In contemporary life there is a high emphasis placed on education. In countries around the world, there is a noticeable link between high literacy rates and a better quality of life. The data analyses the factors of quality of life including; infant mortality, deathrate and GDP per capita. These variables are quantitative and the country and regions of our data are qualitative. The data explored is from the US Government database, presented in the CIA World Factbook 2013. We believe it is a credible source as the dataset was gathered by a well respected major American organisation.
knitr::opts_chunk$set(echo = TRUE)
library(readxl)
selected_countries_by_region <- read_excel("data/selected countries by region.xls")
countries_of_the_world <- read_excel("data/countries-of-the-world.xls")
summary(countries_of_the_world)
## Country Region Infant mortality per 1000 births
## Length:227 Length:227 Min. : 2.29
## Class :character Class :character 1st Qu.: 8.15
## Mode :character Mode :character Median : 21.00
## Mean : 35.51
## 3rd Qu.: 55.70
## Max. :191.19
## NA's :3
## GDP Literacy % Birthrate Deathrate
## Min. : 500 Min. : 17.60 Min. : 7.29 Min. : 2.290
## 1st Qu.: 1900 1st Qu.: 70.60 1st Qu.:12.67 1st Qu.: 5.910
## Median : 5550 Median : 92.50 Median :18.79 Median : 7.840
## Mean : 9690 Mean : 82.84 Mean :22.11 Mean : 9.241
## 3rd Qu.:15700 3rd Qu.: 98.00 3rd Qu.:29.82 3rd Qu.:10.605
## Max. :55100 Max. :100.00 Max. :50.73 Max. :29.740
## NA's :1 NA's :18 NA's :3 NA's :4
par(las=2)
barplot.default(selected_countries_by_region$`Infant mortality per 1000 births`, main="Infant Mortality in Different Regions", ylab="Infant Mortality Rate per 1000 births", names.arg=c("Afghanistan", "Algeria", "Antigua & Barbuda", "Benin", "Bulgaria", "Canada", "Fiji", "France", "Lithuania", "Saudi Arabia", "Turkmenistan"), cex.names=0.85, col="lightblue")
par(las=2)
barplot.default(selected_countries_by_region$`Literacy %`, main="Literacy Rates in Different Regions", ylab="Literacy Rate", names.arg=c("Afghanistan", "Algeria", "Antigua & Barbuda", "Benin", "Bulgaria", "Canada", "Fiji", "France", "Lithuania", "Saudi Arabia", "Turkmenistan"),cex.names=0.85, col="lightblue")
par(las=2)
barplot.default(selected_countries_by_region$'Deathrate', main = "Deathrate (per 1000 births) in Different Regions", ylab = "Deaths per 1000 Births", names.arg=c("Afghanistan", "Algeria", "Antigua", "Benin", "Bulgaria", "Canada", "Fiji", "France", "Lithuania", "Saudi Arabia", "Turkmenistan"),cex.names=0.85, col="lightblue")
par(las=2)
plot(countries_of_the_world$`Literacy %`, countries_of_the_world$`Infant mortality per 1000 births`, main='Scatterplot of Literacy Rate against Infant Mortality', xlab = 'Literacy rate (%)', ylab = 'Infant mortality per 1000 births')
par(las=2)
plot(countries_of_the_world$`Literacy %`, countries_of_the_world$Deathrate, main = 'Scatterplot of Literacy Rate against Deathrate', xlab = 'Literacy rate (%)', ylab = 'Deathrate (individuals per year per 1000 individuals)')
plot(countries_of_the_world$`Literacy %`, countries_of_the_world$'GDP', main = 'Scatterplot of Literacy Rate against GDP per Capita', xlab = 'Literacy rate (%)', ylab = 'GDP per capita ($)')
The impact of literacy on infant mortality rates and deathrate is negatively correlated. In the graphs above there is a clear correlation between the infant mortality rates and the literacy rates in different regions around the world. Particularly in Afghanistan and Benin where there are considerably low literacy rates (below 40%) and noticeably high infant mortality rates (above 80). However, other countries contradict this hypothesis such as Turkmenistan which has a very high literacy rate and a high infant mortality rate in comparison to other countries with high literacy rates.
The link between literacy rate and GDP is positively correlated.The graphs also show what appears to be an exponential relationship between literacy and average GDP, meaning the impact of literacy rate on productivity amplifies as it approaches 100%.
The data explored was from two datasets, one being many countries in regions around the world and the second dataset being one country per region. The second dataset was chosen by country as the best representation of that particular region, we did this as a bar plot is a great was to visually represent data, and through comparing our three bar plots, the correlation between literacy and quality of life indicators is exemplified. Although we considered the country per region to be an accurate representation, this is subject to bias.
Overall, the data shows that the literacy rate is a great indicator of the quality of life. The graphs show the impact of literacy rates on each chosen indicator of the quality of life, which are death rate, GDP per capita, infant mortality rate. We have compared these indicators with different countries around the world to exemplify our data by repetition. The stakeholders could be Government Agencies and Companies concerned with improving the quality of life, as our data shows a clear correlation between the effect of literacy on the quality of life.