7月 022018
 

Looking to connect and engage with other like-minded people in a meaningful way? A #SASchat might just what you’re looking for! #SASChat is SAS’ version of a Twitter chat: a live, online event that works like any get-together. Participating in a #SASchat is a great way to: Engage with an [...]

Everything you need to know about the weekly #SASChat was published on SAS Voices by Kristine Vick

7月 022018
 
Who was the greatest US president? Presidental greatness scores through 2018

Which president of the United States is ranked the greatest by presidential historians? This article visualizes the results of the 2018 Presidential Greatness Survey, which was created and administered by B. Rottinghaus and J. Vaughn. They analyzed 166 responses from experts in political science who ranked the 44 US presidents on a 0–100 "greatness scale" where 0 represents abject failure, 50 is average, and 100 is great. The survey results are presented in their 2018 report.

Table 1 of the report (p. 2-3) provides the average greatness scores of the presidents among all respondents. Just as "beauty is in the eye of the beholder," so too "greatness" is a subjective notion that depends on the values and beliefs of the assessor. Table 2A (p. 6-7) provides the average greatness scores for respondents by the political party and political ideology of the respondents. Of the respondents, 57.2% were Democrats, 30.1% were Independent or Other, and 12.7% were Republicans. Similarly, 58.4% self-identified as liberal or somewhat liberal, 24.1% self-identified as moderate, and 17.5% reported being conservative or somewhat conservative. The relatively large proportion of liberals should be considered when interpreting the survey results.

I imported the survey results into SAS and combined them to produce a visualization of the data. The graph at the right (click to enlarge) shows the presidents ranked by their overall mean greatness score, which is shown by an X. The mean scores for the conservative, moderate, and liberal respondents are shown as filled circles. The party of the president is indicated by a colored bar that shows the range of scores.

A few results are apparent from the graph:

  • Lincoln, Washington, FDR, Teddy Roosevelt, and Jefferson are the five greatest presidents according to these experts.
  • Obama and Reagan are two recent presidents who are ranked highly.
  • Statistically speaking, there are large groups of presidents for which the relative ranking is uncertain. For example, the presidents ranked 12–20 (Madison through Monroe) have mean scores that are similar.
  • The current president, Donald Trump, is ranked last. In fairness, Trump had not yet completed his first year in office at the time of the survey (December 2017). The authors of the report dedicate several pages (p. 10–13) analyzing a survey question about who was the Most Polarizing president, a category that Trump won handily.
  • Other presidents who scored low include James Buchanan (the "Do-Nothing President"), William Henry Harrison (who died 31 days into his term), and Franklin Pierce (a weak leader in a contentious pre-Civil War era).

You can also graph the data by using the political parties of the experts rather than their ideologies. The graph is very similar.

Bias and disagreement in ranking presidents

One of the most interesting aspects of these data is the relative widths of the ranges of most 20th and 21st century presidents. These wide intervals are caused when experts of one political ideology judge a president much differently than experts from another ideology. Typically, the conservative-liberal disparity amounts to 10–15 points on the "greatness" scale, but for several presidents (Coolidge and Obama) the average conservative score is more than 20 points different from the average liberal score.

This disparity is evidence of the partisan state of politics in the US. Even these experts, who presumably use historical facts rather than opinions to guide their ranking, are not immune from seeing current events through the lens of their own personal values and biases.

To better visualize the range of disagreement among the experts, I graphed the difference between the conservative and liberal scores for each president. Again, the color of the line indicates the political party of the president. The 20th century began with McKinley's presidency. For almost every president since McKinley, the gap for Democratic presidents is negative, indicating that the conservative experts judged the presidency less favorably than the liberal experts. For Republican presidents, the direction of the gap is reversed: the conservative experts ranked the presidency more favorably than their liberal counterparts. The exception is Teddy Roosevelt, whose progressive agenda (the "Square Deal" and conservation measures such as national parks and forests) appeals to liberal experts.

Summary

Rottinghaus and Vaughn's 2018 Presidential Greatness Survey analyzes the opinions of 166 experts as to which US presidents are considered great, average, or a failure. The first graph in this blog post shows that although there is general agreement among experts for many 18th and 19th century presidents (Lincoln and Washington were great; Pierce, W. H. Harrison, and Buchanan were not.), there is less agreement for modern presidents. As seen in the second graph, the variation tends to correlate along ideological grounds, with conservatives and liberals having very different views about which presidents were great.

You can download the SAS program that creates the graphs in this article. For a different visualization of these data, see Robert Lawrence's article "All the U.S. Presidents, Ranked by 'Greatness'."

The post Ranking US presidents appeared first on The DO Loop.

6月 302018
 

The Geo Map Visualization has several built-in geographical units, including country and region names and codes, US state names and codes, and US zip codes. You can also define your own geographic units. This paper describes how to identify any geographic point of interest, or collection of points, on a map to create custom maps in SAS.

The post Custom Maps in SAS: My Neighborhood appeared first on SAS Learning Post.

6月 302018
 

Introduced with SAS Visual Analytics 8.2 is a new object named: Key Value. The intent of this object is call attention to an aggregated value for a measure, a category, or both. For additional specifics,

I’ve mocked up several reports to show some of the combinations available to give you an idea of what the Key Value object can look like. Toward the end of the blog, I will add additional reports to provide design ideas for placement and action assignments. Click on any image to enlarge.

Text Style with Measure Value Highlight

Here you can see in the report, I am highlighting two measure values. Both are representing the Highest value but I selected one to show the aggregation and the other not. I was able to mimic similar headings by renaming my data item. Be sure to look at the Options and Roles pane for the assignments to understand how I accomplished this.

Infographic Style with Measure Value Highlight

Here is the same information represented using the Infographic style. Seeing the same report using the different styles allows you to quickly determine the most powerful and appropriate visualization to meet your needs. We cannot control the size of the circle, only the color. In this case, the circles are different thicknesses because of the number of characters used to represent the measure values inside the visual.

Text Style displaying both Measure Value and Category

In this report, I have shown how to use the Text style to display both a Category value and a Measure value. As you can see, only one can be Highlighted, i.e. given the largest font. Notice that in this report I used the object’s Title to help explain the key value being displayed, this is a recommended best practice.

Infographic Style displaying both Measure Value and Category

Below I am using the Infographic style and notice in the Options pane below that I had to use the Additional information attribute to better label the data. Make sure that when you are in the design phase and toggling between text and infographic to review and test the available Key Value Style attributes to better label the visual.

Text Style with Category Value Highlight

In this report, I show how to highlight the Category value using the Text style. Since I chose to not use any of the available label attributes it is critical that I use the object’s Title to better explain the key value displayed.

Infographic Style with Category Value Highlight

In this report, you can see how I changed the layout from the previous report to make the Key Value object side-by-side the other report objects. If you are interested in using the Infographic style with the circle enabled then you may have to adjust your report design to accommodate for the space the circle needs to display. Remember not to shy away from adding white space to your report, it can assist when adding emphasis to a particular visual, in this case a key value.

Summary

Some important things to remember about using the Key Value object in your reports:

  • Use the Key Value object’s Title to inform your users what the number or category value represents so there is no ambiguity as to if they are looking at the maximum or minimum value.
  • When determining which style you prefer, Text or Infographic, it may be easier to make a duplicate of the Page and then adjust the style attributes till you find the desired combination.
  • Take time to adjust the arrangement of objects on your report to get the most pleasing configuration. Don’t shy away from leaving white space in your report. You can also experiment with the Container object using the Precision container type to layer the Key Value object.
  • The Key Value object will be affected by Report and Page Prompts like any other report object and you can even define Actions to filter the Key Value object.

Here are some additional examples of using the Key Value object:

In this example, you can see from the Actions Diagram how the Key Value object is being filtered. First, by the two page prompts and second, there is a direct filter action defined from the List Control object

In this example, we can see from the Actions Diagram that I used the Container object. I then selected the Precision container type and overlaid the Key Value object on the Line Chart. The only filters applied to these report objects are the page prompts.

And in this last example, you can see how I have no Report or Page prompts or any other filters impacting the Key Value objects. Therefore, these values are representative for the entire data.

Key Value Object in SAS Visual Analytics was published on SAS Users.

6月 292018
 

As a resident of Northern California, I was interested in learning more about the causes of wildfires. My area has recently experienced large fires that caused many residents to evacuate their homes and some who have even lost their lives. Last October there were more than 170 fires that burned [...]

Blazing statistics: visualizing wildfire data was published on SAS Voices by Melanie Carey

6月 282018
 

During these hot summers in the southern US, many people regard Willis Carrier (the man who invented modern air conditioning) as a saint. But along with the comfortable indoor temperatures comes the high electricity bill at the end of the month. And how much does the electricity cost? That's a difficult [...]

The post What's the electricity bill for your air conditioner this summer? appeared first on SAS Learning Post.

6月 282018
 

We hear a lot about how various industries are using data visualization and analytics. But what about the education industry? The institutional research office (IR) at universities is the center for data, reports and analytics and provides decision makers with information about the university. The IR teams are working on [...]

How are data visualization and analytics used in higher education? was published on SAS Voices by Georgia Mariani

6月 272018
 

Our company talks to utilities all over the world about the value of analytics. We help utility executives understand what the "digital utility company" looks like and share use cases to illustrate how these companies are using analytics across: assets and operations; customers; portfolio, and corporate operations (see diagram below). [...]

Analytics use cases for utilities: Assets and operations was published on SAS Voices by David Pope