Life Expectancy
Sakshi
2024-10-16
Description
The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countriesTherefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. .
Tasks
- Insight of how different factors affects the life expectancy.
- On what factors a good life span is depended.
Working on the dataset
Prepare Phase
Installing packages like ‘tidyverse’, ‘ggplot2’, ‘lubridate’, ‘dplyr’, ‘tidyr’, ‘here’, ‘skimr’, ‘janitor’ that will help in cleaning, analyzing and plotting our data.
# Loading packages :
library(tidyverse)
library(lubridate)
library(dplyr)
library(ggplot2)
library(tidyr)
library(here)
library(skimr)
library(janitor)Importing datasets
Importing Life Expectancy Data.csv
expectancy<- read.csv("C:/Users/saksh/Desktop/Life Expectancy Data.csv")
View(expectancy)
glimpse(expectancy)## Rows: 2,938
## Columns: 22
## $ Country <chr> "Afghanistan", "Afghanistan", "Afghani…
## $ Year <int> 2015, 2014, 2013, 2012, 2011, 2010, 20…
## $ Status <chr> "Developing", "Developing", "Developin…
## $ Life.expectancy <dbl> 65.0, 59.9, 59.9, 59.5, 59.2, 58.8, 58…
## $ Adult.Mortality <int> 263, 271, 268, 272, 275, 279, 281, 287…
## $ infant.deaths <int> 62, 64, 66, 69, 71, 74, 77, 80, 82, 84…
## $ Alcohol <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.…
## $ percentage.expenditure <dbl> 71.279624, 73.523582, 73.219243, 78.18…
## $ Hepatitis.B <int> 65, 62, 64, 67, 68, 66, 63, 64, 63, 64…
## $ Measles <int> 1154, 492, 430, 2787, 3013, 1989, 2861…
## $ BMI <dbl> 19.1, 18.6, 18.1, 17.6, 17.2, 16.7, 16…
## $ under.five.deaths <int> 83, 86, 89, 93, 97, 102, 106, 110, 113…
## $ Polio <int> 6, 58, 62, 67, 68, 66, 63, 64, 63, 58,…
## $ Total.expenditure <dbl> 8.16, 8.18, 8.13, 8.52, 7.87, 9.20, 9.…
## $ Diphtheria <int> 65, 62, 64, 67, 68, 66, 63, 64, 63, 58…
## $ HIV.AIDS <dbl> 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1…
## $ GDP <dbl> 584.25921, 612.69651, 631.74498, 669.9…
## $ Population <dbl> 33736494, 327582, 31731688, 3696958, 2…
## $ thinness..1.19.years <dbl> 17.2, 17.5, 17.7, 17.9, 18.2, 18.4, 18…
## $ thinness.5.9.years <dbl> 17.3, 17.5, 17.7, 18.0, 18.2, 18.4, 18…
## $ Income.composition.of.resources <dbl> 0.479, 0.476, 0.470, 0.463, 0.454, 0.4…
## $ Schooling <dbl> 10.1, 10.0, 9.9, 9.8, 9.5, 9.2, 8.9, 8…So now, we can see that the file was imported correctly.
Process Phase
And here some cleaning steps I followed:
- I did not found any Spelling errors, Extra and blank space and duplicated value in the data.
- This is the data of 193 distinct countries.
expectancy[!duplicated(expectancy), ] unique(expectancy$Country)
As our data is cleaned we will move to our further analysis.
Analyze Phase
expectancy %>%
select(Life.expectancy,infant.deaths, under.five.deaths,Adult.Mortality, Population, BMI) %>%
summary()## Life.expectancy infant.deaths under.five.deaths Adult.Mortality
## Min. :36.30 Min. : 0.0 Min. : 0.00 Min. : 1.0
## 1st Qu.:63.10 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 74.0
## Median :72.10 Median : 3.0 Median : 4.00 Median :144.0
## Mean :69.22 Mean : 30.3 Mean : 42.04 Mean :164.8
## 3rd Qu.:75.70 3rd Qu.: 22.0 3rd Qu.: 28.00 3rd Qu.:228.0
## Max. :89.00 Max. :1800.0 Max. :2500.00 Max. :723.0
## NA's :10 NA's :10
## Population BMI
## Min. :3.400e+01 Min. : 1.00
## 1st Qu.:1.958e+05 1st Qu.:19.30
## Median :1.387e+06 Median :43.50
## Mean :1.275e+07 Mean :38.32
## 3rd Qu.:7.420e+06 3rd Qu.:56.20
## Max. :1.294e+09 Max. :87.30
## NA's :652 NA's :34I can see that the average life span of a person is 69 and the average of infants death rate is of 30 per 1000 population.
Share Phase
Now let’s visualize some key explorations.
Relationship between Life expectancy and adult mortality according to the country status
ggplot(expectancy, aes( Life.expectancy, Adult.Mortality, colour = Status))+
geom_point(alpha = 0.5)+
geom_smooth()
As i can see that the developed countries have a good life expectancy than the countries which are in the phase of development.
Relationship between life expectancy and alcohol
ggplot(expectancy, aes (Life.expectancy, Alcohol))+
geom_point(alpha=0.5)+
geom_smooth()
I can see the a week positive correlation between the pure alcohol and life expectancy.
Relationship between life expectancy and schooling
ggplot(expectancy, aes (Life.expectancy, Schooling))+
geom_point(alpha=0.5)+
geom_smooth()
There is a positive correlation between the schooling and life expectancy, it means schooling leads to a good life span of a person.
Relationship between life expentancy and income
ggplot(expectancy, aes (Life.expectancy, Income.composition.of.resources ))+
geom_point( alpha= 0.5)+
geom_smooth()
This graph shows a positive correlation between the life expectancy and the income composition of resources.
Relationship between life expectancy and HIV AIDS
ggplot(expectancy,aes( Life.expectancy,HIV.AIDS))+
geom_point(alpha=0.5)+
geom_smooth()
As I can see there is a very week negative correlation between HIV AIDS and life expectancy.
Conclusion
After looking at the data and the insights we created
Schooling effects the life expectancy of the peoples as the more people are educated the more knowledge they have of how they can get a good life span and what factors effect it.
We saw a positive correlation between the income composition of resource and life expectancy, as the people have more income composition of resources the more good life expectancy they have as they invest on themselves.
The HIV ADIS were negatively related to the life expectancy as the less people have HIV and AIDS the more life span they had .
In developed countries the life expectancy was more and adult mortality was less as compared to the countries that were developing where life expectancy was low .
Thank you very much for your interest!
And I would appreciate any comments and recommendations for improvement!