class: center, middle, inverse, title-slide # Data types in R ### Colin Rundel ### 2019-01-17 --- exclude: true --- class: middle count: false # Atomic Vectors --- ## Atomic Vectors R has six atomic vector types: <br/> `typeof` | `mode` :-----------|:------------ logical | logical double | numeric integer | numeric character | character complex | complex raw | raw --- ## Vector types `logical` - boolean values `TRUE` and `FALSE` .pull-left[ ```r typeof(TRUE) ``` ``` ## [1] "logical" ``` ] .pull-right[ ```r mode(TRUE) ``` ``` ## [1] "logical" ``` ] <br/> `character` - text strings <div> .pull-left[ ```r typeof("hello") ``` ``` ## [1] "character" ``` ```r typeof('world') ``` ``` ## [1] "character" ``` ] .pull-right[ ```r mode("hello") ``` ``` ## [1] "character" ``` ```r mode('world') ``` ``` ## [1] "character" ``` ] </div> --- `double` - floating point numerical values (default numerical type) .pull-left[ ```r typeof(1.33) ``` ``` ## [1] "double" ``` ```r typeof(7) ``` ``` ## [1] "double" ``` ] .pull-right[ ```r mode(1.33) ``` ``` ## [1] "numeric" ``` ```r mode(7) ``` ``` ## [1] "numeric" ``` ] <br/> `integer` - integer numerical values (indicated with an `L`) <div> .pull-left[ ```r typeof( 7L ) ``` ``` ## [1] "integer" ``` ```r typeof( 1:3 ) ``` ``` ## [1] "integer" ``` ] .pull-right[ ```r mode( 7L ) ``` ``` ## [1] "numeric" ``` ```r mode( 1:3 ) ``` ``` ## [1] "numeric" ``` ] </div> --- ## Concatenation Atomic vectors can be constructed using the concatenate, `c()`, function. ```r c(1,2,3) ``` ``` ## [1] 1 2 3 ``` -- ```r c("Hello", "World!") ``` ``` ## [1] "Hello" "World!" ``` -- ```r c(1,c(2, c(3))) ``` ``` ## [1] 1 2 3 ``` **Note** - atomic vectors are *always* flat. --- class: split-thirds ## Inspecting 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`. .pull-left[ ```r typeof(1) ``` ``` ## [1] "double" ``` ```r typeof(1L) ``` ``` ## [1] "integer" ``` ```r typeof("A") ``` ``` ## [1] "character" ``` ```r typeof(TRUE) ``` ``` ## [1] "logical" ``` ] .pull-right[ ```r mode(1) ``` ``` ## [1] "numeric" ``` ```r mode(1L) ``` ``` ## [1] "numeric" ``` ```r mode("A") ``` ``` ## [1] "character" ``` ```r mode(TRUE) ``` ``` ## [1] "logical" ``` ] --- ## Type 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) ``` ``` ## [1] FALSE ``` ```r is.integer(1L) ``` ``` ## [1] TRUE ``` ```r is.integer(3:7) ``` ``` ## [1] TRUE ``` ] .col2[ ```r is.double(1) ``` ``` ## [1] TRUE ``` ```r is.double(1L) ``` ``` ## [1] FALSE ``` ```r is.double(3:8) ``` ``` ## [1] FALSE ``` ] .col3[ ```r is.numeric(1) ``` ``` ## [1] TRUE ``` ```r is.numeric(1L) ``` ``` ## [1] TRUE ``` ```r is.numeric(3:7) ``` ``` ## [1] TRUE ``` ] --- ## Other useful predicates * `is.atomic(x)` - returns `TRUE` if `x` is an *atomic vector*. * `is.vector(x)` - returns `TRUE` if `x` is either an *atomic vector* or *list*). ```r is.atomic(c(1,2,3)) ``` ``` ## [1] TRUE ``` ```r is.vector(c(1,2,3)) ``` ``` ## [1] TRUE ``` ```r is.atomic(list(1,2,3)) ``` ``` ## [1] FALSE ``` ```r is.vector(list(1,2,3)) ``` ``` ## [1] TRUE ``` --- ## Type Coercion R is a dynamically typed language -- it will automatically convert between most type without raising warnings or errors. ```r c(1,"Hello") ``` ``` ## [1] "1" "Hello" ``` -- ```r c(FALSE, 3L) ``` ``` ## [1] 0 3 ``` -- ```r c(1.2, 3L) ``` ``` ## [1] 1.2 3.0 ``` --- ## Operator coercion Functions and operators will attempt to coerce object to an appropriate type ```r 3.1+1L ``` ``` ## [1] 4.1 ``` -- ```r log(TRUE) ``` ``` ## [1] 0 ``` -- ```r TRUE & 7 ``` ``` ## [1] TRUE ``` -- ```r FALSE | !5 ``` ``` ## [1] FALSE ``` --- ## 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) ``` ``` ## [1] TRUE ``` ```r as.character(TRUE) ``` ``` ## [1] "TRUE" ``` ```r as.integer(pi) ``` ``` ## [1] 3 ``` ] .pull-right[ ```r as.numeric(FALSE) ``` ``` ## [1] 0 ``` ```r as.double("7.2") ``` ``` ## [1] 7.2 ``` ```r as.double("one") ``` ``` ## Warning: NAs introduced by coercion ``` ``` ## [1] NA ``` ] --- ## 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) ``` ``` ## [1] "logical" ``` ```r typeof(NA+1) ``` ``` ## [1] "double" ``` ```r typeof(NA+1L) ``` ``` ## [1] "integer" ``` ] .pull-right[ ```r typeof(NA_character_) ``` ``` ## [1] "character" ``` ```r typeof(NA_real_) ``` ``` ## [1] "double" ``` ```r typeof(NA_integer_) ``` ``` ## [1] "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 ``` ```r 1 / NA ``` ``` ## [1] NA ``` ```r NA * 5 ``` ``` ## [1] NA ``` ] .pull-right[ ```r mean(c(1,2,3,NA)) ``` ``` ## [1] NA ``` ```r sqrt(NA) ``` ``` ## [1] NA ``` ```r 3^NA ``` ``` ## [1] NA ``` ] --- ## Conditionals and missing values `NA`s can be problematic in some cases (particularly for control flow) ```r 1 == NA ``` ``` ## [1] NA ``` -- ```r if (2 != NA) "Here" ``` ``` ## Error in if (2 != NA) "Here": missing value where TRUE/FALSE needed ``` -- ```r if (all(c(1,2,NA,4) >= 1)) "There" ``` ``` ## Error in if (all(c(1, 2, NA, 4) >= 1)) "There": missing value where TRUE/FALSE needed ``` -- ```r if (any(c(1,2,NA,4) >= 1)) "There" ``` ``` ## [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) ``` ``` ## [1] TRUE ``` ```r is.na(1) ``` ``` ## [1] FALSE ``` ```r is.na(c(1,2,3,NA)) ``` ``` ## [1] FALSE FALSE FALSE TRUE ``` ] .pull-right[ ```r any(is.na(c(1,2,3,NA))) ``` ``` ## [1] TRUE ``` ```r all(is.na(c(1,2,3,NA))) ``` ``` ## [1] FALSE ``` ] --- ## Other Special (double) values * `NaN` - Not a number * `Inf` - Positive infinity * `-Inf` - Negative infinity .pull-left[ ```r pi / 0 ``` ``` ## [1] Inf ``` ```r 0 / 0 ``` ``` ## [1] NaN ``` ```r 1/0 + 1/0 ``` ``` ## [1] Inf ``` ] .pull-right[ ```r 1/0 - 1/0 ``` ``` ## [1] NaN ``` ```r NaN / NA ``` ``` ## [1] NaN ``` ```r NaN * NA ``` ``` ## [1] NaN ``` ] --- ## 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] NA ``` ```r 1/0+1/0 ``` ``` ## [1] Inf ``` ```r 1/0-1/0 ``` ``` ## [1] NaN ``` ] .pull-right[ ```r is.finite(NA) ``` ``` ## [1] FALSE ``` ```r is.finite(1/0+1/0) ``` ``` ## [1] FALSE ``` ```r is.finite(1/0-1/0) ``` ``` ## [1] FALSE ``` ```r is.nan(1/0-1/0) ``` ``` ## [1] TRUE ``` ] --- ## 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) ``` ``` ## Warning: NAs introduced by coercion to integer range ``` ``` ## [1] NA ``` ```r as.integer(NaN) ``` ``` ## [1] NA ``` .pull-left[ ```r as.logical(Inf) ``` ``` ## [1] TRUE ``` ```r as.logical(NaN) ``` ``` ## [1] NA ``` ] .pull-right[ ```r as.character(Inf) ``` ``` ## [1] "Inf" ``` ```r as.character(NaN) ``` ``` ## [1] "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) ``` ``` ## [[1]] ## [1] "A" ## ## [[2]] ## [1] TRUE FALSE ## ## [[3]] ## [1] 0.5 1.0 1.5 2.0 ## ## [[4]] ## 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) ) ``` ``` ## List of 4 ## $ : chr "A" ## $ : logi [1:2] TRUE FALSE ## $ : num [1:4] 0.5 1 1.5 2 ## $ :function (x) ## ..- attr(*, "srcref")= 'srcref' int [1:8] 1 40 1 54 40 54 1 1 ## .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x7fa10181cb00> ``` --- ## 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 of 2 ## $ : num 1 ## $ :List of 3 ## ..$ : num 2 ## ..$ :List of 2 ## .. ..$ : num 3 ## .. ..$ : num 4 ## ..$ : num 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))) ) ``` ``` ## List of 3 ## $ : num 1 ## $ : num 4 ## $ :List of 2 ## ..$ : num 6 ## ..$ : num 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))) ``` ``` ## [1] 1 2 3 4 5 6 ``` ```r unlist( list(1, list(2, list(3, "Hello"))) ) ``` ``` ## [1] "1" "2" "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 of 2 ## $ A: num 1 ## $ B:List of 2 ## ..$ C: num 2 ## ..$ D: num 3 ``` ```r list("knock knock" = "who's there?") ``` ``` ## $`knock knock` ## [1] "who's there?" ``` ```r names(list(ABC=1, DEF=list(H=2, I=3))) ``` ``` ## [1] "ABC" "DEF" ``` --- ## 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" } ] } ``` --- # 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)