Find the mtcars data in R. This is the dataset that you will use to create your graphics.

  1. Creating a pie chart showing the proportion of cars from the mtcars data set that have different cylinder (cyl) values.
library(plotrix)
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
cyl_unique=unique(mtcars$cyl)
cyl_table=table(mtcars$cyl)
cyl_4 <- length(which(mtcars$cyl == 4))
cyl_6 <- length(which(mtcars$cyl == 6))
cyl_8 <- length(which(mtcars$cyl == 8))
cyl_label=c(cyl_4,cyl_6,cyl_8)
cyl_prop=sprintf("%1.1f%%",((cyl_table/sum(cyl_table))*100))
cyl_label=paste(cyl_label,cyl_prop,sep ="\n")
pie3D(cyl_table,labels=cyl_label,explode = 0.1,main="Proportions of Cylinder Values for different cars")

The above Pie chart explains how the 3 different types of cylinder types of the “mtcars” dataset are present alongside it’s Proportions.

  1. Creating a bar graph, that shows the number of each carb type in mtcars.
carb_table=table(mtcars$carb)
carb_names=names(carb_table)
barplot(carb_table,main="Number of each Carb types",xlab = "Number of Carbs",ylab = "Frequency of cars",names.arg = carb_names,col=rainbow(length(carb_names)) )

In the above Bar graph we can visualize the number of carbs types and the count of number of cars.

  1. Next showing a stacked bar graph of the number of each gear type and how they are further divided out by cyl.
cylgear_table=table(mtcars$cyl,mtcars$gear)
cyl.gear = table(mtcars$cyl, mtcars$gear)
barplot(cylgear_table, main="Number of each gear type alongside Cylinders", xlab="Number of Gears",ylab="Number of Cars",legend = rownames(cylgear_table))

In the above stacked Bar graph we can clearly visualize how many number of cars are distributed for each number of Gears alongside each number of cylinders.

  1. Drawing a scatter plot showing the relationship between wt and mpg.
library("ggplot2")
ggplot(data = mtcars, mapping = aes(x = wt,y=mpg)) +
    geom_point()+
  ggtitle("Relationship between wt and mpg") +
    xlab("wt")+
    ylab("mpg")+
    geom_smooth(method = 'lm', color = 'black')+
  theme(plot.title = element_text(hjust = 0.5))

In the above Scatterplot we can clearly visualize and interpret that the datapoints of wt and mpg are having negative linear trend type of relationship.

  1. Designing a visualization of your choice using the data and write a brief summary about why you chose that visualization.
library(corrplot)
plot(mtcars)

corrplot.mixed(cor(mtcars),lower.col = "black", number.cex = .7)

I choose the above two visualization techniques so as to explore the datapoints and it’s characteristics of the “mtcars” datasets alongside the correlation plot which explains the interaction between variable to variable.