##### doplnok output$distPlot <- renderPlot({ input$actionID isolate(paste( x<-airquality[,input$selectID], boxplot(x ~ airquality$Month, col=input$radioID, main=input$textID))) }) ##### Matica 3*3 A <- matrix(nrow = 3, ncol = 3, c(4,2,5,3,2,3,0,-2,1)) B <- c(4,0,-2) solve(A,B) ###### Oprimalizačná úloha/lineárne programovanie(KF) install.packages("lpSolveAPI") library(lpSolveAPI) lpmodel <- make.lp(0,2) # lp pre 2 premnene lp.control(lpmodel, sense="max") #maximalizaca cez lp.control set.objfn(lpmodel,c(50,40)) # hodnoty kriteriálnej funkcie- objective function add.constraint(lpmodel, c(2,3), "<=" , 1500) #prvy riadok add.constraint(lpmodel, c(2,1), "<=", 1000) # druhý riadok lpmodel # riešenie solve(lpmodel) get.objective(lpmodel) get.variables(lpmodel) ##### Optimalizácia / celočíselné programovanie install.packages("lpSolve") # inštalácia potrebých kniznic, tu nám staci IPsolve) library(lpSolve) assign.costs <- matrix(c(5,12,9,8,7,8,10,13,13,7,9,12,11,12,5,10,14,10,13,9),5,5) ## naplnenie matice, poroz na rozmery "matice" lp.assign(assign.costs) #### nevieme co to robi lp.assign(assign.costs)$solution ##### Lineárna regresia (tabulka predaj cena) predaj = c(9,5,18,14,10,12,7,11,5,16,14,11) cena = c(18,24,9,15,17,16,20,15,22,14,15,19) mydata = data.frame(predaj,cena) model = lm(predaj ~ cena, data=mydata) model plot(mydata) abline(model) nove <- data.frame(cena = c(5,10,25)) nove$predaj <- predict(model, newdata = nove) nove ##### Viacnásobná regresia /matus seplength = iris$Sepal.Length petlength = iris$Petal.Length sepwidth = iris$Sepal.Width fit = lm(seplength~petlength) plot(seplength,petlength, xlab = 'sep', ylab = 'pet', main = 'plot') plot(petlength,sepwidth, xlab = 'pet', ylab = 'sep', main = 'plot2') data = data.frame(seplength = 5.8, sepwidth = 3) predictdata = predict(fit, newdata = data) predictdata ###viacnasobna regresia sepl = iris$Sepal.Length sepw = iris$Sepal.Width petl = iris$Petal.Length fit = lm(petl ~ sepl + sepw, data = iris) plot(sepl, petl) plot(sepw, petl) #par("mar") #par(mar = c(4,4,5,2)) newTab1 = data.frame(sepl = 5.8, sepw = 3) newTab1$petl = predict(fit, newdata = newTab1) newTab1$petl ##### Skupina A uloha 3 vyp. korene nelinearnej rovnice/( vracia len jednu hodnotu) curve(5*sin(x), 0, 10) curve(exp(x), add = TRUE, col="red") f = function(x) 5*sin(x) - exp(x) uniroot(f,lower=0,upper=1, tol=1e-9) uniroot(f,lower=1,upper=2, tol=1e-9) ##### Optimalizácia v R library(TSP) konštanty df <- data.frame(x = runif(20), y = runif(20), row.names = LETTERS[1:20]) etsp <- ETSP(df) n_of_cities(etsp) # vypíše [1] 20 labels(etsp) # vypíše názvy [1] "A" "B" "C" .... ###### Nájdenie riešenia a jeho vykreslenie tour <- solve_TSP(etsp) tour plot(etsp, tour, tour_col = "red") ###### Nelineárne rovnice ###### Príklad 2cos(x) - ln(x) = 0 curve(2*cos(x), 0, 10) curve(log(x), add = TRUE, col="red") f = function(x) 2*cos(x) - log(x) uniroot(f,lower=0,upper=2, tol=1e-9) uniroot(f,lower=4,upper=6, tol=1e-9) uniroot(f,lower=6,upper=7, tol=1e-9) ##### Interpolácia set.seed(1) n <- 500 dat <- data.frame( x = 1:n, y = sin(seq(0, 5*pi, length.out = n)) + rnorm(n=n, mean= 0, sd=0.5) ) ####################Ashiny************************ library(shiny) #Define UI for application that draws a histogram ui <- fluidPage( ##### Application title titlePanel("Old Faithful Geyser Data"), ###### Sidebar with a slider input for number of bins sidebarLayout( sidebarPanel( selectInput("atr1", "Vyberte atribut grafu", choices = colnames(state.x77)), radioButtons("radio", "Zvolte farbu grafu", choices = c("red", "blue"), selected = "red"), numericInput("numeric", "Zadajte rozdelenie v histograme", value = 12, min = 2, max = 20), numericInput("tab", "Pocet riadkov tabulky", value = 12, min = 1, max = 50) ), ###### Show a plot of the generated distribution mainPanel( plotOutput("distPlot"), tableOutput("vypis") ) ) ) ##### Define server logic required to draw a histogram server <- function(input, output) { output$distPlot <- renderPlot({ ##### draw the histogram with the specified number of bins hist(state.x77[,input$atr1], breaks = input$numeric, col = input$radio, border = 'white') }) output$vypis <- renderTable({ tail(state.x77,input$tab) }) } ###### Run the application shinyApp(ui = ui, server = server) ####################B shiny********************************* library(shiny) ##### Define UI for application that draws a histogram shinyUI(fluidPage( ##### Application title titlePanel("Data airquality"), ##### Sidebar with a slider input for number of bins sidebarLayout( sidebarPanel( selectInput("atr1", "Vyberte atribut", c(colnames(airquality))), radioButtons("radio","Vyberte farbu grafu", choices = c("green","yellow")), numericInput("numeric", "Pocet riadkov tabulky", value = 3, min = 1, max = 100, step = 5), textInput("text","Nadpis", label = "Zadaj nadpis grafu") ), ##### Show a plot of the generated distribution mainPanel( tableOutput("distTable"), plotOutput("distPlot") ) ) )) library(shiny) ###### Define server logic required to draw a histogram shinyServer(function(input, output) { output$distPlot <- renderPlot({ x <- airquality[, input$atr1] y <- airquality[,"Month"] boxplot(x~y, col = input$radio, main= input$text, xlab = input$atr1, ylab = "Month") }) output$distTable <- renderTable({ tail(airquality, input$numeric) }) }) #######################C shiny********************************* library(shiny) ##### Define UI for application that draws a histogram shinyUI(fluidPage( ##### Application title titlePanel("Data Quakes"), ###### Sidebar with a slider input for number of bins sidebarLayout( sidebarPanel( selectInput("atr1","Vyberte atribut c.1", c(colnames(quakes))), selectInput("atr2", "Vyberte atribut c.2", c(colnames(quakes))), radioButtons("radio", "Vyberte farbu grafu", choices = c("green","yellow","black"), selected = "green"), numericInput("numeric", "Zadajte hrubku bodov", value = 1, min = 1, max = 3) ), ##### Show a plot of the generated distribution mainPanel( textOutput("distText"), plotOutput("distPlot") ) ) )) *server library(shiny) shinyServer(function(input, output) { output$distText <- renderText({paste("Atribut1:",input$atr1,"","Atribut2:",input$atr2,"","Farba grafu:", input$radio,"","Hrubka bodov:", input$numeric)}) output$distPlot <- renderPlot({ x <- quakes[, input$atr1] y <- quakes[, input$atr2] plot(x~y, main = 'Graf', xlab= input$atr1, ylab = input$atr2, col = input$radio, lwd = input$numeric) }) }) #####################D shiny********************************* ###### Define UI for application that draws a histogram library(shiny) shinyUI(fluidPage( ##### Application title titlePanel("Data CO2"), ##### Sidebar with a slider input for number of bins sidebarLayout( sidebarPanel( sliderInput("bins", "Number of bins:", min = 1, max = 50, value = 30), selectInput("art1", "Vyber atribut:", c("Conc","Uptake")), selectInput("art2","Vyberte atribut typu faktor", c("Treatment","Type")), textInput("textX","X", label = "Zadajte popis X suradnice:"), numericInput("numeric","Zadajte farbu grafu", value = 1, min = 1, max = 7) ), ###### Show a plot of the generated distribution mainPanel( plotOutput("distPlot"), textOutput("distText") ) ) )) *server* library(shiny) ##### Define server logic required to draw a histogram shinyServer(function(input, output) { output$distText <- renderText({paste("Atribut 1:", input$art1," ", "Atribut2:",input$art2 ," ", "Popis X súradnice", input$textX," " , "Farba grafu", input$numeric) output$distPlot <- renderPlot({ x <- quakes[, input$art1] y <- quakes[, input$art2] boxplot(x~y, data = CO2, main = "Graf", xlab =input$textX , col= input$numeric) })

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