Compares socio-economic info with suicide rates by year and country

Introduction

This compiled dataset pulled from four other datasets linked by time and place and was built to find signals correlated to increased suicide rates among different cohorts globally, across the socio-economic spectrum.

Materials

I find this dataset in kaggle (https://www.kaggle.com) which is an online community of data scientists and machine learners. There are many options on the list, including ‘the usage of google applications’, ‘vegetable prices’, etc. I chose ‘suicide rate’ because this dataset can reveal multiple dimensions that may help me to explore more content. This dataset includes country, year, sex, age group, count of suicides, population, suicide rate, gdp_per_capital, generation (based on age grouping average). By reviewing it, I can find much unique information that shows humanity stories.

Also, I choose this dataset because it meets the requirements of Tableau Lab: Over 1000 records/rows; 1+ quantitive dimensions; 1+ categorial dimensions; historical (has time-oriented data).

Methods

1. Clean up the data

Fortunately, the data is pretty clean. I don’t need to clean it on my own. 

2. Drop dataset into Tableau

Then, I drop it in my Tableau to start a sheet. The sidebar navigation has two parts. The upper part includes age, country, GDP for the year, generation, sex, the year which can be called qualitative data. The lower part includes, population, suicides No, which is quantitive data. The bars can be switched randomly but I need to think clearly what exactly to look for before I move it, because mess up bars can cause traffic in the software system. 

3. Making the Charts

First, I’m curious about the suicide rate in Europe countries and its comparison with the other countries. I select Europe countries and make them into a group calls ‘Europe’. The bar chart shows numbers very obviously with the suicide population as rows and population as columns. The charts reveal although the population of ‘other countries’ is higher than the population in Europe more than double, Europe has more people to kill themselves. This made me want to dig more data through it.

I use filter to see some qualitative data, like the difference suicide rate in gender. Pai chart is my first choice. If I divide the dataset into male and female and both of them are seen as a whole, I can easily find which part holds more population and which part holds less. I keep the filter of ‘the country group’ because I still want to see a comparison between Europe and other countries. I choose pink as the general representation of females, while blue as males. I manipulate them into the Pai chart. The final result comes up is surprising. The male suicide rate in Europe and others is far beyond than females’ which both close to 45%.

After that exploration, I found Europe’s suicide rate is pretty high so that I decided to dig into data again to explore more specific information. I use circles to see the suicide rate in each Europe country. Also, for those parts of data, I can also use a bar chart to visualize it. However, I still use circles because it shows the quantity difference obviously by multiple sizes of circles and looks more comfortable than vertical bars. From that view, I know there are six countries have a high sucide population include Germany, France, United Kingdom, Italy, Ukraine, and Poland. Among them, Germany, France, and Ukraine really catch my eyes with huge circles.

After that exploration, I found Europe’s suicide rate is pretty high so that I decided to dig into data again to explore more specific information. I use circles to see the suicide rate in each Europe country. Also, for those parts of data, I can also use a bar chart to visualize it. However, I still use circles because it shows the quantity difference obviously by multiple sizes of circles and looks more comfortable than vertical bars. From that view, I know there are six countries have a high sucide population include Germany, France, United Kingdom, Italy, Ukraine, and Poland. Among them, Germany, France, and Ukraine really catch my eyes with huge circles.

I have a curiosity about the reason for this phenomenon in six countries so I start to check out the GDP of each country. In this respect, the bar chart is a good way to choose and I colored six countries to make them easier to be analyzed. While the benefit of this form is it’s quite easy to find similarity,  especially when the bar ends in the same column block. France, Germany, Italy, and the United Kingdom have a pretty similar GDP rate which means their economic development is stable. However, Poland and Ukraine show interesting data that their GDP rates are far behind the other four counties. Ukraine is in the worse situation which makes me speculate that the poor economic situation causes the high suicide population rate.

The generation is another qualitative data that helps me to find more interesting discoveries among those six countries. The dataset divides the population into the six-generation groups: Boomers, G.I. Generation X, Generation Z, Millenials, and Silent. I use the area percentage chart to visualize those data. It’s pretty obvious that Boomers and Silent generation have more suicide populations than others that reveals there must be some potential problems cause this phenomenon. 

4. Create a dashboard

I put all the sheets into dashboard and change the titles of each chart. I didn’t show all of them because some charts may show too many details and multiple colors make the whole dashboard confused. For example, the area chart has six colors for each generation which is the same as the circle chart that represents the suicide rate for six countries. Therefore, I chose to put the area chart into the analyze report.

For me, the limitation of this dashboard is the layouts are all designed by squares which are hard to arrange the sheets. I can’t freely edit each side of the chart.

5. Save to Tableau Public

Click on save to the Tableau Public is a very convenient function. There are huge amount of serves on this website, ‘Tableau online’, etc. If I go to a different channel, it will be hard to find sheets.

Results

https://public.tableau.com/profile/jennie8834#!/vizhome/EuropeSuicide/Dashboard1

1. Through those suicide rates from all over the world. Europe countries have more suicide population than others.
2. All most forty-five percent of suicide population is male and around fifty percent of suicide population is female. The situation is similar all over the world.
3. There are six countries that hold a high suicide rate, Germany, France, Poland, Ukraine, Italy, and the United Kingdom.
4. The GDP of Germany, France, Italy, and the United Kingdom is similar and stable while Poland and Ukraine are in a poor situation. The economic problem may cause the high suicide population among those two countries.
5. Boomers and Silent generation are high suicide groups in six countries.

Reflection

Tableau Public is a pretty useful tool to help me go through the brunch of data and makes data into charts show the content about a certain topic. What I think is important is that dimensions are the key to build charts. It can help me explore what kind of content I want to get. To be more detailed, if the dimensions include too many kinds of data, the final results will become such a mess. Think clearly before creating charts is necessary. Also, understand the difference between qualitative data and quantitive data can help users to use dimensions effectively like gender is a qualitative data help me go through the population in counties, etc. Finally, the most important point from my perspective is to know the advantage and limitations of each chart form. The line chart shows the wave of data while the bar chart is a good way to make a comparison. Moreover, if I use Tableau next time, I will try to use fewer colors and make them more accordance.