Boxplots can be used to get the minimum, median, maximum, and quartiles of the data being plotted. Boxplots also help to identify outliers:

 
b = boxplot(Salaries$salary~Salaries$rank, col=brewer.pal(8,"Set1")) 
 
names(b) 
# [1] "stats" "n"     "conf"  "out"   "group" "names" 
 
b$stats 
#         [,1]     [,2]     [,3] 
# [1,] 63100.0  62884.0  57800.0 
# [2,] 74000.0  82350.0 105890.0 
# [3,] 79800.0  95626.5 123321.5 
# [4,] 88597.5 104331.5 145098.0 
# [5,] 97032.0 126431.0 194800.0 
# attr(,"class") 
# AsstProf  
# "integer"  

The output is as follows:

The pros and cons of using boxplots are as follow:

Pros:

Boxplots can be used to get an understanding of the distribution, the outliers, and skewness of the data through an intuitive plot.

Cons:

• Can only be used with numerical variables
• Due to summarization, the original data is not displayed in the charts