#-----------------------------------------------------
#                P R I K L A D    1
#-----------------------------------------------------
data <- data.frame(nizka = c(75, 79, 73, 13),
                   norma = c(264, 325, 341, 63),
                   vysoka = c(8, 20, 11, 4),
                   row.names = c('ZS', 'SS', 'SSm', 'VS'))
data
sum(data)

apply(data, 1, sum)
apply(data, 2, sum)

#-----------------------------------------------------
#                P R I K L A D    2
#-----------------------------------------------------

lsr:: cramersV(data) # varovna hlaska se tyka testovani, ne Cramerova koeficientu

#-----------------------------------------------------
#                P R I K L A D    3
#-----------------------------------------------------

data <- read.delim('17-anova-newborns-mod.txt')
head(data)
data <- na.omit(data)
data <- data[data$weight.C > 4200, ]
dim(data)

prch <- data$prch.N
prch
min(prch)
prch.sort <- sort(prch)
prch.sort
(prch.sort[11] + prch.sort[12]) / 2
(prch.sort[33] + prch.sort[34]) / 2
quantile(prch, probs = 0.25, type = 2)
quantile(prch, probs = c(0.25, 0.5, 0.75), type = 2)
median(prch, type = 2)

#-----------------------------------------------------
#                P R I K L A D    4
#-----------------------------------------------------

boxplot(prch)

#-----------------------------------------------------
#                P R I K L A D    5
#-----------------------------------------------------

head(data)
wei <- data$weight.C
cor(prch, wei, method = 'spearman')

#-----------------------------------------------------
#                P R I K L A D    6
#-----------------------------------------------------

source('Sbirka-AS-I-2018-funkce.R')
dotplot(prch, wei)
plot(prch, wei)

#-----------------------------------------------------
#                P R I K L A D    7
#-----------------------------------------------------

data <- read.delim('01-one-sample-mean-skull-mf.txt')
head(data)
data <- na.omit(data)
skull.BM <- data[data$sex == 'm', 'skull.B']
skull.BM

n <- length(skull.BM)
(m <- sum(skull.BM) / n)
mean(skull.BM)

(s2 <- 1/n * sum((skull.BM - m)^2))
(s <- sqrt(s2))
min(skull.BM)
quantile(skull.BM, probs = c(0.25, 0.5, 0.75), type = 2)

library(e1071)
skewness(skull.BM, type = 3) # sikmost
e1071::kurtosis(skull.BM, type = 3) # spicatost

#-----------------------------------------------------
#                P R I K L A D    8
#-----------------------------------------------------

skull.LM <- data[data$sex == 'm', 'skull.L']
cor(skull.BM, skull.LM, method = 'pearson')

#-----------------------------------------------------
#                P R I K L A D    9
#-----------------------------------------------------

plot(skull.BM, skull.LM)