This presentation analyzes alcohol consumption across countries and how it relates to GDP per capita, population, year and continent.

Data Source:Kaggle alcohol consumption by country dataset

The variables used:

• Entity: country name
• Year: year
• alcohol: alcohol consumption per capita
• gdp: GDP per capita
• population: population
• Continent: continent

library(ggplot2)
library(plotly)
library(dplyr)

data <- read.csv("alcohol-consumption-vs-gdp-per-capita.csv")

colnames(data)[4] <- "alcohol"
colnames(data)[5] <- "gdp"
colnames(data)[6] <- "population"

data <- data %>%
  filter(!is.na(alcohol), !is.na(gdp), !is.na(population), !is.na(Continent))

data_2018 <- data %>%
  filter(Year == 2018)

#plty plt
plot_ly(data=data_2018,x=~gdp,y=~alcohol,z=~population,type="scatter3d",mode="markers",color=~Continent) %>%
  layout(title="GDP, alcohol consumption, and population in 2018",scene = list(xaxis = list(title = "GDP per Capita"),yaxis = list(title = "Alcohol Consumption"),
      
      
      
      zaxis = list(title = "Population")))

This plot shows three variables at the same time. It helps compare GDP per capita, alcohol consumption, and population across countries in 2018.

plot_ly(data=data_2018,x=~gdp,y=~alcohol,type="scatter",mode="markers",color=~Continent) %>%
  
  
  
  #scter plt
  layout(   title="GDP per capita vs alcohol consumption in 2018",xaxis =list(title="GDP per Capita"),yaxis=list(title="Alcohol consumption")
  )

This scatterplot shows the relationship between GDP per capita and their alcohol consumption, it seems to be some positive relationship but the points are still spread out.

continent_summary<-data_2018 %>%
  group_by(Continent) %>%
  summarise(avg_alcohol = mean(alcohol))
#plottttttt
ggplot(continent_summary,aes(x=Continent,y= avg_alcohol,fill=Continent))+geom_bar(stat="identity")+labs(title= "Average Alcohol Consumption by Continent in 2018",x    = "Continent",y="Average Alcohol Consumption")

#"Continent",y="Avg Alc Consumption")

This bar chart compares average alcohol consumption across continents in 2018

ggplot(data_2018, 
  aes(x=Continent,y=alcohol,    fill=Continent))+ geom_boxplot()+labs(title="alcohol consumption by continent in 2018",x ="continent",y="Alcohol Consumption")

This boxplot shows the spread of alcohol consumption values for each continent and it also helps compare variation between continents.

data_2018 %>%group_by(Continent) %>%summarise(Count= n(),Mean_Alcohol=mean(alcohol),Median_Alcohol= median(alcohol),SD_Alcohol  =sd(alcohol)
  )

## # A tibble: 1 × 5
##   Continent Count Mean_Alcohol Median_Alcohol SD_Alcohol
##   <chr>     <int>        <dbl>          <dbl>      <dbl>
## 1 ""          182         6.11              6       4.13

The summary statistics show that alcohol consumption is different across continents. The regression suggests GDP per capita is related to alcohol consumption although it does not explain everything.

model<-lm(alcohol~gdp,data=data_2018)
summary(model)

## 
## Call:
## lm(formula = alcohol ~ gdp, data = data_2018)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.1588  -2.6036   0.2127   2.5872  13.8430 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.523e+00  3.976e-01  11.376  < 2e-16 ***
## gdp         7.804e-05  1.375e-05   5.675 5.46e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.818 on 180 degrees of freedom
## Multiple R-squared:  0.1518, Adjusted R-squared:  0.1471 
## F-statistic: 32.21 on 1 and 180 DF,  p-value: 5.46e-08

Alcohol consumption is differnet across continents. GDP per capita also appears to be related to alcohol consumption although it is not the only factor. Overall both geography and economics help explain differences across countries.

Dataset source: Kaggle alcohol consumption by country