graphical data analysis on student alcohol consumption
vaishnavi chillara
2023-10-31
College alcoholism affects millions of students every year. The college years are some of the most popular times to experiment with alcohol. Roughly 80% of college students – four out of every five – consume alcohol to some degree. It’s estimated that 50% of those students engage in binge drinking, which involves consuming too much alcohol in too little time. this document will give you a brief clarity of the student alcohol consumption,making it easier to analyse through various plots.
#reading the data
data=read.csv("C:/Users/chvss/OneDrive/Documents/student-mat.csv")summary(data)## school sex age address
## Length:395 Length:395 Min. :15.0 Length:395
## Class :character Class :character 1st Qu.:16.0 Class :character
## Mode :character Mode :character Median :17.0 Mode :character
## Mean :16.7
## 3rd Qu.:18.0
## Max. :22.0
## famsize Pstatus Medu Fedu
## Length:395 Length:395 Min. :0.000 Min. :0.000
## Class :character Class :character 1st Qu.:2.000 1st Qu.:2.000
## Mode :character Mode :character Median :3.000 Median :2.000
## Mean :2.749 Mean :2.522
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000
## Mjob Fjob reason guardian
## Length:395 Length:395 Length:395 Length:395
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## traveltime studytime failures schoolsup
## Min. :1.000 Min. :1.000 Min. :0.0000 Length:395
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.0000 Class :character
## Median :1.000 Median :2.000 Median :0.0000 Mode :character
## Mean :1.448 Mean :2.035 Mean :0.3342
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:0.0000
## Max. :4.000 Max. :4.000 Max. :3.0000
## famsup paid activities nursery
## Length:395 Length:395 Length:395 Length:395
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## higher internet romantic famrel
## Length:395 Length:395 Length:395 Min. :1.000
## Class :character Class :character Class :character 1st Qu.:4.000
## Mode :character Mode :character Mode :character Median :4.000
## Mean :3.944
## 3rd Qu.:5.000
## Max. :5.000
## freetime goout Dalc Walc
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :1.000 Median :2.000
## Mean :3.235 Mean :3.109 Mean :1.481 Mean :2.291
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## health absences G1 G2
## Min. :1.000 Min. : 0.000 Min. : 3.00 Min. : 0.00
## 1st Qu.:3.000 1st Qu.: 0.000 1st Qu.: 8.00 1st Qu.: 9.00
## Median :4.000 Median : 4.000 Median :11.00 Median :11.00
## Mean :3.554 Mean : 5.709 Mean :10.91 Mean :10.71
## 3rd Qu.:5.000 3rd Qu.: 8.000 3rd Qu.:13.00 3rd Qu.:13.00
## Max. :5.000 Max. :75.000 Max. :19.00 Max. :19.00
## G3
## Min. : 0.00
## 1st Qu.: 8.00
## Median :11.00
## Mean :10.42
## 3rd Qu.:14.00
## Max. :20.00# Explore the student alcohol consumption data frame with str()
str(data)## 'data.frame': 395 obs. of 33 variables:
## $ school : chr "GP" "GP" "GP" "GP" ...
## $ sex : chr "F" "F" "F" "F" ...
## $ age : int 18 17 15 15 16 16 16 17 15 15 ...
## $ address : chr "U" "U" "U" "U" ...
## $ famsize : chr "GT3" "GT3" "LE3" "GT3" ...
## $ Pstatus : chr "A" "T" "T" "T" ...
## $ Medu : int 4 1 1 4 3 4 2 4 3 3 ...
## $ Fedu : int 4 1 1 2 3 3 2 4 2 4 ...
## $ Mjob : chr "at_home" "at_home" "at_home" "health" ...
## $ Fjob : chr "teacher" "other" "other" "services" ...
## $ reason : chr "course" "course" "other" "home" ...
## $ guardian : chr "mother" "father" "mother" "mother" ...
## $ traveltime: int 2 1 1 1 1 1 1 2 1 1 ...
## $ studytime : int 2 2 2 3 2 2 2 2 2 2 ...
## $ failures : int 0 0 3 0 0 0 0 0 0 0 ...
## $ schoolsup : chr "yes" "no" "yes" "no" ...
## $ famsup : chr "no" "yes" "no" "yes" ...
## $ paid : chr "no" "no" "yes" "yes" ...
## $ activities: chr "no" "no" "no" "yes" ...
## $ nursery : chr "yes" "no" "yes" "yes" ...
## $ higher : chr "yes" "yes" "yes" "yes" ...
## $ internet : chr "no" "yes" "yes" "yes" ...
## $ romantic : chr "no" "no" "no" "yes" ...
## $ famrel : int 4 5 4 3 4 5 4 4 4 5 ...
## $ freetime : int 3 3 3 2 3 4 4 1 2 5 ...
## $ goout : int 4 3 2 2 2 2 4 4 2 1 ...
## $ Dalc : int 1 1 2 1 1 1 1 1 1 1 ...
## $ Walc : int 1 1 3 1 2 2 1 1 1 1 ...
## $ health : int 3 3 3 5 5 5 3 1 1 5 ...
## $ absences : int 6 4 10 2 4 10 0 6 0 0 ...
## $ G1 : int 5 5 7 15 6 15 12 6 16 14 ...
## $ G2 : int 6 5 8 14 10 15 12 5 18 15 ...
## $ G3 : int 6 6 10 15 10 15 11 6 19 15 ...Data Layer: In the data Layer, the source of the information is to be visualized i.e the mtcars dataset in the ggplot2 package.
library(ggplot2)
#Data Layer
ggplot(data = data) + labs(title ="student alcohol consumption dataset")
aesthetic layer:display and map dataset into certain aesthetics
# Aesthetic Layer
ggplot(data = data, aes(x = sex, y = age, col = reason))+labs(title = "student alcohol consumption")
Geometric layer:In geometric layer control the essential elements, how
our data being displayed using point, line, histogram, bar, boxplot
# Geometric layer
ggplot(data = data, aes(x = age, y = Medu, col = reason)) +
geom_point() +
labs(title = "age vs Medu", x = "age", y = "Medu")
then plotting the Histogram plot # Adding size
ggplot(data = data, aes(x = age, y = absences, size = Medu)) +
geom_point() +
labs(title = "age vs absences", x = "age", y = "absences")
# Adding shape and color
ggplot(data = data, aes(x =age, y = famsup, col = factor(age), shape = factor(famsup))) +geom_point() +
labs(title = "age vs family support", x = "age", y = "family support")
scatter plot
data$age<-factor(data$age)
ggplot(data, aes(x = factor(age), y = reason)) +
geom_point()
# Histogram plot
ggplot(data = data, aes(x = health)) +
geom_histogram(binwidth = 5,color="purple", fill="orange") +
labs(title = "Histogram of health and the age of the students", x = "health", y = "age")
ggplot(data = data, aes(x=as.factor(health), fill=health)) +
geom_bar(stat="count")
pie chart the below pie chart tells us the reason of the alcohol
consumption and analysing the percentage of each reason.
reason = table(data$reason)
data.labels = names(reason)
share = round(reason/sum(reason)*100)
data.labels = paste(data.labels, share)
data.labels = paste(data.labels,"%",sep="")
pie(reason,labels = data.labels,clockwise=TRUE, col=heat.colors(length(data.labels)), main="Frequency of reason")
creating a pie chart analysing the percentage of the consumers and the job they do(males)
Mjob = table(data$Mjob)
data.labels = names(Mjob)
share = round(Mjob/sum(Mjob)*100)
data.labels = paste(data.labels, share)
data.labels = paste(data.labels,"%",sep="")
pie(Mjob,labels = data.labels,clockwise=TRUE, col=heat.colors(length(data.labels)), main="Frequency of males job who consume alcohol")
creating a similar pie analysing the same with the females
Fjob = table(data$Fjob)
data.labels = names(Fjob)
share = round(Fjob /sum(Fjob)*100)
data.labels = paste(data.labels, share)
data.labels = paste(data.labels,"%",sep="")
pie(Fjob,labels = data.labels,clockwise=TRUE, col=heat.colors(length(data.labels)), main="Frequency of jobs (female)")