Univariate descriptive statistics generate the frequency distribution of datasets. Moreover, they can be used to identify the obvious patterns in the data, and the characteristics of the variates, to provide a better understanding of the data from a holistic viewpoint. Additionally, they can provide information about the central tendency and descriptors of the skewness of individual cases. Therefore, it is common to see that univariate analysis is conducted at the beginning of the data exploration process.

To begin the exploration of data, we first load the dataset, mtcars, to an R session. From the data, we apply range, length, mean, median, sd, var, IQR, quantile, min, max, cumin, and cumax to obtain the descriptive statistic of the attribute, mpg. Then, we use the summary function to obtain summary information about mtcars.

Next, we obtain a frequency count of the categorical data (cyl). To obtain a frequency count of the numerical data, we use a stem plot to outline the data shape. Lastly, we use a histogram with the binwidth argument in 2 to generate a plot similar to the stem-and-leaf plot.