IIT Indore

library(shiny)

ui = fluidPage(
   titlePanel("TABLE"),
    sidebarLayout(
      sidebarPanel(
        sliderInput("num", "integer", 1, 200, 1,step = 1, animate =
animationOptions(interval=400, loop=TRUE))),
      mainPanel(
        tableOutput("iit")
      )) )

server = function(input, output) {
    output$iit = renderPrint({ x<-input$num
    for(i in 1:10){
      a=x*i
      cat(x,"x",i,"=",a,"<br>")
       }})}

shinyApp(ui = ui, server = server)
_______________________________________________________

library(shiny)
ui<-fluidPage(
  sidebarPanel(
    sliderInput("num","integer",1,200,5,step=1,animate = animationOptions(interval = 1000,loop = TRUE))),
  radioButtons("radio",label = h1("Choose one"),choices = list("Table"=1,"Even/Odd"=2,"Fact"=3),selected = 1),
  hr(),
  mainPanel(
    tableOutput("abc")
  )
)


server<-function(input,output)
{

    output$abc<-renderPrint({
      r<-input$radio
      x<-input$num
      if(r==1){
        for(i in 1:10)
        {
          cat(x,"X",i,"=",x*i,"<br>")
        }
      }else if(r==2)
      {
        if(x%%2==0)
        {
          print("Even")
        }else{
          print("Odd")
        }
      }
      else{
        print(factorial(x))
      }
    })
 
} 
 
shinyApp(ui=ui,server=server)

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# Splitting the dataset into the Training set and Test set

install.packages('caTools')

library(caTools)

set.seed(123)

split = sample.split(dataset$Purchased, SplitRatio = 0.75)

training_set = subset(dataset, split == TRUE)

test_set = subset(dataset, split == FALSE)

View(training_set)

View(test_set)

# Feature Scaling

training_set[-3] = scale(training_set[-3])

test_set[-3] = scale(test_set[-3])

View(training_set)

View(test_set)

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

# Fitting SVM to the Training set

install.packages('e1071')

library(e1071)

classifier = svm(formula = Purchased ~ .,

                 data = training_set,

                 type = 'C-classification',

                 kernel = 'linear')

print(classifier)

A=classifier$SV

Print(A)

# Predicting the Test set results

y_pred = predict(classifier, newdata = test_set[-3])

# Making the Confusion Matrix

cm = table(test_set[, 3], y_pred)

print(cm)

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

#Visualizing the Training set results

# installing library ElemStatLearn

library(ElemStatLearn)

# Plotting the training data set results

set = training_set

X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)

X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)

grid_set = expand.grid(X1, X2)

colnames(grid_set) = c('Age', 'EstimatedSalary')

y_grid = predict(classifier, newdata = grid_set)

plot(set[, -3],

     main = 'SVM (Training set)',

     xlab = 'Age', ylab = 'Estimated Salary',

     xlim = range(X1), ylim = range(X2))

contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)

points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'coral1', 'aquamarine'))

points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))

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

#Visualizing the Test set results

set = test_set

X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)

X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)

grid_set = expand.grid(X1, X2)

colnames(grid_set) = c('Age', 'EstimatedSalary')

y_grid = predict(classifier, newdata = grid_set)

plot(set[, -3], main = 'SVM (Test set)',

     xlab = 'Age', ylab = 'Estimated Salary',

     xlim = range(X1), ylim = range(X2))

contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)

points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'coral1', 'aquamarine'))

points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))

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