Introducing Google Trends Database

Most of great tech companies have a tech blog to demonstrate their progress, and Google is not an exception. As I was browsing Google’s blog today, I found a very interesting article, which talks about a database called Google Trends, that collects real-time big data for various topics search through Google. The topic can be the popularity of one basketball game, the interest of one literature project over time, and elections in 2016. Also, you can view those real-time data in different formats, like line chart, bar chart, map and so on. With Google providing unlimited correlations and topics, we can retrieve useful data in a more convenient way.  W

With Google providing unlimited correlations and topics, we can retrieve useful data in a more convenient way.  What’s more, Google has also announced the acquisition of data analysis platform Kaggle this week, showing its great interest in utilizing data to deliver more valuable information.

Those two piece of news are enough to show Google’s focus on data. Thus for us, data visualization, the way to present a large amount of data will also become more and more popular and that it might be as common as Office tools in today. And I think it’s a good time to say that let’s grab this technique and move to the future along with great tech companies. 😀

Interactivity in Tableau (continued, again!)

We are focusing on Actions this week. The set of features within Actions allows the user to connect multiple visualization/dashboard and link a visualization to an external URL. The first feature i.e. connecting multiple visualizations, allows a visualization to act as a filter item for another visualization or as a data highlight item. Sounds confusing? Let’s take a detailed look at these features –

  • Use as a filter action: This action can be used when a visualization has to be used as a filter item for another visualization (this holds good for a dashboard, as well). This is particularly useful when there is data that is interlinked between various visualizations. Instead of keeping a filter card, the user can have an additional visualization, which acts as a filter, thereby making better use of space and adding more context into the analysis. While creating the filter, the user has to select the source and target visualizations which will be used by the filter action. The action trigger can be set using the following three options:
    • Hover: The trigger for this action is when the user brings the mouse over the data point in the source visualization. This kind of action is good in cases when the user is performing data exploration, rather than data analysis. This is because while doing data exploration the user will be quickly scanning across the data points rather than stopping at a single data point to perform any analysis. Also, if the number of records in the data set is large, hover action could cause performance lags.
    • Select: The trigger for this action is when the user selects the data point in the source visualization. This action is suggested when the user has to perform data analysis or the number of target visualizations that get affected are large.
    • Menu: The trigger for this action is when the user selects a data point and selects the appropriate option provided in the context menu. A menu action is best used when the user wants to provide the user with an extra layer of choice before applying the action. A good practice is to use this action for cases where the user has to be navigated away from the existing screen (it could be a different dashboard/visualization/URL).

The user also has options to choose from when he/she clears the action trigger. An example from our speed violation data set could be if we have two visualization in a dashboard. The first visualization shows the map of Chicago with the addresses marked as per violations reported. The addresses are also clustered based on geographical zones – north, south, east, and west. The second visualization shows the history of violations for each address, along with additional statistical data such as deviation from daily average, max/min for that address, etc. We may want to put a hover filter here on the map for each cluster so that the user can see a subset of the total addresses which are grouped based on their geographical proximity.

  • Use as a highlight action: This action is used when the user needs to highlight data points based on the trigger action. The non-highlighted data points still remain on the view but are grayed out, whereas in the case of the filter action the “out data” points are removed from the view temporarily. This kind of action is useful in the following scenarios:
    • The data set is large.
    • The non-highlighted data set is important for spatial reasons i.e. the proximity of highlighted/non-highlighted items is important to the user in his/her analysis.
  • Use as a URL action: This action is used when the user should be redirected to a resource outside of Tableau environment, such as a file path, URL, or send a mail. A user should be navigated away from the visualization/dashboard only when it absolutely necessary or is part of the visual discovery process. Therefore, this feature needs to be used with caution. Some sample use cases for using this feature are as follows:
    • User needs to be showed data that is not in a format that can be represented in a visual format. For e.g. links to legal documentation in the case of contract negotiation dashboards.
    • User needs to be navigated to a different but relevant data set that is not part of the data set being presented. This could be done in cases where the subset of the data, that is being linked, is needed for verification purpose.
    • The dashboard/visualization is part of an enterprise application. The data point that is being used as the action trigger is passed a parameter to the enterprise application for further business processing.
    • A user needs to be alerted/informed by mail based on a data point in the dashboard. The email feature can be setup to send an alert/information mail with the data present in the user’s trigger.

Actions provide a new dimension for the user to interact with data, in addition, to the visual representation. While setting up interactivity with actions, the points to consider are the number of source/target visualizations, whether filtering applies to each element, and the user workflow.

References:

The Rhythm of Food

Over the years Key food trends can be revealed via Google search. From the rise and fall of recipes over diets and drinks to cooking trends and regional cuisines.

The first visualization shows the top Google search volume for different things related to food i.e. Veganism, Moscow Mule, Fat-free etc.

Another best thing about this visualization is that it appropriately shows the collected weekly trend of a number of dishes and ingredients over the past 12 years. These searches were then plotted on a year clock.

The attractive thing about this visualization is that after plotting on a year clock we can investigate the seasons and rhythm of food around the world. It shows that when is the google search for dishes or ingredients at peak, a radius of the clock gives us the number of the google search, The volume of the google searches in particular months highlights the natural season of the food.

The best part is that the visualization has shown which food is at the peak in a month. It shows the natural season of the food including food and veggies.

Also, it shows the most common patterns across the year, i.e, which items fade in and fade out in a year, which food items are at peak during the holidays or special events.

It also shows that the seasonality varies across the world.

 

Reference – http://rhythm-of-food.net/

Image Reference – http://rhythm-of-food.net/

Financial KPI Dashboard For Executive

The first thing that comes to mind when thinking of an executive dashboard is tracking fiscal performance. This dashboard displays financial KPIs like current revenue, quick ratio, and short term assets. The idea behind any executive dashboard is to provide a concise, but accurate view of business performance so executives can get the information they need “at-a-glance” to drive the business forward. In the quest for brevity, however, it is important to provide executives with an easy way to to get a more granular view of the business.

  • Left top corner is the Sales Growth, which measure the pace at which your organization’s sales revenue is increasing or decreasing. This is a key metric for any organization to monitor since it is an essential part of growth projections and is instrumental in strategic decision-making.
  • Left bottom corner is the Quick Ratio, which measures the ability of your organization to meet any short-term financial obligations with assets that can be quickly converted into cash.
  • Right part is about Working Capital, which measures your organization’s financial health by analyzing readily available assets that could be used to meet any short-term financial liabilities.

Reference: https://www.klipfolio.com/resources/dashboard-examples/executive/financial-performance

How to create a simple Bar chart using D3.js? Part 1

For many classes I have sat wondering why should I learn to use D3.js when I can create a simple bar chart in Tableau within seconds. However, after reading a couple of articles online and also a few blogposts from this course I got intrigued and wanted to write on my understanding of the creation of a bar chart using D3.js.

D3.js as we all know is a Javascript library which helps visualize data using HTML, SVG and CSS.

All of us are aware of HTML and CSS. So, what is this SVG after all? SVG is an XML based vector image format with support for interactivity and animation. SVG allows three types of graphic objects:

  • Vector graphic shapes such as outlines (boundary for your bar graph) consisting of straight lines and curves
  • Bitmap images
  • Text

These graphical objects can be grouped, styled and transformed into previously rendered objects. SVG is like your paint tool which draws your chart based on the measurements you give and axis and boundary defined by you.  You can include interactivity with your SVG as well and bring in animation to your chart through javascript that accesses the SVG DOM (document object model) Document_Object_Model.

So to first start your bar graph, you need an svg element to plot it and render it with the available data.

<svg id=”visualisation” width=”1000″ height=”500″></svg>

This code defines the svg element’s border or a small box with the mentioned height and width.

Next step is to create the x and the y axis of your chart for which you need a fixed range and domain for both the axis.

Domain – defines the maximum and minimum value of the data set displayed on the graph

Range – amount of the domain that the svg will be covering.

How will you fix the domain and the range? What sets that minimum and maximum value? The axis needs to scale according to your data and we need to take this into consideration while setting the values for the domain and the range.

For this example, I am using the following dataset:

var lineData = [{ x: 1, y: 5}, {  x: 20, y: 20 }, { x: 40,  y: 10 }, {  x: 60, y: 40 }, {  x: 80, y: 5}, { x: 100,  y: 60 }];

Now that the SVG is defined next step is to define a margin because each HTML element is considered as box on any web page. It consists of margin, border, padding and content. The width and height of the entire bar chart should also be set initially.

var vis = d3.select(‘#visualisation’), WIDTH = 1000, HEIGHT = 500, MARGINS = { top: 20,  right: 20,  bottom: 20, left: 50 },

<!— The x-range and the y-range represent the domain of the x- axis and the y-axis.

The range value takes into consideration the left and the right margin.

The next step would be to tie the domain to the data set using d3. Max() and d3.min() methods. –>

xRange = d3.scale.linear().range([MARGINS.left, WIDTH – MARGINS.right]).domain([d3.min(lineData, function(d) {

return d.x;

}), d3.max(lineData, function(d) {

return d.x;

})]),

yRange = d3.scale.linear().range([HEIGHT – MARGINS.top, MARGINS.bottom]).domain([d3.min(lineData, function(d) {

return d.y;

}), d3.max(lineData, function(d) {

return d.y;

})]),

<! – – Both the axis are appended to the SVG element by use of the append function – ->

xAxis = d3.svg.axis() .scale(xRange)  .tickSize(5) .tickSubdivide(true),

<! – the Y axis needs to be oriented towards the left hence orient function is used. – ->

yAxis = d3.svg.axis().scale(yRange) .tickSize(5) .orient(‘left’).tickSubdivide(true);

Both the axes have been transformed keeping the defined margin in view so that the axis do not touch the SVG margin. I will be continuing this post next week to add the lines of the bar chart and also CSS styling element. Hope this post encourages you all to try a simple chart on D3.js.

Reference : https://www.sitepoint.com/creating-simple-line-bar-charts-using-d3-js/

 

 

 

The Start Up Boom

Living in silicon valley means you will be surrounded by numerous start-ups. But how many of these startups actually succeed? Here success is identified as crossing the ‘Billion Dollar’ mark. Most of the companies are venture-backed private companies. The claim of this visualization is to show how the Billion dollar start-up boom happened after 2014.

The visualization tracks all the start-ups who touched the billion dollar mark and how they went beyond that in a span of 2 years. The visualization defines a semi-circle of 3 radii. The first one marks the billion dollar mark, the second marks $10 billion and the 3rd one marks $40 billion. The start-ups across 3 major geographic locations are marked, viz., United States, Asia, Europe, and others. The visualization being interactive has a time slider below which shows dates from the year 2014 to 2017. Also, you can differentiate the companies on the basis of the industry. The drop down list shows different types of industries through which you can drill down to a specific industry or you can look at all the industries at once.

The visualization strongly supports the claim to show the boom of startups after the year 2014. The idioms used could have been a bit different so as to show the name of the companies extensively but the main claim of the visualization is to show the ‘Boom’ which appropriately displayed.

 

Reference: http://graphics.wsj.com/billion-dollar-club/

Data Visualization of Recession Reshaped the Economy

In 2009, great recession overwhelmed the United States’ economy. This data visualization clearly shows the performance (jobs number and salary number) of each industry in this ten years.

How the Recession Reshaped the Economy, in 255 Charts By JEREMY ASHKENAS and ALICIA PARLAPIANO Updated: JUNE 6, 2014
How the Recession Reshaped the Economy, in 255 Charts By JEREMY ASHKENAS and ALICIA PARLAPIANO Updated: JUNE 6, 2014

Visualization Link

The first visualization gives a big picture of the overall domestic performance and gives the overview of the performance of every industry in the United States. It uses color to categorize how industry performs and also gives the details information of ten years performance of each industry.

  1. The second visualization highlights the data from the low-paying job such as fast food and high-paying job such as consulting. It finds that both sectors are growing.
  2. The third visualization highlights medical industry and especial those in middle-wage industries.
  3. The fourth visualization displays Housing industry. It is seriously suffering until now.
  4. The fifth visualization shows the manufacturing industry. It compares each sector with another.
  5. The sixth visualization shows oil industry performance. Almost all of sectors in this industry grow dramatically.
  6. In this ten years, we also face digital revolution. The seventh visualization compares traditional media industry with IT industry.
  7. Last but not least, it shows the hottest and fast growing industries’ performance in the United States.

This Visualization did a great job at grouping the data, using color to classify the performance, and arranging, for example, it put poor performance industry at the lower part. Moreover, it tries to look at the same data from different angles and makes story interesting.

Reference: https://www.nytimes.com/interactive/2014/06/05/upshot/how-the-recession-reshaped-the-economy-in-255-charts.html?&_r=2&abt=0002&abg=0

 

Building KPI Dashboards in Tableau

The main task of dashboards is to provide the insights. Key Performance Indicators (KPIs), as the name suggests, help us to know how a business is doing or how is a specific process doing. This usually involves comparing current values or historical trends against a target value.

I have two methods to point you towards the blogs, for building KPI dashboards:

1. Tableau provides dynamic shapes to show good news/challenges. The upward pointing green triangle is shown for any good news/success whereas the downward pointing red arrow reflects failure or a lower result from the last one.

It’s very simple to generate these in Tableau and the steps are explained on the website:

http://www.thedataschool.co.uk/emily-chen/building-kpi-dashboards-with-shapes/

2. This method involves making two views – one showing the current values, the other showing the historic trend (in this case, revenue). This post explains how to align these two views together or how to deal with them by interlinking the two separate sheets on a dashboard.

https://www.interworks.com/sites/default/files/RobertKPI1.png

This is explained on the following website:

A New Way to Visualize KPIs in Tableau

Personally, I liked both. It totally depends on which one you choose to present your research and insights.

References:

A New Way to Visualize KPIs in Tableau


http://www.thedataschool.co.uk/emily-chen/building-kpi-dashboards-with-shapes/

Surplus Singles

Original Visualization

The visualization shows the data for surplus singles in the US. Gender is shown by the color and the size of the circle determines the number of surplus singles. This chart just shows the number of surplus singles, I believe the audience would be confused of how big or large the number is if they do not have an idea about the population of the city. There is no different chart for different age groups to have targeted claims.

Redesigned Visualization

This visualization was created to overcome the above disadvantages. The visualization shows the number of surplus singles for every 1000 instead of the actual number, this gives the audience more background of the “how many extra” singles. This gives a nice and clear information to the audience. This visualization also has Slider using which the audience can select the age group they are interested in, this would make the claim to be more targeted.
Usage of map, color and size of the circle is a very easy way for the audience to grasp what the visualization represents. By redesigning the visualization the author has solved the problem the audience had with the original visualization i.e. understand how many more unmatched for 1000 rather than just the entire unmatched singles.

Understanding human perception first, then visualization comes second

We’ve learned the nine principles of how to make effective visualizations and applied it into our exercises.

Today I’d like to share what I have learned from aesthetics session and how important that is to understand human perception when it comes to creating a good visualizations.

Bellow questions can answer the aesthetic lecture in different angle.

  1. Does it meet physical attributes of vision? Our vision sensitively recognizes length, position, size, shape and color of views. Those determines how we think and how we decide. Sometimes pie charts, donut charts can’t fully represents the attributes of vision. Understanding attributes of our visions can help us to have better design when it comes to visualization.
  2. Does eyes can discern and brains can understand? First our eyes see and then our brain understand. If the visualization is not easy to understand, not clearly indicates the relationship of the data then our brain can hardly understand what we see.
  3. Does our seeing In-Balance with our thinking? In class professor taught eye beats memory principles. Our brain has limitation of working memory. If what we see is not balance with what we thinking then the visualization can’t convey the accurate message that it suppose to deliver.

Reference

Data Visualization for Human Perception

https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception