library(datasets)
data(mtcars)
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
  1. Draw a pie chart showing the proportion of cars from the mtcars data set that have different carb values. ‘Printing’ the dataset, and creating a table isolating the ‘carb’ variable.
print(mtcars)
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
factor(mtcars$carb)
##  [1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2
## Levels: 1 2 3 4 6 8
w = table(mtcars$carb)
w
## 
##  1  2  3  4  6  8 
##  7 10  3 10  1  1

We could also use the easier way with the ‘plyr’ package and the function count().

library(plyr)
## Warning: package 'plyr' was built under R version 3.4.4
count(mtcars, 'carb')
##   carb freq
## 1    1    7
## 2    2   10
## 3    3    3
## 4    4   10
## 5    6    1
## 6    8    1
carb.frequency <- table(mtcars$carb)
lbls <- names(carb.frequency)
pct <- round(carb.frequency/sum(carb.frequency)*100)
lbls <- paste(lbls, pct)
lbls <- paste(lbls,"%",sep="")
pie(carb.frequency,labels = lbls,col=rainbow(length(lbls)), main="Cars/Carb Distribution")

We notice that there is no cars with 5 or 7 carburetors. So, we can create the pie chart from here. There are 32 observations in the dataset, so: 7/32 = 0.21875 = 21.875% 10/32 = 0.3125 = 31.25% 3/32 = 0.09375 = 9.375% 10//32 = 0.3125 = 31.25% 1/32 = 0.03125 = 3.125% 1/32 = 0.03125 = 3.125%

knitr::include_graphics("image1.jpeg")

  1. Draw a bar graph, that shows the number of each gear type in mtcars. Using the ‘count’ function:
count(mtcars, 'gear')
##   gear freq
## 1    3   15
## 2    4   12
## 3    5    5
count <- table(mtcars$gear)
barplot(count, main="Car Distribution", xlab="Gears",ylab="Cars",names.arg=c("3", "4", "5"),cex.names=0.8,col=c("green","red","blue"))

knitr::include_graphics("image4.jpeg")

This bar chart indicates that there are more 3 gear cars (15) than 4 gear cars (12) and 5 gear cars (5).

  1. 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="Cars by Gears / Cylinders",
      xlab="Gears", 
      names.arg=c("3", "4", "5"),
      cex.names=0.8,
      ylab="Cars",
      col=c("red","blue","green"),
      legend = rownames(counts))

Cars with 3 gears and 8 cylinders are equal to the total of 4 gear cars. The number of cars with 4 gears and 4 cylinders and the double of cars with 4 gears and 6 cylinders. There is the same number of car with 3 gears and 4 cylinders as cars with 5 gears and 6 cylinders.

knitr::include_graphics("image3.jpeg")

  1. Draw a scatter plot showing the relationship between wt and mpg.
plot(mtcars$wt, mtcars$mpg, main="Weight VS Mpg", 
      xlab="Weight", ylab="MPG", pch=16)
abline(lm(mtcars$mpg~mtcars$wt), col="green") 
lines(lowess(mtcars$wt,mtcars$mpg), col="blue")

The green line represents the linear regression, and the blue line represents also the lowess. Both lines show a negative correlation between the weight of the cars and the consumption in miles per gallon. The heavier a car is, the more fuel it consumes.

  1. Design a visualization of your choice using the data.
plot(mtcars$hp, mtcars$mpg, main="Horsepower vs MPG", xlab="Horsepower", ylab="MPG", pch=21, bg="blue")