Cindy Wang

10月 132017

Every year in early October, the eyes of the world turn to Sweden and Norway, where the Nobel Prize winners are announced to the world. The Nobel Prize is considered the world's most prestigious award. Since 1901, the Prize has been presented to individuals and organizations that have made significant achievements in the fields of physics, chemistry, physiology or medicine, world peace and literature in each year (there were several exceptions during war years). In 1968, Sveriges Riksbank established the Sveriges Riksbank Prize in Economic Sciences in memory of Alfred Nobel, founder of the Nobel Prize. Today, individuals or organizations who are awarded Nobel Prizes and the Prize in Economic Sciences are called Nobel Laureates.

So far, more than 900 Nobel Laureates have been awarded. In this post, I wanted to learn a little more about these impressive individuals. Where were these Nobel Laureates from? Why do they get awarded? Is there any common characteristics you’ll find in these Laureates? Below you’ll find a preliminary analysis of Nobel Laureates using SAS Visual Analytics.

The analysis is based on data from List_of_Nobel_laureates, List of Nobel laureates by university affiliation and Nobel Laureates datasets at Kaggle, which definitely has some missing and inconsistent values. I have cleaned the data to correct for some obvious inconsistency as possible for my analysis.

How many Nobel Laureates have their been so far?

Recently, 12 new Nobel Laureates were awarded by the 2017 Nobel Prizes and Prize in Economic Sciences, and that makes 923 Laureates in total since the first Nobel Prize in 1901. Some Laureates share one prize, so we see more shared Laureates total in below table. While we see 27 organization winners of the Peace prize, most Laureates are individual winners.

analysis of the Nobel Laureates

The chart below shows the overall trend of annual total Nobel Laureates is increasing year-over-year, as more and more winners are sharing the Prize. The purple circle on the plot indicates that there are shared winners in that year. The average number of winners is about eight each year. Yet there was only one winner in 1916 for the Literature Prize. The most winners came in 2001, with 15 Laureates sharing the prizes. I also note from the chart that during the First World War, there were very few Nobel Prizes awarded, and during the Second World War, there were none.

Moreover, we know that most Nobel Laureates are awarded one Nobel Prize, yet I learned from childhood that the female scientist Marie Curie received two Nobel Prizes. If you search the datasets for winners awarded more than one Prize, you’ll find four scientists accomplished this feat. They are: Marie Curie, Linus Pauling, John Bardeen and Frederick Sanger.

Do Nobel Laureates live longer?

The answer is YES, per the research by Prof. Andrew Oswald from University of Warwick. Winning a Nobel prize adds about 1.5 years to the lifespan of Nobel Laureates compared to those who were merely nominated. Of course, it is not because of the monetary benefits that come with the Nobel Prize, but because of ‘the deep links between mind and body’, and that ‘happiness’ may make people live longer, which makes sense to me.

Since I don’t have the data of Nobel Prize nominees, let’s only test the lifespan of the Nobel Laureates and the ages they got awarded. The average life age of all Nobel Laureates is nearly 80, much older than the global average life expectancy of 71.4 years-old (according to World Health Organization 2015). Digging a bit more, we see Martin Luther King is the Nobel laureate (Peace, 1964) who died at youngest age. He was assassinated at 39 years old. Laureates who lived longest are Rita Levi-Montalcini (Medicine, 1986) and Ronald H. Coase (Economics, 1991), who both lived to 103 years old. You may also notice that the distribution of the Laureates’ lifespan is left skewed, the Nobel Prize winners certainly live longer than most.

In addition, something more worth noting:

  • The most laureates with the longest lifespans are from the Economics and Medicine categories. The Nobel Prize winning economists live longer than other categories’ winners on average. The average lifespan of these economists is about 86 years-old, five years longer than the second category of Medicine.
  • Economics winners are winning the awards at the highest age – 67 years-old on average. More digging shows that the oldest awarded age is 90 when Leonid Hurwicz (Economics, 2007) was awarded his Prize. We see the average awarded age of Physics winners is 56, which is 10+ years younger than that of the Economics winners. Thus, we get the impression that economists need more time to have outstanding achievements.
  • If we compare the time span between Laurates’ average awarded ages and their lifespan, the Physics Prize winners enjoy the longest life time after winning the award – about 20 years on average.
  • It is also worth noting that the Nobel Peace winners have the largest span of awarded age, about 70 years’ span. That’s because the youngest Nobel Laureates Malala Yousafzai, who got awarded of Nobel Peace Prize at 17 years-old in 2014.

The chart below is created in SAS Visual Analytics and shows the awarded ages of all individual Nobel Laureates in different prize categories. The reference line is the average awarded age of 59. It is very easy to note that no Nobel Prize was awarded during 1940-1943 due to the Second World War.

From which universities have Nobel Laureates graduated?

Next, let’s look at the educational background of Nobel Laureates. The left chart below obviously shows that much more Nobel winners hold Doctorate degrees than those of Bachelor or Master degrees. If we see the chart for Literature and Peace categories on the right, the difference is not that big. From the data, we know that the educational background of Nobel Laureates in Physics, Chemistry, Medicine and Economics categories (I call these four categories the scientific categories for easier description later) has the higher percentage of doctorate than that of winners in the Literature and Peace categories.

To learn more about the universities the Laureates in these scientific categories are graduated from, I ranked the top 10 university affiliations for the scientific categories in below chart, and their distribution among these categories, as well as the countries in which these universities are located.

The top 10 university affiliations were selected basing on the highest degree of the scientific categories’ Laureates obtained. That is, if one winner held a Master degree from Harvard University and a Doctorate degree from University of Cambridge, he/she is counted in University of Cambridge but not in the Harvard University. From the parallel coordinates plot, you may have noticed that the Physics in University of Cambridge and the Medicine in Harvard University are their greatest majors respectively. On the right, it shows the countries where these top 10 university affiliations are in United States, United Kingdom, France and Germany. The bar charts on the left show the percentage of educational degrees (Doctorate, Master, Bachelor) of each in the scientific categories (according to the available dataset). In the bottom chart, top 10 universities are ranked by their percentages. Perhaps now you have a great university in your mind for future education?

Next, I created the chart below to show the top eight countries having the university affiliations that more Nobel Prize winners graduated from. (Here the chart only shows for scientific categories, thus it excludes the Nobel Literature Prize and Peace prize.). An obvious trend we see from the chart is that the United States has the most Laureates spanning in the scientific categories after the Second World War, while Germany has more Laureates in the scientific categories comparatively before World War II.

Why do the Nobel Laureates get awarded?

Per the ‘’, in his excerpt of the will, Alfred Nobel (1833-1896) dictates that his entire remaining estate should be used to endow "prizes to those who, during the preceding year, shall have conferred the greatest benefit to mankind." So Alfred's interests are reflected in the Prize, which said “The whole of his remaining realizable estate constitutes a fund, and the annually interest shall be divided into five equal parts, which shall be apportioned as follows: one part to the person who shall have made the most important discovery or invention within the field of physics; one part to the person who shall have made the most important chemical discovery or improvement; one part to the person who shall have made the most important discovery within the domain of physiology or medicine; one part to the person who shall have produced in the field of literature the most outstanding work in an ideal direction; and one part to the person who shall have done the most or the best work for fraternity between nations, for the abolition or reduction of standing armies and for the holding and promotion of peace congresses.”

Since it’s not easy to seek evidence in the datasets that Nobel Laureates are awarded by fulfilling Alfred’s will, what I do is to use SAS Visual Analytics text topics analysis performing some preliminary text analysis of the ‘Motivation’ field in the dataset for a validation to some extent. The ‘Motivation’ is given by ‘’ for why the Laureate gets awarded. The analysis shows that the most frequently mentioned word is ‘discovery’, while the most 5 frequently appeared words include ‘work’, ‘development’, ‘contribution’, and ‘theory’. And from the topics analysis result, the top 10 topics are about ‘discovery’, ‘human”, “structure”, “economic”,” technique”, etc., which are reflecting Alfred Nobel‘s will in establishing the Prize. Moreover, the sentimental analysis result shows that the statements in the ‘Motivation’ field are mainly neutral (being ‘objective’), even though there are few positive and negative sentimental statements.


I hope you’ve found this analysis of Nobel Laureates data interesting. I believe there are still many other perspectives you can analyze to get insights. Is there anything interesting you see?

A preliminary analysis of the Nobel Laureates was published on SAS Users.

2月 252017

As a practitioner of visual analytics, I read the featured blog of ‘Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Python, SAS’ last year with great interest. In the post, the blogger Tim Matteson asked the readers to guess which software was used to create his 18 graphs. My buddy, Emily Gao, suggested that I should see how SAS VA does recreating these visualizations. I agreed.

SAS Visual Analytics (VA) is better known for its interactive visual analysis, and it’s also able to create nice visualizations. Users can easily create professional charts and visualizations without SAS coding. So what I am trying to do in this post, is to load the corresponding data to SAS VA environment, and use VA Explorer and Designer to mimic Matteson’s visualizations.

I want to specially thank Robert Allison for his valuable advices during the process of writing this post. Robert Allison is a SAS graph expert, and I learned a lot from his posts. I read his blog on creating 18 amazing graphs using purely SAS code, and I copied most data from his blog when doing these visualization, which saved me a lot time preparing data.

So, here’s my attempt at recreating Matteson’s 18 visualization using SAS Visual Analytics.

Chart 1

This visualization is created by using two customized bar charts in VA, and putting them together using precision layout so it looks like one chart. The customization of bar charts can be done by using the ‘Custom Graph Builder’ in SAS VA, which includes: set the reverse order for X axis, set the axes direction to horizontal, and don’t show axis label for X axis and Y axis, uncheck the ‘show tick marks’, etc. Comparing with Matteson’s visualization, my version has the tick values on X axis displayed as non-negative numbers, as people generally would expect positive value for the frequency.

Another thing is, I used the custom sort for the category to define the order of the items in the bar chart. This can be done by right click on the category and select ‘Edit Custom Sort…’ to get the desired order. You may also have noticed that the legend is a bit strange for the Neutral response, since it is split into Neutral_1stHalf and Neutral_2ndHalf, which I need to gracefully show the data symmetrically in the visualization in VA.

Chart 2

VA can create a grouped bar chart with desired sort order for the countries and the questions easily. However, we can only put the questions texts horizontally atop of each group bar in VA. VA uses vertical section bar instead, with its tooltip to show the whole question text when the mouse is hovered onto it. And we can see the value of each section in bar interactively in VA when hovering the mouse over.

Chart 3

Matteson’s chart looks a bit scattered to me, while Robert’s chart is great at label text and markers for the scatterplot matrix. Here I use VA Explorer to create the scatterplot matrix for the data, which omitted the diagonal cells and its diagonal symmetrical part for easier data analysis purpose. It can then be exported to report, and change the color of data points.

Chart 4

I used the ‘Numeric Series Plot’ to draw this chart of job losses in recession. It was straightforward. I just adjust some setting like checking the ‘Show markers’ in the Properties tab, unchecking the ‘Show label’ in X Axis and unchecking the ‘Use filled markers’, etc. To make refinement of X axis label of different fonts, I need to use the ‘Precision’ layout instead of the default ‘Tile’ layout. Then drag the ‘Text’ object to contain the wanted X axis label.

Chart 5

VA can easily draw the grouped bar charts automatically. Disable the X axis label, and set the grey color for the ‘Header background.’ What we need to do here, is to add some display rules for the mapping of color-value. For the formatted text at the bottom, use the ‘Text’ object. (Note: VA puts the Age_range values at the bottom of the chart.)

Chart 6

SAS VA does not support drawn 3D charts, so I could not make similar chart as Robert did with SAS codes. What I do for this visualization, is to create a network diagram using the Karate club dataset. The grouped detected communities (0, 1, 2, 3) are showing with different colors. The diagram can be exported as image in VAE.

***I use the following codes to generate the necessary data for the visualization: 

/* Dataset of Zachary’s Karate Club data is from:  
This dataset describes social network friendships in karate club at a U.S. university.
data LinkSetIn;
   input from to weight @@;
 0  9  1  0 10  1  0 14  1  0 15  1  0 16  1  0 19  1  0 20  1  0 21  1
 0 23  1  0 24  1  0 27  1  0 28  1  0 29  1  0 30  1  0 31  1  0 32  1
 0 33  1  2  1  1  3  1  1  3  2  1  4  1  1  4  2  1  4  3  1  5  1  1
 6  1  1  7  1  1  7  5  1  7  6  1  8  1  1  8  2  1  8  3  1  8  4  1
 9  1  1  9  3  1 10  3  1 11  1  1 11  5  1 11  6  1 12  1  1 13  1  1
13  4  1 14  1  1 14  2  1 14  3  1 14  4  1 17  6  1 17  7  1 18  1  1
18  2  1 20  1  1 20  2  1 22  1  1 22  2  1 26 24  1 26 25  1 28  3  1
28 24  1 28 25  1 29  3  1 30 24  1 30 27  1 31  2  1 31  9  1 32  1  1
32 25  1 32 26  1 32 29  1 33  3  1 33  9  1 33 15  1 33 16  1 33 19  1
33 21  1 33 23  1 33 24  1 33 30  1 33 31  1 33 32  1
/* Perform the community detection using resolution levels (1, 0.5) on the Karate Club data. */
proc optgraph
   data_links            = LinkSetIn
   out_nodes             = NodeSetOut
   graph_internal_format = thin;
      resolution_list    = 1.0 0.5
      out_level          = CommLevelOut
      out_community      = CommOut
      out_overlap        = CommOverlapOut
      out_comm_links     = CommLinksOut;
/* Create the dataset of detected community (0, 1, 2, 3) for resolution level equals 1.0 */ 
proc sql;
	create table mylib.newlink as 
	select a.from,, b.community_1, c.nodes from LinkSetIn a, NodeSetOut b, CommOut c
 	where a.from=b.node and and c.resolution=1 ;

Chart 7

I created this map using the ‘Geo Coordinate Map’ in VA. I need to create a geography variable by right clicking on the ‘World-cities’ and selecting Geography->Custom…->, and set the Latitude to the ‘Unprojected degrees latitude,’ and Longitude to the ‘Unprojected degrees longitude.’ To get the black continents in the map, go to VA preferences, check the ‘Invert application colors’ under the Theme. Remember to set the ‘Marker size’ to 1, and change the first color of markers to black so that it will show in white when application color is inverted.

Chart 8

This is a very simple scatter chart in VA. I only set transparency in order to show the overlapping value. The blue text in left-upper corner is using a text object.

Chart 9

To get this black background graph, set the ‘Wall background’ color to black. Then change the ‘Line/Marker’ color in data colors section accordingly. I’ve also checked the ‘Show markers’ option and changed the marker size to bigger 6.

Chart 10

There is nothing special for creating this scatter plot in VA. I simply create several reference lines, and uncheck the ‘Use filled markers’ with smaller marker size. The transparency of the markers is set to 30%.

Chart 11

In VA’s current release, if we use a category variable for color, the marker will automatically change to different markers for different colors. So I create a customized scatterplot using VA Custom Graph Builder, to define the marker as always round. Nothing else, just set the transparency to clearly show the overlapping values. As always, we can add an image object in VA with precision layout.

Chart 12

I used the GEO Bubble Map to create this visualization. I needed to create a custom Geography variable from the trap variable using ‘lat_deg’ and ‘lon_deg’ as latitude and longitude respectively. Then rename the NumMosquitos measure to ‘Total Mosquitos’ and use it for bubble size. To show the presence of west nile virus, I use the display rule in VA. I also create an image to show the meaning of the colored icons for display rule. The precision layout is enabled in order to have text and images added for this visualization.

Chart 13

This visualization is also created with GEO bubble map in VA. First I did some data manipulation to make the magnitude squared just for the sake of the bubble size resolution, so it shows contrast in size. Then I create some display rules to show the significance of the earth quakes with different colors, and set the transparency of the bubble to 30% for clarity. I also created an image to show the meaning of the colored icons.

Be aware that some data manipulation is needed for original longitude data. Since the geographic coordinates will use the meridian as reference, if we want to show the data of American in the right part, we need to add 360 to the longitude, whose value is negative.

Chart 14

My understanding that one of the key points of this visualization Matteson made, is to show the control/interaction feature. Great thing is, VA has various control objects for interactive analysis. For the upper part in this visualization, I simply put a list table object. The trick here is how to use display rule to mimic the style. Before assigning any data to the list table in VA, I create a display rule with Expression, and at this moment we can specify the column with any measure value in an expression. (Otherwise, you need to define the display rule for each column with some expressions.) Just define ‘Any measure value’ is missing or greater than a value with proper filled color for cell. (VA doesn’t support filling the cell with certain pattern like Robert did for missing value. Therefore, I use grey for missing value to differentiate from 0 with a light color.)

For the lower part, I create a new dataset for interventions to hold the intervention items, and put it in the list control and a list table. The right horizontal bar chart is a target bar chart with the expected duration as the targeted value. The label on each bar shows the actual duration.

Chart 15

VA does not have solid-modeling animation like Matteson made in his original chart, yet VA has animation support for bubble plots in an interactive mode. So I made this visualization using Robert’s animation dataset, trying to make an imitation of the famous animation by the late Hans Rosling as a memorial. I set the dates for animation by creating the dates variable with the first day in each year (just for simplicity). One customization here is: I use the custom graph builder to add a new role so that it can display the data label in the bubble plot, and set the country name as the bubble label in VA Designer. Certainly, we can always filter the interested countries in VA for further analysis.

VA can’t show only a part of the bubble labels as Robert did using SAS codes. So in order to clearly show the labels of those interested countries, I made a rank of top 20 countries of average populations, and set a filter to show data between year 1950 to 2011. I use a capture screen tool to have the animation saved as a .gif file. Be sure to click the chart to see the animation.

Chart 16

I think Matteson’s original chart is to show the overview axis in the line chart, since I don’t see specialty of the line chart otherwise. So I draw this time series plot with the overview axis enabled in VA using the SASHELP.STOCK dataset. It shows the date on X axis with tick marks splitting to months, which can be zoomed in to day level in VA interactively. The overview axis can do the zooming in and out, as well as movement of the focused period.

Chart 17

For this visualization, I use a customized bubble plot (in Custom Graph Builder, add a Data Label Role for Bubble Plot.) so it will have bubble labels displayed. I use one reference line with label of Gross Avg., and 2 reference lines for X and Y axis accordingly, thus it visually creats four quadrants. As usual, add 4 text objects to hold the labels at each corner in the precision layout.

Chart 18

I think Matteson made an impressive 3D chart, and Robert recreated a very beautiful 3D chart with pure SAS codes. But VA does not have any 3D charts. So for this visualization, I simply load the data in VA, and drag them to have a visualization in VAE. Then choose the best fit from the fit line list, and export the visualization to report. Then, add display rules according to the value of Yield. Since VA shows the display rules at information panel, I create an image for colored markers to show them as legend in the visualization and put it in the precision layout.

There you have it. Matteson’s 18 visualizations recreated in VA.

How did I do?

18 Visualizations created by SAS Visual Analytics was published on SAS Users.