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

[1]Create a pie chart showing the proportion of cars from the mtcars data set that have different carb values.
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
carb_prop<-table(mtcars$carb)
lbls <- names(carb_prop)
carb_pct<- carb_prop/sum(carb_prop)*100
lbls <- paste(lbls, carb_pct, sep = ", ") 
lbls <- paste(lbls,"%") 
pie(carb_prop,labels = lbls,col=rainbow(length(lbls)), main="Car proportion by different carb values")

Summary: From this pie chart we can see that most cars have carb2 or carb4(31.25% respectively), followed by carb1(21.875%). Carb3, carb6 and carb8 are minority.

[2].Create a bar graph, that shows the number of each gear type in mtcars.
barplot(table(mtcars$gear),main="Car Distribution by gear number",xlab = "Number of Gears", ylab = "Number of cars")

Summary: In the mtcars database, most cars have 3 gear, followed by 4 gear and 5 gear. No car has other gear number.

[3].Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyl.
counts <- table(mtcars$cyl, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and Cylinders",
  xlab="Number of Gears", names.arg=c("3 Gears", "4 Gears", "5   Gears"),
  ylab="Number of Cars", col=c("purple","red","blue"),
   legend = rownames(counts))

Summary: Most 3 gear cars have 8 cylinders. None of the 4 gear car has 8 cylinders, and the number of 4 cylinders cars are twice as the number of 6 cylinders cars in the same category. 5 gear cars are possible to carry either 4, 6 or 8 cylinders.

[4] Draw a scatter plot showing the relationship between wt and mpg.
plot(mtcars$wt, mtcars$mpg, main="Scatterplot of Weight and Miles Per Gallon", 
    xlab="Car Weight ", ylab="Miles Per Gallon ",pch=21,bg="red")

Summary: Car weights and MPG have negative correlation. While car weights increasing, MPG decreasing.

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

Inspired by the question 4, I’m interested in buliding a 3-factor Scatterplot using the weight, MPG and cylinder number. From the visualization plot, there is a trend that high cylinder cars (especially 8 cylinder) tend to be heavier and have lower MPG. We can conclude that MPG has negative correlation to car weight and cylinder, while cylinder and weight have positive correlation to each other.

library(ggplot2)
qplot(wt, mpg, data = mtcars, col = cyl,
      main="Relationship between Weight, MPG and Cylinders", xlab="Car Weight ", ylab="Miles Per Gallon ")