit
tree JohnSnowLabs/motor-vehicle-fatal-crashes-by-posted-speed-limit
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/motor-vehicle-fatal-crashes-by-posted-speed-limit/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/motor-vehicle-fatal-crashes-by-posted-speed-limit/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/motor-vehicle-fatal-crashes-by-posted-speed-limit/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/motor-vehicle-fatal-crashes-by-posted-speed-limit/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/motor-vehicle-fatal-crashes-by-posted-speed-limit/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/motor-vehicle-fatal-crashes-by-posted-speed-limit/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/motor-vehicle-fatal-crashes-by-posted-speed-limit/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