R language Coding

 R language Coding

# starting...

x=1
y=4
plot(x,y,col="red",pch=19,main="gaurav point",xlab="hello")
plot(dbinom,0,3,col="red",pch=19,main="gaurav point",xlab="hello")
mean(x,y)
mean(x)
mean(y)
clear
rm()
ss=sample(c(1,2,3,4,5,6),size=100,replace=TRUE)
factorial(5)
choose(4,6)
table(ss/length(ss))
table()

#to see the current directory
getwd()

#to set the directory as current working directory
setwd("C:/Users/gaura/OneDrive/Desktop/r language/data")

#read data form csv file
mydata = read.csv("C:/Users/gaura/OneDrive/Desktop/r language/data/marks.csv")

#print data 
print(mydata)

somedata=mydata[1:3]
print(somedata)

#creating and writing data in a file
write.csv(somedata,"somedata.csv")

read.csv("C:/Users/gaura/OneDrive/Desktop/r language/data/somedata.csv")

x=rdunif(100,1,20)

# Defining Function
rdu<-function(n,k){sample(1:k,n,replace=T)}

pdu<-function(x,k){ifelse(x<1,0,ifelse(x<=k,floor(x)/k,1))}

#calling fucntion
pdu(5,6)

rdu(10,5)

#doubt
x1= rdunif(100,1,20)

hist(x1)


x=sample((80:100),100,replace = TRUE)
length(x)
x
hist(x)
table(x)

rand.unif<-runif(1000,min=-2,max=0.8)
hist(rand.unif,freq=FALSE,density=90)
plot(table(x),type="s")

mean(x)
var(x)

barplot(table(x)/length(x),ylim=c(0,0.3))

require(graphics)
library(graphics)
probabilities<-100*dbinom(x=c(0:10,probabilities,type="h"))

dbinom(x=c(0:4), size = 10, prob = 1/3)

Mam code 

#import export all types of files

install.packages("purrr")

library(purrr)

#E:\1 Teaching\GGSIPU\Probability and statistics May 2022

getwd()

setwd("E:/1 Teaching/GGSIPU/Probability and statistics May 2022")

mydata=read.csv("AIML B2 attendence sheet.csv")

setwd("E:/1 Teaching/GGSIPU/Probability and statistics May 2022")

setwd("C:/Users/DELL/OneDrive/Documents")

print(mydata)# print mydata

mydata = read.csv("E:/1 Teaching/GGSIPU/Probability and statistics May 2022/AIML B2 attendence sheet.csv")

read.csv("E:/1 Teaching/GGSIPU/Probability and statistics May 2022/example 2.csv")

outputdata=mydata[2:3]

write.csv(outputdata,"outputdata_example.csv")

write.csv(outputdata,"E:/1 Teaching/GGSIPU/Probability and statistics May 2022/outputdata_example.csv")

#fitting of binomial, poisson

#mean, variance, histogram, pdf, cdf, etc for distribution

#show graphs of all distributions with different sample

#size and actual graph also(take image from net and make

#word ppt for them)

#Discrete Uniform in (1,20)

rdu<-function(n,k) {sample(1:k,n,replace=T)}

pdu<-function(x,k) {ifelse(x<1,0,ifelse(x<=k,floor(x)/k,1))}

rdu(10)

x=rdunif(1000,1,20)

#1 to k, mean=(k+1)/2

hist(x)

length(x[(x==1)])

length(x[(x==4)])

table(x)

rand.unif <- runif(10000, min = -2, max = 0.8)

hist(rand.unif, freq = FALSE, xlab = 'x', density = 20)

plot(table(x)/length(x))

plot(table(x),type="l")

plot(table(x),type="s")

mean(x)#actual mean is k+1/2

var(x)#actual variance is k^2-1/12

barplot(table(x)/length(x),ylim=c(0,.3))

rdunif(10000,1,100)

x=rdunif(10000,1,100)

k=100

(k+1)/2#theoritical_mean

(k^2-1)/12#theoritical_var

mean(x)

var(x)

#Binomial

#P(Y=6)P(Y=6) if Y∼b(n=10,p=0.75)

probabilities <- 100*dbinom(x = c(0:10), size = 10, prob = 1 / 6)

data.frame(x, probabilities)#distribution tableplot(0:10, probabilities, type = "h")

plot(x, probabilities, type = "h")#pmf function

dbinom(x = 0:4, size = 4, prob = 0.75)#The probability of flipping an unfair coin 10 times and seeing 6 heads, if the probability of heads is 0.75

#

probabilities=dbinom(x = 0:10, size = 10, prob = 0.05)

hist(probabilities)

#P(X<=x)>=.1 Quantile function

#dbinom(k, n, p) P(X=k)

dbinom(6,10,0.75)

k=0:10

plot(k,dbinom(k,10,0.75),type="l")

k=8

pbinom(k,10,0.75)#P(X<=k) CDF function

k=c(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1)

#probability mass function of binomial plot

n=10

x <- 0:n

p=0.6

plot(x, dbinom(x, size = n, prob = p), main = "Probability mass function for Bin(13,0.7)")

#P(X<=x)>=0.5, smallest x

qbinom(0.75, 10, 0.75)#median(50) 25th quantile P(X<=x)=>25/100 minimum x, 25th, 50th, 75th

x=rbinom(1000, 10, 0.75)

#QP(X<=x)>=0.66

qbinom(0.66, 10, 0.75)

cdfprobabilities = dbinom(x = c(0:10), size = 10, prob = 1/6)

data.frame(probabilities)#give all the probabilities

#pbinom(k, n, p) : P(X<=k)

pbinom(3, size = 13, prob = 1/6)

pbinom(13, size = 13, prob = 1/6)

pbinom(3, size = 13, prob = 1 / 6)

plot(0:10, pbinom(0:10, size = 10, prob = 0.8), type = "l")#CDF

#qbinom():This function is used to find the nth quantile, that is if P(x <= k) is given, it finds k.

#qbinom(P, n, p) P(X<=k)=P, K=?

qbinom(0.8419226, size = 13, prob = 1 / 6)

x <- seq(0, 1, by = 0.1)

y <- qbinom(x, size = 13, prob = 1 / 6)

plot(x, y, type = 'l')

#rbinom(n, N, p): generates n random binomial variables

x=rbinom(10000, size = 13, prob = 1 / 6)

hist(rbinom(10000, size = 13, prob = 1 / 6))

print("graph of binomial")

x=rbinom(10000,15,0.5)

 15*0.5#theoritical_mean

 15*0.5*0.5#theoritical_var

mean(x)

var(x)

######################

#Poisson

x=5

lambda=0.5

dpois(x,lambda)#gives the density

ppois(x,lambda)#gives the cdf

qpois(p,lambda)#gives quantile function, which is the smallest

#integer x such that P(X<=x)>=p

rpois(n,lambda)#generates a random poisoon number

k=0:200

prob=ppois(k,lambda=100)#P(X<=k)

plot(x=k,y=prob, main="Poisson Probability Mass Function",type="h")

dpois(x=2,lambda=4)#P(X=2)

dpois(2,4)

#P(2<X<=4)=F(4)-F(2)

ppois(4,4)-ppois(2,4)

n=100

lambda=5

k=0:n

pmfpois=dpois(k,lambda)

plot(k,pmfpois,type="h")

x=rpois(100000,4)

mean(x)

var(x)

###############3

print("Math")

x <- "Probability and statistics"

print(x)

for(i in 1:20) print(1:i)

##################

##geometric: definition in R is slightly different,

#k is the number of failures before a success

#p(x)=p(1-p)^x,x=0,1,2,..

k=0:10

prob=dgeom(x=k,prob=0.6)

#P(X=4) for our defined distribution will be

dgeom(x=4-1,prob=0.6)#p(X=4)

plot(x=k,y=prob, main="Geometric distribution PMF",type="h")

pgeom(x=4-1,prob=0.6)#P(X<=4)

qgeom(p,0.6)#gives x such that P(X<=x+1)>=p

x=rgeom(n=10,p=0.6)

mean(x)

var(x)