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R Language Charts Data Visualization Assignment Binus University, Assignments of Data Mining

R scripts for creating charts and analyzing the mtcars dataset. The charts show the distribution of cars' MPG, the data distribution based on the number of cylinders and MPG, and the composition of cars based on the number of cylinders and carburetor barrels. The insights gained from the charts are also provided. The document can be useful for students studying data visualization, statistics, and data analysis.

Typology: Assignments

2022/2023

Available from 03/18/2023

JasonMalvor
JasonMalvor 🇮🇩

6 documents

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bg1
Name: Jason Pangestu
NIM: 2602107650
Types of
Charts
Create your own charts and write the insights!
Distribution
Script:
par (
cex=1.1
)
hist(mtcars$mpg,
col="Red",
breaks=9,
xlab="MPG",
ylab="Frequency",
main="Distribution of Cars' MPG"
)
Insights: Based on the mtcars dataset,
there are many cars that have Miles/(US)
Gallon (MPG) in the range of 15 to 21.
Script:
par (
cex=0.75
)
boxplot(mpg ~ cyl,
data = mtcars,
xlab="Number of Cylinders",
ylab="MPG",
main="Data Distribution Based on
The Number of Cylinders and MPG",
col="Purple"
)
Insights: We can see here on the dataset
that the more cylinders a car has, the
more likely that car has lower MPG.
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NIM: 2602107650

Types of Charts Create your own charts and write the insights! Distribution Script: par ( cex=1. ) hist(mtcars$mpg, col="Red", breaks=9, xlab="MPG", ylab="Frequency", main="Distribution of Cars' MPG" ) Insights: Based on the mtcars dataset, there are many cars that have Miles/(US) Gallon (MPG) in the range of 15 to 21. Script: par ( cex=0. ) boxplot(mpg ~ cyl, data = mtcars, xlab="Number of Cylinders", ylab="MPG", main="Data Distribution Based on The Number of Cylinders and MPG", col="Purple" ) Insights: We can see here on the dataset that the more cylinders a car has, the more likely that car has lower MPG.

NIM: 2602107650

Composition Script: par ( cex=0. 8 ) x1 = sum(mtcars$cyl==4) x2 = sum(mtcars$cyl==6) x3 = sum(mtcars$cyl==8) y1 = (x1/(x1+x2+x3))* y2 = (x2/(x1+x2+x3))* y3 = (x3/(x1+x2+x3))* pie( c(y1, y2, y3), labels=c(paste(y1, "%"), paste(y2, "%"), paste(y 3 , "%")), main="Number of Cylinders Composition", col=rainbow( 3 ) ) legend("topleft", c("4 Cylinders", " Cylinders", "8 Cylinders"), fill=rainbow(3)) Insights: Based on the dataset, almost 50% of the cars have 8 cylinders while the other cars have only 4 or 6 cylinders. Script: par ( cex=0. 8 ) x1 = sum(mtcars$carb==1) x2 = sum(mtcars$carb==2) x3 = sum(mtcars$carb==3) x4 = sum(mtcars$carb==4) x5 = sum(mtcars$carb==6) x6 = sum(mtcars$carb==8) y1 = (x1/(x1+x2+x3+x4+x5+x6))* y2 = (x2/(x1+x2+x3+x4+x5+x6))* y3 = (x3/(x1+x2+x3+x4+x5+x6))* y4 = (x4/(x1+x2+x3+x4+x5+x6))* y5 = (x5/(x1+x2+x3+x4+x5+x6))* y6 = (x6/(x1+x2+x3+x4+x5+x6))* pie( c(y1, y2, y3, y4, y5, y6), labels=c(paste(y1, "%"), paste(y2, "%"), paste(y3, "%"), paste(y4, "%"), paste(y5, "%"), paste(y6, "%")), main="Number of Carburetor Barrels Composition", col=rainbow(6) ) legend("topleft", c("1", "2", "3", "4", "6", "8"), fill=rainbow(6)) Insights: Based on the data visualization above, we can see that cars with 4 carburetor barrels are the majority followed by cars with 2 and 1 carburetor barrels.

NIM: 2602107650

table, col=c("Red", "Blue", "Green"), xlab="Number of Gears", ylab="Frequency", main="Number of Cars Based on The Number of Gears", ) Insights: Based on the dataset, cars with 3 gears are the majority of cars followed by cars that have 4 and then 5 gears. xlab="Year", ylab="Sunspot Number", main="Sunspot Number from 1700 until 1988" ) Insights: We can see here from the dataset that the sunspot number kept changing up and down very often.