Simple Graph Spec Tutorial

examples

Files Size Format Created Updated License Source
2 66kB csv zip 1 week ago CBOE VIX Page
This is an example dataset, that demonstrates how to build the simple and nice graphs using the "Simple Graph Spec". We are using CBOE Volatility Index (VIX) time-series dataset for 2015-2016 as an example to create line and bar charts. Views We assume that you are familiar with what read more
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Data Files

File Description Size Last changed Download Other formats
vix-daily [csv] 11kB vix-daily [csv] vix-daily [json] (30kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 13kB datapackage_zip [zip]

vix-daily  

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

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
VIXOpen 2 number
VIXHigh 3 number
VIXLow 4 number
VIXClose 5 number

datapackage_zip  

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

Read me

This is an example dataset, that demonstrates how to build the simple and nice graphs using the “Simple Graph Spec”. We are using CBOE Volatility Index (VIX) time-series dataset for 2015-2016 as an example to create line and bar charts.

Views

We assume that you are familiar with what datapackage.json is and its specifications.

To create graphs for your tabular data, the datapackage.json should include the views attribute that is responsible for visualizations.

“Simple Graph Spec” is the quickest and easiest way to build a graph. To use it, inside views you should set specType to simple and define some graph specifications in spec property. See example datapackage.json:

{
  "name": "simple-graph-spec",
  "title": "Simple Graph Spec Tutorial",
  "sources": [{
    "name": "CBOE VIX Page",
    "web": "http://www.cboe.com/micro/vix/historical.aspx"
  }],
  "resources": [
    {
      "name": "vix-daily",
      "path": "data/vix-daily.csv",
      "format": "csv",
      "mediatype": "text/csv",
      "schema": {
        "fields": [
          {
            "name": "Date",
            "type": "date",
            "description": ""
          },
          {
            "name": "VIXOpen",
            "type": "number",
            "description": ""
          },
          {
            "name": "VIXHigh",
            "type": "number",
            "description": ""
          },
          {
            "name": "VIXLow",
            "type": "number",
            "description": ""
          },
          {
            "name": "VIXClose",
            "type": "number",
            "description": ""
          }
        ],
        "primaryKey": "Date"
      }
    }
  ],
  "views": [
    {
      "name": "simple-view-line",
      "title": "tutorial-on-simple-views-line",
      "resources": ["vix-daily"],
      "specType": "simple",
      "spec": {
        "type": "line",
        "group": "Date",
        "series": ["VIXHigh", "VIXLow"]
      }
    },
    {
      "name": "simple-view-bar",
      "title": "tutorial-on-simple-views-bar",
      "resources": ["vix-daily"],
      "specType": "simple",
      "spec": {
        "type": "bar",
        "group": "Date",
        "series": ["VIXOpen", "VIXOpen", "VIXHigh", "VIXLow"]
      }
    }
  ]
}

Only 3 properties enough to define graph specifications inside spec property:

Attribute Type Description
type String line, bar, pie (defaults to line)
group String Field name, that will be used as abscissa (usually date field)
series Array Field name(s) that will be used as ordinate

You can define multiple views for your dataset. For example, to display line graph as presented above, we defined graph type to be a line:

  ...
  "spec": {
    "type": "line",
    ...
  }

Similarly to display bar chart we’ve used bar type:

  ...
  "spec": {
    "type": "bar",
    ...
  }

We use Date field to display data over time, by setting group attribute to the field name:

  ...
  "spec": {
    ...
    "group": "Date",
    ...
  }

You can set any number of fields to display in series attribute as an array:

  ...
  "spec": {
    ...
    "series": [
      "VIXHigh",
      "VIXLow"
    ]
 }

In our case we’ve displayed line graph for VIXHigh and VIXLow and similarly, in the bar chart, we use all four series and all of them are presented in chart.

Outside of spec attribute there are some other important parameters to note:

Attribute Type Description
name String Unique identifier for view within list of views (lines 51 and 62)
title String Title for the graph (lines 52 and 63)
resources Array Data sources for this spec. It can be either resource name or index. By default it is the first resource (lines 53 and 64)
specType String Available options: simple, vega, plotly (Required)

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/examples/simple-graph-spec/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/examples/simple-graph-spec/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/examples/simple-graph-spec/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/examples/simple-graph-spec/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/examples/simple-graph-spec/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