Using elastic stack to analyze Olympic data (II)

This is my last article “ Use Elastic Stack To analyze the Olympic data （ One ）” The sequel of . In the last article , I explained in detail how to upload data to Elasticsearch in . In today's article , I will focus on how to achieve this olympic Index for visualization .

Visualize Olympic data

In the last article , We can see such data ：

end 2016, Altogether 271,116 Athletes compete . We can create one Dashboard To visualize this data .

Get the number of participants

 

 

  

 

  So we get our first visualization . In the past 140 In the year , share 271,116 Athletes took part in the competition .

 

Check the sex ratio

We still follow the above routine , Click on Create visualization：

This time, , What we are dragging is sex Field . We can see from above that the man is standing 72.51% The proportion of . Click on Save & return：

 

This forms our second Visualization . on top ,  We can also add a title to each diagram . Next I want to get the oldest of all the athletes .

The oldest athlete

Click on the Create visualization：

 

Click on the top Save & return：

 

So we get our third Visualization . It shows that the oldest contestant is 97 year . A careful reader , You may see one in the figure above 24 It's worth it . It is actually a median age for all athletes . In other words, more than half of the athletes are older than 24 year . Of course, we can also find the average age . About other indicators , I will not show them here . Maybe people are interested in the youngest athlete . Remember when we were cleaning data , If age The value of the field is a NAN (not a number), We'll set it to 0. In the table , In fact, some athletes have no age , So it is set to 0. The youngest athlete is 0, It's not surprising .

Number of previous parameters

In our Discover Interface , We have seen the number of previous parameters . So how do we visualize ？ Same as before , stay Dashboard Interface Click Create visualization：

 

 

This also gives us an overview of the number of previous parameters . From the picture , We can see that , In the early days, fewer people participated , And there are two periods in between , There is no Olympic Games . In the later period , We can see that the summer Olympic Games and the Winter Olympic Games are held every two years .

View the parameter number of the previous winter Olympic Games

Aiming at this , We use Lens It's a little difficult , Because we only use part of the data for visualization , That is to say season by Winter The data of . stay Lens We can't use filter To filter . We have two ways to visualize ：

1） Use a filtered data set .

We click Save：

 

We keep olympic_winter Data sets . We have to use aggregations Tools for visualization ：

 

 

 

 

 

In this way, we have got the visualization of the Winter Olympic Games .

2） Use TSVB To visualize

Another way is to use TSVB To visualize . We don't need to save a filtered data set .

 

 

 

on top , We choose Bar, And click the Save & return：

 

We can see the same result as the first method above .

Age distribution

Let's look at the age distribution of Olympic athletes . stay Dashboard Click on the Create visualization：

 

 

 

From the above we can see that the median age of athletes is 24 year . Some very old athletes are also active in the Olympic Games . 

 

Medals are ranked by country  

We then rank according to the number of medals we have won . The number of medals can be gold medals , Silver and bronze . In this case , It also has a filter, That is to say medal Not for None The athletes who win medals are the ones who win medals . We can create a filtered data set following the previous method ：

  

 

Let's use   Aggregation based Visualization tools for visualization ：

 

 

 

 

 

 

 

 

 

From this picture , We can see that before each session 5 Award winning countries .

Next, we would like to know the overall awards of all previous countries . We do the following operations ：

 

 

 

 

 

 

 

 

 

In this way, we have won the top ten award-winning countries . From this picture we can see ,USA By the end of 2016 In so far , Proportion of awards 15.95%. It is the country that has won the most medals .

Find out all the Winter Olympic events

Let's go back to the question in my last article . Let's find out all the events of the Winter Olympics first , And find out the project with the largest number of participants . We use the following method ：

 

 

on top , We select the previously saved olympic_winter Data sets ：

 

 

 

 

So we get the names of all the events of the Winter Olympics . We can see Cross Country Skiing It is the event with the largest number of participants .

 

China , The United States , Russia , Medals of Japan and Germany

Next, we want to compare the last three Winter Olympic Games （2006,2010,2014） China , The United States , Russia , The overall awards of Japan and Germany . We use Lens To complete . Same as before , stay Dashboard Choose from Create visualization：

 

 

on top , I enter the following KQL：

year: "2006"  and not medal:"None" and (NOC: CHN or NOC :GER or NOC :RUS or NOC :USA or NOC :JPN)

Follow the same method , Let's add 2010 And 2014 Filter of the year . Also remember to set the display mode to Bar vertical：

 

 

Let's configure Y Axis ：

 

 

 

 

 

 

such , We have created something that we are interested in 5 A country 2006 year ,2010 year ,2014 All the circumstances of the year . 

 

Find out what we are interested in 5 A comparison of sports  

We want to find out what we are interested in 5 Neck movement , The United States , Japan , Germany , The situation in Russia and China . Similar to the above method , It's just that we're filter Add the conditions we need ：

We can go through Clone panel To replicate the previous visualization , It is easier to achieve the goal ：

 

This time, , We choose Edit lens. This time, , We are filter Enter the following conditions in ：

year: "2006"  and not medal:"None" and (NOC : CHN or NOC :GER or NOC :RUS or NOC :USA or NOC :JPN) and (sports : "Cross Country" or  "Skiing" or "Ice Hockey" or "Speed Skating"  or Biathlon)

  We are aiming at 2010 And 2014 Revised separately in . At the same time, the mode we chose to show is Bar vertical stacked：

 

In this way, we can get a comparison of several events in several countries that we are interested in in the Winter Olympic Games . 

 

Before the last three gold medals 10 Country of name

This is also the topic that I am most concerned about ： Gold medal list . We want to get 2006,2010,2014 The gold medal list of the 2008 Winter Olympic Games . List the top ten countries . Remember we used enrich processor Add the name of the country to the final region Fields ？ ad locum , I will use table To show . We also use them region To describe . A lot of people NOC The name of is still unfamiliar .

stay Dashboard in , We click Create visualziation：

 

We create the following filter:

year:"2014" and medal:"Gold" 

  At the same time, we select the display mode as table. be modeled on , We created 2010 And 2014 filter ：

 

 

 

 

 

 

on top , We have seen the number of gold medals in various countries . Like I said before , In the table above , We used  RUS,NOR, These abbreviations may not be familiar to many readers . We can use region To describe ：

 

Let's add the host city to this table ：

 

 

In this way, we have formed a table that we hope . In the table above , We modify the table header：

 

Click on the top Save & return：

 

So now we have 2006,2010,2014 Gold medal ranking in .

thus , We have completed the following Dashboard：

 

I hope you have learned something in the whole exercise . More information about Kibana Visual tutorial for , see also ：

• Kibana：Kibana introduction （ One ）

• Kibana：Kibana introduction （ Two ）

• Kibana：Kibana introduction （ 3、 ... and ）