Jazyk_R, zaklady

Základne prikazy

while ( ! $success ) $try ++ ;

#PRAVDEPODOBNOSTNE ROZDELENIA v R
##normalove rozdelenie
# Generovanie 10 náhodných čísel z NR
rnorm(10,mean=0,sd=1)
# Hustota pravdepodobnosti v bude 10 (výška rozdelenia v bode 10)
dnorm(10,mean=0,sd=1,log=FALSE)
# Sumár hustoty pravdepodobnosti po daný bod (10) z ľava
pnorm(10, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
pnorm(1) # 0.8413447
pnorm(1,lower.tail=FALSE) # 0.1586553 od daného bodu napravo
pnorm(0)
# Inverzná k pnorm, vráti bod pri zadaní určitej pravdepodobnosti
qnorm(0.5) # 0
qnorm(0.85) # 1.036433
v=c(0.0,0.25,0.5,0.75,1.0) #vektor
qnorm(v) # [1] -Inf -0.6744898 0.0000000 0.6744898 Inf

##binomicke rozdelenie
rbinom(20, 100, prob = 0.1) #nahodne
rbinom(20, 100, prob = 0.5)
dbinom(20, 40, prob = 0.5) #hustota
pbinom(30, 40, p = 0.5) #sumar hustoty

#dalsie rozdelenia pravdepodobnosti - unif; pois; exp; chisq; geom; ...

runif(20, min = 0, max = 1)
rpois(20, lambda = 1)
dpois(20, lambda = 2, log = FALSE)
pexp(1, rate = 1, lower.tail = TRUE, log.p = FALSE)
rchisq(10, 2, ncp = 0)
rgeom(20, 0.5)

# Generovanie náhodných čísel
x <- rnorm(10)

# Sample - generovanie vzoriek
set.seed(1)
sample(1:10, 4) #generovanie 4 nahodnych cisel od 1 po 10
sample(letters, 5) #generovanie piatoch nahodnych pismen
sample(1:10) # permutácia prvkov od 1 do 10
sample(1:10, replace = TRUE) # výber s nahradením (môžu sa opakovať prvky)

set.seed(100)
rnorm(5) #generovanie 5 nahodnych cisel normaloveho rozdelenia


# Generovanie linearneho modelu
set.seed(20)
x <- rnorm(100)
e <- rnorm(100, 0, 2)
y <- 0.5 + 2 * x + e #linearna funkcia
summary(y)
plot(x, y) #graf xy
abline(lm(y~x)) #linearny model ako priamka v grafe


# Lineárne rovnice - riesenie pomocou SOLVE
A = matrix(nrow = 3, ncol = 3, data = c(6, 1, 2, 3, -3, 1, -2, 2, 1)) #prepis do matice A
b = c(2,5,9) #prepis do vektory b
solve(A,b) #riesenie rovnice pomocou prikazu SOLVE
A2 = matrix(nrow = 4, ncol = 4, data=c(4,3,2,5,-3,-2,-1,-3,2,1,0,1,-1,-3,5,-8))
b2 = c(8,7,6,1)
solve(A2,b2)

# Nelineárne rovnice - riesenie pomocou UNIROOT
# Príklad 2cos(x) - ln(x) = 0
#vykreslenie jednotlivych funkcii
curve(2*cos(x), 0, 10) #vykreslenie kosinusovej funkcie
curve(log(x), add = TRUE, col="red") #vykreslenie logaritmu
f = function(x) 2*cos(x) - log(x) #zapisanie funkcie
uniroot(f,lower=0,upper=2, tol=1e-9) #riesenie nelinearnej rovnice pomocou uniroot s ohranicenim (0,2) a presnostou
uniroot(f,lower=4,upper=6, tol=1e-9) #riesenie nelinearnej rovnice pomocou uniroot s ohranicenim (4,6) a presnostou
uniroot(f,lower=6,upper=7, tol=1e-9) #riesenie nelinearnej rovnice pomocou uniroot s ohranicenim (6,7) a presnostou

## x3+2x+4=0 - riesenie polynomialnej rovnice
polyroot(c(4,2,0,1))


# Lineárna regresia
x = c(3,8,9,3,13,6,11,21,1,16) #prepis vektora
y = c(30,57,64,72,36,43,59,90,20,83) #prepis vektora
mydata = data.frame(x,y) #vytvorenie tabulky
model = lm(y ~ x, data=mydata) #vytvorenie modelu linearnej regresie pomocou funkcie LM
plot(mydata) #zobrazenie dat v grafe
abline(model) #zobrazenie modelu v podobe priamky
pr1 <- data.frame(x = c(10,15,20))
pr1$y <- predict(model, newdata = pr1) #predikovanie hodnot

# Viacnásobná regresia
year <- rep(2008:2010, each = 4) #roky
quarter <- rep(1:4, 3) #kvartale
cpi <- c(162.2, 164.6, 166.5, 166, 166.2, 167,
168.6, 169.5, 171, 172.1, 173.3, 174) #hodnoty
plot(cpi, xaxt = "n", ylab = "CPI", xlab = "") #graf
# vykresli popis x-osi, kde 'las=3' zabezpeci vertikalny text
axis(1, labels = paste(year, quarter, sep = "Q"), at = 1:12, las = 3) #popis osi
fit <- lm(cpi ~ year + quarter)
data2011 <- data.frame(year = 2011, quarter = 1:4)
cpi2011 <- predict(fit, newdata = data2011) #predikovanie buducich hodnot
style <- c(rep(1, 12), rep(2, 4))
plot(c(cpi, cpi2011), xaxt = "n",ylab = "CPI", xlab = "",pch = style, col = style) #ich zobrazenie
axis(1, at = 1:16, las = 3,
labels = c(paste(year, quarter, sep = "Q"), "2011Q1", "2011Q2", "2011Q3", "2011Q4")) #zobrazenie osi s dalsimi kvartalmi


# Interpolácia - hladanie kriviek k zadanym bodov
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)
)

approxData <- data.frame(with(dat, approx(x, y, method = "linear")),
metoda = "approx")
splineData <- data.frame(with(dat, spline(x, y) ),metoda = "spline default")
splineData2 <- data.frame(with(dat, spline(x, y, xout = seq(1, n, by = 10), method = "fmm") ), metoda = "spline krok 10")
smoothData <- data.frame(x = 1:n, y = as.vector(smooth(dat$y)), metoda = "smooth")
loessData <- data.frame(x = 1:n, y = predict(loess(y~x, dat, span = 0.1)), metoda = "loess span 0.1")
loessData2 <- data.frame(x = 1:n, y = predict(loess(y~x, dat, span = 0.5)), metoda = "loess span 0.5")
library(ggplot2)
ggplot(rbind(approxData, splineData, splineData2, smoothData, loessData, loessData2), aes(x, y)) + geom_point(dat = dat, aes(x, y), alpha = 0.2, col = "red") + geom_line(col = "blue") + facet_wrap(~metoda) + ggtitle("Príklad - vybrané interpolačné a vyhladzovacie funkcie v R") + theme_bw(16)


# Lineárne programovanie
# install.packages("lpSolveAPI")
library(lpSolveAPI)
lpmodel <- make.lp(0, 2) # prazdny LP solver s 2 premennymi
#prepis zadanej ulohy
lp.control(lpmodel, sense="max") # maximalizacia
set.objfn(lpmodel, c(143, 60)) # definicia KF (v anglictine casto objective function)
add.constraint(lpmodel, c(120, 210), "<=", 15000) #ohranicenia
add.constraint(lpmodel, c(110, 30), "<=", 4000) #ohranicenia
add.constraint(lpmodel, c(1, 1), "<=", 75) #ohranicenia
lpmodel #zobrazenie modelu
solve(lpmodel) #riesenie
get.objective(lpmodel) #hodnota KF (maximalna/minimalna)
get.variables(lpmodel) #hodnoty premennych X a Y pre optimum

# Celočíselné programovanie
# install.packages("lpSolve")
library(lpSolve)
assign.costs <- matrix (c(7, 7, 3, 2, 2, 7, 7, 2, 1, 9, 8, 2, 7, 2, 8, 10), 4, 4) #prepis
lp.assign (assign.costs) #sucet najmensich nakladov
lp.assign (assign.costs)$solution #riesenie v podobe matice

#nelinearna

-4*cos(x)-exp(x)

curve(-4*cos(x), -10,1)
curve(exp(x), add = TRUE, col ="red")
f = function(x) -4*cos(x)-exp(x)
uniroot(f, lower=-10, upper = 1, tol= 1e-4)
uniroot(f, lower=-2, upper=0, tol = 1e-4)

curve(5*sin(x),-5,5)
curve(-exp(x),add = TRUE,col="red")
f=function(x){5*sin(x)-exp(x)}
uniroot(f,lower = -4,upper = -2,tol = 1e-4)
uniroot(f,lower = -1,upper = 1,tol = 1e-4)

• Príklady pravdepodobnostných rozdelení (distribúcií) – Normálne (*norm) – Uniformné (*unif) – Poissonovo (*pois) – Binomické (*binom) – Exponenciálne (*exp) – Chi-kvadrát rozdelenie (*chisq) – Geometrické (*geom)

Normálne rozdelenie •
Gaussovo rozdelenie – mean (def. 0), sd (def. 1) –
Funkcie
*rnorm(n,mean=0,sd=1) – generuje n čísel z NR
*dnorm(x,mean=0,sd=1,log=FALSE) – vráti „výšku“ rozdelenia v bode x => hustotu (density) pravdepodobnosti v bode x
*pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE) – kumulatívna distribučná funkcia = v podstate udáva sumár hustoty pravdepodobnosti po daný bod q zľava (ak lower.tail = FALSE, tak od daného bodu napravo), log.p je pre logaritmické hodnoty pnorm(0) # 0.5;
pnorm(1) # 0.8413447; pnorm(0,lower.tail=FALSE) # 0.5; pnorm(1,lower.tail=FALSE) # 0.1586553

*qnorm(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE) – kvantilová funkcia = inverzná k pnorm = zadáme pravdepodobnosť a získame bod z pre ktorý by platilo pnorm(z)=p
qnorm(0.5) # 0; qnorm(0.85) # 1.036433; v=c(0.0,0.25,0.5,0.75,1.0); qnorm(v) # [1] -Inf -0.6744898 0.0000000 0.6744898 Inf

Generovanie náhodných čísel
x <- rnorm(10); x
[1] 1.38380206 0.48772671 0.53403109 0.66721944
[5] 0.01585029 0.37945986 1.31096736 0.55330472
[9] 1.22090852 0.45236742
> x <- rnorm(10, 20, 2)
> x
[1] 23.38812 20.16846 21.87999 20.73813 19.59020
[6] 18.73439 18.31721 22.51748 20.36966 21.04371
> summary(x)
Min. 1st Qu. Median Mean 3rd Qu. Max. 18.32 19.73 20.55 20.67 21.67 23.39
# uniformny vyber
> runif(3, min=0, max=100)
[1] 17.28769 96.69770 52.30550 # napr. uniformny vyber integer cisla ….. uniformne vybery je vsak lepsie robit cez sample funkciu
> floor(runif(3, min=0, max=100))
[1] 9 38 8
> sample(1:100, 3, replace=TRUE) # 3 krat integer od 1 po 100 s možnosťou znovu vybrať rovnaké číslo
> sample(1:100, 3, replace=FALSE) # detto ale rovnake cislo nemozem vybrat znovu (t.j. nevratim ho spat do mnoziny na dalsi vyber

Sample – generovanie vzoriek
> set.seed(1)
> sample(1:10, 4)
[1] 3 4 5 7
> sample(1:10, 4)
[1] 3 9 8 5
> sample(letters, 5)
[1] "q" "b" "e" "x" "p"
> sample(1:10) # permutácia prvkov od 1 do 10
[1] 4 7 10 6 9 2 8 3 1 5
> sample(1:10)
[1] 2 3 4 1 9 5 10 8 6 7
> sample(1:10, replace = TRUE) # výber s nahradením (môžu sa opakovať prvky)
[1] 2 9 7 8 2 8 5 9 7 8

Opakovateľnosť experimentov
> set.seed(158)
> rnorm(5)
[1] 0.8316425 0.2856389 -0.3573470 -1.1607888 0.4869541
> rnorm(5)
[1] -0.94285749 0.05782252 -0.07641639 -1.30975637 -1.37445786
> set.seed(158)
> rnorm(5)

Generovanie lineárneho modelu
> set.seed(20)
> x <- rnorm(100)
> e <- rnorm(100, 0, 2)
> y <- 0.5 + 2 * x + e
> summary(y)
Min. 1st Qu. Median Mean 3rd Qu. Max. -6.4080 -1.5400 0.6789 0.6893 2.9300 6.5050
> plot(x, y)
> abline(lm(y~x))

Generalizovaný lineárny model
> set.seed(1)
> x <- rnorm(100)
> log.mu <- 0.5 + 0.3 * x
> y <- rpois(100, exp(log.mu))
> summary(y)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 1.00 1.00 1.55 2.00 6.00
> plot(x, y)

Generovanie separovateľných dát
generuj2D2k = function(){
d = 0
while(d<1.0){ s = rnorm(4); d = sqrt((s[1]-s[2])^2 + (s[3]-s[4])^2)}
x1 = s[1] + rnorm(50,0,0.5);
x2 = s[2] + rnorm(50,0,0.5)
y1 = s[3] + rnorm(50,0,0.5); y2 = s[4] + rnorm(50,0,0.5)
data.frame(a = c(x1,x2), b = c(y1,y2),f = factor(c(rep("A",50),rep("B",50)))) }
> set.seed(12548)
> mydata = generuj2D2k()
> plot(mydata$a,mydata$b,col=mydata$f)

jednovýberový KS test
ks.test(x, pnorm)
One-sample Kolmogorov-Smirnov
test data: x
D = 0.26225, p-value = 0.02612 alternative hypothesis: two-sided
> ks.test(x, punif)
One-sample Kolmogorov-Smirnov
test data: x
D = 0.3, p-value = 0.006852 alternative hypothesis: two-sided

dvojvýberový KS test
> y = rnorm(50)
> z = runif(50)
> ks.test(y,z) Two-sample Kolmogorov-Smirnov
test data: y and z
D = 0.54, p-value = 4.929e-07 alternative hypothesis: two-sided

Sústava lineárnych rovníc
A = matrix(nrow = 3, ncol = 3, data = c(6, 1, 2, 3, -3, 1, 2, 2, 1))
b = c(2,5,9)
solve(A,b)
[1] 1 2 5 # riesenie sustavy s A a b
A2 = matrix(nrow = 4, ncol = 4, data=c(4,3,2,5,-3,-2,-1,3,2,1,0,1,-1,-3,5,-8))
b2 = c(8,7,6,1)
solve(A2,b2) # system s A2 a b2 nema riesenie
Error in solve.default(A2, b2) : system is computationally singular: reciprocal condition number = 3.26536e-18 SSvHI

Riešenie nelinárnych rovníc
> curve(2*cos(x), 0, 10)
> curve(log(x), add = TRUE, col="red")

# definujeme funkciu a potom hladame jej korene pre dany interval a presnost
> f = function(x) 2*cos(x) - log(x)
> uniroot(f,lower=0,upper=2, tol=1e-9)
$root [1] 1.401289
$f.root [1] -5.406786e-14
$iter [1] 6 $init.it [1] NA
$estim.prec [1] 5.000007e-10
> uniroot(f,lower=4,upper=6, tol=1e-9) # najde koren 5.782918
> uniroot(f,lower=4,upper=6, tol=1e-9) # najde koren 6.616946

• polyroot – riešenie polynomiálnej rovnice – Príklad: x3 + 2x + 4 = 0 .... konstanty od najnižšieho stupňa 4,2,0,1
> polyroot(c(4,2,0,1)) # vstupný vektor = konštanty pri stupňoch od najnižšieho
[1] 0.589755+1.744543i -1.179509+0.000000i 0.589755-1.744543i

LG model (RK4, Euler)
# derivácia logistickej funkcie
logist <- function(t, x, parms) {
with(as.list(parms), {
dx <- r * x[1] * (1 - x[1]/K)
list(dx)
})
}
time <- 0:100; N0 <- 0.1
r <- 0.5; K <- 100;
parms <- c(r = r, K = K); x <- c(N = N0) # vykreslenie analytického riešenia
plot(time, K/(1 + (K/N0-1) * exp(-r*time)), ylim = c(0, 120), type = "l", col = "red", lwd = 2) # rozumné numerické riešenie cez Runge-Kutta (rk4)
time <- seq(0, 100, 2);
out <- as.data.frame(rk4(x, time, logist, parms))
points(out$time, out$N, pch = 16, col = "blue", cex = 0.5)
# rovnaký časový krok, Eulerova metóda
time <- seq(0, 100, 2);
out <- as.data.frame(euler(x, time, logist, parms)) points(out$time, out$N, pch = 1)
legend("bottomright", c("analytical","rk4, h=2", "euler, h=2"), lty = c(1, NA, NA), lwd = c(2, 1, 1), pch = c(NA, 16, 1), col = c("red", "blue", "black"))

lineárna regresia (LR)
> x = c(3,8,9,3,13,6,11,21,1,16)
> y = c(30,57,64,72,36,43,59,90,20,83)
> mydata = data.frame(x,y)
> model = lm(y ~ x, data=mydata)
> model Call: lm(formula = y ~ x, data = mydata) Coefficients: (Intercept) x 32.337 2.534
> plot(mydata)
> abline(model)

Viacnásobná regresia
year <- rep(2008:2010, each = 4)
quarter <- rep(1:4, 3)
cpi <- c(162.2, 164.6, 166.5, 166, 166.2, 167, 168.6, 169.5, 171, 172.1, 173.3, 174)
plot(cpi, xaxt = "n", ylab = "CPI", xlab = "") # vykresli popis x-osi, kde 'las=3' zabezpeci vertikalny text
axis(1, labels = paste(year, quarter, sep = "Q"), at = 1:12, las = 3)

Výpočet modelu + predikcia
data2011 <- data.frame(year = 2011, quarter = 1:4)
cpi2011 <- predict(fit, newdata = data2011)
style <- c(rep(1, 12), rep(2, 4))
plot(c(cpi, cpi2011), xaxt = "n",
ylab = "CPI", xlab = "",
pch = style, col = style)
axis(1, at = 1:16, las = 3,
labels = c(paste(year, quarter, sep = "Q"), "2011Q1", "2011Q2", "2011Q3", "2011Q4"))

Interpolácia kriviek v R
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)
)
approxData <- data.frame( with(dat, approx(x, y, method = "linear") ), metoda = "approx“ )
splineData <- data.frame( with(dat, spline(x, y) ), metoda = "spline default")
splineData2 <- data.frame( with(dat, spline(x, y, xout = seq(1, n, by = 10), method = "fmm") ), metoda = "spline krok 10")
smoothData <- data.frame( x = 1:n, y = as.vector(smooth(dat$y)), metoda = "smooth")
loessData <- data.frame( x = 1:n, y = predict(loess(y~x, dat, span = 0.1)), metoda = "loess span 0.1")
loessData2 <- data.frame( x = 1:n, y = predict(loess(y~x, dat, span = 0.5)), metoda = "loess span 0.5")

Porovnanie interpolácie v grafe
library(ggplot2)
ggplot(rbind(approxData, splineData, splineData2, smoothData, loessData, loessData2), aes(x, y)) + geom_point(dat = dat, aes(x, y), alpha = 0.2, col = "red") + geom_line(col = "blue") + facet_wrap(~metoda) + ggtitle("Príklad - vybrané interpolačné a vyhladzovacie funkcie v R") + theme_bw(16)

lineárne programovanie
> install.packages("lpSolveAPI“)
> library(lpSolveAPI)
> lpmodel <- make.lp(0, 2) # prazdny LP solver s 2 premennymi
> lp.control(lpmodel, sense="max") # maximalizacia
> set.objfn(lpmodel, c(143, 60)) # definicia KF (v anglictine casto objective function)
> add.constraint(lpmodel, c(120, 210), "<=", 15000)
> add.constraint(lpmodel, c(110, 30), "<=", 4000)
> add.constraint(lpmodel, c(1, 1), "<=", 75)
> lpmodel

Celočíselné programovanie
> library(lpSolve)
> assign.costs <- matrix (c(7, 7, 3, 2, 2, 7, 7, 2, 1, 9, 8, 2, 7, 2, 8, 10), 4, 4)
> lp.assign (assign.costs)
> lp.assign (assign.costs)$solution

Optimalizácia v R
ibrary(TSP)
# vytvorenie dát – náhodných „miest“, mená priradené z letters konštanty
df <- data.frame(x = runif(20), y = runif(20), row.names = LETTERS[1:20])
# vytvorenie Euklidovskeho TSP
etsp <- ETSP(df)
# výpis detailov – počet miest, názvy miest
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")

A jéje, už zasa...

df-kári, prečo toto niekto robí, čo myslíte? Aký to má účel?

Asi chce niekto dať jasne najavo, že keď zaniknú náboženstvá, tak budeme diskutovať v jazyku R.

čítam tu: # vytvorenie dát – náhodných „miest“, mená priradené z letters konštanty df
pýtam sa: aké sú pôvodné konštanty df?

aké sú pôvodné konštanty df?

seriózna diskusia
vecné argumenty
tolerancia

18,....ten čas príde veľmi rýchlo! A budú to politicke zložky tohto sveta,ktoré to urobia,....pretože vidia v náboženstve prekážku jednoty! Čo je fakt! Len zabudnú na jednu vec,....že špinavá voda z vaničky sa nesmie vylievať aj s dieťatkom! A to sa im a ostatným stane osudné!

Zvýraznenie textu – Italic - *italic* alebo _italic_ – Bold - **bold** alebo __bold__
Prečiarknuté písmo ~~slovo~~,
horný index x^2^
Nadpisy – # Header 1 ... ## Header 2 ... ### Header 3 ... atď
Zoznamy – Nečíslované zoznamy * Item 1 * Item 2 + Item 2a + Item 2b
Číslovaný zoznam 1. Item 1 2. Item 2 + Item 2a + Item 2b
Jednoduchý zoznam - Item 1 - Item 2 - Item 3
Pre napísanie pomlčky: - - Pre dlhšiu verziu: - - - Pre „trojbodku“ : ...

Priamo uvedieme linku ... odkaz
alebo s vl. textom ... [moj text](odkaz

obrázky ![moj text](odkaz

Blok „citácie“ > Toto je citácia v bloku
Horizontálna čiara ********
tabuľky Názov 1 | Názov 2 | Názov 3
------- | ------- | -------
Hodnota 1.1 | Hodnota 1.2 | Hodnota 1.3
Hodnota 2.1 | Hodnota 2.2 | Hodnota 2.3

Inline rovnice (Latex) $A = \pi*r^{2}$

Inline vloženie kódu: Dva plus dva je `r 2 + 2`.
Kúsky kódu v texte ``` {r} dim(iris) ```
Nastavenia pre evaluáciu a výpis – eval ... TRUE/FALSE – vypíše/nevypíše „výpočet“ (def. TRUE)
– echo ... TRUE/FALSE – vypíše/nevypíše „zdroják“ (def. TRUE)
Príklad: ``` {r, eval=FALSE} dim(iris) ```

Príklad – interaktívny dokument
--- title: "Histogram pre rôzny počet častí (bins)"
runtime: shiny
output: html_document ---

Histogram trvania erupcie gejzíra.
```{r, echo=FALSE}
inputPanel(
selectInput("n_breaks", label = "Number of bins:",
choices = c(10, 20, 30), selected = 30) )
renderPlot({
hist(faithful$eruptions, breaks = as.numeric(input$n_breaks),
xlab = "Duration (minutes)", main = "Geyser eruption duration") }) ```

Príklad – prezentácia (cez ioslides)
--- title: "Moja prezentácia„
output: beamer_presentation ---
## Slajd s odrážkami
- Názov 1 - Názov 2 - Názov 3
## Slajd s R kódom a výstupom
```{r} summary(cars) ```
## Slajd s grafom
```{r, echo=FALSE}
plot(cars) ```

Príklad – server.R
library(shiny)
shinyServer(function(input, output) {
output$distPlot <- renderPlot({
# generuje bins (rozdelenia) na základe input$bins z ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# kresli histogram pre specifikovany pocet binov
hist(x, breaks = bins, col = 'darkgray', border = 'white') }) })

Príklad – ui.R
library(shiny)
shinyUI(fluidPage(
titlePanel("Gejzírové dáta - histogram"),
sidebarLayout(
# sidebar panel – panel so slajderom pre vyber poctu binov
sidebarPanel(
sliderInput("bins", "Number of bins:", min = 1, max= 50,value = 30)
),
# Hlavny panel so zobrazenim generovanej distribucie
mainPanel( plotOutput("distPlot")
) ) ))

Reaktívnosť prvkov v R Shiny
function(input, output) {
x <- reactive({as.numeric(input$text1)+100})
output$text1 <- renderText({x() })
output$text2 <- renderText({x() + as.numeric(input$text2)}) }

Nereaktivita (?) reaktívnych prvkov
function(input, output) {
output$text1 <- renderText({input$text1})
output$text2 <- renderText({input$text2})
output$text3 <- renderText({
input$goButton
isolate(paste(input$text1, input$text2)) } ) }

NAMESPACE súbor
Základné direktívy – export("<function>") – import("<package>") – importFrom("<package>", "<function>") • Ďalšie dôležité direktívy – exportClasses("<class>") – exportMethods("<generic>")

príklad -gpclib balík ... NAMESPACE súbor:
importFrom(graphics, plot)
import(methods)
exportClasses("gpc.poly", "gpc.poly.nohole")
exportMethods("show", "get.bbox", "plot", "intersect", "union", "setdiff", "[", "append.poly", "scale.poly", "area.poly", "get.pts", "coerce", "tristrip", "triangulate") export("read.polyfile", "write.polyfile")
useDynLib(gpclib)

library(shiny)
shinyUI(fluidPage(

# Application title
titlePanel("Old Faithful Geyser Data"),

# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30),

actionLink("link","Zadajte link: "),

actionButton("button","Potvrd"),

checkboxGroupInput("checkbox", "Výber možnosti",
choices = c("ano", "nie", "mozno"), selected = "ano"),

checkboxInput("checkbox1", "Zaškrtni možnosť"),

dateInput("datum","Zadajte datum", value = Sys.Date(), format = "dd.mm.yyyy",
min = Sys.Date() - 5, max = Sys.Date()+ 5, language = "sk", startview = "year",
weekstart = 3),

dateRangeInput("datum2", "Zadajte rozsah",
start = Sys.Date() - 6, end = Sys.Date() + 4 ,min = NULL, max = NULL,
format = "yyyy-mm-dd", startview = "month", weekstart = 0,
language = "en", separator = "do"),

fileInput("subor", "Nahrajte súbor", multiple = TRUE,
accept = NULL),

numericInput("cislo", "Zadajte číslo", value = 18, min = 1, max = 25,
step = 3),

passwordInput("heslo","Zadajte heslo", value = "qwertz"),

radioButtons("radio","Vyberte jednú z možností", choices = c(1,2,5),
selected = 5),

selectInput("select", "Vyberte možnosť", choices = c("včera","dnes","zajtra"),
selected = "zajtra", multiple = TRUE, selectize = TRUE, width = "400px"),

# submitButton("GO"),

wellPanel(

textInput("text","Zadajte vstup", value = "a"),

actionButton("goButton", "Spusť")

),

conditionalPanel(condition = "input.cislo == 18",
selectInput("select2", "Výber", choices = c("rok", "mesiac", "den"),
selected = "mesiac"))


),





# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot"),
verbatimTextOutput("distPrint"),
tableOutput("distTable"),
dataTableOutput("distDataTable"),
textOutput("distText"),
textOutput("Text"),
textOutput("Text2"),
textOutput("Text3")


)


)
)
)


library(shiny)
shinyServer(function(input, output) {

x <- reactive({as.numeric(input$text)+100})

output$distPlot <- renderPlot({

# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)

# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')

})

output$distPrint <- renderPrint({
print(input$text)
})

output$distTable <- renderTable({
head(iris)
})

output$distDataTable <- renderDataTable({
head(mtcars)
})

output$distText <- renderText({
paste("Zadali ste rozsah dátumu ", input$datum2[1], " do ", input$datum2[2])
})

output$Text <- renderText({
x()
})

output$Text2 <- renderText({
x() + as.numeric(input$cislo)
})

output$Text3 <- renderText({
input$goButton
isolate(paste("Zadali ste čísla ", input$text, " a" ,input$cislo))
})

})

library(shiny)
shinyUI(fluidPage(

titlePanel("Old Faithful Geyser Data"),

flowLayout(
actionLink("link","Zadajte link: "),
actionButton("button","Potvrd"),
checkboxGroupInput("checkbox", "Výber možnosti",
choices = c("ano", "nie", "mozno"), selected = "ano")
),

splitLayout(
actionLink("link","Zadajte link: "),
radioButtons("radio","Vyberte jednú z možností", choices = c(1,2,5),
selected = 5)
# checkboxGroupInput("checkbox", "Výber možnosti",
# choices = c("ano", "nie", "mozno"), selected = "ano")
),

verticalLayout(
actionLink("link","Zadajte link: "),
actionButton("button","Potvrd"),
checkboxGroupInput("checkbox", "Výber možnosti",
choices = c("ano", "nie", "mozno"), selected = "ano")
),

# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30)
),

# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot")
)
)
))

library(shiny)

shinyUI(fluidPage(

tabsetPanel(
tabPanel(
title = "Page"

),
tabPanel("tab2",
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30)

),

tabPanel("tab3",
plotOutput("distPlot"))
)
)
)


library(shiny)

shinyServer(function(input, output) {

output$distPlot <- renderPlot({

# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)

# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')

})

})

library(shiny)

shinyServer(function(input, output) {

output$ui <- renderUI({
if (is.null(input$input_type))
return()

# Depending on input$input_type, we'll generate a different
# UI component and send it to the client.
switch(input$input_type,
"slider" = sliderInput("dynamic", "Dynamic",
min = 1, max = 20, value = 10),
"text" = textInput("dynamic", "Dynamic",
value = "starting value"),
"numeric" = numericInput("dynamic", "Dynamic",
value = 12),
"checkbox" = checkboxInput("dynamic", "Dynamic",
value = TRUE),
"checkboxGroup" = checkboxGroupInput("dynamic", "Dynamic",
choices = c("Option 1" = "option1",
"Option 2" = "option2"),
selected = "option2"
),
"radioButtons" = radioButtons("dynamic", "Dynamic",
choices = c("Option 1" = "option1",
"Option 2" = "option2"),
selected = "option2"
),
"selectInput" = selectInput("dynamic", "Dynamic",
choices = c("Option 1" = "option1",
"Option 2" = "option2"),
selected = "option2"
),
"selectInput (multi)" = selectInput("dynamic", "Dynamic",
choices = c("Option 1" = "option1",
"Option 2" = "option2"),
selected = c("option1", "option2"),
multiple = TRUE
),
"date" = dateInput("dynamic", "Dynamic"),
"daterange" = dateRangeInput("dynamic", "Dynamic")
)
})

output$input_type_text <- renderText({
input$input_type
})

output$dynamic_value <- renderPrint({
str(input$dynamic)
})

})

"checkboxGroup" = checkboxGroupInput("dynamic", "Dynamic",

Má to sexistický nádych.

Sviňa jedna.

Priklady A

library(shiny)

shinyUI(fluidPage(

# Application title
titlePanel("Old Faithful Geyser Data"),

# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30),


selectInput("select", "Vyberte atribut", choices = c(" "," "," "),
multiple = TRUE, selectize = TRUE, width = "400px"),

radioButtons("radio","Vyberte farbu grafu", choices = c("blue","red"),
),

numericInput("cislo", "Zadajte rozdelenie v histograme", value = 12, min = 2, max = 20,
),

numericInput("cislo", "Pocet riadkov tabulky", value = 12, min = 1, max = 50,
),



),


mainPanel(
plotOutput("distPlot"),



)


)
)
)

markdow

---
title: "Skupina A"
output: html_document
---
#Struktura systemu R
**R** system je rozdeleny do *dvoch* konceptualnych casti:

1.R base system

-Cran (priestor pre zdielanie balikov)

2.Vsetko ostatne

********************

#Relevantne simulacne nastroje

Nazov | Vyhody | Nevyhody | Open-source
------- | ------- | ------- | -------
R | Podpora kniznic | N?ro?nej?? | ?no
Matlab | Podpora matic | Podpora ?metod | Nie

#Relevantne simulacne nastroje

Data mtcars obsahuju tieto nazvy stlpcov:

```{r}
colnames(mtcars)
```

```{r, echo=FALSE}
plot(mtcars$hp,mtcars$wt, col = "blue")
```

Priklady B

---
title: "skupina B"
output: html_document
---

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <odkaz

When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

#Preco R?
- Statisticky **softver** + jazyk + [link R](odkaz
- Je volne dostupny --- *open source*

###Kvadraticka rovnica
Diskriminant vypocitame pomocou vzorca $D=b{2}-4*a*c$

> Citácia na vzorec: odkaz

##Data state.x77
Data state.x77 obsahuj? spolu `r 25+25` riadkov a `r 16/2` stlpcov.

#Graf
R Markdown — Dynamic Documents for R
R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports from R. It combines the core syntax of markdown (an easy-to-write plain text format) with embedded R code chunks that are run so their output can be included in the final document. R Markdow…
rmarkdown.rstudio.com

a ešte pokračuje Garfom tu
#Graf
hist(state.x77$Income,breaks=10,col="red",ylab = "Frekvencia", xlab = "Prijem")
ale ten graf má byť trochu inak napísaný
Ďalej toto je jedna časť 3ky
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)

pr1 <- data.frame(cena = c(5,10,25))
pr1$predaj <- predict(model, newdata = pr1)
a toto je UI u 2ke

ale aj tam mám chyby v zátvorke, ale finálne to mám odstránené

library(shiny)

shinyUI(fluidPage (

titlePanel("Data Airquality"),

sidebarLayout(
sidebarPanel(

selectInput("select", "Vyberte atribut", c("slider", "text", "numeric", "checkbox",
"checkboxGroup", "radioButtons", "selectInput",
"selectInput (multi)", "date", "daterange")),

radioButtons("radio","Vyberte farbu grafu", choices = c("zelena","zlta")),

numericInput("cislo", "Pocet riadkov tabulky", value = 3, min = 1, max = 100,
step = 5),

textInput("text","Zadajte nadpis grafu", value = "Nadpis"),

mainPanel(
tableOutput("distTable"),
plotOutput("distPlot"),



)


)
)
)

# Lineárna regresia
x = c(3,8,9,3,13,6,11,21,1,16)
y = c(30,57,64,72,36,43,59,90,20,83)
mydata = data.frame(x,y)
model = lm(y ~ x, data=mydata)
model
plot(mydata)
abline(model)

pr1 <- data.frame(x = c(10,15,20))
pr1$y <- predict(model, newdata = pr1)

# Viacnásobná regresia
year <- rep(2008:2010, each = 4)
quarter <- rep(1:4, 3)
cpi <- c(162.2, 164.6, 166.5, 166, 166.2, 167,
168.6, 169.5, 171, 172.1, 173.3, 174)
plot(cpi, xaxt = "n", ylab = "CPI", xlab = "")
# vykresli popis x-osi, kde 'las=3' zabezpeci vertikalny text
axis(1, labels = paste(year, quarter, sep = "Q"), at = 1:12, las = 3)
fit <- lm(cpi ~ year + quarter)
data2011 <- data.frame(year = 2011, quarter = 1:4)
cpi2011 <- predict(fit, newdata = data2011)
style <- c(rep(1, 12), rep(2, 4))
plot(c(cpi, cpi2011), xaxt = "n",ylab = "CPI", xlab = "",pch = style, col = style)
axis(1, at = 1:16, las = 3,
labels = c(paste(year, quarter, sep = "Q"), "2011Q1", "2011Q2", "2011Q3", "2011Q4"))

A

SERVER
# Define server logic required to draw a histogram
shinyServer(function(input, output) {




output$distPlot <- renderPlot({

# generate bins based on input$bins from ui.R

#bins <- seq(min(x), max(x), length.out = input$bins + 1)

# draw the histogram with the specified number of bins
x <- state.x77[,input$vyber]
bins <- seq(min(x), max(x), length.out = input$rozdelenie + 1)

hist(x,breaks = bins,col = input$farba)

})
output$distTable= renderTable(head(state.x77,input$kolko))



})


UI

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("vyber", "Vyberte atribut", choices = colnames(state.x77)),
radioButtons("farba","Vyberte farbu grafu0", choices = c("blue","red")),
numericInput("rozdelenie","zadajte rozdelenie v grafe",min = 2,max=20,value = 12),
numericInput("kolko","pocet riadkov tabulky",min = 1,max=50,value = 12)

),

# Show a plot of the generated distribution
mainPanel(



plotOutput("distPlot"),
tableOutput("distTable")
)
)
))

B
SERVER

# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# odkaz
#

library(shiny)

shinyServer(function(input, output) {

output$distPlot <- renderPlot({

# generate bins based on input$bins from ui.R
x <- airquality[,input$vyber]
# ZAKOMENTOVAT !!!! bins <- seq(min(x), max(x), length.out = input$bins + 1)

# draw the histogram with the specified number of bins
boxplot(x~airquality$Month, col = input$farba, border = 'white', main=input$nadpis)

})
output$distTable= renderTable(
tail(airquality,input$cislo)
)

})


UI


# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# odkaz
#

library(shiny)

shinyUI(fluidPage(

# Application title
titlePanel("Data airquality"),

# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
selectInput("vyber","Vyberte atribut",choices = colnames(airquality)),
radioButtons("farba","Vyberte farbu grafu", choices=c("green","yellow")),
numericInput("cislo","Pocet riadkov tabulky",min = 1,max = 100,value = 3,step = 5),
textInput("nadpis","Zadajte nadpis grafu",value = "Nadpis")
),

# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot"),
tableOutput("distTable")
)
)
))

C
SERVER
# Define server logic required to draw a histogram
shinyServer(function(input, output) {

output$distText <- renderText({

vypis = c("Vybrali ste si",input$farba, "farbu vybrali ste atributy",input$vyber,"a",input$vyber2)
})


output$distPlot <- renderPlot({

# generate bins based on input$bins from ui.R

#bins <- seq(min(x), max(x), length.out = input$bins + 1)

# draw the histogram with the specified number of bins
x=quakes[,input$vyber]
y=quakes[,input$vyber2]
hist(x,col=input$farba,lwd=input$hrubka)

})



})


UI

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("vyber", "Vyberte atribut c.1", choices = colnames(quakes)),
selectInput("vyber2","vyberte atribut c.2",choices = colnames(quakes)),
radioButtons("farba","Vyberte farbu grafu0", choices = c("yellow","black")),
numericInput("hrubka","Vyberte hrubku bodov",min = 1,max=3,value = 1)

),

# Show a plot of the generated distribution
mainPanel(

textOutput("distText"),
plotOutput("distPlot")
)
)
))

D
SERVER
# Define server logic required to draw a histogram
shinyServer(function(input, output) {


output$distPlot <- renderPlot({

# generate bins based on input$bins from ui.R

#bins <- seq(min(x), max(x), length.out = input$bins + 1)

# draw the histogram with the specified number of bins
x=CO2[,input$selectID]
y=CO2[,input$vyber2]
boxplot(x~y,col=input$farba,xlab=input$text)

})
output$distText <- renderText({

vypis = c("Vybrali ste si",input$selectID1, "a zaroven", input$vyber2, "zadali ste text z nazvom",input$text,"a farba je",input$farba)
})


})

UI

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("selectID", "Vyberte atribut", choices = c(colnames(CO2$conc,CO2$uptake))),
selectInput("vyber2","vyberte atribut typu faktor",choices = c(colnames(factor(CO2$Type,CO2$Treatment)))),
textInput("text","Zadajte nazov xovej suradnice",value = "X"),
numericInput("farba","Vyberte farbu grafu",min = 1,max=7,value = 1)

),

# Show a plot of the generated distribution
mainPanel(

plotOutput("distPlot"),
textOutput("distText")
)
)
))

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)

tab=data.frame(predaj,cena)
model=lm(cena~predaj,data = tab)
plot(tab)
abline(model)
pr1=data.frame(cena=c(5,10,25))
pr1$predaj <- predict(model,newdata = pr1)

NEW
---
title: "Skupina B 2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Vlastnosti R
- Beží na takmer každom *štandartnom OS*
- Má časté vydávanie nových verzií
- Grafické možnosti sú *pokročilé*
- Dokumentácia je veľmi dobrá
- Je to voľný softvér

## Relevantné simulačné nástroje
Názov | Výhody | Nevýhody | Open-source
------|--------|----------|------------
SPSS |Podpora ako dopis|dopis|Nie
Excel|Jednoduchý vizuálny|dopis|Nie


![logo R](odkaz

Logaritmická hodnota **hustoty (výšky) pravdepodobnosti** pre bod 8.

```{r = TRUE}
dbinom(8, 16, prob = 0.8, log = FALSE)
```





##Dáta mtcars
Zobrazenie bodového grafu pre atribúty **hp** a **wt** z dát *mtcars*



```{r, echo=FALSE}

plot(mtcars$hp, mtcars$wt, col="purple", main = "Plot of atrributes ", xlab = "hp", ylab = "wt")

```