Sasha  Lee

Sasha Lee


Clojure Data Visualisation Library, Based on Statistiker and D3


Envision is a small, easy to use Clojure library for data processing, cleanup and visualisation. If you've heard about Incanter, you may see a couple of things that we do in a similar way.

You can check out a couple of rendered examples here.

Project Maturity

Envision is a relatively young project. Since it's never meant to be used in hard- production (e.g. it will never be something user-facing), and is intended to be used by people who'd like to yield some information from their data, it should be stable enough from the very early releases.

Dependency Information (Artifacts)

Envision artifacts are released to Clojars. If you are using Maven, add the following repository definition to your pom.xml:


The Most Recent Version

With Leiningen:

[clojurewerkz/envision "0.1.0-SNAPSHOT"]

With Maven:


General Approach

Main idea of this library is to make exploratory analysis more interactive and visual, although in programmer's way. Envision creates a "throwaway environment" every time you, for example, make a line chart. You can modify chart the way you want, change all the possible configuration parameters, filter data, add exponents the ways we wouldn't be able to program for you.

We concluded that visual environments are often constraining, and creating an API for every since feature would make it amazingly big and bloated. So we do a bare minimum, which is already helpful by default through the API and let you configure everything you could've possibly imagined yourself: adding interactivity, combining charts, customizing layouts and so on.


Main entrypoint is clojurewerkz.envision.core/render. It creates a temporary directory with all the required dependencies and returns you a path to it. For example, let's generate some data and render a line and area charts:

(ns my-ns
  (:require [clojurewerkz.envision.core         :as envision]
            [clojurewerkz.envision.chart-config :as cfg]

   [(envision/histogram 10 (take 100 (distribution/normal-distribution 5 10))
               {:tick-format "s"})

     (flatten (for [i (range 0 20)]
                [{:year (+ 2000 i)
                  :income (+ 10 i (rand-int 10))
                  :series "series-1"}
                 {:year (+ 2000 i)
                  :income (+ 10 i (rand-int 20))
                  :series "series-2"}]
     [:year :income :series])
     {:id            "line"
      :headline      "Line Chart"
      :x             "year"
      :y             "income"
      :x-config      {:order-rule "year"}
      :series-type   "line"
      :data          (flatten (for [i (range 0 20)]
                                [{:year (+ 2000 i)
                                  :income (+ 10 i (rand-int 10))
                                  :series "series-1"}
                                 {:year (+ 2000 i)
                                  :income (+ 10 i (rand-int 20))
                                  :series "series-2"}]
      :series        "series"
      :interpolation :cardinal
     {:id            "area"
      :headline      "Area Chart"
      :x             "year"
      :y             "income"
      :x-config      {:order-rule "year"}
      :series-type   "area"
      :data          (into [] (for [i (range 0 20)] {:year (+ 2000 i) :income (+ 10 i (rand-int 10))}))
      :interpolation :cardinal

Function will return a tmp folder path, like:


cd into this path and start an HTTP Server on most systems you'd have Python 2.7 installed.

python -m SimpleHTTPServer

After that you can point your browser to


If you don't want to start an HTTP server, or don't have Python installed, just open templates/index_file.html static file in your browser.

You can check out a couple of example graphs rendered as static files here.

We decided to use an simple HTTP server by default, since sometimes d3 doesn't like file:// protocol. However, you can always just open templates/index_file.html in your browser and get pretty much same result.

Chart configuration

In order to configure chart, you have to specify:

  • id, a unique string literal identifying the chart
  • data, sequence of maps, where each map represents an entry to be displayed
  • x, key that should be taken as x value for each rendered point
  • y, key that should be taken as y value for each rendered point
  • series-type, one of line, bubble, area and bar for line charts, Scatterplots, area charts and barcharts, correspondingly

Optionally, you can specify:

  • series, which will split your data, grouping or color-coding charts by given keys keys should be given either as a string or a vector or strings.
  • interpolation, interpolation type to be used in area or line chart, usually you want to use linear, basis, or step-after, but there're more options, which will be mentioned in a corresponding section.
  • x-config specifies a configuration for X axis

x-config options:

  • order-rule specifies a key to sort data points on x axis, if it's not x
  • override-min overrides minimum for an axis


  • Histograms
  • Scatterplots
  • Boxplots
  • Barcharts
  • Regression lines
  • Cluster visualisation

Supported Clojure Versions

Envision supports Clojure 1.4+.


To subscribe for announcements of releases, important changes and so on, please follow @ClojureWerkz on Twitter.

Envision Is a ClojureWerkz Project

Envision is part of the group of libraries known as ClojureWerkz, together with Monger, Elastisch, Langohr, Welle, Titanium and several others.


Envision uses Leiningen 2. Make sure you have it installed and then run tests against all supported Clojure versions using

lein2 all test

Then create a branch and make your changes on it. Once you are done with your changes and all tests pass, submit a pull request on Github.

Author: clojurewerkz
Source Code:

#machine-learning #DataVisualisation

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Clojure Data Visualisation Library, Based on Statistiker and D3
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