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  • Last week, data visualisation specialist Andy Kirk presented the final talk of the year in Parliament’s series of seminars on Exploring Digital. The session explored the challenges of visualising data effectively and the approaches practitioners could take to meet them.

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Myth</span></span></a></span>, <br/> <span title="Date last updated" itemprop="dateModified" content="2014-01-21 14:50:00" class="date"><br/> 21 Jan 2014</span><br/> <br/> <a rel="nofollow" title="The Code Project Open License (CPOL)" class="license" href="http://www.codeproject.com/info/cpol10.aspx" id="ctl00_LicenseLink">CPOL</a><div class="tooltip anchorLink" name="CurRat_693841" style="cursor:pointer;margin-top: 5px" id="ctl00_CurRat"><br/> <br/> <br/><br/><table itemprop="aggregateRating" class="small-text" itemtype="http://schema.org/AggregateRating" itemscope="" cellspacing="0" cellpadding="0"> <br/><tbody><tr><br/> <br/> <td class="nowrap"><br/><br/> <br/> <meta itemprop="bestRating" content="5"> <br/> <meta itemprop="worstRating" content="1"><br/> <br/><br/> <span id="ctl00_ArticleRating_VI"><br/> <div class="nowrap rating-stars-medium" style="height:18px;width:80px;position:relative;"><br/> <div class="clipped align-left float-left" style="height:16px;width:77px;"><br/> <img 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By clicking and selecting different events in graphs, you can filter the entire dashboard to drill into a particular event.</p></div>&lt;/form></div></div></td></tr></tbody></table>

  • This guide is part of The Complete Guide to ES6 with Babel 6 series. If you’re having trouble upgrading to Babel 6, start with Six Things You Need To Know About Babel 6.

     

    The Babel CLI is great for compiling ES6 to ES5 on a file-by-file basis. However, when Babel encounters an import statement, it outputs a require call – which won’t get you very far in the browser.

     

    To make our Babel output browser friendly, we’ll need to bundle it. My favourite tool for this is Webpack, and as it happens, Webpack has great Babel support through babel-loader

  • How to exchange data between iframes

     

    Sometimes it's necessary to be able to send some data (access a variable) across iframes using JavaScript, but due to security restrictions placed by the same origin policy by the browser it is not as easy as using a frame reference with the variable (function) name if the domain name, port or protocol (including http versus https) of the frames do not much. Luckily of are in control of the both frames there are a few methods of passing and accessing data between them depending on the browser and the url.

     

    Frames have the same origin

     

    In this simplest case you can simply call methods of the other frame by obtaining and using reference to it like for example parent.function() in a child frame.

     

    Both frames have the same top level domain (TLD)

     

    If both frames are coming from the same top level domain (for example subdomain.example.com and example.com) you can set JavasScript document.domain property equal to the top domain and from there sending data would be possible:

  •   28  down vote   favorite 
    19
       
           
     
      

    I have data set where some of the field values are arrays and I'd like to use crossfilter and d3.js or dc.js to display histogram of how many times each of those values was present in the dataset.

      

    Here's an example:

      
    var data = [     {"key":"KEY-1","tags":["tag1", "tag2"]},     {"key":"KEY-2","tags":["tag2"]},     {"key":"KEY-3","tags":["tag3", "tag1"]}];  var cf = crossfilter(data);  var tags = cf.dimension(function(d){ return d.tags;}); var tagsGroup = tags.group();   dc.rowChart("#chart")     .renderLabel(true)     .dimension(tags)     .group(tagsGroup)     .xAxis().ticks(3);  dc.renderAll();
      

    And JSFiddle http://jsfiddle.net/uhXf5/2/

      

Jul 22, 16

"
tptacek 1163 days ago [-]

It's good to do the exercise of doubling your salary and backing it out to an hourly to figure out what your cost floor is, but it's easy to show how this still gets you an under-the-market result: consulting companies can pay market salaries, handle 70-80% utilization, and still have strong margins.
The "correct" way to work out your rates is to raise them until you drop down to a target utilization rate by shedding the clients who don't value your work. It's awfully hard to do this in practice, but it gives you an idea of what you're aiming for.
As always, let me remind you: don't bill hourly. You are not a furniture mover."


  •   It's good to do the exercise of doubling your salary and backing it out to an hourly to figure out what your cost floor is, but it's easy to show how this still gets you an under-the-market result: consulting companies can pay market salaries, handle 70-80% utilization, and still have strong margins.

    The "correct" way to work out your rates is to raise them until you drop down to a target utilization rate by shedding the clients who don't value your work. It's awfully hard to do this in practice, but it gives you an idea of what you're aiming for.

    As always, let me remind you: don't bill hourly. You are not a furniture mover.

Jul 22, 16

"If you want to query by a "single" term, then you set it to be not_analyzed
on the index mapping. store has nothing to do with the analysis process, it
just controls if you can fetch the field "on its own", without the _source,
when you search.

-shay.banon

On Wed, Aug 4, 2010 at 6:31 PM, elastic searcher
wrote: "

Jul 22, 16

"Creating Mappings Manually
Very soon after starting to use Elasticsearch in real-world projects you come to realize the Mapping defaults are far from perfect. The most common requirement is to make certain string fields not_analyzed; or to use a different non-default analyzer. Basically, to control how they are being searched for. For that, you need to override the defaults, or provide your own Mapping before the defaults are applied.

You can define a mapping up-front when creating an index, by simply creating the index explicitly and passing a Mapping JSON while at it:

PUT /twitter
{
"tweet" : {
"properties" : {
"message" : {"type" : "string" },
"user" : {"type" : "string", "index": "not_analyzed" }
}
}
}
Not all fields have to be defined up-front in the mapping (just like defining the mapping up-front is optional). If new fields are introduced as you are indexing documents, Elasticsearch's default will be applied to it. Read more details in the Put Mapping API documentation.

However, this is still bothersome to do. Creating an index explicitly still requires more code and more maintenance, and when an index gets deleted the mapping gets deleted with it. Quite the party pooper especially for development environments!"

  • Creating Mappings Manually

     

    Very soon after starting to use Elasticsearch in real-world projects you come to realize the Mapping defaults are far from perfect. The most common requirement is to make certain string fields not_analyzed; or to use a different non-default analyzer. Basically, to control how they are being searched for. For that, you need to override the defaults, or provide your own Mapping before the defaults are applied.

     

    You can define a mapping up-front when creating an index, by simply creating the index explicitly and passing a Mapping JSON while at it:

     
    PUT /twitter {     "tweet" : {         "properties" : {             "message" : {"type" : "string" },             "user" : {"type" : "string", "index": "not_analyzed" }         }     } } 
     

    Not all fields have to be defined up-front in the mapping (just like defining the mapping up-front is optional). If new fields are introduced as you are indexing documents, Elasticsearch's default will be applied to it. Read more details in the Put Mapping API documentation.

     

    However, this is still bothersome to do. Creating an index explicitly still requires more code and more maintenance, and when an index gets deleted the mapping gets deleted with it. Quite the party pooper especially for development environments!

Jul 21, 16

"Takeaways:
An HTML form is creating using the <form> tag with opening and closing tag
Common attribute value pairs for an HTML form tag are the id, action, and method
Almost all HTML forms will have an input element with an attribute value pair of type="submit"
Using D3, you can create a selection of different parts of HTML Form Elements
Once you have the D3 selection, you can add event listeners to each element of the selection just like you have with the SVG event listeners
For instance, the HTML Form radio input button element has the event "change" which can be listened to
You can then use the HTML Form events to trigger changes to a D3 Data Visualization using D3 Transitions"

    • Takeaways:   
       
       

       

         
      • An HTML form is creating using the <form> tag with opening and closing tag
      •  
      • Common attribute value pairs for an HTML form tag are the id, action, and method
      •  
      • Almost all HTML forms will have an input element with an attribute value pair of type="submit"
      •  
      • Using D3, you can create a selection of different parts of HTML Form Elements
      •  
      • Once you have the D3 selection, you can add event listeners to each element of the selection just like you have with the SVG event listeners
      •  
      • For instance, the HTML Form radio input button element has the event "change" which can be listened to
      •  
      • You can then use the HTML Form events to trigger changes to a D3 Data Visualization using D3 Transitions

    • Features

        
         
      • One-to-one mapping with REST API and the other official clients
      •  
      • Generalized, pluggable architecture. See Extending Core Components
      •  
      • Configurable, automatic discovery of cluster nodes
      •  
      • Persistent, Keep-Alive connections
      •  
      • Load balancing (with pluggable selection strategy) across all available nodes.
      •  
        

  • ElasticSearch is a great open-source search tool that’s built on Lucene (like SOLR) but is natively JSON + RESTful. Its been used quite a bit at the Open Knowledge Foundation over the last few years. Plus, as its easy to setup locally its an attractive option for digging into data on your local machine.

      

    While its general interface is pretty natural, I must confess I’ve sometimes struggled to find my way around ElasticSearch’s powerful, but also quite complex, query system and the associated JSON-based “query DSL” (domain specific language).

      

    This post therefore provides a simple introduction and guide to querying ElasticSearch that provides a short overview of how it all works together with a good set of examples of some of the most standard queries.

Jul 20, 16

"What dock are you using? The following firmwares need to be intalled while docked - I've managed to fix several issues just by applying these two updates. Good Luck! http://www.dell.com/support/home/us/en/19/Drivers/DriversDetails?driverId=1XFPM
http://www.dell.com/support/home/us/en/04/Drivers/DriversDetails?driverId=29D8V"

Jun 13, 16

"Introduction

Machine learning has become a central part of our life – as consumers, customers, and hopefully as researchers and practitioners! Whether we are applying predictive modeling techniques to our research or business problems, I believe we have one thing in common: We want to make “good” predictions! Fitting a model to our training data is one thing, but how do we know that it generalizes well to unseen data? How do we know that it doesn’t simply memorize the data we fed it and fails to make good predictions on future samples, samples that it hasn’t seen before? And how do we select a good model in the first place? Maybe a different learning algorithm could be better-suited for the problem at hand?"

  • Introduction

      

    Machine learning has become a central part of our life – as consumers, customers, and hopefully as researchers and practitioners! Whether we are applying predictive modeling techniques to our research or business problems, I believe we have one thing in common: We want to make “good” predictions! Fitting a model to our training data is one thing, but how do we know that it generalizes well to unseen data? How do we know that it doesn’t simply memorize the data we fed it and fails to make good predictions on future samples, samples that it hasn’t seen before? And how do we select a good model in the first place? Maybe a different learning algorithm could be better-suited for the problem at hand?

  • The wacky Indian guys who made "Endhiran The Robot" could probably do an okay Bruce Sterling movie. You should see that flick. You'll thank me later.
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