Have you ever had one of those “of course I can do that in QlikView” moments? As a Qlik developer it’s not uncommon to find yourself struggling to create a chart or pull off a complex script that nobody asked for, but for some strange reason, you decided to tas a personal challenge. Well, I just had one of those moments.
I couple of days ago I was wandering around my usual blogs when I found a post by Alberto Cairo where he talked about an interactive visualization created by the FiveThirtyEight team regarding the political preferences in the United States. [By the way, if you don’t follow those blogs you are missing a lot of great stuff!]
Besides being a stunning way to present What-If scenarios, I liked the usage of the Tile Grid Maps, which have become a pretty popular visualization lately. What is a Tile Grid Map, you ask? Well, think of it as a mix between a choropleth map and a table heat map. If you want to see more examples, you can visit Bloomberg, The Washington Post or The New York Times.
It is true that most of the times we strive for more accurate charts but ironically, this technique’s boon is that it disregards the actual size of the regions and translates them to equal-sized shapes (in this case, squares) so it becomes easier to see even the smallest areas. Therefore, this solution is excellent when the geographic size is not the most important thing, but maintaining a loose spatial distribution is.
So anyways… In today’s tutorial we’ll build something like this:
The Qlik platform is all about analyzing data and making discoveries. However, in order to get valuable insights for your organization, you can’t just go around loading any data source and creating random charts. On the contrary, a good QlikView developer will always strive to use the most appropriate objects for each type of analysis.
Even though classic visualizations such as bar, line or pie charts are essential components of most applications, complex inquiries usually require more sophisticated tools to gain full understanding of the situation and make the best decisions possible. In this regard, one of my favorite visualizations is the scatter plot (Well, scatter plots and histograms, but we’ve already talked about those).
Although not very common, when used adequately, these charts can be real eye-openers. Sadly, its usage is still covered in a veil of mystery for the majority of the business users who –for a strange reason– seem to fear its power. But anyways, back to the story…
This chart stands out due its ability to elegantly handle great amounts of data. Though its simplest form only combines one dimension and two expressions plotted along the x and y axes, you can enrich them in several ways. Let’s start with an easy example:
Each bubble in this chart represents one of On Nom Nom Nom’s food trucks. As the y-axis embodies the sales amount, the higher the bubble is, the “stronger” the food truck. On the other end, the x-axis represents the Margin %. Therefore, a bubble far in the right could be categorized as “more intelligent” due to its higher profitability. In this case, the best scenario for the company would be to have most of the bubbles in the upper right corner, meaning that all the food trucks sell a lot but also have good margins.
To make this visualization clearer, we can add reference lines and define static of dynamic thresholds with variables and traditional expressions: Continue reading