#######################
### JEDANAESTI CAS ###
######################

# 1.
library(palmerpenguins)
attach(penguins)
plot(flipper_length_mm ~ species)
jda1 <- aov(flipper_length_mm ~ species)
summary(jda1)
# postoji znacajna razlika medju vrstama

# 2.
pairwise.t.test(flipper_length_mm, species) 

TukeyHSD(jda1)
plot(TukeyHSD(jda1))

# 3.
library(palmerpenguins)
attach(penguins)
jda2 <- aov(flipper_length_mm ~ species + year)
summary(jda2)

# 4. 
lm.model <- lm(flipper_length_mm ~ species)
summary(lm.model)

# 5. 
mtcars
mtcars$mpg
library(BSDA)
SIGN.test(mtcars$mpg, md = 25, alternative = "less")

# 6.
poeni <- c(31.2, 33.3, 26.4, 29.3, 31.3, 26.9, 32.5, 30.8, 30.6, 31.9)
poeni
wilcox.test(poeni, mu = 30)
# implementacija testa peske
poeni-30
sort(abs(poeni-30))

# 7.
x <- c(4.4, 3.9, 5.2, 4.6, 4.3, 3.6, 4.4)
y <- c(3.4, 3.2, 2.2, 2.8, 3.1, 2.6, 3.2)
d <- x-y
SIGN.test(x, y, md = 0, alternative = "greater")
SIGN.test(d, md = 0, alternative = "greater")

# 8. 
A <- c(1.5, 1.4, 1.4, 1, 1.1, 0.9, 1.3, 1.2, 1.1, 0.9, 0.7, 1.8)
B <- c(2, 1.8, 0.7, 1.3, 1.2, 1.5, 1.1, 0.9, 1.5, 1.7, 0.9, 0.9)
# test rankova
wilcox.test(A-B, mu = 0, alternative = "less")


# Vilkoksonov test zbira rangova
A <- c(1.5, 1.4, 1.4, 1, 1.1, 0.9, 1.3, 1.2, 0.7, 1.8)
B <- c(2, 1.8, 0.7, 1.3, 1.2, 1.5, 1.1, 0.9, 1.5, 1.7, 0.9, 0.9)
wilcox.test(A-B, mu = 0, alternative = "less")

# 9.
PlantGrowth
levels(PlantGrowth$group)
kruskal.test(weight ~ group, data = PlantGrowth)

# 10. 

pacijenti <- data.frame(person = rep(1:5, each = 4),
                   drug = rep(c(1, 2, 3, 4), times = 5),
                   score = c(30, 28, 16, 34, 14, 18, 10, 22, 24, 20,
                             18, 30, 38, 34, 20, 44, 26, 28, 14, 30))
pacijenti
friedman.test(y = pacijenti$score, groups = pacijenti$drug, blocks = pacijenti$person)