(well, just S3, S4 and R7)
Michael Jones
24 August 2022
Have you ever wondered about print()
and summary()
?
print()
[1] 1 2 3 4 5
Call:
lm(formula = mpg ~ hp, data = mtcars)
Coefficients:
(Intercept) hp
30.09886 -0.06823
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
summary()
Min. 1st Qu. Median Mean 3rd Qu. Max.
1 2 3 3 4 5
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
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
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
but:
data.frame()
and start talking about bond
, equity
or patient
, hospital
dog.bark(at = "postman")
interact(dog, postman)
human <- function(name, dob) { output <- list( name = name, dob = dob ) class(output) <- "human" output } alice <- human(name = "Alice", dob = as.Date("1980-04-10")) alice
human <- function(name, dob) { output <- list( name = name, dob = dob ) class(output) <- "human" output } alice <- human(name = "Alice", dob = as.Date("1980-04-10")) alice
human <- function(name, dob) { output <- list( name = name, dob = dob ) class(output) <- "human" output } alice <- human(name = "Alice", dob = as.Date("1980-04-10")) alice
human <- function(name, dob) { output <- list( name = name, dob = dob ) class(output) <- "human" output } alice <- human(name = "Alice", dob = as.Date("1980-04-10")) alice
human <- function(name, dob) { output <- list( name = name, dob = dob ) class(output) <- "human" output } alice <- human(name = "Alice", dob = as.Date("1980-04-10")) alice
human <- function(name, dob) { output <- list( name = name, dob = dob ) class(output) <- "human" output } alice <- human(name = "Alice", dob = as.Date("1980-04-10")) alice
$name
[1] "Alice"
$dob
[1] "1980-04-10"
attr(,"class")
[1] "human"
print.human <- function(h) {
age <- (as.Date(Sys.time()) - h$dob) / 365
cat("A human called", h$name, "who is", age, "years old")
}
print(alice)
A human called Alice who is 42.39726 years old
print.<class>
print(alice)
print()
something"human"
print.human()
functionprint.human(alice)
summary()
is a generic, summary.numeric()
and summary.lm()
are methods# Generic introduce <- function(x, ...) { UseMethod("introduce") } # Method introduce.human <- function(h) { cat("Hello, my name is", h$name) } introduce(alice)
# Generic introduce <- function(x, ...) { UseMethod("introduce") } # Method introduce.human <- function(h) { cat("Hello, my name is", h$name) } introduce(alice)
# Generic introduce <- function(x, ...) { UseMethod("introduce") } # Method introduce.human <- function(h) { cat("Hello, my name is", h$name) } introduce(alice)
# Generic introduce <- function(x, ...) { UseMethod("introduce") } # Method introduce.human <- function(h) { cat("Hello, my name is", h$name) } introduce(alice)
Hello, my name is Alice
introduce(bob)
?hint:
an object is whatever it’s class
says it is:
Harder to enforce quality.
And S3 only does single dispatch. Methods are only called based on the first argument.
setClass( "Human", slots = c( name = "character", dob = "Date" ) )
setClass( "Human", slots = c( name = "character", dob = "Date" ) )
setClass( "Human", slots = c( name = "character", dob = "Date" ) )
setClass( "Human", slots = c( name = "character", dob = "Date" ) )
setClass( "Human", slots = c( name = "character", dob = "Date" ) )
setClass( "Human", slots = c( name = "character", dob = "Date" ) )
We get some error checking for free:
Error in validObject(.Object): invalid class "Human" object: invalid object for slot "dob" in class "Human": got class "numeric", should be or extend class "Date"
You can do:
Note we’re using @
to access slots, rather than $
.
But it’s better to make getters and setters:
[1] "name"
[1] "name<-"
[1] "favPackage"
We can inherit directly
We can inherit directly
Generic:
setGeneric( "Meets", function(x, y) standardGeneric("Meets"), signature = c("x", "y") )
setGeneric( "Meets", function(x, y) standardGeneric("Meets"), signature = c("x", "y") )
setGeneric( "Meets", function(x, y) standardGeneric("Meets"), signature = c("x", "y") )
[1] "Meets"
Method:
setMethod( "Meets", signature = c("Human", "Human"), function(x, y) { cat(name(x), "says hello to", name(y)) }) Meets(alice, bob)
setMethod( "Meets", signature = c("Human", "Human"), function(x, y) { cat(name(x), "says hello to", name(y)) }) Meets(alice, bob)
setMethod( "Meets", signature = c("Human", "Human"), function(x, y) { cat(name(x), "says hello to", name(y)) }) Meets(alice, bob)
Alice Bloggs says hello to Bob
Is a powerful way to allow control how many different classes interact
[1] Meets,Human,Human-method Meets,Ruser,Human-method Meets,Ruser,Ruser-method
see '?methods' for accessing help and source code
human <- new_class( "human", properties = list( name = class_character, dob = class_Date ) ) alice <- human( name = "alice", dob = as.Date("1980-04-10")) alice
human <- new_class( "human", properties = list( name = class_character, dob = class_Date ) ) alice <- human( name = "alice", dob = as.Date("1980-04-10")) alice
human <- new_class( "human", properties = list( name = class_character, dob = class_Date ) ) alice <- human( name = "alice", dob = as.Date("1980-04-10")) alice
human <- new_class( "human", properties = list( name = class_character, dob = class_Date ) ) alice <- human( name = "alice", dob = as.Date("1980-04-10")) alice
<human>
@ name: chr "alice"
@ dob : Date[1:1], format: "1980-04-10"
It seems that @
is not discouraged
introduce <- new_generic( "introduce", "x" ) method(introduce, human) <- function(x) { cat("Hello, I'm", x@name) } introduce(alice)
introduce <- new_generic( "introduce", "x" ) method(introduce, human) <- function(x) { cat("Hello, I'm", x@name) } introduce(alice)
introduce <- new_generic( "introduce", "x" ) method(introduce, human) <- function(x) { cat("Hello, I'm", x@name) } introduce(alice)
introduce <- new_generic( "introduce", "x" ) method(introduce, human) <- function(x) { cat("Hello, I'm", x@name) } introduce(alice)
Hello, I'm alice
r_user <- new_class( "ruser", parent = human, properties = list( favourite_package = class_character ) ) bob <- r_user( name = "Bob", dob = as.Date("1985-10-13"), favourite_package = "ggplot2" )
r_user <- new_class( "ruser", parent = human, properties = list( favourite_package = class_character ) ) bob <- r_user( name = "Bob", dob = as.Date("1985-10-13"), favourite_package = "ggplot2" )
r_user <- new_class( "ruser", parent = human, properties = list( favourite_package = class_character ) ) bob <- r_user( name = "Bob", dob = as.Date("1985-10-13"), favourite_package = "ggplot2" )
meets <- new_generic( "meets", c("x", "y") ) method(meets, list(human, human)) <- function(x, y) {cat(x@name, "greets", y@name)} method(meets, list(r_user, r_user)) <- function(x, y) {cat(x@name, "tells", y@name, "all about", x@favourite_package)}
meets <- new_generic( "meets", c("x", "y") ) method(meets, list(human, human)) <- function(x, y) {cat(x@name, "greets", y@name)} method(meets, list(r_user, r_user)) <- function(x, y) {cat(x@name, "tells", y@name, "all about", x@favourite_package)}
meets <- new_generic( "meets", c("x", "y") ) method(meets, list(human, human)) <- function(x, y) {cat(x@name, "greets", y@name)} method(meets, list(r_user, r_user)) <- function(x, y) {cat(x@name, "tells", y@name, "all about", x@favourite_package)}