setwd("~/Desktop/Lectures/2023-2024/Statistical Tools/R practice/Lab 2")
data
# Step 1: Load Your Data
setwd("~/Desktop/Lectures/2023-2024/Statistical Tools/R practice/Lab 2")
data <- read.csv("daily_csv.csv")
data
head(data)
head(data)
dim(data)
is.na(data$Price)
missing_values_count <- sum(is.na(data$Price))
missing_values_count
str(data)
data$Price
is.na(data$Price)
missing_values_count <- sum(is.na(data$Price))
missing_values_count
data$Price[which(is.na(data$Price))]
position<-which(is.na(data$Price))
position
(position-4):(position+4)
data$Price[(position-4):(position+4)]
data$Date[position]
data<-na.omit(data)
data
mean(data$Price)
M_a<-function(x){
n<-length(x)
res<-sum(x)/n
return(res)
}
mean(data$Price)
M_a(data$Price)
M_g1<-function(x){
n<-length(x)
res<-(prod(x))^(1/n)
return(res)
}
M_g1(data$Price)
y<-data$Price[1:100]
y
M_g1(y)
M_g2<-function(x){
res<-exp(mean(log(x)))
return(res)
}
M_g1(y)
M_g2(y)
M_g2(data$Price)
median_value <- median(data$Price)
median_value
M_a(data$Price)
density_estimation <- density(data$Price)
mode<-density_estimation$x[which.max(density_estimation$y)]
mode
