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📜  R内置函数

📅  最后修改于: 2021-01-08 09:39:08             🧑  作者: Mango

R内置功能

在编程框架中已经创建或定义的功能称为内置函数。 R具有丰富的功能集，可用于为用户执行几乎所有任务。这些内置功能根据其功能分为以下几类。

数学函数

R提供了各种数学函数来执行数学计算。这些数学函数对查找绝对值，平方值和更多计算非常有用。在R中，使用了以下功能：

S. No Function Description Example
1. abs(x) It returns the absolute value of input x. 
x<- -4
print(abs(x))

Output

[1]  4
2. sqrt(x) It returns the square root of input x. 
x<- 4
print(sqrt(x))

Output

[1]  2
3. ceiling(x) It returns the smallest integer which is larger than or equal to x. 
x<- 4.5
print(ceiling(x))

Output

[1]  5
4. floor(x) It returns the largest integer, which is smaller than or equal to x. 
x<- 2.5
print(floor(x))

Output

[1]  2
5. trunc(x) It returns the truncate value of input x. 
x<- c(1.2,2.5,8.1)
print(trunc(x))

Output

[1]  1  2  8
6. round(x, digits=n) It returns round value of input x. 
x<- -4
print(abs(x))

Output

4
7. cos(x), sin(x), tan(x) It returns cos(x), sin(x) value of input x. 
x<- 4
print(cos(x))
print(sin(x))
print(tan(x))

Output

[1]  -06536436
[2]  -0.7568025
[3]  1.157821
8. log(x) It returns natural logarithm of input x. 
x<- 4
print(log(x))

Output

[1]  1.386294
9. log10(x) It returns common logarithm of input x. 
x<- 4
print(log10(x))

Output

[1]  0.60206
10. exp(x) It returns exponent. 
x<- 4
print(exp(x))

Output

[1]  54.59815

字符串功能

R提供了各种字符串函数来执行任务。这些字符串函数使我们能够从字符串，搜索模式等中提取子字符串。R中包含以下字符串函数：

S. No Function Description Example
1. substr(x, start=n1,stop=n2) It is used to extract substrings in a character vector. 
a <- "987654321"
substr(a, 3, 3)

Output

[1]  "3"
2. grep(pattern, x , ignore.case=FALSE, fixed=FALSE) It searches for pattern in x. 
st1 <- c('abcd','bdcd','abcdabcd')
pattern<- '^abc'
print(grep(pattern, st1))

Output

[1]  1  3
3. sub(pattern, replacement, x, ignore.case =FALSE, fixed=FALSE) It finds pattern in x and replaces it with replacement (new) text. 
st1<- "England is beautiful but no the part of EU"
sub("England', "UK", st1)

Output

[1]  "UK is beautiful but not a part of EU"
4. paste(…, sep=””) It concatenates strings after using sep string to separate them. 
paste('one',2,'three',4,'five')

Output

[1]  one 2 three 4 five
5. strsplit(x, split) It splits the elements of character vector x at split point. 
a<-"Split all the character"
print(strsplit(a, ""))

Output

[[1]]
[1]  "split"    "all"     "the"     "character"
6. tolower(x) It is used to convert the string into lower case. 
st1<- "shuBHAm"
print(tolower(st1))

Output

[1]  shubham
7. toupper(x) It is used to convert the string into upper case. 
st1<- "shuBHAm"
print(toupper(st1))

Output

[1]  SHUBHAM

统计概率函数

R提供了各种统计概率函数来执行统计任务。这些统计功能对于查找法线密度，法线分位数和更多计算非常有用。在R中，使用了以下功能：

S. No Function Description Example
1. dnorm(x, m=0, sd=1, log=False) It is used to find the height of the probability distribution at each point to a given mean and standard deviation 
a <- seq(-7, 7, by=0.1)
b <- dnorm(a, mean=2.5, sd=0.5)
png(file="dnorm.png")
plot(x,y)
dev.off()
2. pnorm(q, m=0, sd=1, lower.tail=TRUE, log.p=FALSE) it is used to find the probability of a normally distributed random numbers which are less than the value of a given number. 
a <- seq(-7, 7, by=0.2)
b <- dnorm(a, mean=2.5, sd=2)
png(file="pnorm.png")
plot(x,y)
dev.off()
3. qnorm(p, m=0, sd=1) It is used to find a number whose cumulative value matches with the probability value. 
a <- seq(1, 2, by=002)
b <- qnorm(a, mean=2.5, sd=0.5)
png(file="qnorm.png")
plot(x,y)
dev.off()
4. rnorm(n, m=0, sd=1) It is used to generate random numbers whose distribution is normal. 
y <- rnorm(40)
png(file="rnorm.png")
hist(y, main="Normal Distribution")
dev.off()
5. dbinom(x, size, prob) It is used to find the probability density distribution at each point. 
a<-seq(0, 40, by=1)
b<- dbinom(a, 40, 0.5)
png(file="pnorm.png")
plot(x,y)
dev.off()
6. pbinom(q, size, prob) It is used to find the cumulative probability (a single value representing the probability) of an event. 
a <- pbinom(25, 40,0.5)
print(a)

Output

[1]  0.9596548
7. qbinom(p, size, prob) It is used to find a number whose cumulative value matches the probability value. 
a <- qbinom(0.25, 40,01/2)
print(a)

Output

[1]  18
8. rbinom(n, size, prob) It is used to generate required number of random values of a given probability from a given sample. 
a <- rbinom(6, 140,0.4)
print(a)

Output

[1]  55  61  46  56  58  49
9. dpois(x, lamba) it is the probability of x successes in a period when the expected number of events is lambda (λ) 
dpois(a=2, lambda=3)+dpois(a=3, lambda=3)+dpois(z=4, labda=4)

Output

[1]  0.616115
10. ppois(q, lamba) It is a cumulative probability of less than or equal to q successes. 
ppois(q=4, lambda=3, lower.tail=TRUE)-ppois(q=1, lambda=3, lower.tail=TRUE)

Output

[1]  0.6434504
11. rpois(n, lamba) It is used to generate random numbers from the poisson distribution. 
rpois(10, 10)
[1]  6  10  11  3  10  7  7  8  14  12
12. dunif(x, min=0, max=1) This function provide information about the uniform distribution on the interval from min to max. It gives the density. 
dunif(x, min=0, max=1, log=FALSE)
13. punif(q, min=0, max=1) It gives the distributed function 
punif(q, min=0, max=1, lower.tail=TRUE, log.p=FALSE)
14. qunif(p, min=0, max=1) It gives the quantile function. 
qunif(p, min=0, max=1, lower.tail=TRUE, log.p=FALSE)
15. runif(x, min=0, max=1) It generates random deviates. 
runif(x, min=0, max=1)

其他统计功能

除上述功能外，还有一些其他有用的功能可用于统计目的。有以下功能：

S. No Function Description Example
1. mean(x, trim=0, na.rm=FALSE) It is used to find the mean for x object 
a<-c(0:10, 40)
xm<-mean(a)
print(xm)

Output

[1]  7.916667
2. sd(x) It returns standard deviation of an object. 
a<-c(0:10, 40)
xm<-sd(a)
print(xm)

Output

[1]  10.58694
3. median(x) It returns median. 
a<-c(0:10, 40)
xm<-meadian(a)
print(xm)

Output

[1]  5.5
4. quantilie(x, probs) It returns quantile where x is the numeric vector whose quantiles are desired and probs is a numeric vector with probabilities in [0, 1]
5. range(x) It returns range. 
a<-c(0:10, 40)
xm<-range(a)
print(xm)

Output

[1]  0  40
6. sum(x) It returns sum. 
a<-c(0:10, 40)
xm<-sum(a)
print(xm)

Output

[1]  95
7. diff(x, lag=1) It returns differences with lag indicating which lag to use. 
a<-c(0:10, 40)
xm<-diff(a)
print(xm)

Output

[1]  1  1  1  1  1  1  1  1  1  1  30
8. min(x) It returns minimum value. 
a<-c(0:10, 40)
xm<-min(a)
print(xm)

Output

[1]  0
9. max(x) It returns maximum value 
a<-c(0:10, 40)
xm<-max(a)
print(xm)

Output

[1]  40
10. scale(x, center=TRUE, scale=TRUE) Column center or standardize a matrix. 
a <- matrix(1:9,3,3)
scale(x)

Output

[,1]
 [1,] -0.747776547
 [2,] -0.653320562
 [3,] -0.558864577
 [4,] -0.464408592
 [5,] -0.369952608
 [6,] -0.275496623
 [7,] -0.181040638
 [8,] -0.086584653
 [9,]  0.007871332
[10,]  0.102327317
[11,]  0.196783302
[12,]  3.030462849
attr(,"scaled:center")
[1] 7.916667
attr(,"scaled:scale")
[1] 10.58694