To gain a quick overview of the data, given below is summary of the variables of the “mtcars” dataset

str(mtcars)
## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
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

Question 1

Create a pie chart showing the proportion of cars from the mtcars data set that have different carb values.

ggplot(data=mtcars, 
       aes(x = factor(1), fill = factor(carb))) + 
       ylab("Proportions of cars that have different carb values") + xlab("") +
       geom_bar(width = 1) + 
       coord_polar(theta = "y")

Graphic Summary 1: We can see that there are 6 different carb values in mtcars (1,2,3,4,6,8). From the graph, approximately one-third of the cars have carb value 2, and another one-third have carb value 4. About half of that seem to have carb value 1, and carb value 6 and 8 make up the least proportions of the cars.

Question 2

Create a bar graph, that shows the number of each gear type in mtcars.

ggplot(data=mtcars, aes(x=gear)) + geom_bar(stat="count")

Graphic Summary 2: There are 3 types of gears in mtcars: Type-3, Type-4, and Type-5. There are 15 Type-3 cars, 12 Type-4 cars and 5 Type-5 cars,

Question 3

Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyl.

ggplot(mtcars, 
       aes(x = factor(cyl), fill = factor(gear))) +  
       xlab("Number of Cylinders'") +
       ylab("Number of Cars'") +
       geom_bar()

Graphic Summary 3: Four cylinder and 6 cylinder cars have more Type-4 geared cars than Type-3 and Type-5. Eight cylinder cars have more Type-3 geared cars, very less Type-5 gears and no Type-4 gears.

Question 4

Draw a scatter plot showing the relationship between wt and mpg.

ggplot(mtcars, aes(x = wt, y = mpg)) +
       xlab("Weight of the car") + ylab("Miles Per Gallon") +
       geom_point() +
       ggtitle("Relationship between Weight and MPG") +
       stat_smooth(method = "lm")

Graphic Summary 4: There is a clear indication that, heavier the car, lesser is its mileage per gallon. Wt and mpg and negatively correlated.

Question 5

Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.

ggplot(mtcars, aes(x = hp, y = mpg)) +
       xlab("Horse Power") + ylab("Miles Per Gallon") +
       geom_point() +
       ggtitle("Relationship between Horse Power and MPG") +
       stat_smooth(method = "lm")

Graphic Summary 5: This is a scatter plot depicting the relationship between mileage of the car with its horse power. It is common knowledge that, greater the horsepower, lesser the mileage, and this visualization ascertains that fact.