Seminar9.R

library(igraph)
library(decisionSupport)

# Path impact way model
hail_path <- graph.formula(HailNet -+ Yield, 
                           HailNet -+ Cost, 
                           HailEvent -+ Yield,
                           Yield -+ MarketPrice, 
                           MarketPrice -+ NPV,
                           Cost -+ NPV,
                           Discount -+ NPV)

plot(hail_path)


# Input estimates
hail_estimates <- data.frame(variable = c("yield", 
                                          "var_CV", 
                                          "initial_investment", 
                                          "price",
                                          "p_hail"),
                             lower = c(6000, 20, 500, 5, 0.02),
                             median = NA,
                             upper = c(14000, 20, 1000, 80, 0.2),
                             distribution = c("posnorm",
                                              "const", 
                                              "posnorm", 
                                              "posnorm",
                                              "posnorm"),
                             label = c("Yield (kg/ha)", 
                                       "Coefficient of variation", 
                                       "Investment cost (USD)", 
                                       "Market price (EUR/kg)",
                                       "% chance hail"),
                             Description = c("Yield under normal conditions", 
                                             "Coefficient of variation (measure of relative variability)",
                                             "Investment cost", 
                                             "Market price achieved for yields (EUR/kg)",
                                             "Probability of the hail storm"))

hail_estimates

hail_function <- function(){
  
  # use vv() to add variability to the 
  # random draws of yield and of  price 
  # over a 20 year simulation 
  yields <- vv(var_mean = yield, 
               var_CV = var_CV, 
               n = 20)
  
  prices <- vv(var_mean = price, 
               var_CV = var_CV, 
               n = 20)
  
  # use rep() to simulate the initial_investment 
  # only in the first year (assuming the net lasts 20 years)
  invest_costs <- c(initial_investment, rep(0, 19))
  
  # use p_hail in the chance_event() 
  # to adjust yield for probability of hail
  # assuming no yield at all in the event of hail
  hail_adjusted_yield <- chance_event(chance = p_hail, 
                                      value_if = 0,
                                      value_if_not = yield,
                                      n = 20)
  
  # calculate profit without net
  profit_no_net <- hail_adjusted_yield*prices
  
  # calculate profit with the net
  profit_with_net <- (yields*prices)-invest_costs
  
  # use 'discount' to calculate net present value 
  # 'discount_rate' is expressed in percent
  NPV_no_net <- discount(profit_no_net, discount_rate = 5, calculate_NPV = TRUE)
  NPV_net <- discount(profit_with_net, discount_rate = 5, calculate_NPV = TRUE)
  
  # calculate the overall NPV of the decision (do - don't do)
  NPV_decision <- NPV_net-NPV_no_net
  
  return(list(NPV_no_net =  NPV_no_net,
              NPV_net =  NPV_net, 
              NPV_decision = NPV_decision))
}

DT2[intersect(which(V3==1), 1:5), V2 := 1]
hail_estimates[intersect(which("variable" == "initial_investment"), c(2,4)),]
hail_estimates[hail_estimates$variable=="initial_investment", c(2,4)]
library(microbenchmark)
set.seed(1234)
x <- x <- c(0, 0, 20, 40, 80, 90)
microbenchmark(times = 1000,
  filter = {
    x[x < 10] <- 10
    x[x > 55] <- 55
    x
  },
  ifelse = {
    x <- ifelse(x < 10, 10, x)
    x <- ifelse(x > 55, 55, x)
    x
  },
  for_loop = {
    for (i in 1:length(x)){
      if ((x[i]) < 10){
        x[i] <- 10
      }
      if (x[i] > 55){
        x[i] <- 55
      }
    }
    x
}
)