--- title: "Data types in R" author: "Colin Rundel" date: "2018-08-30" output: xaringan::moon_reader: css: "slides.css" lib_dir: libs nature: highlightStyle: github highlightLines: true countIncrementalSlides: false --- exclude: true ```{r, message=FALSE, warning=FALSE, include=FALSE} options( htmltools.dir.version = FALSE, # for blogdown width=80 ) htmltools::tagList(rmarkdown::html_dependency_font_awesome()) ``` --- class: middle count: false # Atomic Vectors --- ## Atomic Vectors R has six atomic vector types:
`typeof` | `mode` | `storage.mode` :-----------|:------------|:---------------- logical | logical | logical double | numeric | double integer | numeric | integer character | character | character complex | complex | complex raw | raw | raw --- ## Vector types `logical` - boolean values `TRUE` and `FALSE` .pull-left[ ```{r} typeof(TRUE) ``` ] .pull-right[ ```{r} mode(TRUE) ``` ]
`character` - text strings
.pull-left[ ```{r} typeof("hello") typeof('world') ``` ] .pull-right[ ```{r} mode("hello") mode('world') ``` ]
--- `double` - floating point numerical values (default numerical type) .pull-left[ ```{r} typeof(1.33) typeof(7) ``` ] .pull-right[ ```{r} mode(1.33) mode(7) ``` ]
`integer` - integer numerical values (indicated with an `L`)
.pull-left[ ```{r} typeof( 7L ) typeof( 1:3 ) ``` ] .pull-right[ ```{r} mode( 7L ) mode( 1:3 ) ``` ]
--- ## Concatenation Atomic vectors can be constructed using the concatenate, `c()`, function. ```{r} c(1,2,3) ``` -- ```{r} c("Hello", "World!") ``` -- ```{r} c(1,c(2, c(3))) ``` **Note** - atomic vectors are *always* flat. --- class: split-thirds ## Testing types * `typeof(x)` - returns a character vector (length 1) of the *type* of object `x`. * `mode(x)` - returns a character vector (length 1) of the *mode* of object `x`. * `storage.mode(x)` - returns a character vector (length 1) of the *storage mode* of object `x`. .col1[ ```{r} typeof(1) typeof(1L) typeof("A") typeof(TRUE) ``` ] .col2[ ```{r} mode(1) mode(1L) mode("A") mode(TRUE) ``` ] .col3[ ```{r} storage.mode(1) storage.mode(1L) storage.mode("A") storage.mode(TRUE) ``` ] --- ## Logical Predicates * `is.logical(x)` - returns `TRUE` if `x` has *type* logical. * `is.character(x)` - returns `TRUE` if `x` has *type* character. * `is.double(x)` - returns `TRUE` if `x` has *type* double. * `is.integer(x)` - returns `TRUE` if `x` has *type* integer. * `is.numeric(x)` - returns `TRUE` if `x` has *mode* numeric. .col1[ ```{r} is.integer(1) is.integer(1L) is.integer(3:7) ``` ] .col2[ ```{r} is.double(1) is.double(1L) is.double(3:8) ``` ] .col3[ ```{r} is.numeric(1) is.numeric(1L) is.numeric(3:7) ``` ] --- ## Other useful predicates * `is.atomic(x)` - returns `TRUE` if `x` is an *atomic vector*. * `is.vector(x)` - returns `TRUE` if `x` is either type of vector (i.e. either *atomic vector* or *list*). ```{r} is.atomic(c(1,2,3)) is.vector(c(1,2,3)) is.atomic(list(1,2,3)) is.vector(list(1,2,3)) ``` --- ## Type Coercion R is a dynamically typed language -- it will automatically convert between most type without raising warnings or errors. ```{r} c(1,"Hello") ``` -- ```{r} c(FALSE, 3L) ``` -- ```{r} c(1.2, 3L) ``` --- ## Operator coercion Functions and operators will attempt to coerce object to an appropriate type ```{r} 3.1+1L ``` -- ```{r} log(TRUE) ``` -- ```{r} TRUE & 7 ``` -- ```{r} FALSE | !5 ``` --- ## Explicit Coercion Most of the `is` functions we just saw have an `as` variant which can be used for *explicit* coercion. .pull-left[ ```{r} as.logical(5.2) as.character(TRUE) as.integer(pi) ``` ] .pull-right[ ```{r} as.numeric(FALSE) as.double("7.2") as.double("one") ``` ] --- ## Missing Values R uses `NA` to represent missing values in its data structures, what may not be obvious is that there are different `NA`s for the different types. .pull-left[ ```{r} typeof(NA) typeof(NA+1) typeof(NA+1L) ``` ] .pull-right[ ```{r} typeof(NA_character_) typeof(NA_real_) typeof(NA_integer_) ``` ] --- ## Stickiness of Missing Values Because `NA`s represent missing values it makes sense that any calculation using them should also be missing. .pull-left[ ```{r} 1 + NA 1 / NA NA * 5 ``` ] .pull-right[ ```{r} mean(c(1,2,3,NA)) sqrt(NA) 3^NA ``` ] --- ## Conditionals and missing values `NA`s can be problematic in some cases (particularly for control flow) ```{r error=TRUE} 1 == NA ``` -- ```{r error=TRUE} if (2 != NA) "Here" ``` -- ```{r error=TRUE} if (all(c(1,2,NA,4) >= 1)) "There" ``` -- ```{r error=TRUE} if (any(c(1,2,NA,4) >= 1)) "There" ``` --- ## Testing for `NA` To explicitly test if a value is missing it is necessary to use `is.na` (often along with `any` or `all`). .pull-left[ ```{r} is.na(NA) is.na(1) is.na(c(1,2,3,NA)) ``` ] .pull-right[ ```{r} any(is.na(c(1,2,3,NA))) all(is.na(c(1,2,3,NA))) ``` ] --- ## Other Special (double) values * `NaN` - Not a number * `Inf` - Positive infinity * `-Inf` - Negative infinity .pull-left[ ```{r} pi / 0 0 / 0 1/0 + 1/0 ``` ] .pull-right[ ```{r} 1/0 - 1/0 NaN / NA NaN * NA ``` ] --- ## Testing for `inf` and `NaN` `NaN` and `Inf` don't have the same testing issues that `NA` has, but there are still convenience functions for testing for .pull-left[ ```{r} NA 1/0+1/0 1/0-1/0 1/0-1/0 ``` ] .pull-right[ ```{r} is.finite(NA) is.finite(1/0+1/0) is.finite(1/0-1/0) is.nan(1/0-1/0) ``` ] --- ## Coercion for infinity and NaN First remember that `Inf`, `-Inf`, and `NaN` have type double, however their coercion behavior is not the same as for other double values. ```{r} as.integer(Inf) as.integer(NaN) ``` .pull-left[ ```{r} as.logical(Inf) as.logical(NaN) ``` ] .pull-right[ ```{r} as.character(Inf) as.character(NaN) ``` ] --- ## Exercise 1 **Part 1** What is the type of the following vectors? Explain why they have that type. * `c(1, NA+1L, "C")` * `c(1L / 0, NA)` * `c(1:3, 5)` * `c(3L, NaN+1L)` * `c(NA, TRUE)` **Part 2** Considering only the four (common) data types, what is R's implicit type conversion hierarchy (from highest priority to lowest priority)? *Hint* - think about the pairwise interactions between types. --- class: middle count: false # Generic Vectors --- ## Lists Lists are _generic vectors_, in that they are 1 dimensional (i.e. have a length) and can contain any type of R object. ```{r} list("A", c(TRUE,FALSE), (1:4)/2, function(x) x^2) ``` --- ## Structure Often we want a more compact representation of a complex object, the `str` function is useful for this particular task ```{r} str( list("A", c(TRUE,FALSE), (1:4)/2, function(x) x^2) ) ``` --- ## Recursive lists Lists can contain other lists, meaning they don't have to be flat ```{r} str( list(1, list(2, list(3, 4), 5)) ) ``` --- ## List Coercion By default a vector will be coerced to a list (as a list is more generic) if needed ```{r} str( c(1, list(4, list(6, 7))) ) ``` -- We can coerce a list into an atomic vector using `unlist` - the usual type coercion rules then apply to determine its type. ```{r} unlist(list(1:3, list(4:5, 6))) unlist( list(1, list(2, list(3, "Hello"))) ) ``` --- ## Named lists Because of their more complex structure we often want to name the elements of a list (we can also do this with vectors). This can make reading and accessing the list more straight forward. ```{r} str(list(A = 1, B = list(C = 2, D = 3))) list("knock knock" = "who's there?") names(list(ABC=1, DEF=list(H=2, I=3))) ``` --- ## Exercise 2 Represent the following JSON data as a list in R. ```json { "firstName": "John", "lastName": "Smith", "age": 25, "address": { "streetAddress": "21 2nd Street", "city": "New York", "state": "NY", "postalCode": 10021 }, "phoneNumber": [ { "type": "home", "number": "212 555-1239" }, { "type": "fax", "number": "646 555-4567" } ] } ``` --- class: middle count: false # Functions --- ## When to use functions The goal of a function should be to encapsulate a *small* *reusable* piece of code. * Name should make it clear what the function does (think in terms of simple verbs). * Functionality should be simple enough to be quickly understood. * The smaller and more modular the code the easier it will be to reuse elsewhere. * Better to change code in one location than code everywhere. --- ## Function Parts The two parts of a function are the arguments (`formals`) and the code (`body`). ```{r} gcd = function(long1, lat1, long2, lat2) { R = 6371 # Earth mean radius in km # distance in km acos(sin(lat1)*sin(lat2) + cos(lat1)*cos(lat2) * cos(long2-long1)) * R } ``` -- .pull-left[ ```{r} formals(gcd) ``` ] .pull-right[ ```{r} body(gcd) ``` ] --- ## Return values There are two ways of returning values in R: explicit or implicit return values.
*Explicit* - includes one or more `return` statements ```{r} f = function(x) { return(x*x) } ```
*Implicit* - value of the last statement is returned. ```{r} f = function(x) { x*x } ``` --- ## Returning multiple values If we want a function to return more than one value we can group things using either a vector or a list. ```{r} f = function(x) { c(x, x^2, x^3) } f(2) f(2:3) ``` --- ## Argument names When defining a function we are also implicitly defining names for the arguments, when calling the function we can use these names to pass arguments in a different order. ```{r} f = function(x,y,z) { paste0("x=",x," y=",y," z=",z) } ``` .pull-left[ ```{r,error=TRUE} f(1,2,3) f(z=1,x=2,y=3) ``` ] .pull-right[ ```{r,error=TRUE} f(y=2,1,3) f(y=2,1,x=3) ``` ] ```{r,error=TRUE} f(1,2,3,m=1) ``` --- ## Argument defaults It is also possible to give function arguments default values so that they don't need to be provided every time the function is called. ```{r error=TRUE} f = function(x,y=1,z=1) { paste0("x=",x," y=",y," z=",z) } ``` ```{r error=TRUE} f() f(x=3) f(y=2,2) ``` --- ## Scope R has generous scoping rules, if it can't find a variable in the functions body, it will look for it in the next higher scope, and so on. ```{r} y = 1 f = function(x) { x+y } f(3) ``` ```{r} g = function(x) { y=2 x+y } g(3) ``` --- ## Additionally, variables defined within a scope only persist for the duration of that scope, and do not overwrite variables at higher scopes (unless you use the global assignment operator `<<-`, *which you shouldn't*) ```{r} x = 1 y = 1 z = 1 f = function() { y = 2 g = function() { z = 3 return(x + y + z) } return(g()) } f() c(x,y,z) ``` --- ## Lazy evaluation Arguments to R functions are lazily evaluated - meaning they are not evaluated until they are used ```{r, error=TRUE} f = function(x) { cat("Hello world!\n") x } f(stop()) ``` --- ## Everything is a function ```{r} `+` typeof(`+`) x = 4:1 `+`(x,2) ``` --- ## Getting Help Prefixing any function name with a `?` will open the related help file for that function. ```{r, eval=FALSE} ?`+` ?sum ``` For functions not in the base package, you can generally see their implementation by entering the function name without parentheses (or using the `body` function). ```{r} lm ``` --- ## Less Helpful Examples ```{r} list `[` sum `+` ``` --- # Acknowledgments ## Acknowledgments Above materials are derived in part from the following sources: * Hadley Wickham - [Advanced R](http://adv-r.had.co.nz/) * [R Language Definition](http://stat.ethz.ch/R-manual/R-devel/doc/manual/R-lang.html)