Since Learning QlikView Data Visualization was published, the url to download the SVG map extension created by Brian Munz and highlighted in chapter 7 has changed to https://github.com/brianwmunz/svgReader-QV11.
If you have any questions about the content of the book, don’t hesitate to ask.
See you around,
Packt Publishing is going to be giving away 2 free copies of Learning QlikView Data Visualization this week.
- Explore the basics of data discovery with QlikView
- Perform rank, trend, multivariate, distribution, correlation, geographical, and what-if analysis
- Deploy data visualization best practices for bar, line, scatterplot, heat map, tables, histogram, box plot, and geographical charts
How to Participate
During the last giveaway, I asked for ideas on what data visualizations you would like to add to QlikView. Now, I can’t imagine the development team at QlikTech being able to focus on creating and improving every chart and graph in QlikView, so let’s help them focus on the best data visualization techniques, and therefore leave behind some of the charts and graphs in QlikView that don’t do a good job of helping us discover data.
Continue reading “Let’s do it again! Win a Free Copy of Packt’s Learning QlikView Data Visualization.”
Thanks to all those who responded to my post in QlikCommunity and in this blog about what data visualization they would like to see in QlikView. I learned a few things about what people would love to do in QlikView. Most visualizations that were posted are possible to create in QlikView, but what frustrates many users seems to be the difficulty creating these visualizations through tricks, extensions or third-parties.
I think we can expect great advances in data visualization in QlikView after the company bought NComVA, a company dedicated to data visualization. Along with QlikView’s continuous improvements, I believe it is important to continue creating new and improving existing extensions. There is always going to be a visualization that QlikView cannot create out-of-box, and that’s why extensions are such a key element.
So, enough talking. The winners of the three Learning QlikView Data Visualization e-books are:
- Philipe Grenier
- Rebeca Gums
- Sokkorn Cheav
If you are a winner please send me a message in QlikCommunity with your e-mail address so that I can send you your e-book.
Thanks again for all those who participated.
Hope to see you around,
This week I’m giving away three e-book copies of Learning QlikView Data Visualization. To be eligible to win all you have to do is post what data visualization method you would like to do in QlikView that you have not been able to do until now. It could either be a chart or a technique.
For example, I would like to do be able to do scatterplot matrices like the one below.
Post your idea in LinkedIn, Twitter, Google+, or Facebook. You can either respond to my post or create your own post mentioning me.
Don’t forget to include a link that explains the data visualization method you would like to do in QlikView.
Please respond by Monday, Nov. 4th and I will announce the winners on Wednesday, Nov. 6th after an old-fashioned drawing. Soon after the e-books will be delivered to the winners’ e-mails as a Kindle e-book.
You don’t need a kindle to read the e-book. You can download the kindle application from amazon.com and view it from your laptop or iPad.
Those who have read Learning QlikView Data Visualization may wonder why I didn’t talk about pie charts in the book. Some may even assume that I am a data visualization idealist that tries to deny people’s temptations to create pie charts, but while I do support those idealists, I have created more than a few pie charts in my eight years working with QlikView.
I have strived to create alternate charts that more effectively show a parts-to-whole relationship, but some users with not accept any other chart that is not a pie chart. So, inevitably pie charts sometimes end up in my QlikView applications.
Continue reading “Pie charts or parts-to-whole analysis”
In chapter 4 of Learning QlikView Data Visualization, I explain how to perform multivariate analysis with heat maps and mini-charts. However, I left out the possibility of using a parallel coordinates chart to analyze the relationship between several variables. Honestly, this method is rarely great for communicating your discoveries to others, as Stephen Few explains so well in his white paper “Multivariate Analysis Using Parallel Coordinates“, but it can be a great tool for data discovery and analysis.
Parallel coordinates chart with qualitative values
A parallel coordinates chart is commonly used to link common attributes of the dimension. For example, it could show us the most common characteristics of a customer (i.e. age group, gender, marital status, etc.) We will create this type of parallel coordinates chart in a later post.
Continue reading “KPI Parallel Coordinates Chart”
Many times users lose a great opportunity to use color to make their QlikView interface more appealing and insightful when they choose to make a rainbow-colored bar chart. One of the most important subjects I had to leave out of Learning QlikView Data Visualization was how to apply the coloring technique of a heat map to a bar chart. In the book, I left the exercise of combining these two data visualization techniques to the reader. In this blog, let’s review how we combine the two by adding data about the profit generated from each customer to a simple bar chart that ranks customers by their net sales. If you want to follow along with the exercise below, download Sales_Project_Analysis_Sandbox.qvw. Before starting the exercise, let’s review what colors we want to use to represent data in a heat map. Colorbrewer is a great site to choose color-blind safe colors schemes that are either sequential or diverging.
- Sequential color scheme – The greater the data value, the darker the shade of one color.
- Diverging color scheme – The combination of two sequential schemes that are divided at some center point (e.g. zero). The greater the distance from the center point, the darker the shade of either of the two colors.
Since we will be adding data about profit which can either be positive or negative, we use the diverging color scheme. Also, we choose orange and blue as the two colors that make up the color scheme in order to ensure the involvement of those of us who are color blind. I’ll assume we’ve already created a simple bar chart that shows the top-selling customers based on net sales like the one pictured below.
In the properties window of our bar chart, let’s perform the following steps.
- In the Expressions tab, click the plus sign next to the expression to expand its properties.
- Select Background Color and click … in the Definition text area to the right.
- In the File menu of the Edit Expression window, select Colormix Wizard…
- Click Next >.
- In the Value Expression text box, type Sum ([Profit Margin]) and click Next >.
- In the Upper Limit section, click the green color button twice and then in the Color window select blue (Red=0, Green=128, Blue=255).
- Select the Intermediate checkbox and type 0 in the text box below.
- In the Intermediate section, click the yellow color button twice and then in the Color window select light gray (Red=192, Green=192, Blue=192)
- In the Lower Limit section, click the red color button twice and then in the Color window select orange (Red=255, Green=128, Blue=64)
- Click Next >.
- Clear the Enhanced Colors checkbox to make extreme values stand out.
- Click Finish.
After adding adequate labeling and accepting the changes made in the chart properties window, we now have the following chart that is both appealing and insightful.
Here’s the video tutorial.
Hope to see you around, Karl