class: center, middle, inverse, title-slide # Programming Tools in Data Science ## Lecture #8: Function II ### Samuel Orso ### 8 November 2022 --- # Function <img src="images/lego-674354_1280.jpg" width="600" height="400" style="display: block; margin: auto;" /> --- # ''Everything is a function call'' - A function has three components: **arguments**, a **body** and an **environment**. - Signalling conditions: **errors** (sever problem), **warnings** (mild problem), **messages** (informative). - Lexical scoping: how to find a value associated with a name (**dynamic lookup** and **name masking**). - Environments (**current**, **global**, **empty**, **execution**, **package**). - Function composition through **nesting** or **piping**. --- # Ready to continue? &nbsp; <center> <iframe src="https://giphy.com/embed/xT8qBvH1pAhtfSx52U" width="480" height="270" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/baby-sleepy-face-first-xT8qBvH1pAhtfSx52U">via GIPHY</a></p> </center> --- # S3 OOP system * Object-oriented programming (OOP) is one of the most popular programming paradigm. * The type of an object is a **class** and a function implemented for a specific class is a **method**. * It is mostly used for **polymorphism**: the function interface is separated from its implementation. In other words, the function behaves differently according to the class. * This is related to the idea of **encapsulation**: the object interface is separated from its internal structure. In other words, the user doesn't need to worry about details of an object. Encapsulation avoids spaghetti code (see [Toyota 2013 case](http://archive.wikiwix.com/cache/index2.php?url=https%3A%2F%2Fwww.usna.edu%2FAcResearch%2F_files%2Fdocuments%2FNASEC%2F2016%2FCYBER%2520-%2520Toyota%2520Unintended%2520Acceleration.pdf)). * `R` has several OOP systems: S3, S4, R6, ... * S3 OOP system is the first R OOP system, it is rather informal (easy to modify) and widespread. --- # Example of polymorphism ```r summary(cars$speed) ``` ``` ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 4.0 12.0 15.0 15.4 19.0 25.0 ``` ```r summary(lm(cars$speed~cars$dist)) ``` ``` ## ## Call: ## lm(formula = cars$speed ~ cars$dist) ## ## Residuals: ## Min 1Q Median 3Q Max ## -7.5293 -2.1550 0.3615 2.4377 6.4179 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 8.28391 0.87438 9.474 1.44e-12 *** ## cars$dist 0.16557 0.01749 9.464 1.49e-12 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 3.156 on 48 degrees of freedom ## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438 ## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12 ``` --- What is happening? ```r sloop::s3_dispatch(summary(cars$speed)) ``` ``` ## summary.double ## summary.numeric ## => summary.default ``` ```r sloop::s3_dispatch(summary(lm(cars$speed~cars$dist))) ``` ``` ## => summary.lm ## * summary.default ``` * `*` indicates the method is defined; * `=>` indicates the method is used. ```r class(cars$speed) ``` ``` ## [1] "numeric" ``` ```r class(lm(cars$speed~cars$dist)) ``` ``` ## [1] "lm" ``` --- What is happening? ```r summary ``` ``` ## function (object, ...) ## UseMethod("summary") ## <bytecode: 0x561be263f920> ## <environment: namespace:base> ``` ```r head(summary.default) ``` ``` ## ## 1 function (object, ..., digits, quantile.type = 7) ## 2 { ## 3 if (is.factor(object)) ## 4 return(summary.factor(object, ...)) ## 5 else if (is.matrix(object)) { ## 6 if (missing(digits)) ``` ```r head(summary.lm) ``` ``` ## ## 1 function (object, correlation = FALSE, symbolic.cor = FALSE, ## 2 ...) ## 3 { ## 4 z <- object ## 5 p <- z$rank ## 6 rdf <- z$df.residual ``` --- # `...` * Using `...` is a special argument which allows to pass any number of additional arguments. * You can catch it into a list: ```r f <- function(...){list(...)} f(a=1, b=2) ``` ``` ## $a ## [1] 1 ## ## $b ## [1] 2 ``` * It is useful for example when defining a **generic** method. * The role of a generic is to **dispatch**: find the specific method for a **class**. --- * A generic is easy to create ```r my_new_generic <- function(x, ...) { UseMethod("my_new_generic") } ``` * Then, you can create methods ```r # default method my_new_generic.default <- function(x, ...){ print("this is default method") } # method for object of class `lm` my_new_generic.lm <- function(x, ...){ print("this is method for class `lm`") } ``` ```r my_new_generic(cars$speed) ``` ``` ## [1] "this is default method" ``` ```r my_new_generic(lm(cars$speed~cars$dist)) ``` ``` ## [1] "this is method for class `lm`" ``` --- * check the dispatch ```r sloop::s3_dispatch(my_new_generic(cars$speed)) ``` ``` ## my_new_generic.double ## my_new_generic.numeric ## => my_new_generic.default ``` ```r sloop::s3_dispatch(my_new_generic(lm(cars$speed~cars$dist))) ``` ``` ## => my_new_generic.lm ## * my_new_generic.default ``` * Why are there several output when running `sloop::s3_dispatch(my_new_generic(cars$speed))`? --- * check the dispatch ```r sloop::s3_dispatch(my_new_generic(cars$speed)) ``` ``` ## my_new_generic.double ## my_new_generic.numeric ## => my_new_generic.default ``` ```r sloop::s3_dispatch(my_new_generic(lm(cars$speed~cars$dist))) ``` ``` ## => my_new_generic.lm ## * my_new_generic.default ``` * Why are there several output when running `sloop::s3_dispatch(my_new_generic(cars$speed))`? ```r sloop::s3_class(cars$speed) ``` ``` ## [1] "double" "numeric" ``` * `cars$speed` has class "numeric" and implicit class "double" (check `typeof(cars$speed)`). --- # Inheritance * An object can have several classes like a child has parents and ancestors. For example: ```r class(glm(cars$speed~cars$dist)) ``` ``` ## [1] "glm" "lm" ``` * If a method is not found for the 1st class, then `R` looks for the 2nd class and so on. ```r sloop::s3_dispatch(summary(glm(cars$speed~cars$dist))) ``` ``` ## => summary.glm ## * summary.lm ## * summary.default ``` ```r sloop::s3_dispatch(plot(glm(cars$speed~cars$dist))) ``` ``` ## plot.glm ## => plot.lm ## * plot.default ``` --- # Create your own S3 class * S3 is rather informal and straightforward (be careful!) ```r set.seed(123) # for reproducibility image <- matrix(rgamma(100, shape = 2), 10, 10) class(image) <- "pixel" ``` * `class` is a special attribute. ```r attributes(image) ``` ``` ## $dim ## [1] 10 10 ## ## $class ## [1] "pixel" ``` --- * Alternatively, it is neater to create a S3 object using `structure` ```r set.seed(123) # for reproducibility image <- structure( matrix(rgamma(100, shape = 2), 10, 10), class = "pixel" ) ``` * There is no method yet for this class ```r plot(image) ``` <img src="lecture08_function_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" /> --- ```r sloop::s3_dispatch(plot(image)) ``` ``` ## plot.pixel ## => plot.default ``` ```r plot ``` ``` ## function (x, y, ...) ## UseMethod("plot") ## <bytecode: 0x561be76faaa8> ## <environment: namespace:base> ``` --- * Generally, it is a bad practice to create methods for a generic you don't own. * But it is common for generics with `...` arguments such as `mean`, `sum`, `summary`, `plot`, `coefficients`, ... ```r plot.pixel <- function (x, ...) { heatmap(x, ...) } sloop::s3_dispatch(plot(image)) ``` ``` ## => plot.pixel ## * plot.default ``` --- * New plot for class `pixel` ```r plot(image) ``` <img src="lecture08_function_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" /> --- * Thanks to `...`, you can pass other arguments (be careful) (see for meaningful arguments `?heatmap`) ```r plot(image, col = cm.colors(256), xlab = "x axis", ylab = "y axis") ``` <img src="lecture08_function_files/figure-html/unnamed-chunk-22-1.png" style="display: block; margin: auto;" /> --- # To go further * More details and examples in the book [An Introduction to Statistical Programming Methods with R](https://smac-group.github.io/ds/section-functions.html) * More advanced remarks in [Advanced R](https://adv-r.hadley.nz/index.html), Chapters 6, 7 and 8 for functions, Chapters 12 to 16 for object-oriented programming. --- # In-class exercises (10 min) 1. Create a `summary` function for the class `pixel`. Verify the dispatch before and after implementing the summary function. 2. Describe the difference between `t.test()` and `t.data.frame()`. What happens if you run the following code and why? ```r x <- structure(1:10, class = "test") t(x) ``` --- <ol start="3"> <li>Read the documentation for <code class="remark-inline-code">UseMethod()</code> and explain why the following code returns the results that it does.</li></ol> ```r g <- function(x) { x <- 10 y <- 10 UseMethod("g") } g.default <- function(x) c(x = x, y = y) x <- 1 y <- 1 g(y) ``` <ol start="4"><li> What do you expect this code to return? What does it actually return and why?</li></ol> ```r generic2 <- function(x) UseMethod("generic2") generic2.a1 <- function(x) "a1" generic2.a2 <- function(x) "a2" generic2.b <- function(x) { class(x) <- "a1" NextMethod() } generic2(structure(list(), class = c("b", "a2"))) ``` --- class: sydney-blue, center, middle # Question ? .pull-down[ <a href="https://ptds.samorso.ch/"> .white[<svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M369.9 97.9L286 14C277 5 264.8-.1 252.1-.1H48C21.5 0 0 21.5 0 48v416c0 26.5 21.5 48 48 48h288c26.5 0 48-21.5 48-48V131.9c0-12.7-5.1-25-14.1-34zM332.1 128H256V51.9l76.1 76.1zM48 464V48h160v104c0 13.3 10.7 24 24 24h104v288H48z"></path></svg> website] </a> <a href="https://github.com/ptds2022/"> .white[<svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> GitHub] </a> ]