Content
Comparison in each year 1990 – 2019: 3
https://www.fiverr.com/share/942dQj
Abstract:
The data of life expectancy has been collected and integrated to have a clear answer of the question, in which place around the world life expectancy increases, and people well survive in certain places? So, the maximum number of years an individual human species can live, is the calculation method to respond to this question.
Key Word: Life – Expectancy – Rate – world
Introduction
The data set include (186 country) content region and states of countries all under evaluation of life expectation rate, the sample of data include some region as well, this study gives us a high vision of the availability of basic necessities of life such as food, health and education etc.., as well as the number of survivals is over increase or in the contrary increasing the number of mortality or even if it’s the situation of the life expectation is stable?
By computing some statistics, the questions could be clearer and more satisfying.
Data Exploration
As a matter of fact, data interrogation and integration were the most important challenges to be handled so in the next we will show we have achieved this problem.
Data collection:
Data has been collected from the (Kaggle) website (https://www.kaggle.com/chrisrarig/life-expectancy/notebook) and it contains 1969 observations and 34 attributes with missing values.
All attributes of the dataset were of (String) type, which could be difficult to compute statistical operations. We’ve taken the decision to give each attribute with a numerical type (integer) and we leave the other attributes as (string) type as mentioned next.
The attribute with string type: ((Country_Code, Level, Region, Country))
The attribute with Double type: age average statistics ((years from 1990 to 2019)).
The data we collected from the (186) countries, according to the observations for each country we can see from the data visualised below, in the upper left side we have the United State which is had the maximum number of observations, and gradually from the top to down in right side we can notice that the minimum observation in which Kuwait has obtained.
The table was showing the data of 1990 but we have obtained the same situation for years to 2019. We can conclude that data are treated equally in all years of under study and this diagram below is showing the treatment of years in which data were collected.
Missing Data Handling:
There are some missing values on the data set in a certain time from 1990 to 1998 as maximum in (Angola, Azerbaijan, Belarus, Bhutan, Bosnia and Herzegovina, Burkina Faso etc.) the table below is showing top 20 missing values.
Operations:
By computing these operations I’ve obtain an optimal result these steps have been computed to fill missing values
Giving (median) values for the attribute of integer types, calculating all values from the observation which is related to.
Other attributes like (Country_Code, Level, Region, Country) where having string type of data are without missing values
We have calculated the media for all years from 1990 to 2019
https://public.tableau.com/app/profile/khalid2173/viz/HumanlifeExpectancy1990-2019/HumanlifeExpectancy1990-2019
After we have had data cleaned, the next step was to present these data to be more readable and understandable. If we take a look at the map above, in 1990 some countries like (Italy, Australia, Canada, United States of America, UK and Scandinavian countries), the age average was high, the reason why the map showing a dark green colour people in these countries have a high expectation to live long.
On the other hand if we keep our attention in Africa, we can conclude that average ratios were the lowest, in particular and because of war in Rwanda and Sierra leone, they obtain the minimum averages in which 33.42 is signed in Rwanda and 38.81 in Sierra leone.
By the continuity of years the situation is getting better, and according to our individual notice, the interventions of medical assistance and political accords are becoming more effective to improve the quality life of people in what can increase the average rate of ages. In the next slide we can show how average rates in each country under evaluations in 2019.
The map which is below is presenting the improvement of the ages average in 2019. In 1990 we have taken Rwanda as an example of low rate of rate average, but in 2019 the country under is jump to 69.48 as a media of life age but we still have (Chad, Somalia, cot d’ivoire, Lesotho, Sierra leone, Guinea Bissau and South Sudan) which are signing a minimum number of average rates around 50-60 as media.
Anyhow, we can conclude that as a minimum achieved in 1990 compared of minimum in 2019, we can summaries the averages of ages are getting more better and somehow stable, and we can keep our attention to the African Country which is signing the lowest rate as we can see at the map of age average 2019.
https://public.tableau.com/app/profile/khalid2173/viz/HumanlifeExpectancy1990-2019/HumanlifeExpectancy1990-2019
Comparison in each 10 years
In this part we’ll show the difference of the increasing age average in every 10 years.
So, we can conclude that the lowest age averages had been signed in 1990 which was (56.483), by the increasing of the in each year with un stable value, until the last year of the study in which we obtain a highest value of age average (73.150).
Comparison in each year 1990 – 2019:
All previous steps were a prelude to this step of the presentation, which will appear in the following:
In this presentation we can take our attention to the area of confidence interval in which we decided to be 95% for the total of the sample obtained. So, we can conclude that a 95% in 29 years starting from 1990 to 2019 life expectancy ranging between (68.554-70.495)
Conclusion
As long as there is a quaite suffusion of diagrams that could be used to demonstrate a good visual reading of age average during the years under studies, but we have taken our decision to demonstrate the map in above to give a clearer understanding to the user by computing the latitude and longitude which were been calculated taking the country names.
Some statistical valuation to verify the graphic presentation has been used by a sample of 4 people, by calculating visibility, consistency, flexibility and how the presentation is understood.
Reference
https://www.knime.com/
https://public.tableau.com/app/profile/khalid2173/viz/HumanlifeExpectancy1990-2019/HumanlifeExpectancy1990-2019
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