Data Visualization

3月 272017

When senior leaders at the University of Louisville (UofL) approached Vice Provost Bob Goldstein in early April 2016 with a request for a fully functioning data visualization platform by start of the 2016 fall semester—just four months away—he did not panic. Instead, Goldstein, along with Becky Patterson, Executive Director of [...]

Data visualization helps University of Louisville achieve its 2020 strategic plan was published on SAS Voices by Georgia Mariani

3月 172017

I’m drawn to immersive analytics (IA) because it covers areas I’ve been looking at since 2012, and have been publishing on since early 2014, like virtual reality and data worlds. I’m retroactively applying the cool new term IA (not to be confused with AI for artificial intelligence) to all of my activities [...]

Immersive analytics: yes or no? was published on SAS Voices by Michael Thomas

3月 102017

Once again, SAS is sponsoring the North Carolina Scholastic Chess Championship. The 43 year-old event is at the Raleigh Convention Center this weekend, March 11-12th. SAS sponsors this event because of the STEM education benefits of youth chess. Hundreds of players have been preparing and studying. A few even practiced [...]

North Carolina's youth chess championship by the numbers was published on SAS Voices by Michael Thomas

3月 102017

Once again, SAS is sponsoring the North Carolina Scholastic Chess Championship. The 43 year-old event is at the Raleigh Convention Center this weekend, March 11-12th. SAS sponsors this event because of the STEM education benefits of youth chess. Hundreds of players have been preparing and studying. A few even practiced [...]

North Carolina's youth chess championship by the numbers was published on SAS Voices by Michael Thomas

2月 232017

Some would say that it's impossible for blind users to see charts and graphs. Those same people might have once said it was impossible for the visually impaired to see the particles that comprise an atom, or galaxies that are billions of light years away. Innovation would prove them wrong [...]

SAS Graphics Accelerator makes charts and graphs visible for blind users was published on SAS Voices by Ed Summers

1月 312017

You have all seen, or perhaps even created, some really bad graphics: Cluttered, confusing, too small, incomprehensible. Or worse, the author may have committed one of the three unforgivable sins of data visualization by deceptively distorting a map, truncating the axis so as to misrepresent the data, or used double […]

How to make your pie chart worse was published on SAS Voices.

12月 162016

“Analytics” and “data scientist” aren’t new terms, but they are trending buzzwords. The popularity of these concepts has created a false impression: Analytics are mysterious abstractions that can only be decoded if you have a white lab coat and an advanced degree in computer science. The reality couldn’t be more different. […]

No data scientist? No analytics platform? No problem. was published on SAS Voices.

10月 272016

Consumers want content 24 hours a day, seven days a week, all around the world. It's a tall order for media & entertainment (M&E) companies and a 180 degree shift from days past. How do they provide enough content to meet demand? Audiences are binge watching over-the-top (OTT) programming, creating […]

Digital transformation = increased expectations for media & entertainment was published on SAS Voices.

10月 182016

In JMP 12, an interactive HTML Profiler was added, as I had previously blogged about. That change mainly updated the existing Flash functionality to HTML5 technology, making it available on mobile devices like an iPad, but it also introduced a few new features. Among these was the option of exporting the Fit Model Least Squares platform report as a whole with an interactive Profiler embedded within it.

After users got to try this tool, the response was overwhelmingly positive. They found it a great way to explore cross-sections of predicted responses across multiple factors with other people who don’t have JMP yet. However, the feedback was that users would like to see Profilers available in other platforms as well.

In JMP 13, three more platforms have embedded Profilers that are available in interactive HTML.


In JMP, you can analyze your data using Neural Networks. I will use the  Diabetes data set from the sample data library to illustrate some of the differences between this platform and Generalized Regression below. Note the curved responses for Age, BMI, and BP as well as the elongated report (only the first five factors out of 10 are shown).


Generalized Regression

Generalized Regression embedded Profilers are supported for export from JMP Pro 13. This example also shows an additional enhancement for Interactive HMTL in JMP 13 that allows you to pick how many plots are displayed in a row when you have a lot of factors. You'd do this in JMP by selecting the red triangle, going to Appearance and selecting Arrange in Rows to provide the number you want before exporting.  This allows you to explore many factors in Interactive HTML with a nice layout (which can be useful on a mobile device with a smaller screen). You can see the same factors analyzed as in the Neural platform above, but more are visible in the same width display due to this feature.


Generalized Linear Model

Generalized Linear Model is the third platform to support interactive HTML embedded Profilers in JMP 13.


In addition to making embedded Profilers in those three platforms available in interactive HTML, JMP 13 includes new features to make exploring your data a little easier. That's what I'll cover in  the following sections.

Adapt Y Axis

In JMP 12, you could explore data outside of the initial range of the numeric factors by typing in a value in the edit box below the curve. But what if this causes the curve to move outside the initial range of the response? You could see the value displayed in red on the Y axis, but no longer see the curve itself. Now there is an option to have the Y axis automatically adapt to show the min and max values of the curve.  Simply click the menu button above the Profiler and check “Adapt Y Axis”.


Formatted Variables

Some data requires analyzing a formatted X factor such as a date, time, or geographic location. In JMP 12, you could click or drag anywhere within the Profiler to change the value, but there was no way to provide a precise value for this type of data.  Now X variables in these formats are displayed as a button that, when clicked, launches a dialog to enter the individual fields of the format.


Apply Mixture

Similarly, in JMP 12, if you tried to precisely set a Profiler with a mixture constraint to a set of values that you knew satisfied the constraint, you couldn’t do it; every time you set one value, the others were altered to satisfy the mixture. In JMP 13, mixture values are applied by clicking an apply button.

For example, the amounts of three ingredients used to make a plastic in the following Profiler must sum to 1 and stay within the ranges shown. The values  0.7, 0.1, and 0.2 sum to 1 exactly. So, by entering these values in the edit boxes and then clicking apply, the Profiler is set to those precise values.


The images shown here as well as a few other examples are available as live interactive HTML files to explore on the web.

JMP offers a wide variety of math functions, special features and powerful algorithms that haven’t all been implemented in HTML, so not every Profiler will come out interactively. If you need to share work with someone who doesn’t have JMP and export your reports to Interactive HTML, we’ve added messages to the log to try to indicate why a particular Profiler has come out as a static image. Armed with this knowledge, we hope you will try your own Profilers and give us feedback on what features and platforms you want to see in the future.

tags: Data Visualization, Interactive HTML, JMP 13, Modeling, Profiler

The post Interactive HTML: Profilers in 3 more platforms in JMP 13 appeared first on JMP Blog.

10月 112016

This is a continuation of a series of blog posts on interactive HTML for Graph Builder reports in JMP 13. Here, I'm discussing support for Points, Box Plots, Heat Maps and Map Shapes. These Graph Builder elements are highlighted in the figure below.


Since this blog post describes interactive web pages output from JMP, images and animations below were captured from a web browser.


Points exist in many graphs in JMP where you can customize the point color, shape, and size, usually by opening a dialog box. Graph Builder’s drag-and-drop interface makes it easy to create colorful graphs with points of all shapes and sizes. The example below using Diamonds Data from the sample data library in JMP sets the following point attributes:

  • Size based on the Table column data
  • Color based on the Depth column data
  • Shape based on the Clarity column data

In addition to these attributes, Price versus Cut and grouping by Carat Weight was employed to understand what influences diamond prices the most. Of course, JMP provides capabilities that specifically target this question, but that’s a topic for another blog post.


This combination of attributes made supporting Graph Builder point plots in Interactive HTML challenging because there are now more ways to determine the size, shape and color of each point. The challenge was increased additionally by the fact that each point in Graph Builder can represent a statistical summary of multiple rows of data.

In the following Interactive HTML example, each point represents diamonds of a given Cut and Clarity. Although the legend is rearranged, the shape and color are still determined by Clarity and Depth respectively. To  accentuate the difference between the diamonds' Table dimensions, a column transform named Relative Table was used in the Size role rather than the raw Table column data. DiamondsPointsMean

Box Plots

The summarized points above may provide too little information and the raw points may be too busy, so how about a compromise using box plots? In this graph, we see the distribution of prices for each Cut, Clarity, and Carat Weight combination. The legend was moved to the bottom and drawn horizontally to match the arrangement of box plots in each group.


Heat Maps

So far, it might be difficult to see what influences diamond prices the most. We’ve only covered three of the four C’s in diamond quality. So, here’s a heat map including all four. Maybe now it’s easier to make some conclusions.


Adding support for heat maps in Graph Builder gave us a bonus outside of Graph Builder: The Uplift graph in the Uplift Platform is now interactive and can display X Factors and X/Y ranges.


Map Shapes

Map shapes can be used in Graph Builder for location-based data, like population data. Grouping can help the viewer focus on one region at a time. With the ‘Show Missing Shapes’ option enabled, the region of interest can be seen in context of the whole country.


Map Shapes can be scaled according to a size variable (Population) while being colored by another variable (Vegetable Consumption).



To see that some of the interactive power of JMP is available in Interactive HTML, it helps to interact with combined graphs. In JMP this can be accomplished with Combined Windows, Application Builder, or Dashboard Builder. Below are some combination examples using the graph types described above.

This example explores Crime data with a Heat Map and geographical Map Shapes.


The following example uses Points, Box Plots, a Heat Map, and a custom Map Shape to explore office temperatures.


One new feature for Points and Map Shapes in Interactive HTML is the ability to display images in tooltips.


Note that these are just animations. You can interact with the Interactive HTML files shown in this blog here:

tags: Data Visualization, Graph Builder, Interactive HTML, JMP 13

The post Interactive HTML: Points, box plots and more for Graph Builder appeared first on JMP Blog.