Choose Right Chart Type for Data Visualization. Part 7: Geovisualization with Maps (Geo-Related Data) May 24th, 2017 by Andrey
Geovisualization and map-based analysis of geo-data sets related to given territories or spatial environments can provide significant insight into trends and assist greatly in exploring and associating the impacts of variables. Nowadays, maps are used for data visualization very frequently, both as standalone geovisualizations and part of complex dashboards.
In this new article within the framework of our Choose Right Chart Type for Data Visualization series, we’ll write about map charts and explain how (when) to properly use each of corresponding types.
Choose Right Chart Type for Data Visualization. Part 6: Resource/Project Management May 17th, 2017 by Andrey
Achieving and maintaining effective Project Management is one of the most important challenges for every company. And high-quality visualization of resource usage and activity processes is a great tool for significantly improving the overall performance at the end. Keeping that in mind, the we simply could not omit the information on how to properly represent project and resource data.
Read this article to learn what (specific) chart types you can choose to use in such cases.
Choose Right Chart Type for Data Visualization. Part 5: Single-Value Data (Indicators) May 10th, 2017 by Andrey
In the previous articles from the Choose Chart Type for Data Visualization series, we covered the ways to visually represent information for data comparison, composition and distribution analysis, and observing trends over time. The current post sheds light on a situation when you only have Single-Value Data that can serve as Indicators of the current performance. In this quite widespread case, plotting each value on a separate chart often makes sense. And that is where Gauges and Bullet charts enter the scene.
Choose Right Chart Type for Data Visualization. Part 4: Data Distribution May 4th, 2017 by Andrey
Displaying and researching some Data Distribution and relationship between data sets instead of studying precise values in each category is a quite common task in data analysis. It can be solved with the help of the chart types that we are going to identify and explain in this article.
Depending on a situation – what kind of data you have and what specific questions you’d like it to provide answers to – you can pick one approach or another. Just be careful when choosing between one chart type and another for the subsequent data distribution analysis. You want the visualization to clarify data, not obscure it or deliver any sort of confusion. Well, simply make sure you understand the following aspects, mind all the details of your situation, and you will have no problem with visualizing data distribution correctly.
- Closure library and Closure compiler were updated to version 20161024.
Now, you are welcome to read the SitePoint article about GraphicsJS. Please do not forget to ask your questions, if any. You may do that by leaving a comment here right below the article.
Choose Right Chart Type for Data Visualization. Part 3: Data Over Time (Trend Context) April 26th, 2017 by Andrey
We are continuing the effort to better familiarize you with the world of chart types. This time let’s talk about good ways to visualize and explore Data Over Time.
Watching the change in data over time helps identify trends and dynamics in diverse timeline based sets of values. And choosing a right chart type is very important here. When applying an inappropriate form of visualization to your data, you might end up with a wrong or simply inaccurate idea of what happened in the past, is taking place now, and/or will occur in the future. But we’ll do our best to help you avoid any mistakes in this field so you always make only right decisions based on your date/time data.
So, let’s get to the gist now.
Choose Right Chart Type for Data Visualization. Part 2: Data Composition, Parts to Whole April 20th, 2017 by Andrey
Illustrating part-to-whole relationships for further analysis is a very popular objective in data visualization. Basically, it is one of the most widespread ones, e.g. along with data comparison. With that in mind, the second part of the Choose Right Chart Type for Data Visualization series on our blog focuses on how to display Data Composition properly.
In particular, this article will show you the best ways to present the share percentages of simple values, compositional patterns in large data sets and hierarchical data (also with subordination), and stages in a process.
Choose Right Chart Type for Data Visualization. Part 1: Data Comparison April 12th, 2017 by Andrey
When it comes to building data analytics and reporting solutions, choosing the right chart type for a certain data visualization task remains a common challenge. What to pick for data comparison, studying distribution, observing data over time, or another certain purpose? It all can be very tricky! To help you overcome with this challenge to the best effect, today we are launching a series of articles entitled Choose Right Chart Type for Data Visualization. The series is designed to quickly explain what chart types you should pick for one purpose of data analysis or another. With that said, each article here will be devoted to a specific, yet still big question that you want your data to answer.
The current (first) guide of the series is all about chart types that work best for finding out the differences in data: Data Comparison. In fact, it is one of the most frequently established purposes of data analysis. And sometimes many people use wrong chart types to fulfil it correctly. Now, finally, let’s get to the very point and see what visualization forms work best for comparing data.
How to Name a Graph: Tips for Writing Great Chart Captions April 5th, 2017 by Andrey
Charts are one of the best ways to display your data in a way that’s meaningful to readers, but if you don’t have great chart captions, your readers may interpret that meaningful information incorrectly.
Readers’ attention spans are waning by the second. In fact, humans now have a shorter attention span than goldfish (yes that cute, little fish you won from the carnival can pay attention better than the average person). This means that most people are scanning through your work. Without a clear, concise chart caption, your chances of getting your message across are slim.
We as ‘mere’ humans are not very good at processing raw statistical data visually when it’s delivered to us in an unrefined form. We are, however, very good in detecting complex patterns when data is presented to us in a graph or a chart. It is therefore no wonder that as a developer you often get the requirement to represent data in a more comprehensible form. When you want to visualize data in the SAP Web UI in a more graphical way the standard possibilities available to you are rather limited. Luckily there are ways to overcome these limitations. When you combine SAP with the graphical power of AnyChart JS Charts a whole range of new possibilities will become available to you.
In this blog I will describe how you can integrate AnyChart in the SAP Web UI with relative ease, how you can feed AnyChart objects with SAP data using both a ‘pull’ and a ‘push’ mechanism and how you can respond in the SAP backend to the events triggered from user interaction with an AnyChart object.
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