[R语言] Vectors 向量操作《R for data sc

《R for Data Science》第二十章 Vectors 啃书知识点积累
参考链接：R for Data Science

Vector basics

向量有两种类型：

1. Atomic vectors, of which there are six types: logical, integer, double, character, complex, and raw. Integer and double vectors are collectively known as numeric vectors. (homogeneous)
2. Lists, which are sometimes called recursive vectors because lists can contain other lists. (heterogeneous)

NULL is often used to represent the absence of a vector.
NA is used to represent the absence of a value in a vector.

• Every vector has two key properties:

1. Its type, which you can determine with typeof().

   typeof(letters)
   #> [1] "character"
   typeof(1:10)
   #> [1] "integer"
   

2. Its length, which you can determine with length().

   x <- list("a", "b", 1:10)
   length(x)
   #> [1] 3
   

- augmented vectors

• Factors are built on top of integer vectors.
• Dates and date-times are built on top of numeric vectors.
• Data frames and tibbles are built on top of lists.

Important types of atomic vector

- Logical

Logical vectors can take only three possible values: FALSE, TRUE, and NA.
(尤其注意NA是逻辑型)

c(TRUE, TRUE, FALSE, NA)
#> [1]  TRUE  TRUE FALSE    NA

- Numeric

To make an integer, place an L after the number

typeof(1)
#> [1] "double"
typeof(1L)
#> [1] "integer"
1.5L
#> [1] 1.5

# integer和double的取值差异，不重要
.Machine$integer.max
#> [1] 2147483647


.Machine$double.xmax
#> [1] 1.8e+308
.Machine$double.base
#> [1] 2
.Machine$double.digits
#> [1] 53
.Machine$double.exponent
#> [1] 11
.Machine$double.eps
#> [1] 2.22e-16
.Machine$double.neg.eps
#> [1] 1.11e-16

需要注意的integer和double区别：

1. Doubles are approximations.

x <- sqrt(2) ^ 2
x
#> [1] 2

x - 2
#> [1] 4.44e-16

x - 2 == 0
#> [1] FALSE

dplyr::near(x - 2, 0)
#> [1] TRUE

# near的原理：不比较精确相等，而是有个判断 
dplyr::near
# function (x, y, tol = .Machine$double.eps^0.5) 
# {
#   abs(x - y) < tol
# }
# <bytecode: 0x000002bd0ce7c7e8>
# <environment: namespace:dplyr>

2. Integers have one special value: NA,
   while doubles have four: NA, NaN, Inf, -Inf.

c(-1, 0, 1) / 0
#> [1] -Inf  NaN  Inf

（X 表示TRUE）

# 可以注意到NA和NaN有限和无限判断均为FALSE
is.infinite(NA)
# [1] FALSE
is.finite(NA)
# [1] FALSE

# 举一个更明确的例子
x <- c(0, NA, NaN, Inf, -Inf)
is.finite(x)
#> [1]  TRUE FALSE FALSE FALSE FALSE
!is.infinite(x)
#> [1]  TRUE  TRUE  TRUE FALSE FALSE

• double to integer

tibble(
  x = c(
    1.8, 1.5, 1.2, 0.8, 0.5, 0.2,
    -0.2, -0.5, -0.8, -1.2, -1.5, -1.8
  ),
  `Round down` = floor(x),
  `Round up` = ceiling(x),
  `Round towards zero` = trunc(x),
  `Nearest, round half to even` = round(x)
)

- Character

R uses a global string pool.
This means that each unique string is only stored in memory once.
This reduces the amount of memory needed by duplicated strings.

x <- "This is a reasonably long string."
pryr::object_size(x)
#> Registered S3 method overwritten by 'pryr':
#>   method      from
#>   print.bytes Rcpp
#> 152 B

y <- rep(x, 1000)
pryr::object_size(y)
#> 8.14 kB

原因：
A pointer is 8 bytes, so 1000 pointers to a 136 B string is 8 * 1000 + 136 = 8.13 kB.

- Missing values

Note that each type of atomic vector has its own missing value:

NA            # logical
#> [1] NA
NA_integer_   # integer
#> [1] NA
NA_real_      # double
#> [1] NA
NA_character_ # character
#> [1] NA

Using atomic vectors

- Test functions

Base R provides many functions like is.vector() and is.atomic(), but they often return surprising results.
Instead, it’s safer to use the is_* functions provided by purrr, which are summarised in the table below.

• 如果检查是否是标量可以用scalar

x <- c(TRUE)
y <- c(TRUE, FALSE)

is_scalar_logical(x)
# [1] TRUE
is_scalar_logical(y)
# [1] FALSE

- Scalars and recycling rules

The vectorised functions in tidyverse will throw errors when you recycle anything other than a scalar.

tibble(x = 1:4, y = 1:2)
#> Error: Tibble columns must have consistent lengths, only values of length one are recycled:
#> * Length 2: Column `y`
#> * Length 4: Column `x`

tibble(x = 1:4, y = rep(1:2, 2))
#> # A tibble: 4 x 2
#>       x     y
#>   <int> <int>
#> 1     1     1
#> 2     2     2
#> 3     3     1
#> 4     4     2

tibble(x = 1:4, y = rep(1:2, each = 2))
#> # A tibble: 4 x 2
#>       x     y
#>   <int> <int>
#> 1     1     1
#> 2     2     1
#> 3     3     2
#> 4     4     2

- Naming vectors

两种方法：c()内部设置和purrr::set_names()

c(x = 1, y = 2, z = 4)
#> x y z 
#> 1 2 4

set_names(1:3, c("a", "b", "c"))
#> a b c 
#> 1 2 3

• purrr::set_name和setNames

setNames(1:4, c("a", "b", "c", "d"))
#> a b c d 
#> 1 2 3 4
purrr::set_names(1:4, c("a", "b", "c", "d"))
#> a b c d 
#> 1 2 3 4
# 即使多个向量但符合数据长度也可以
purrr::set_names(1:4, "a", "b", "c", "d")
#> a b c d 
#> 1 2 3 4

setNames(1:4, c("a", "b"))
#>    a    b <NA> <NA> 
#>    1    2    3    4
# 如果名字长度和数据长度不同则set_names无法起作用
purrr::set_names(1:4, c("a", "b"))
#> `nm` must be `NULL` or a character vector the same length as `x`

- Subsetting

• By repeating a position, you can actually make a longer output than input:

# 允许重复取子集下标
x[c(1, 1, 5, 5, 5, 2)]
#> [1] "one"  "one"  "five" "five" "five" "two"

• It’s an error to mix positive and negative values:

x[c(1, -1)]
#> Error in x[c(1, -1)]: only 0's may be mixed with negative subscripts

• The error message mentions subsetting with zero, which returns no values:

x[0]
#> character(0)

• 利用逻辑值取子集

x <- c(10, 3, NA, 5, 8, 1, NA)

x[x > 0]
# [1] 10  3 NA  5  8  1 NA
subset(x, x > 0)
# [1] 10  3  5  8  1
# 可去除NA

[[ only ever extracts a single element, and always drops names.

• x[x >= 0] 和 x[- which(x < 0)]的区别

x
# [1] 10  4 NA  5  8  1 NA
x[x >= 0]
# [1] 10  4 NA  5  8  1 NA
x[-which(x < 0)]
# numeric(0)

# 如果which取子集取不到，则无法删除和取反

y
# [1] 10 -4 NA  5  8  1 NA
y[y >= 0]
# [1] 10 NA  5  8  1 NA
y[-which(y < 0)]
# [1] 10 NA  5  8  1 NA

# 可取到子集则相同

Recursive vectors (lists)

Lists are a step up in complexity from atomic vectors, because lists can contain other lists.

x_named <- list(a = 1, b = 2, c = 3)
str(x_named)
#> List of 3
#>  $ a: num 1
#>  $ b: num 2
#>  $ c: num 3


y <- list("a", 1L, 1.5, TRUE)
str(y)
#> List of 4
#>  $ : chr "a"
#>  $ : int 1
#>  $ : num 1.5
#>  $ : logi TRUE

# 嵌套list
z <- list(list(1, 2), list(3, 4))
str(z)
#> List of 2
#>  $ :List of 2
#>   ..$ : num 1
#>   ..$ : num 2
#>  $ :List of 2
#>   ..$ : num 3
#>   ..$ : num 4

- Visualising lists

x1 <- list(c(1, 2), c(3, 4))
x2 <- list(list(1, 2), list(3, 4))
x3 <- list(1, list(2, list(3)))

- Subsetting

str(a[1:4])
# List of 4
# $ a: int [1:3] 1 2 3
# $ b: chr "a string"
# $ c: num 3.14
# $ d:List of 2
#   ..$ : num -1
#   ..$ : num -5

str(a[2:3])
# List of 2
# $ b: chr "a string"
# $ c: num 3.14

str(a[4])
#> List of 1
#>  $ d:List of 2
#>   ..$ : num -1
#>   ..$ : num -5

• list的两个操作符: [[ $

(1) [[ extracts a single component from a list. It removes a level of hierarchy from the list.

str(a[4])
# List of 1
# $ d:List of 2
#  ..$ : num -1
#  ..$ : num -5

str(a[[4]])
# List of 2
#  $ : num -1
#  $ : num -5

(2) $ is a shorthand for extracting named elements of a list.

a$d
#  [[1]]
# [1] -1
# 
#  [[2]]
# [1] -5

Attributes

x <- 1:10
attr(x, "greeting")
#> NULL
attr(x, "greeting") <- "Hi!"
attr(x, "farewell") <- "Bye!"
attributes(x)
#> $greeting
#> [1] "Hi!"
#> 
#> $farewell
#> [1] "Bye!"

涉及了泛型函数generic functions的概念

methods("as.Date")
#> [1] as.Date.character   as.Date.default     as.Date.factor     
#> [4] as.Date.numeric     as.Date.POSIXct     as.Date.POSIXlt    
#> [7] as.Date.vctrs_sclr* as.Date.vctrs_vctr*
#> see '?methods' for accessing help and source code

For example, if x is a character vector, as.Date() will call as.Date.character(); if it’s a factor, it’ll call as.Date.factor().

You can see the specific implementation of a method with getS3method():

getS3method("as.Date", "default")
#> function (x, ...) 
#> {
#>     if (inherits(x, "Date")) 
#>         x
#>     else if (is.logical(x) && all(is.na(x))) 
#>         .Date(as.numeric(x))
#>     else stop(gettextf("do not know how to convert '%s' to class %s", 
#>         deparse(substitute(x)), dQuote("Date")), domain = NA)
#> }
#> <bytecode: 0x4f30d48>
#> <environment: namespace:base>
getS3method("as.Date", "numeric")
#> function (x, origin, ...) 
#> {
#>     if (missing(origin)) 
#>         stop("'origin' must be supplied")
#>     as.Date(origin, ...) + x
#> }
#> <bytecode: 0x84fa058>
#> <environment: namespace:base>

Augmented vectors

- Factors

- Dates

x <- as.Date("1971-01-01")
unclass(x)
#> [1] 365

typeof(x)
#> [1] "double"
attributes(x)
#> $class
#> [1] "Date"

- Date-times

x <- lubridate::ymd_hm("1970-01-01 01:00")
unclass(x)
#> [1] 3600
#> attr(,"tzone")
#> [1] "UTC"

typeof(x)
#> [1] "double"
attributes(x)
#> $class
#> [1] "POSIXct" "POSIXt" 
#> 
#> $tzone
#> [1] "UTC"

If you find you have a POSIXlt, you should always convert it to a regular data time lubridate::as_date_time().

- Tibbles

Tibbles are augmented lists: they have class “tbl_df” + “tbl” + “data.frame”, and names (column) and row.names attributes

• Q: Try and make a tibble that has columns with different lengths. What happens?

# 如果是标量会循环遍历，不等长非标量则无法创建
tibble(x = 1, y = 1:5)
#> # A tibble: 5 x 2
#>       x     y
#>   <dbl> <int>
#> 1     1     1
#> 2     1     2
#> 3     1     3
#> 4     1     4
#> 5     1     5

tibble(x = 1:3, y = 1:4)
#> Tibble columns must have consistent lengths, only values of length one are recycled:
#> * Length 3: Column `x`
#> * Length 4: Column `y`