`x[, 1]` | `x[1, , drop=FALSE]`

`x[, 1, drop=FALSE]` Factor | `x[1:4, drop=TRUE]` | `x[1:4]` Data frame | `x[, 1]`

`x[[1]]` | `x[, 1, drop=FALSE]`

`x[1]` By preserving we mean retaining the attributes. It is good practice to use `drop = FALSE` when subsetting a n-dimensional object, where $n > 1$.

The drop argument for factors controls whether the levels are preserved or not. It defaults to `drop = FALSE`. --- ## Subsetting data frames Recall that data frames are lists with attributes `class`, `names`, `row.names`. Thus, they can be subset using `[`, `[[`, and `$`. They also support matrix-style subsetting (specify rows and columns to subset). ```{r} df <- data.frame(coin = c("BTC", "ETH", "XRP"), price = c(10417.04, 172.52, .26), vol = c(21.29, 8.07, 1.23), stringsAsFactors = FALSE) ``` -- What will the following return? .pull-left[ ```{r eval=FALSE} df[1] df[c(1, 3)] df[1:2, 3] df[, "price"] ``` ] .pull-right[ ```{r eval=FALSE} df[[1]] df[["vol"]] df[[c(1, 3)]] df[[1, 3]] ``` ] ??? What will the following return? .tiny[ .pull-left[ ```{r} df[1] df[c(1, 3)] df[1:2, 3] df[, "price"] ``` ] .pull-right[ ```{r} df[[1]] df[["vol"]] df[[c(1, 3)]] df[[1, 3]] ``` ] ] --- class: inverse, center, middle # Subsetting extras --- ## Subassignment Indexing can occur on the right-hand-side of an expression for extraction or on the left-hand-side for replacement. ```{r} x <- c(1, 4, 7) ``` ```{r} x[2] <- 2 x ``` -- ```{r} x[x %% 2 != 0] <- x[x %% 2 != 0] + 1 x ``` -- ```{r} x[c(1, 1, 1, 1)] <- c(0, 7, 2, 3) ``` What is `x` now? -- ```{r} x ``` ??? Subassignment is done sequentially, so if an index is specified more than once the latest assigned value for an index will result. --- .pull-left[ ```{r} x <- 1:6 x[c(2, NA)] <- 1 x ``` ```{r} x <- 1:6 x[c(TRUE, NA)] <- 1 x ``` ] .pull-right[ ```{r} x <- 1:6 x[c(-1, -3)] <- 3 x ``` ```{r} x <- 1:6 x[] <- 6:1 x ``` ] --- ## Adding list and data frame elements ```{r} df <- data.frame( x = rnorm(4), y = rt(4, df = 1) ) ``` -- .tiny[ ```{r} df$z <- rchisq(4, df = 1) df ``` ] -- .tiny[ ```{r} df["a"] <- rexp(4) df ``` ] --- ## Removing list and data frame elements ```{r} df <- data.frame(coin = c("BTC", "ETH", "XRP"), price = c(10417.04, 172.52, .26), vol = c(21.29, 8.07, 1.23), stringsAsFactors = FALSE) ``` ```{r} df["coin"] <- NULL str(df) df[[1]] <- NULL str(df) df$vol <- NULL str(df) ``` --- ## Exercises Use the built-in data frame `longley` to answer the following questions. 1. What year was the percentage of people employed relative to the population highest? Return the result as a data frame. 2. The Korean war took place from 1950 - 1953. Filter the data frame so it only contains data from those years. 3. What years did the number of people in the armed forces exceed the number of people unemployed? Give the result as an atomic vector. ??? ## Solutions 1. .tiny[ ```{r} longley[which.max(longley$Employed / longley$Population), "Year", drop=FALSE] ``` ] 2. .tiny[ ```{r} longley[longley$Year %in% 1950:1953, ] ``` ] 3. .tiny[ ```{r} longley$Year[longley$Armed.Forces > longley$Unemployed] ``` ] --- class: inverse, center, middle # S3 objects --- ## Introduction >S3 is R’s first and simplest OO system. S3 is informal and ad hoc, but there is a certain elegance in its minimalism: you can’t take away any part of it and still have a useful OO system. For these reasons, you should use it, unless you have a compelling reason to do otherwise. S3 is the only OO system used in the base and stats packages, and it’s the most commonly used system in CRAN packages.

Hadley Wickham

R has many object oriented programming (OOP) systems: S3, S4, R6, RC, etc. This introduction will focus on S3. --- ## Polymorphism How are certain functions able to handle different types or classes of inputs? ```{r} summary(c(1:10)) ``` -- ```{r} summary(c("A", "A", "a", "B", "b", "C", "C", "C")) ``` -- ```{r} summary(factor(c("A", "A", "a", "B", "b", "C", "C", "C"))) ``` --- ```{r} summary(data.frame(x = 1:10, y = letters[1:10])) ``` -- ```{r} summary(as.Date(0:10, origin = "2000-01-01")) ``` --- ## Terminology - An **S3 object** is a base type object with at least a class attribute. - The implementation of a function for a specific class is known as a **method**. - A **generic function** defines an interface that performs method dispatch. ![](images/s3-diagram-generic.png) --- ## Example ![](images/s3-diagram-summary.png) ```{r} x <- factor(c("A", "A", "a", "B", "b", "C", "C", "C")) summary(x) ``` --- ## Example .pull-left[ ```{r} summary.factor(x) summary.default(x) ``` ] .pull-right[ ```{r error=TRUE} summary.lm(x) summary.matrix(x) ``` ] --- ## Working with the S3 OOP system A few approaches for working with the S3 system: 1. build methods off existing generics for a newly defined class 2. define a new generic, build methods off existing classes 3. some combination of 1 and 2 --- ## Approach 1 First, define a class. S3 has no formal definition of a class. The class name can be any string. ```{r} x <- "hello world" attr(x, which = "class") <- "string" attributes(x) ``` -- Second, define methods that build off existing generic functions. Functions `summary()` and `print()` are existing generic functions. ```{r} summary.string <- function(x) { length(unlist(strsplit(x, split = ""))) } ``` -- ```{r} print.string <- function(x) { print(unlist(strsplit(x, split = ""))) } ``` --- ## Approach 1 in action ```{r} summary(x) ``` -- ```{r} print(x) ``` -- ```{r} y <- "hello world" summary(y) print(y) ``` --- ## Approach 2 First, define a generic function. ```{r} trim <- function(x, ...) { UseMethod("trim") } ``` -- Second, define methods based on existing classes. ```{r} trim.default <- function(x) { x[-c(1, length(x))] } ``` -- ```{r} trim.data.frame <- function(x, col=TRUE) { if (col){ x[-c(1, dim(x)[2])] } else { x[-c(1, dim(x)[1]), ] } } ``` --- ## Approach 2 in action .tiny.pull-left[ ```{r} trim(1:10) trim(c("a", "ab", "abc", "abcd")) trim(c(T, F, F, F, T)) trim(factor(c("a", "ab", "abc", "abcd"))) ``` ] .tiny.pull-right[ ```{r} df <- data.frame(x = 1:5, y = letters[1:5], z = c(rep(T, 5))) df ``` ```{r} trim(df) trim(df, col = FALSE) ``` ] --- ## Helpful tips - When creating new classes follow Hadley's recommendation of constructor, validator, and helper functions. See section [13.3](https://adv-r.hadley.nz/s3.html#s3-classes) in Advanced R. - Only write a method if you own the generic or class. - A method must have the same arguments as its generic, except if the generic has the `...` argument. ``` > print function (x, ...) UseMethod("print") > print.data.frame function (x, ..., digits = NULL, quote = FALSE, right = TRUE, row.names = TRUE, max = NULL) ``` - Package `sloop` has useful functions for finding generics and methods. Specifically, `ftype()`, `s3_methods_generic()`, `s3_methods_class()`. - Use the generic function and let method dispatch do the work, i.e. use `print(x)` and not `print.data.frame(x)` if `x` is a data frame. --- ## Exercises 1. Use function `sloop::ftype()` to see which of the following functions are S3 generics: `mean`, `summary`, `print`, `sum`, `plot`, `View`, `length`, `[`. 2. Choose 2 of the S3 generics you identified above. How many methods exist for each? Use function `sloop::s3_methods_generics()`. 3. How many methods exist for classes `factor` and `data.frame`. Use function `sloop::s3_methods_class()`. 4. Consider a class called dollars. If a numeric vector has class dollars, function `print()` should print the vector with a \$ in front of each number and round digits to two decimals. ```{r echo=FALSE} print.dollar <- function(x) { paste0("$", round(x, digits = 2)) } ``` .tiny[ ```{r} x <- 1:5 class(x) <- "dollar" print(x) ``` ```{r} y <- c(4.292, 134.1133, 50.111) class(y) <- "dollar" print(y) ``` ] ??? ## Part 4 ```{r eval=FALSE} print.dollar <- function(x) { paste0("$", round(x, digits = 2)) } ``` ```{r eval=FALSE} x <- 1:5 class(x) <- "dollar" print(x) ``` ```{r eval=FALSE} y <- c(4.292, 134.1133, 50.111) class(y) <- "dollar" print(y) ``` --- ## Looking ahead - Complete Task 1 of Homework 2 before Lab on Friday. - Begin to brainstorm ideas for Task 3. - Sit with you homework team in Friday's lab. --- ## References - Wickham, H. (2019). Advanced R. https://adv-r.hadley.nz/ - R Language Definition. (2019). Cran.r-project.org. https://cran.r-project.org/doc/manuals/r-release/R-lang.html