R中的函数

总结R中使用过的函数的语法。

A

函数：append

Usage

append(x, values, after = length(x))

Arguments 参数

x
values
after

说明

Examples

1
2
3

# NOT RUN {
append(1:5, 0:1, after = 3)
# }

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函数：as.POSIX*

Usage

as.POSIXct(x, tz = "", …)
as.POSIXlt(x, tz = "", …)
# S3 method for character
as.POSIXlt(x, tz = "", format,
           tryFormats = c("%Y-%m-%d %H:%M:%OS",
                          "%Y/%m/%d %H:%M:%OS",
                          "%Y-%m-%d %H:%M",
                          "%Y/%m/%d %H:%M",
                          "%Y-%m-%d",
                          "%Y/%m/%d"),
           optional = FALSE, …)
# S3 method for default
as.POSIXlt(x, tz = "",
           optional = FALSE, …)
# S3 method for numeric
as.POSIXlt(x, tz = "", origin, …)

# S3 method for POSIXlt
as.double(x, …)

Arguments 参数

x 用来转换的R对象
tz 如果需要，则进行时区规范化。系统特定（参见时区），但””是当前时区，“GMT”是UTC（世界时，协调）。在某些带有警告的平台上，无效值通常被视为UTC。
… 要传递给其他方法或从其他方法传递的其他参数
format 字符串给出strptime使用的日期时间格式
tryFormats 如果fromat未指定，则使用此字符串作为向量进行尝试
optional 逻辑指示如果格式猜测不成功则返回NA（而不是发出错误信号）
origin

说明

Examples

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B

b1

C

函数：chartr

Usage

chartr(old, new, x)
tolower(x)
toupper(x)
casefold(x, upper = FALSE)

Arguments 参数

x 字符向量，或者可以通过as.character强制转换为字符的对象。
old 一个字符串用来指定要转换的字符。如果提供长度为2或更大的字符向量，则第一个元素将与警告一起使用。
new 指定翻译的字符串
upper 累计值，转换为大写还是小写

说明

Examples

> x <- "MiXeD cAsE 123" 
> chartr ("iXs" , "why" , x) # w替换i，h替换X，y替换s
[1] "MwheD cAyE 123"
> chartr ("a-cX" , "D-Fw" , x) # 
[1] "MiweD FAsE 123"
> tolower (x)
[1] "mixed case 123"
> toupper (x)
[1] "MIXED CASE 123"
x <- "MiXeD cAsE 123"
chartr("iXs", "why", x)
chartr("a-cX", "D-Fw", x)
tolower(x)
toupper(x)

## "Mixed Case" Capitalizing - toupper( every first letter of a word ) :

.simpleCap <- function(x) {
    s <- strsplit(x, " ")[[1]]
    paste(toupper(substring(s, 1, 1)), substring(s, 2),
          sep = "", collapse = " ")
}
.simpleCap("the quick red fox jumps over the lazy brown dog")
## ->  [1] "The Quick Red Fox Jumps Over The Lazy Brown Dog"

## and the better, more sophisticated version:
capwords <- function(s, strict = FALSE) {
    cap <- function(s) paste(toupper(substring(s, 1, 1)),
                  {s <- substring(s, 2); if(strict) tolower(s) else s},
                             sep = "", collapse = " " )
    sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s)))
}
capwords(c("using AIC for model selection"))
## ->  [1] "Using AIC For Model Selection"
capwords(c("using AIC", "for MODEL selection"), strict = TRUE)
## ->  [1] "Using Aic"  "For Model Selection"
##                ^^^        ^^^^^
##               'bad'       'good'

## -- Very simple insecure crypto --
rot <- function(ch, k = 13) {
   p0 <- function(...) paste(c(...), collapse = "")
   A <- c(letters, LETTERS, " '")
   I <- seq_len(k); chartr(p0(A), p0(c(A[-I], A[I])), ch)
}

pw <- "my secret pass phrase"
(crypw <- rot(pw, 13)) #-> you can send this off

## now ``decrypt'' :
rot(crypw, 54 - 13) # -> the original:
stopifnot(identical(pw, rot(crypw, 54 - 13)))
# }

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函数：CRS-class

Class “CRS” Of Coordinate Reference System Arguments
PROJ.4 projection system的接口类。
该类被定义为空存根用来接受sp包中的NA值。
如果rgdal包可用，则该类将允许空间数据与坐标参考系统相关联。
必须完全按照PROJ.4文档中的参数输入参数，特别是+=字符串中不能有任何空格，并且连续的此类字符串只能用空格分隔。
请注意，地理坐标只接受“+proj=longlat +ellps=WGS84”，必须指定（东边，北边）; 对于最新版本的PROJ.4库，必须给出（或从给定的“+datum=”值内部扩展）“+ellps=”定义，并且应该设置为适当的值。

Usage

CRS(projargs, doCheckCRSArgs=TRUE)
identicalCRS(x,y)

Arguments 参数

projargs 字符串类型，表示投影参数；此参数必须完全按照PROJ.4文档输入; 如果投影未知，则使用as.character（NA），它可能会丢失或为零长度的空字符串，然后将设置为缺失值。
doCheckCRSArgs 默认为TRUE，包开发人员必须将其设置为FALSE，包括S4类定义中的CRS，以避免无法控制地加载rgdal命名空间
x 具有proj4string方法的对象，或者如果缺少y，则列出具有proj4string方法的对象
y 类Spatial的对象，或者有proj4string方法的对象

说明

Examples

CRS()
CRS("")
CRS(as.character(NA))
CRS("+proj=longlat +datum=WGS84")
if (require(rgdal)) {
  print(CRSargs(CRS("+proj=longlat +datum=NAD27")))
  print(CRSargs(CRS("+init=epsg:4267")))
  print(CRSargs(CRS("+init=epsg:26978")))
  print(CRSargs(CRS(paste("+proj=sterea +lat_0=52.15616055555555",
 "+lon_0=5.38763888888889 +k=0.999908 +x_0=155000 +y_0=463000 +ellps=bessel",
 " +towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812 +units=m"))))
  print(CRSargs(CRS("+init=epsg:28992")))
}

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函数：crs

Usage

st_crs(x, ...)

"st_crs"(x, ...)

"st_crs"(x, ...)

st_crs(x) <- value

"st_crs"(x) <- value

"st_crs"(x) <- value

Arguments 参数

说明

Examples

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函数：cumsum

简介：
返回一个向量，其元素是参数元素的累积和，乘积，最小值或最大值。

Usage

cumsum(x)
cumprod(x)
cummax(x)
cummin(x)

Arguments 参数

x 数字或复数（不是cummin或cummax）对象，或可以强制转换为其中一种的对象。

说明

Examples

> cumsum(100)
[1] 100
>  cumsum(1:10) #累加
 [1]  1  3  6 10 15 21 28 36 45 55
>  cumprod(1:10) #累乘
 [1]       1       2       6      24     120     720    5040   40320  362880 3628800
>  cummin(c(3:1, 2:0, 4:2)) #后面的数不能大于前一个，大于的按等于前一个处理。
[1] 3 2 1 1 1 0 0 0 0
>  cummax(c(3:1, 2:0, 4:2)) #后面的数一定要大于前一个，小的按等于前一个处理。
[1] 3 3 3 3 3 3 4 4 4

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D

函数：difftime

可以用来将时间转化为R中两个时间之差的格式，以方便进行比较。

Usage

time1 - time2
difftime(time1, time2, tz,
         units = c("auto", "secs", "mins", "hours",
                   "days", "weeks"))

as.difftime(tim, format = "%X", units = "auto")

# S3 method for difftime
format(x, ...)
# S3 method for difftime
units(x)
# S3 method for difftime
units(x) <- value
# S3 method for difftime
as.double(x, units = "auto", ...)

## Group methods, notably for round(), signif(), floor(),
## ceiling(), trunc(), abs(); called directly, *not* as Math():
# S3 method for difftime
Math(x, …)

Arguments 参数

说明

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函数：dim

简介：
检测或设置一个对象的维度参数。

Usage

dim(x)
dim(x) <- value

Arguments 参数

x
一个R对象，比如：矩阵、数组、数据框（data frame）
value
For the default method, either NULL or a numeric vector, which is coerced to integer (by truncation).
对于默认方法，可以是NULL或数值向量，它被强制转换为整数（通过截断）。

说明

维数为value的元素个数，每一维度的长度是value向量对应的分量的值。
比如（3，4，5）向量设置的含义就是，三维，有3行，有4列，有5层，共计60个元素。

Examples

1
2
3

> x <- 1:12 ; dim(x) <- c(3,4)
> x
> View(x)

结果：

m <- matrix(1:12, 3, 4)
dim(m)      # 3 4

d <- data.frame(a = 1:3, b = 4:6, c = 7:9, d = 10:12)
dim(d)     # 3 4

a <- array(1:24, dim = 2:4)
dim(a)     # 2 3 4

v <- 1:12
dim(v)     # NULL

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函数：dist2Line

计算点到线的距离
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函数：distHaversine

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函数：distm

Usage

distm(x, y, fun=distHaversine)

Arguments 参数

x 点的精度/纬度
y 同x，如果缺省，则使用x的值
fun 距离计算函数 distCosine, distHaversine, distVincenty等

说明

http://en.wikipedia.org/wiki/Great_circle_distance

Examples

xy <- rbind(c(0,0),c(90,90),c(10,10),c(-120,-45))
distm(xy)
xy2 <- rbind(c(0,0),c(10,-10))
distm(xy, xy2)

t1=c(103.994191,30.589908)
t2=c(103.994644,30.588221)
aa=rbind(t1=t1,t2=t2)
distm(aa)

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函数：dtw

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F

函数：fix

简介：
相当于可以调用工具对R对象的值进行修改，调用的工具和对象类型有关。

Usage

fix(x, …)

Arguments 参数

说明

Examples

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K

函数：kalmanFilter

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函数：Kinhom.Track

Usage

Kinhom.Track(X,timestamp,correction=c(“border”, “bord.modif”,”isotropic”,”translate”),q,sigma=c(“default”,”bw.diggle”,”bw.ppl”,” bw.scott”),…)

Arguments 参数

说明

Examples

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L

函数：LCSS

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N

函数：names

简介：
查看变量所具有的名称，或给变量修改元素名称

Usage

names(x)
names(x) <- value

Arguments 参数

x R对象
value 一个与x长度相同的字符向量，或NULL
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说明

使用demo

Examples

z <- list(a = 1, b = "c", c = 1:3)
names(z)
# change just the name of the third element.
names(z)[3] <- "c2"
z

z <- 1:3
names(z)
## assign just one name
names(z)[2] <- "b"
z

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函数：Normal 正态

Usage

dnorm(x, mean = 0, sd = 1, log = FALSE)
pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
qnorm(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
rnorm(n, mean = 0, sd = 1)

Arguments 参数

x, q vector of quantiles.
p vector of probabilities.
n number of observations. If length(n) > 1, the length is taken to be the number required.
mean vector of means.
sd
log, log.p
lower.tail

说明

资料1
资料2
资料3

Examples

# NOT RUN {
require(graphics)

dnorm(0) == 1/sqrt(2*pi)
dnorm(1) == exp(-1/2)/sqrt(2*pi)
dnorm(1) == 1/sqrt(2*pi*exp(1))

## Using "log = TRUE" for an extended range :
par(mfrow = c(2,1))
plot(function(x) dnorm(x, log = TRUE), -60, 50,
     main = "log { Normal density }")
curve(log(dnorm(x)), add = TRUE, col = "red", lwd = 2)
mtext("dnorm(x, log=TRUE)", adj = 0)
mtext("log(dnorm(x))", col = "red", adj = 1)

plot(function(x) pnorm(x, log.p = TRUE), -50, 10,
     main = "log { Normal Cumulative }")
curve(log(pnorm(x)), add = TRUE, col = "red", lwd = 2)
mtext("pnorm(x, log=TRUE)", adj = 0)
mtext("log(pnorm(x))", col = "red", adj = 1)

## if you want the so-called 'error function'
erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
## (see Abramowitz and Stegun 29.2.29)
## and the so-called 'complementary error function'
erfc <- function(x) 2 * pnorm(x * sqrt(2), lower = FALSE)
## and the inverses
erfinv <- function (x) qnorm((1 + x)/2)/sqrt(2)
erfcinv <- function (x) qnorm(x/2, lower = FALSE)/sqrt(2)
# }

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M

函数：merge

Usage

merge(x, y, …)
# S3 method for default
merge(x, y, …)
# S3 method for data.frame
merge(x, y, by = intersect(names(x), names(y)),
by.x = by, by.y = by, all = FALSE, all.x = all, all.y = all,
sort = TRUE, suffixes = c(“.x”,”.y”), no.dups = TRUE,
incomparables = NULL, …)

Arguments 参数

x, y
by, by.x, by.y
all
all.x
all.y
sort
suffixes
no.dups
incomparables
…

说明

Examples

# NOT RUN {
authors <- data.frame(
    ## I(*) : use character columns of names to get sensible sort order
    surname = I(c("Tukey", "Venables", "Tierney", "Ripley", "McNeil")),
    nationality = c("US", "Australia", "US", "UK", "Australia"),
    deceased = c("yes", rep("no", 4)))
authorN <- within(authors, { name <- surname; rm(surname) })
books <- data.frame(
    name = I(c("Tukey", "Venables", "Tierney",
             "Ripley", "Ripley", "McNeil", "R Core")),
    title = c("Exploratory Data Analysis",
              "Modern Applied Statistics ...",
              "LISP-STAT",
              "Spatial Statistics", "Stochastic Simulation",
              "Interactive Data Analysis",
              "An Introduction to R"),
    other.author = c(NA, "Ripley", NA, NA, NA, NA,
                     "Venables & Smith"))

(m0 <- merge(authorN, books))
(m1 <- merge(authors, books, by.x = "surname", by.y = "name"))
 m2 <- merge(books, authors, by.x = "name", by.y = "surname")
stopifnot(exprs = {
   identical(m0, m2[, names(m0)])
   as.character(m1[, 1]) == as.character(m2[, 1])
   all.equal(m1[, -1], m2[, -1][ names(m1)[-1] ])
   identical(dim(merge(m1, m2, by = NULL)),
             c(nrow(m1)*nrow(m2), ncol(m1)+ncol(m2)))
})

## "R core" is missing from authors and appears only here :
merge(authors, books, by.x = "surname", by.y = "name", all = TRUE)


## example of using 'incomparables'
x <- data.frame(k1 = c(NA,NA,3,4,5), k2 = c(1,NA,NA,4,5), data = 1:5)
y <- data.frame(k1 = c(NA,2,NA,4,5), k2 = c(NA,NA,3,4,5), data = 1:5)
merge(x, y, by = c("k1","k2")) # NA's match
merge(x, y, by = "k1") # NA's match, so 6 rows
merge(x, y, by = "k2", incomparables = NA) # 2 rows
# }

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O

函数：outer

简介：
The outer product of the arrays X and Y is the array A with dimension c(dim(X), dim(Y)) where element A[c(arrayindex.x, arrayindex.y)] = FUN(X[arrayindex.x], Y[arrayindex.y], …).
数组X和Y的外积是数组A，则数组A的维度是c(dim(X), dim(Y))
元素 A[c(arrayindex.x, arrayindex.y)] = FUN(X[arrayindex.x], Y[arrayindex.y], …)

Usage

outer(X, Y, FUN = “*”, …)
X %o% Y

Arguments 参数

X, Y
FUN

说明

X %o% Y ：

Examples

>test1 <- 1:9
>test2 <- 1:2
> outer(test2, test1, "^")
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,]    1    1    1    1    1    1    1    1    1
[2,]    2    4    8   16   32   64  128  256  512

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P

函数：par

Usage

par(…, no.readonly = FALSE)
(…, = )

Arguments 参数

…
no.readonly

说明

Examples

# NOT RUN {
op <- par(mfrow = c(2, 2), # 2 x 2 pictures on one plot
          pty = "s")       # square plotting region,
                           # independent of device size

## At end of plotting, reset to previous settings:
par(op)

## Alternatively,
op <- par(no.readonly = TRUE) # the whole list of settable par's.
## do lots of plotting and par(.) calls, then reset:
par(op)
## Note this is not in general good practice

par("ylog") # FALSE
plot(1 : 12, log = "y")
par("ylog") # TRUE

plot(1:2, xaxs = "i") # 'inner axis' w/o extra space
par(c("usr", "xaxp"))

( nr.prof <-
c(prof.pilots = 16, lawyers = 11, farmers = 10, salesmen = 9, physicians = 9,
  mechanics = 6, policemen = 6, managers = 6, engineers = 5, teachers = 4,
  housewives = 3, students = 3, armed.forces = 1))
par(las = 3)
barplot(rbind(nr.prof)) # R 0.63.2: shows alignment problem
par(las = 0)  # reset to default

require(grDevices) # for gray
## 'fg' use:
plot(1:12, type = "b", main = "'fg' : axes, ticks and box in gray",
     fg = gray(0.7), bty = "7" , sub = R.version.string)

ex <- function() {
   old.par <- par(no.readonly = TRUE) # all par settings which
                                      # could be changed.
   on.exit(par(old.par))
   ## ...
   ## ... do lots of par() settings and plots
   ## ...
   invisible() #-- now,  par(old.par)  will be executed
}
ex()

## Line types
showLty <- function(ltys, xoff = 0, ...) {
   stopifnot((n <- length(ltys)) >= 1)
   op <- par(mar = rep(.5,4)); on.exit(par(op))
   plot(0:1, 0:1, type = "n", axes = FALSE, ann = FALSE)
   y <- (n:1)/(n+1)
   clty <- as.character(ltys)
   mytext <- function(x, y, txt)
      text(x, y, txt, adj = c(0, -.3), cex = 0.8, ...)
   abline(h = y, lty = ltys, ...); mytext(xoff, y, clty)
   y <- y - 1/(3*(n+1))
   abline(h = y, lty = ltys, lwd = 2, ...)
   mytext(1/8+xoff, y, paste(clty," lwd = 2"))
}
showLty(c("solid", "dashed", "dotted", "dotdash", "longdash", "twodash"))
par(new = TRUE)  # the same:
showLty(c("solid", "44", "13", "1343", "73", "2262"), xoff = .2, col = 2)
showLty(c("11", "22", "33", "44",   "12", "13", "14",   "21", "31"))
# }

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函数：paste

difference-between-paste-and-paste0

Usage

paste (…, sep = “ “, collapse = NULL)
paste0(…, collapse = NULL)

Arguments 参数

…
one or more R objects, to be converted to character vectors.
字符向量，用“,”分隔
sep
a character string to separate the terms. Not NA_character_.
用于分割的字符串
collapse
an optional character string to separate the results. Not NA_character_.
连接符

说明

会将传入的n个向量进行连接，连接次数以向量中分量最多的向量为准，其它向量则循环使用。
sep用来分隔（连接）分量，collapse用来将连接后的分量进行连接为一个字符串。
比如一个向量为（a,b,c,d），另一个向量为（1,2,3），还有（1）。
则连接次数为4。
（1，2，3）则循环为：1，2，3，1
（1）则循环为：1，1，1，1
如果不设置参数，则为：

1 2	> paste(c('a','b','c','d'),c(1,2,3),1) [1] "a 1 1" "b 2 1" "c 3 1" "d 1 1"

如果设置参数，比如sep=‘_’，则：

1 2	> paste(c('a','b','c','d'),c(1,2,3),1,sep = '_') [1] "a_1_1" "b_2_1" "c_3_1" "d_1_1"

如果设置参数，比如sep=‘_’，collapse=’&’则：

1 2	> paste(c('a','b','c','d'),c(1,2,3),1,sep = '_',collapse='&') [1] "a_1_1&b_2_1&c_3_1&d_1_1"

Examples

> paste('one',2,'three',4,'five')
[1] "one 2 three 4 five"
> paste('X',1:5,sep='')
[1] "X1" "X2" "X3" "X4" "X5"
> paste(c('one','two','three','four'),collapse=' and ')
[1] "one and two and three and four"
> paste(c('X','Y'),1:5,sep='_',collapse=' and ')
[1] "X_1 and Y_2 and X_3 and Y_4 and X_5"
> paste(1:5,6:9)
[1] "1 6" "2 7" "3 8" "4 9" "5 6"
> paste(1:5,6:9,10:11)
[1] "1 6 10" "2 7 11" "3 8 10" "4 9 11" "5 6 10"
> paste(1:2,6:9,10:11)
[1] "1 6 10" "2 7 11" "1 8 10" "2 9 11"
> paste(1:2,6:9,sep='_',collapse=' and ')
[1] "1_6 and 2_7 and 1_8 and 2_9"
> paste(1:2,6:9,10:11,sep='_',collapse=' and ')
[1] "1_6_10 and 2_7_11 and 1_8_10 and 2_9_11"

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函数：plot

画图函数

Usage

plot(x, y, …)

Arguments 参数

x plot中点的坐标。或者，任何可以提供单个绘图结构，函数或具有绘图方法的R对象。
y plot中点的y坐标，如果x是合适的结构（比如包含了完善的位置信息），则可以没有y。
… 传递给方法的参数，比如 graphical parameters (see par)。许多方法接受以下参数：
- type plot应该画什么类型的图形，可能是以下图形中的一种：
  - "p" 点
  - "l" 线
  - "b" for both,
  - "c" for the lines part alone of "b",
  - "o" for both ‘overplotted’,
  - "h" 直方图 like (or ‘high-density’) vertical lines,
  - "s" 阶梯图
  - "S" for other steps, see ‘Details’ below,
  - "n" for no plotting.
- main 画出的图的标题: see title.
- sub 子标题: see title.
- xlab x轴标题: see title.
- ylab y轴标题: see title.
- asp y / x纵横比, see plot.window.

说明

Examples

# NOT RUN {
require(stats) # for lowess, rpois, rnorm
plot(cars)
lines(lowess(cars))

plot(sin, -pi, 2*pi) # see ?plot.function

## Discrete Distribution Plot:
plot(table(rpois(100, 5)), type = "h", col = "red", lwd = 10,
     main = "rpois(100, lambda = 5)")

## Simple quantiles/ECDF, see ecdf() {library(stats)} for a better one:
plot(x <- sort(rnorm(47)), type = "s", main = "plot(x, type = \"s\")")
points(x, cex = .5, col = "dark red")
# }

plot(1:100, (1:100) ^ 2, main = "plot(1:100, (1:100) ^ 2)")

plot(
  1:25, 
  cex  = 3,
  lwd  = 3,
  pch  = 1:25, 
  col  = rainbow(25), 
  bg   = c(rep(NA, 20), terrain.colors(5)),
  main = "plot(1:25, pch = 1:25, ...)"
)

plot(
  (1:100) ^ 2, 
  type = "l", 
  main = 'plot((1:100) ^ 2, type = "l")'
)

plot(
  (1:100) ^ 2, 
  type = "l", 
  lty  = "dashed",
  lwd  = 3,
  col  = "chocolate", 
  main = 'plot((1:100) ^ 2, type = "l", lty = "dashed", ...)'
)

with(
  cars, 
  plot(speed, dist, main = "with(cars, plot(speed, dist))")
)

官网案例挺多的

官网简介
 参考资料1–R语言plot函数参数合集

Q

函数：q1

Usage

rep(x, …)
rep.int(x, times)
rep_len(x, length.out)

Arguments 参数

x
a vector (of any mode including a list) or a factor or (for rep only) a POSIXct or POSIXlt or Date object; or an S4 object containing such an object.
…
further arguments to be passed to or from other methods. For the internal default method these can include:
times
an integer-valued vector giving the (non-negative) number of times to repeat each element if of length length(x), or to repeat the whole vector if of length 1. Negative or NA values are an error. A double vector is accepted, other inputs being coerced to an integer or double vector.
length.out
non-negative integer. The desired length of the output vector. Other inputs will be coerced to a double vector and the first element taken. Ignored if NA or invalid.
each
non-negative integer. Each element of x is repeated each times. Other inputs will be coerced to an integer or double vector and the first element taken. Treated as 1 if NA or invalid.
times, length.out
see … above

说明

Examples

官网简介

R

函数：rep

Usage

rep(x, …)
rep.int(x, times)
rep_len(x, length.out)

Arguments 参数

x
…
times：each处理后的重复次数
len：总分量个数
each：每个重复多少次
times, length.out

说明

依次把值赋值给 times -> len -> each
作用的优先级是 len > times > each
即先each生成一个向量，然后times处理后生成一个向量，最后len处理得到最终的向量

Examples

rep(2:5, 2) 
rep(2:5, 2,2,2,2)

rep(2:5, 2,2,2)
# 依次把值赋值给 times -> len -> each
# 作用的优先级是 len > times > each
# 即先each生成一个向量，然后times处理后生成一个向量，最后len处理得到最终的向量


# NOT RUN {
rep(1:4, 2)
rep(1:4, each = 2, len = 4)       # not the same.
rep(1:4, c(2,2,2,2), len = 4)     # same as second.


rep(1:4, c(2,1,2,1), len = 4)

rep(1:4, each = 2, len = 4)    # first 4 only.
rep(1:4, each = 2, len = 10)   # 8 integers plus two recycled 1's.
rep(1:4, each = 2, times = 3)  # length 24, 3 complete replications
rep(1:4, each = 2, times = 3,len =10)  # length 24, 3 complete replications

rep(1, 40*(1-.8)) # length 7 on most platforms
rep(1, 40*(1-.8)+1e-7) # better

## replicate a list
fred <- list(happy = 1:10, name = "squash")
rep(fred, 5)

# date-time objects
x <- .leap.seconds[1:3]
rep(x, 2)
rep(as.POSIXlt(x), rep(2, 3))

## named factor
x <- factor(LETTERS[1:4]); names(x) <- letters[1:4]
x
rep(x, 2)
rep(x, each = 2)
rep.int(x, 2)  # no names
rep_len(x, 10)
# }

官网简介

函数：rnorm

Usage

rnorm.acomp(n,mean,var)
rnorm.rcomp(n,mean,var)
rnorm.aplus(n,mean,var)
rnorm.rplus(n,mean,var)
rnorm.rmult(n,mean,var)
rnorm.ccomp(n,mean,var,lambda)
dnorm.acomp(x,mean,var)
dnorm.aplus(x,mean,var)
dnorm.rmult(x,mean,var)

Arguments 参数

n 要模拟的数据集数量
mean 要模拟的数据集的平均值
var 方差协方差矩阵
lambda 预计总数
x 采样空间中的向量

说明

Examples

# NOT RUN {
MyVar <- matrix(c(
0.2,0.1,0.0,
0.1,0.2,0.0,
0.0,0.0,0.2),byrow=TRUE,nrow=3)
MyMean <- c(1,1,2)

plot(rnorm.acomp(100,MyMean,MyVar))
plot(rnorm.rcomp(100,MyMean,MyVar))
plot(rnorm.aplus(100,MyMean,MyVar))
plot(rnorm.rplus(100,MyMean,MyVar))
plot(rnorm.rmult(100,MyMean,MyVar))
x <- rnorm.aplus(5,MyMean,MyVar)
dnorm.acomp(x,MyMean,MyVar)
dnorm.aplus(x,MyMean,MyVar)
dnorm.rmult(x,MyMean,MyVar)
# }

官网简介

S

函数：sample

Usage

sample(x, size, replace = FALSE, prob = NULL)
sample.int(n, size = n, replace = FALSE, prob = NULL,
useHash = (!replace && is.null(prob) && size <= 2=”” n=”” &&=””> 1e7))

Arguments 参数

x ：提供的采样范围、训练集
n ：最终需要
size ：总计需要抽取多少个样本
replace ：是否放回，如果是，则抽出的结果可能有重复的，反之，则没有
prob ：概率
useHash ：

说明

Examples

# NOT RUN {
x <- 1:12
# a random permutation
sample(x)
# bootstrap resampling -- only if length(x) > 1 !
sample(x, replace = TRUE) #相当于有重复的组合，用的是x中的元素

# 100 Bernoulli trials
sample(c(0,1), 100, replace = TRUE)

## More careful bootstrapping --  Consider this when using sample()
## programmatically (i.e., in your function or simulation)!

# sample()'s surprise -- example
x <- 1:10
sample(x[x >  8]) # length 2
sample(x[x >  9]) # oops -- length 10!
sample(x[x > 10]) # length 0

## safer version:
resample <- function(x, ...) x[sample.int(length(x), ...)]
resample(x[x >  8]) # length 2
resample(x[x >  9]) # length 1
resample(x[x > 10]) # length 0

## R 3.x.y only
sample.int(1e10, 12, replace = TRUE)
sample.int(1e10, 12) # not that there is much chance of duplicates
# }

官网简介

函数：scale

Usage

scale(x, center = TRUE, scale = TRUE)

Arguments 参数

x 数字矩阵
center 长度等于x的列数的逻辑值或类似数字的向量，其中’numeric-alike’表示如果is.numeric（）不为true，即不为数字，则as.numeric（）将应用，即强制转换为数字。
scale 长度等于x的列数的逻辑值或类似数字的向量

说明

在我们做数据的时候，一个数据会有很多特征；比如在描述影响房价的因素，有房子面积，房间数量等。而不同的特征存在不同的量纲，为了消除量纲、数值差异等，我们就需要对数据进行中心化和标准化；

那什么是中心化，什么是标准化呢？
所谓中心化就是将数据减去均值后得到的，比如有一组数据(1，2，3，4，5，6，7)，它的均值是4，中心化后的数据为(-3，-2，-1，0，1，2，3)
而标准化则是在中心化后的数据基础上再除以数据的标准差
在R语言中可以通过scale函数直接进行数据的中心化和标准化，具体如下：

Scale(x,center,scale)
参数解释：x—即需要标准化的数据
center—表示是否进行中心化
scale—表示是否进行标准化

1、数据的中心化
所谓数据的中心化是指数据集中的各项数据减去数据集的均值。
例如有数据集1, 2, 3, 6, 3，其均值为3
那么中心化之后的数据集为1-3,2-3,3-3,6-3,3-3，即：-2,-1,0,3,0

2、数据的标准化
所谓数据的标准化是指中心化之后的数据在除以数据集的标准差，即数据集中的各项数据减去数据集的均值再除以数据集的标准差。
例如有数据集1, 2, 3, 6, 3，其均值为3，其标准差为1.87
那么标准化之后的数据集为(1-3)/1.87,(2-3)/1.87,(3-3)/1.87,(6-3)/1.87,(3-3)/1.87，即：-1.069,-0.535,0,1.604,0

Examples

官网简介
 参考资料1
参考资料2

函数：seq

参考资料1
参考资料2

函数：sink

说明

Examples

简介：
sink将R的输出转移到连接（并在之后使用sink停止这种转移）
sink.number() 显示有多少个正在使用的连接数
sink.number(type = “message”) 报告当前用于错误消息的连接数

Usage

sink(file = NULL, append = FALSE, type = c(“output”, “message”),split = FALSE)
sink.number(type = c(“output”, “message”))

Arguments 参数

file a writable connection or a character string naming the file to write to, or NULL to stop sink-ing.
一个可写的连接或要写入的文件的文件名或NULL（用来停止sink）
append
值为TRUE/FALSE，如果为TRUE，输出会追加到文件；否则，重写文件，不保留之前的内容（如果有的话）
type
字符串类型，输出流或消息流，名称将部分匹配，因此可以缩写。
split
值为TRUE/FALSE，如果为TRUE，输出将被发送到新的sink和当前的输出流，就像Unix程序tee一样。

说明

Examples

# NOT RUN {
sink("sink-examp.txt")
i <- 1:10
outer(i, i, "*")
sink()
# }
# NOT RUN {

# }
# NOT RUN {
## capture all the output to a file.
zz <- file("all.Rout", open = "wt")
sink(zz)
sink(zz, type = "message") #会将日志写入输出流中去	
try(log("a"))
## revert output back to the console -- only then access the file!
sink(type = "message")
sink()
file.show("all.Rout")
# }

官网简介

函数： stplot

Usage

Arguments 参数

说明

Examples

官网简介

T

函数：tracks

Usage

Arguments 参数

说明

Examples

官网简介

U

函数：unlist

Usage

unlist(x, recursive = TRUE, use.names = TRUE)

Arguments 参数

x R对象，通常是列表或向量
recursive 逻辑值是否应该将非列表化应用于x的列表组件
use.names 逻辑值名字是否保留

说明

Examples

# NOT RUN {
unlist(options())
unlist(options(), use.names = FALSE)

l.ex <- list(a = list(1:5, LETTERS[1:5]), b = "Z", c = NA)
unlist(l.ex, recursive = FALSE)
unlist(l.ex, recursive = TRUE)

l1 <- list(a = "a", b = 2, c = pi+2i)
unlist(l1) # a character vector
l2 <- list(a = "a", b = as.name("b"), c = pi+2i)
unlist(l2) # remains a list

ll <- list(as.name("sinc"), quote( a + b ), 1:10, letters, expression(1+x))
utils::str(ll)
for(x in ll)
  stopifnot(identical(x, unlist(x)))
# }

官网简介