summary() in R
It is also a generic function that implements polymorphism in R. It is used to produce result summaries of the results of various model fitting functions.
Example 1:
R
# R program to illustrate# polymorphosim# Rainbow colors and let us see summary of itcolors <- c("violet", "indigo", "blue", "green", "yellow", "orange", "red")summary(colors) |
Output:
Length Class Mode 7 character character
Example 2:
Let us check for summarized results for state.region. In R it usually displays what are the regions available under “Northeast”, “South”, “North Central”, and “West”. Using summary() function either one can pass state.region as 1st parameter and as a second step, (optionally) pass “maxsum” argument. “maxsum” indicates how many levels should be shown for factors in output.
R
# R program to illustrate# polymorphosimstate.region# Provides summarised results under each regionsummary(state.region) # As maxsum is given as 3, totally we should have 3 regions# But here we have 4 regions and hence highest count region,# next highest count region is displayed and the other # regions are clubbed under Othersummary(state.region, maxsum = 3) |
Output:
> state.region
[1] South West West South West West
[7] Northeast South South South West West
[13] North Central North Central North Central North Central South South
[19] Northeast South Northeast North Central North Central South
[25] North Central West North Central West Northeast Northeast
[31] West Northeast South North Central North Central South
[37] West Northeast Northeast South North Central South
[43] South West Northeast South West South
[49] North Central West
Levels: Northeast South North Central West
> summary(state.region)
Northeast South North Central West
9 16 12 13
> summary(state.region, maxsum = 3)
South West (Other)
16 13 21
Example 3:
If the data set is very large then let’s have a look at how the summary() function works.
R
# R program to illustrate# polymorphosim# 10 different data sets are taken using stats::rnormx <- stats::rnorm(10)x# Let us cut the dataset to lie between -3 and 3 and # in this case, it will be# (-3,-2] (-2,-1] (-1,0] (0,1] (1,2] (2,3]c <- cut(x, breaks = -3:3)c# Summarized the available dataset under the given levelssummary(c) |
Output:
> x
[1] 0.66647846 -0.29140286 -0.29596477 -0.23432541 -0.02144178 1.56640107 0.64575227
[8] -0.23759734 0.73304657 -0.04201218
> c
[1] (0,1] (-1,0] (-1,0] (-1,0] (-1,0] (1,2] (0,1] (-1,0] (0,1] (-1,0]
Levels: (-3,-2] (-2,-1] (-1,0] (0,1] (1,2] (2,3]
> summary(c)
(-3,-2] (-2,-1] (-1,0] (0,1] (1,2] (2,3]
0 0 6 3 1 0
Till now, we have described the plot() and summary() function which is a polymorphic function. By means of different inputs, plot() function behavior is changing and producing outputs. Here we can see the polymorphism concept. Similarly, in summary(), function, by means of varying parameters, the same method is applicable to provide different statistical outputs. Now let’s create our own generic methods.
Polymorphism in R Programming
R language is evolving and it implements parametric polymorphism, which means that methods in R refer to functions, not classes. Parametric polymorphism primarily lets you define a generic method or function for types of objects you haven’t yet defined and may never do. This means that one can use the same name for several functions with different sets of arguments and from various classes. R’s method call mechanism is generics which allows registering certain names to be treated as methods in R, and they act as dispatchers.
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