About Slopegraphs and Bumps Charts

Hey everyone! Today’s post answers a request from one of our readers and features two not-so-common visualizations that you can use to make time comparisons: slopegraphs and bumps charts. Although these objects have a very simple nature, they’re also quite elegant and intuitive, so there’s a good chance that they will help you get some unexpected insights in your analyses.

We’ll also discuss one or two tips that can help you improve their readability and make them more meaningful by adding extra context to the data. As requested, the football dataset that we’ve been using lately will give life to our examples! 😉


Slopegraphs are a great way to compare two points in time. However, instead of describing the ups and downs along the way like traditional line charts, they focus on how the journey started and how it ended. This visualization is the perfect candidate for “then and now” analyses (much like that TV show that presents celebrities and how they looked 30 years ago). * Note to self: stop making that kind of references, people will laugh at you. You should recommend Cole’s blog instead. *

When working with these charts, our brain can easily spot patterns and recognize the distinctive slopes of certain elements in terms of direction or magnitude. It is also an intuitive way of showing how the rankings changed from a point in time to another, as the lines will intersect each other and end up in a different order.


In this example, we can visualize the performance of all the teams in the Premier League over the last two seasons. Each squad is portrayed by a line and its position and slope depend on the rank achieved in both the 2015 and 2016 tournaments. In order to improve the chart’s readability, you can highlight the selected team by adding some color and changing its line’s width.


* Fun Fact: This chart is so cool that it made it to the cover of one of my favorite books about data visualization, The Functional Art by Alberto Cairo. Continue reading

Tile Grid Maps

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.25_30

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:

25_27 Continue reading

Creating Stunning Dashboards with QlikView

It’s been a while since the last time I shared a post here, but believe me, I have a great excuse! Over the last few weeks, I’ve been working very hard with the guys from Packt Publishing on a book that covers two of my favorite topics: dashboards and data visualization. So, without further ado, I gladly present you “Creating Stunning Dashboards with QlikView”.


This tome guides you through the process of creating effective and engaging dashboards that deliver tangible value to the business. Throughout its chapters, you will learn how to apply some of the best practices in the field of data visualization, create a robust navigation schema, work with colors, choose the best chart types for each scenario and many other things that will help you create useful applications that will uncover all the stories behind your data.

Though you might think that this is the average “do this, do that” handbook, this publication was written much like my blog; it contains technical stuff, best practices, opinions, tutorials and even some humor. However, this time I had an amazing group of people that helped me out: Larissa Pinto, Priyanka Mehta, Shruti Iyer and Parag Topre from Packt; Hakan Hansson, Speros Kokenes, Mark O’Donovan and Karl Pover in charge of the technical review and QlikView All-Star Bill Lay who was kind enough to write the foreword.

PACKT LogoAmazon LogoBarnes Noble Logo


If you want to know more about this book, go to its section in this site (you didn’t know there were other pages in this blog right? Surprise!!!). Although Amazon usually delivers faster, I prefer Packt because you have immediate access to the digital version in multiple formats*


I really like the end result, and I hope you find it useful and have a good time reading it, so be sure to check it out. Also, don’t forget to share your thoughts about it, I’ll be looking forward to hearing your feedback!

Hope to see you around,

Julian Villafuerte


It’s not a bar chart!

Every professional is as good as his toolset and as a QlikView developer, there’s always room for one more trick under your sleeve. Today I will show you one of the most powerful –yet underused– chart for analyzing data: the histogram. Even though it is easy to create it I haven’t seen a lot of developers take advantage of it.

The important lesson here is that histograms are not exactly bar charts. The main difference is that bar charts are used to compare categorical variables whilst histograms represent distributions. Sounds interesting? No? Well, here’s an example.

A couple of days ago I was looking for a data set to try some functions and I got my hands on the ENEM results for 2011. The ENEM is a national exam taken by brazilian high school graduates that evaluates each institution (private and public) in subjects like mathematics, language and natural sciences.

With a traditional bar chart, you can address some questions like: Are there more public or private schools? Which state has the most schools? Which schools are the best ranked in mathematics?


But as your inner data analyst grows more interested, you will start asking more complex questions. When we create a bar chart for the top 10 schools in mathematics, we may realize that there’s a big discrepancy between the best and the worst elements:


Are there some extremely good schools raising the national average? Or alarming bad institutions brining it down? Are they all consistent? Can we separate them in groups (good, normal, bad)? Where’s the majority of the schools? Is there a significant difference between public and private institutions? Or between states?

Let’s see what QlikView can do for us. First, we’re going to change our classic conception of a bar chart by using the X-axis as the grade and the Y-axis as number of schools that got it. As you can see, the data adopts a shape that gives us a better perspective of the situation:


Far in the right, we’ve got great schools (there are not a lot of them, but their scores are pretty high). On the other end, those who might need a little help (grades below 420 points) and in the middle, the majority of the schools. We can appreciate that the curve is skewed to the left, with most of the schools scoring from 440 to 560. Remember, a higher bar represents a bigger number of schools. For example, the red bar (the highest of all the histogram) is conformed by 296 schools that got grades between 500 and 505 points.

Is there a difference between public and private high schools? Well, if we separate our histogram using a second dimension we’ll get something like this:


Some thoughts that will probably cross your mind are:

Continue reading


Calendar View

Last week a customer asked me for a simple –but quite friendly– visualization for the daily sales. These guys are in the fast food business, and their operational supervisors (one on each restaurant across the country) needed a general overview of the daily behavior of the sales, orders and average ticket. My first draft was a straight table with the main KPIs compared to the last year accompanied by this chart:13.14

The blue line represents the current year while the gray one embodies the last one. As you can see, their sales are extremely predictable: slow weekdays and busy weekends. Sadly, they didn’t feel comfortable with it. They could compare a Thursday with its contiguous days (Wednesday and Friday), but it was hard to compare it with other Thursdays.

The answer was clear; they needed a Cross Table that involved the weekday. However, when I created the object, I realized it was hard to read. In the end, I came up with this:


Quite simple, but it covered the operational needs. Here’s how to do it. As always, you can download the QVW file here.


1.- First of all, let’s create some fields in the script:


Continue reading



In today’s post, I want to share a simple / not incredibly useful app that I use very often as a QlikView trainer. In my courses, I like to guide most of the exercises step by step and give tips and tricks as we encounter new functions or objects. However, some exercises have better results when working alone, especially when working with charts. For example, when I present themes, containers, tabs and auto-minimize I like to give the students a few minutes to decide the way their applications would look like.

In the beginning, I used to tell them the approximate time that we were going to spend on the exercise and the expected finish hour. As you can imagine, nobody kept an eye on the clock and we always had delays, so one day I created this application. [Download]

12.2The idea is to store the expected time for the exercise in a variable using an input box and after clicking START, the gauge in the right would represent the remaining time using the function now(). When the clock hits the “Red Zone” (another variable), the gauge turns red. Continue reading


Infographics in QlikView Vol. 2

I was wandering around my usual QlikView blogs when I found a post by Rebecca Camper (follow her design blog, INTUIQLIK for stylish ideas!) where she showed some cool data visualizations from Simon Spring, a canadian designer. All of them were easy to the eye, but I found this one especially interesting: RebCam Coincidentally, later that day I found this other post in LinkedIn that used infographics to illustrate employee satisfaction. It showed some curious numbers about why people stay in their jobs, reward systems and work-life balance.

So anyway, long story short, inspiration came and here’s the second volume of Infographics in QlikView. As you remember, in our last tutorial I showed you how to use bar / block charts, images with transparency and layers to create something like this: informejor
Today, we’re going to work with pivot tables and some images to create these: New2



As always, you can download the ZIP package that contains the QVW, images and data sources needed to create all the objects shown above.

1.- First, we will create a pivot table with two calculated dimensions (same expression for both): Continue reading