How to do it...

Perform the following steps:

1. First, load the mtcars data into a DataFrame with a variable named mtcars:

        > data(mtcars)  

1. To obtain the vector range, the range function will return the lower and upper bound of the vector:

        > range(mtcars$mpg)
        Output:
    
        [1] 10.4 33.9

1. Compute the length of the variable:

        > length(mtcars$mpg)
        Output:
    
        [1] 32  

1. To obtain the mean of mpg:

        > mean(mtcars$mpg)
        Output:
    
        [1] 20.09062  

1. To obtain the median of the input vector:

        > median(mtcars$mpg)
        Output:
    
        [1] 19.2

1. To obtain the standard deviation of the input vector:

        > sd(mtcars$mpg)
        Output:
    
        [1] 6.026948  

1. To obtain the variance of the input vector:

        > var(mtcars$mpg)
        Output:
    
        [1] 36.3241 

1. The variance can also be computed with the square of the standard deviation:

        > sd(mtcars$mpg) ^ 2
        Output:
    
        [1] 36.3241  

1. To obtain the Interquartile Range (IQR):

        > IQR(mtcars$mpg)
        Output:
    
        [1] 7.375 

1. To obtain the quantile:

        > quantile(mtcars$mpg,0.67)
        Output:
    
        67% 
        21.4

1. To obtain the maximum of the input vector:

        > max(mtcars$mpg)
        Output:
    
        [1] 33.9  

1. To obtain the minima of the input vector:

        > min(mtcars$mpg)
        Output:
    
        [1] 10.4  

1. To obtain a vector with elements that are the cumulative maxima:

        > cummax(mtcars$mpg)
        Output:
    
        [1] 21.0 21.0 22.8 22.8 22.8 22.8 22.8 24.4 24.4 24.4 24.4 24.4
        24.4 24.4 24.4 24.4    
        [17] 24.4 32.4 32.4 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9
        33.9 33.9 33.9 33.9  

1. To obtain a vector with elements that are the cumulative minima:

        > cummin(mtcars$mpg)
        Output:
    
        [1] 21.0 21.0 21.0 21.0 18.7 18.1 14.3 14.3 14.3 14.3 14.3 14.3
        14.3 14.3 10.4 10.4
        [17] 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4
        10.4 10.4 10.4 10.4

1. To summarize the dataset, you can apply the summary function:

        > summary(mtcars)

1. To obtain a frequency count of the categorical data, take cyl of mtcars as an example:

        > table(mtcars$cyl)
        Output:
    
        4  6  8 
        11  7 14  

1. To obtain a frequency count of numerical data, you can use a stem plot to outline the data shape; stem produces a stem-and-leaf plot of the given values:

        > stem(mtcars$mpg)
        Output:
          The decimal point is at the |

          10 | 44
          12 | 3
          14 | 3702258
          16 | 438
          18 | 17227
          20 | 00445
          22 | 88
          24 | 4
          26 | 03
          28 | 
          30 | 44
          32 | 49

1. You can use a histogram of ggplot to plot the same stem-and-leaf figure:

        > library(ggplot2)
        > qplot(mtcars$mpg, binwidth=2)

        > pie(table(mtcars$cyl), main="Number of Cylinders")