12.2 OOP: Big Ideas
- Polymorphism. Function has a single interface (outside), but contains (inside) several class-specific implementations.
# imagine a function with object x as an argument
# from the outside, users interact with the same function
# but inside the function, there are provisions to deal with objects of different classes
some_function <- function(x) {
if is.numeric(x) {
# implementation for numeric x
} else if is.character(x) {
# implementation for character x
} ...
}
Example of polymorphism
# data frame
summary(mtcars[,1:4])
#> 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
# statistical model
lin_fit <- lm(mpg ~ hp, data = mtcars)
summary(lin_fit)
#>
#> Call:
#> lm(formula = mpg ~ hp, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -5.7121 -2.1122 -0.8854 1.5819 8.2360
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 30.09886 1.63392 18.421 < 2e-16 ***
#> hp -0.06823 0.01012 -6.742 1.79e-07 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.863 on 30 degrees of freedom
#> Multiple R-squared: 0.6024, Adjusted R-squared: 0.5892
#> F-statistic: 45.46 on 1 and 30 DF, p-value: 1.788e-07
- Encapsulation. Function “encapsulates”–that is, encloses in an inviolate capsule–both data and how it acts on data. Think of a REST API: a client interacts with with an API only through a set of discrete endpoints (i.e., things to get or set), but the server does not otherwise give access to its internal workings or state. Like with an API, this creates a separation of concerns: OOP functions take inputs and yield results; users only consume those results.