Questions

summary(mtcars)
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000
#To understand the dataset "mtcars" and have an overview of the different variables.

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

  1. Create a pie chart using ggplot showing the proportion of cars from the mtcars data set that have different cylinder (cyl) values.
library(ggplot2)
Cylinder = factor(mtcars$cyl)
piechart <- ggplot(mtcars, aes(x = factor(1), fill = Cylinder)) + geom_bar()
piechart + coord_polar(theta = "y")

#From the pie chart, we can know that there are three types of cylinders 4, 6, & 8. Of which 8 cylinder cars are more in number.
  1. Create a bar graph using ggplot, that shows the number of each carb type in mtcars.
barchart <- ggplot(mtcars, aes(x = carb)) +   geom_bar(width = 0.2)
barchart + ylim(0,10) + xlab("Carburetors") + 
ylab("Count")

#From the barplot we see that there are cars with distinct number of carburetors 1, 2, 3, 4, 6, & 8. Among them, cars with carburetors with 2 & 4 are the highest at 10 cars each and 6 & 8 carburetors are the least with 1 car each.
  1. Next show a stacked bar graphusing ggplot of the number of each gear type and how they are further divided out by cyl.
# place the code to import graphics here
ggplot(mtcars,aes(x=factor(gear),fill=Cylinder)) +
xlab("gear") + 
ylab("cylinder") + 
geom_bar()

#Among the three different number of gear cars (3, 4, & 5), cars with 4 gear do not have 8 cylinder cars. Whereas 3 gear and 5 gear cars have all the three types of cylinders' cars and 8 cylinder cars are at the highest count. However, 5 gears cars equal number of 4 cylinder cars and 8 cylinder cars.
  1. Draw a scatter plot using ggplot showing the relationship between wt and mpg.
#library(ggplot2)
# Basic scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point() +
xlab("Weight (1000 lbs)") + 
ylab("Mile/(US) gallon")

#We realize from the scatter plot that as the weight of the car increases, the mpg of the car is reducing.
  1. Design a visualization of your choice using ggplot using the data and write a brief summary about why you chose that visualization.
Engine_Shape = factor(mtcars$vs,
                           levels = 0:1,
                           labels = c("V-Shaped","Straight"))
ggplot(mtcars,aes(x=factor(Engine_Shape),fill=Cylinder)) +
xlab("Engine Shape") + 
ylab("Cylinder") + 
geom_bar(width = 0.5)

#I'm interested to visualize the shape of the engine and the number of cylinders in each shape on a stacked bar chart. From the chart we can observe that 8 cylinder cars are not available in the straight engine shape but available in v-shaped engines. We can assume that making a 8 cylinder car on a straight shape engine might be design constraint.