Create a new version of paste:

// - if else
x<-as.integer(readline(“Enter first number:”))
Enter first number:10
y<-as.integer(readline(“Enter second number:”))
Enter second number:20
z<-as.integer(readline(“Enter third number:”))
Enter third number:30
if (x > y && x > z) {
 print(paste("The greatest number is:", x))
 } else if (y > z) {
 print(paste("The greatest number is:", y))
 } else {
 print(paste("The greatest number is:", z))
 } 
// - switch
month <- as.integer(readline(“Enter month number:”))
Enter month number:5
season <- switch(month,
"Winter","Spring","Spring","Summer","Summer"," Summer ","Summer",
"Rainy","Rainy","Rainy","Winter","Winter")
print(season) 

// looping 
// for loop
n <- as.integer(readline(“Enter a number:”)
Enter a number:5
sum <- 0
for(i in 1:n){
 sum=sum+i
}
print(sum) 

// while loop
n <- as.integer(readline(“Enter a number:”)
Enter a number:6
sum <- 0
i <- 1
while(i<=n){
 sum=sum+i
 i=i+1
}
print(sum) 
                
// factorial
num=readline("Enter a Number:")
Enter a Number:10
num=as.integer(num)
factorial=1
if(num<0)
 {
 print("Factorial does not exisit")
 } else if(num==0){
 print("Factorial of 0 is 1")
 } else{
 for(i in 1:num)
 {
 factorial=factorial*i
 }
 print(paste("Factorial of the Given Number:" ,factorial))
 } 

// factorial
# take input from the user
num = as.integer(readline(prompt="Enter a number: "))
factorial = 1
# check is the number is negative or possitive
if(num < 0) {
print("Not possible for negative numbers")
} else if(num == 0) {
print("The factorial of 0 is 1")
} else {
for(i in 1:num) {
	factorial = factorial * i
}
print(paste("The factorial of", num ,"is",factorial))
}

// prime number
isprime <- function(n) {
    lim <- n/2
    prime <- T
    for( i in 2:lim) {
      if(n %% i == 0)
        prime <- FALSE
     
  }

if(n==2) prime <- T
    if(prime) print(paste(n," is a Prime Number"))
    else print(paste(n," is a Composite Number"))
}

------------------------------------------------------------
#Implementation of Random Forest using Iris Dataset
------------------------------------------------------------
library(readxl)
iris<-read_excel("C:/Downloads/iris.xlsx")
#Loadingdata
data(iris)
head(iris)
tail(iris)
#Structure
str(iris)
#Installingpackages
install.packages("caTools")
library(caTools)
install.packages("randomForest")
library(randomForest)
install.packages("caret")
library(caret)
#Splittingdataintotrainingandtestingsets
split<-caTools::sample.split(iris,SplitRatio=0.7)
split
train<-subset(iris,split=="TRUE")
test<-subset(iris,split=="FALSE")
#FittingRandomForesttothetraindataset
control<-trainControl(method="repeatedcv",number=10,repeats=3)
seed<-7
metric<-"Accuracy"
set.seed(seed)
rf<-train(Species~.,data=iris,method="rf",metric=metric,tuneLength=15,
trControl=control)
print(rf)
# Grid Search
tunegrid <- expand.grid(.mtry=c(1:4))
rf_gridsearch <- train(Species~., data=iris, method="rf", metric=metric,
 tuneGrid=tunegrid, trControl=control)
print(rf_gridsearch)
plot(rf_gridsearch)

--------------------------------------
// Titanic Survival Prediction using Naive Bayes Algorithm
---------------------------------------

install.packages(c("dplyr", "caret", "e1071", "ggplot2"))
library(dplyr)
library(caret)
library(e1071)
library(ggplot2)

titanic <- read.csv("titanic_data.csv") %>%
  select(survived, pclass, sex, sibsp, parch) %>%
  na.omit()

titanic$survived <- factor(titanic$survived)
titanic$pclass <- factor(titanic$pclass, levels = c(3, 2, 1))

ggplot(titanic, aes(x = survived)) + 
  geom_bar(width = 0.5, fill = "coral") + 
  geom_text(stat = 'count', aes(label = stat(count)), vjust = -0.5) + 
  theme_classic()

train_test_split <- function(data, fraction = 0.8, train = TRUE) {
  total_rows <- nrow(data)
  train_rows <- fraction * total_rows
  sample <- sample.int(total_rows, train_rows)
  if (train) {
    return(data[sample, ])
  } else {
    return(data[-sample, ])
  }
}

train <- train_test_split(titanic)
test <- train_test_split(titanic, train = FALSE)

nb_model <- naiveBayes(survived ~., data = train)
nb_predict <- predict(nb_model, test)
table_mat <- table(nb_predict, test$survived)
table_mat

nb_accuracy <- sum(diag(table_mat)) / sum(table_mat)
paste("The accuracy is : ", nb_accuracy)


------------------------------------------------------------
#titanic survival
------------------------------------------------------------
# Load necessary libraries
library(dplyr)
library(e1071)
library(ggplot2)
# Load the Titanic dataset
titanic <- read.csv("C:\\Downloads\\titanic_data.csv")
# Keep only the relevant columns
titanic <- titanic %>%
 select(survived, pclass, sex, sibsp, parch) %>%
 na.omit()
# Convert columns to factors
titanic$survived <- factor(titanic$survived)
titanic$pclass <- factor(titanic$pclass, levels = c(3, 2, 1))
# Plot the survival distribution
ggplot(titanic, aes(x = survived)) +
 geom_bar(width = 0.5, fill = "coral") +
 geom_text(stat = 'count', aes(label = stat(count)), vjust = -0.5) +
 theme_classic()
# Build the Naive Bayes model
nb_model <- e1071::naiveBayes(survived ~ ., data = titanic)
# Make predictions on the test set
nb_predict <- predict(nb_model, titanic)
# Create a confusion matrix
table_mat <- table(nb_predict, titanic$survived)
table_mat
# Calculate accuracy
nb_accuracy <- sum(diag(table_mat)) / sum(table_mat)
paste("The accuracy is:", nb_accuracy)

-----------------------
K Means Clustering
-------------------------

install.packages("factoextra")
library(factoextra)

mydata <- read.csv("D:/R programming/USArrests.csv")

USdata <- scale(mydata[, -1])

kmeans_results <- lapply(2:5, function(k) {
  kmeans(USdata, centers = k, nstart = 20)
})

cluster_assignments <- lapply(kmeans_results, function(result) result$cluster)

lapply(kmeans_results, function(result) fviz_cluster(result, data = USdata))

fviz_nbclust(USdata, FUN = kmeans, method = "silhouette")

------------------------------------------------------------
#is_prime
------------------------------------------------------------
function(n) {
 lim <- n/2
 prime <- TRUE
 for(i in 2:lim) {
 if(n %% i == 0) {
 prime <- FALSE
 break
 }
 }

 if(n == 2) prime <- TRUE
 if(prime) {
 print(paste(n, " is a Prime Number"))
 } else {
 print(paste(n, " is a Composite Number"))
 }
}
------------------------------------------------------------
#data_visualization
------------------------------------------------------------
mydata<-read.csv("C:/Users/hp/Downloads/airquality.csv")
str(mydata)
# Horizontal Bar Plot for Ozone concentration in air
barplot(airquality$Ozone,
 main = 'Ozone Concenteration in air',
 xlab = 'ozone levels', horiz = TRUE)
# Vertical Bar Plot for Ozone concentration in air
barplot(airquality$Ozone, main = 'Ozone Concenteration in air',
 xlab = 'ozone levels', col ='blue', horiz = FALSE)
# Histogram for Maximum Daily Temperature
data(airquality)
hist(airquality$Temp, main ="La Guardia Airport's\
Maximum Temperature(Daily)",
 xlab ="Temperature(Fahrenheit)",
 xlim = c(50, 125), col ="yellow",
 freq = TRUE)
# Box plot for average wind speed
data(airquality)
boxplot(airquality$Wind, main = "Average wind speed\
at La Guardia Airport",
 xlab = "Miles per hour", ylab = "Wind",
 col = "orange", border = "brown",
 horizontal = TRUE, notch = TRUE)
# Multiple Box plots, each representing an Air Quality Parameter
boxplot(airquality[, 0:4],
 main ='Box Plots for Air Quality Parameters')
# Scatter plot for Ozone Concentration per month
data(airquality)
plot(airquality$Ozone, airquality$Month,
 main ="Scatterplot Example",
 xlab ="Ozone Concentration in parts per billion",
 ylab =" Month of observation ", pch = 19)
# Set seed for reproducibility
# set.seed(110)
# Create example data
data <- matrix(rnorm(50, 0, 5), nrow = 5, ncol = 5)
# Column names
colnames(data) <- paste0("col", 1:5)
rownames(data) <- paste0("row", 1:5)
# Draw a heatmap
heatmap(data)
#3D Graphs in R
# Adding Titles and Labeling Axes to Plot
cone <- function(x, y){
 sqrt(x ^ 2 + y ^ 2)
}
# prepare variables.
x <- y <- seq(-1, 1, length = 30)
z <- outer(x, y, cone)
# plot the 3D surface
# Adding Titles and Labeling Axes to Plot
persp(x, y, z,
 main="Perspective Plot of a Cone",
 zlab = "Height",
 theta = 30, phi = 15,
 col = "orange", shade = 0.4)
---------------------------------------
control and looping statements
----------------------------------------
for (i in 1:5) {
 print(i)
}
i <- 1
while (i <= 5) {
 print(i)
 i <- i + 1
}
x <- 10
if (x > 5) {
 print("x is greater than 5")
}
x <- 5
if (x < 0) {
 print("x is negative")
} else if (x == 0) {
 print("x is zero")
} else {
 print("x is positive")
}