Function10.

knitr::opts_chunk$set(echo = TRUE)
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#Function name: Matrix Function 
#Use two variables rows and col. 
#First create a matrix (x) fill with 0 values with the correct number of rows, and cols, according to the variables. 
#Second create to for loops to fill up the matrix x. This for loop will loop in i in the first column and nrowX. Then will loop in j to select first row and ncol in x. lastly, will take the values i and j and will create a value multiplying both. 
matrix_function <- function(rows, cols) {
  x<-matrix(rep(0, rows*cols), nrow = rows, ncol = cols)
  for (i in 1:nrow(x)) {
    for (j in 1:ncol(x)) {
      x[i, j] <- i * j  
    }
  }
  
  return(x)
  
}
#-------------------------------------------------------------

#----------------------------------------------------------------

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#Function name: generate_random_data
#Use: Create a random generate data using a for loop. 
#Variables required and predate: n_groups=4, n_obs_min=25, n_obs_max=100, mean_min=12, mean_max=40, sd_min=2, sd_max=5
#How it works: Itiate with an empty list to store independent df for each group. Second creacte for loop to generate random parameter for each group base on n_groups.
#Finally combine all dataset in one unique df. 

generate_random_data <- function(n_groups=4, n_obs_min=25, n_obs_max=100, mean_min=12, mean_max=40, sd_min=2, sd_max=5) {
  group_data <- list()
  for (i in 1:n_groups) {
    n_obs <- sample(n_obs_min:n_obs_max, 1)
    group_mean <- runif(1, mean_min, mean_max)
    group_sd <- runif(1, sd_min, sd_max)
    group_data[[i]] <- data.frame(Group = rep(paste0("Group_", i), n_obs),
                                  Value = rnorm(n_obs, mean = group_mean, sd = group_sd))
  }
  
  # Combine data frames for all groups into a single data frame
  all_data <- do.call(rbind, group_data)
  
  return(all_data)
}

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#Funtion name: Suffle_response
#Function. basicaly suffles the values of any data set, the of column name value, independently of other colums. 
#Calculte the mean for each group after shuffling. 
#output. Vector of means of each group. 
shuffle_response <- function(data) {
  data$Value <- sample(data$Value) 
  mean_df <- aggregate(Value ~ Group, data = data, FUN = mean)
  return(mean_df$Value)
}
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