Difference between revisions of "Goherr: Fish consumption study"

  
library(OpasnetUtils)
library(ggplot2)
library(reshape2)

objects.latest("Op_en7749", "preprocess")

for(i in c(5:6, 16, 29:30, 46:49, 85:86, 95:98, 135)) {
  temp <- survey[!is.na(survey[[i]]),]
    p <- ggplot(temp, aes(x = temp[[i]])) + geom_bar() +
    theme_gray(base_size = 18) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4)) +
    labs(title = colnames(temp[i])) + xlab("") + facet_wrap(~ Country)
  print(p)
}

temp <- melt(survey, measure.vars = 145:153, variable.name = "Changing.factor", value.name = "Impact")
levs <- c("-5 strongly disagree", "-4", "-3", "-2", "-1", "0 Neutral", "1", "2", "3", "4", "5 strongly agree", "I don't know")
temp$Impact <- factor(temp$Impact, levels = levs, labels = levs, ordered = TRUE)

ggplot(temp, aes(x = Impact, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Changing.factor)+
  theme_gray(base_size = 24) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4))

surveytemp <- subset(survey, survey[[31]] == "Yes")

temp <- melt(surveytemp, measure.vars = 38:43, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + labs(title = "Baltic salmon sources")

temp <- melt(surveytemp, measure.vars = 50:59, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + labs(title = "Reasons to eat Baltic salmon")

temp <- melt(surveytemp, measure.vars = 73:82, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4)) + labs(title = "Baltic salmon actions")

surveytemp <- subset(survey, survey[[31]] == "No")

temp <- melt(surveytemp, measure.vars = 62:70, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + labs(title = "Reasons not to eat Baltic salmon")


surveytemp <- subset(survey, survey[[86]] == "Yes")

temp <- melt(surveytemp, measure.vars = 87:92, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + labs(title = "Baltic herring sources")

temp <- melt(surveytemp, measure.vars = 99:108, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + labs(title = "Reasons to eat Baltic herring")

temp <- melt(surveytemp, measure.vars = 123:132, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.4)) + labs(title = "Baltic herring actions")

surveytemp <- subset(survey, survey[[86]] == "No")

temp <- melt(surveytemp, measure.vars = 111:120, variable.name = "Variable", value.name = "Value")
ggplot(temp, aes(x = Value, fill = Country)) + geom_histogram(position = "dodge") + facet_wrap(~ Variable)+
   theme_gray(base_size = 24) + labs(title = "Reasons not to eat Baltic herring")

library(OpasnetUtils)

############## Generic functions and objects are defined first.

### webropol.convert converts a csv file from Webropol into a useful data.frame.

webropol.convert <- function(
  data, # Data.frame created from a Webropol csv file. The first row should contain headings.
  rowfact, # Row number where the factor levels start (in practice, last row + 3)
  textmark = "Other open" # The text that is shown in the heading if there is an open sub-question.
) {
  out <- dropall(data[2:(rowfact - 3) , ])
  subquestion <- t(data[1 , ])
  subquestion <- gsub("\xa0", " ", subquestion)
  subquestion <- gsub("\xb4", " ", subquestion)
  subquestion <- gsub("\n", " ", subquestion)
  #  subquestion <- gsub("\\(", " ", subquestion)
  #  subquestion <- gsub("\\)", " ", subquestion)
  textfield <- regexpr(textmark, subquestion) != -1
  subquestion <- strsplit(subquestion, ":") # Divide the heading into a main question and a subquestion.
  subqtest <- 0 # The previous question name.
  for(i in 1:ncol(out)) {
    #print(i)
    if(subquestion[[i]][1] != subqtest) { # If part of previous question, use previous fact.
      fact <- as.character(data[rowfact:nrow(data) , i]) # Create factor levels from the end of Webropol file.
      fact <- fact[fact != ""] # Remove empty rows
      fact <- gsub("\xa0", " ", fact)
      fact <- gsub("\xb4", " ", fact)
      fact <- gsub("\n", " ", fact)
      fact <- strsplit(fact, " = ") # Separate value (level) and interpretation (label)
    }
    if(length(fact) != 0 & !textfield[i]) { # Do this only if the column is not a text type column.
      out[[i]] <- factor(
        out[[i]], 
        levels = unlist(lapply(fact, function(x) x[1])), 
        labels = unlist(lapply(fact, function(x) x[2])), 
        ordered = TRUE
      )
    }
    subqtest <- subquestion[[i]][1]
  }
  return(out)
}

# merge.questions takes a multiple checkbox question and merges that into a single factor.
# First levels in levs have priority over others, if several levels apply to a row.

merge.questions <- function(
  dat, # data.frame with questionnaire data
  cols, # list of vectors of column names or numbers to be merged into one level in the factor
  levs, # vector (with the same length as cols) of levels of factors into which questions are merged.
  name # text string for the name of the new factor column in the data.
) {
  for(i in length(cols):1) {
    temp <- FALSE
    for(j in rev(cols[[i]])) {
      temp <- temp | !is.na(dat[[j]])
    }
    dat[[name]][temp] <- levs[i]
  }
  dat[[name]] <- factor(dat[[name]], levels = levs, ordered = TRUE)
  return(dat)
}

############# Data preprocessing

# Get the data either from Opasnet or your own hard drive.

#Survey original file: N:/Ymal/Projects/Goherr/WP5/Goherr_fish_consumption.csv

survey <- opasnet.csv("5/57/Goherr_fish_consumption.csv", sep = ";", fill = TRUE, quote = "\"")
surcol <- t(survey[1,])

survey <- webropol.convert(survey, 2121, textmark = ":Other open") # Data file is converted to data.frame using levels at row 1269.

# Take the relevant columnames from the table on the page.
colframe <- opbase.data("Op_en7749", subset = "Questions in the Goherr questionnaire")
colnames(survey) <- colframe$Result

survey$Row <- 1:nrow(survey)
survey$Weighting <- as.double(levels(survey$Weighting))[survey$Weighting]
survey$Ages <- ifelse(as.numeric(as.character(survey$Age)) < 46, "18-45",">45")

freqlist <- c(
  "less than once a year",
  "A few times a year",
  "1 - 3 times per month",
  "once a week",
  "2 - 4 times per week",
  "5 or more times per week"
)

amlist <- c(
  "1/6 plate or below (50 grams)",
  "1/3 plate (100 grams)",
  "1/2 plate (150 grams)",
  "2/3 plate (200 grams)",
  "5/6 plate (250 grams)",
  "full plate (300 grams)",
  "overly full plate (500 grams)"
#  "Not able to estimate"
)

sidel <- c(
  "1/6 plate or below (50 grams)",
  "1/4 plate (100 grams)",
  "1/2 plate (150 grams)",
  "2/3 plate (200 grams)",
  "5/6 plate (250 grams)"
#  "Not able to estimate"
)

ansl <- list(
  `How often eat fish` = freqlist,
  `How often Baltic salmon` = c("Never", freqlist),
  `How much Baltic salmon` = amlist,
  `How often side Baltic salmon` = c("Never", freqlist),
  `How much side Baltic salmon` = sidel,
  `How often Baltic herring` = c("Never", freqlist),
  `How much Baltic herring` = amlist,
  `How often side Baltic herring` = c("Never", freqlist),
  `How much side Baltic herring` = sidel
)

for (i in names(ansl)) {
  survey[[i]] <- factor(survey[[i]], levels = ansl[[i]])
}

objects.store(survey, surcol)
cat("objects survey, surcol were stored.\n")

# This is code Op_en7749/bayes on page [[Goherr: Fish consumption study]]

library(OpasnetUtils)
library(ggplot2)
library(reshape2)
library(rjags)
library(car)

# Fish intake in humans
# Data from data.frame survey from page [[Goherr: Fish consumption study]]
# Start with salmon questions 46:49 (amounts eaten)
# Some preprocessing should be moved away from this code.
# Model assumes that questionnaire data follows binomial distribution with parameter p and max value levels.
# p depends on country (4 groups), sex (2), age (2) that are known individually.
# We should test whether sex makes a difference or whether it can be omitted.
# Predicted p for each question, determined by the group, are produced from the bayes model.
# p is used to sample answers, which are then combined with estimates of amounts related to each answer.
# Total amount eaten of each fish per modelled individual is finally calculated.

objects.latest("Op_en7749", "preprocess") # [[Goherr: Fish consumption study]]: survey, surcol

agel <- as.character(unique(survey$Ages))
countryl <- sort(as.character(unique(survey$Country)))
genderl <- sort(as.character(unique(survey$Gender)))
fisl <- c("Salmon", "Herring")

# Interesting fish eating questions
surv <- survey[c(1,3,158,16,29,30,31,46:49,86,95:98)]
colnames(surv)
#[1] "Country"                       "Gender"                       
#[3] "Ages"                          "Fish eating"                  
#[5] "How often eat fish"            "Salmon eating"                
#[7] "Baltic salmon"                 "How often Baltic salmon"      
#[9] "How much Baltic salmon"        "How often side Baltic salmon" 
#[11] "How much side Baltic salmon"   "Eat Baltic herring"           
#[13] "How often Baltic herring"      "How much Baltic herring"      
#[15] "How often side Baltic herring" "How much side Baltic herring" 

surv$Ages <- match(surv$Ages, agel) # Not a factor, coerce to integer
surv <- as.data.frame(lapply(surv, FUN = function(x) as.integer(x))) # Coerce to integers
surv[is.na(surv[[12]]) | surv[[12]] == 3 , 12] <- 1 # Eat Baltic herring: I don't know --> No
surv[4:16] <- lapply(surv[4:16], FUN = function(x) x-1) # Scale to start from zero

# Row numbers for respondents that have eaten fish, Baltic salmon, and Baltic herring
eatfish <- (1:nrow(surv))[surv[[4]] %in% 1]
eatsalm <- (1:nrow(surv))[!(surv[[7]] %in% 0 | is.na(surv[[7]]))]
eatherr <- (1:nrow(surv))[surv[[12]] %in% 1 & !is.na(rowSums(surv[13:16]))]

# Oletetaan, että covarianssimatriisi on vakio kaikille maille ja sukupuolille yms
# mutta keskiarvo on spesifi näille ja kysymykselle.

qlen <-  c(4,2,2,2,6,2,2,7,7,7,5,2,7,7,7,5) # Number of options in each question


agel
countryl
genderl
fisl

#################################################
cat("Version with both binomial and multivariate normal.\n")

mod <- textConnection("
  model{
    for(i in 1:S) {
      for(j in 1:4) {
        surv[i,j] ~ dbin(p[j], qlen[j])
      }
    surv.scaled[i,1:4] ~ dmnorm(mu0[], Omega[,])
    }
    for(j in 1:4) {
      p[j] ~ dbeta(1,1)
    }
#    mu[1:4] ~ dmnorm(mu0[1:4], S2[1:4,1:4])
    Omega[1:4,1:4] ~ dwish(S3[1:4,1:4],4)
  }
")

jags <- jags.model(
  mod,
  data = list(
    surv = surv[eatherr,13:16],
    surv.scaled = scale(surv[eatherr,13:16]),
    qlen = qlen[13:16],
    S = length(eatherr),
    mu0 = rep(0,4),
#    S2 = diag(4)/100000,
    S3 = diag(4)/10000
  ),
  n.chains = 4,
  n.adapt = 100
)

update(jags, 100)
samps.j <- jags.samples(jags, c('Omega', 'p'), 1000)
j <- array(
  samps.j$p,
  dim = c(4,1000,4),
  dimnames = list(Question = 1:4, Iter = 1:1000, Seed = 1:4)
)
scatterplotMatrix(t(j[,,1]))
j.melt <- melt(j)
ggplot(j.melt, aes(x = Iter, y = value, colour = as.character(Seed)))+
  geom_line()+facet_wrap(~ Question)

j.Omega <- array(
  samps.j$Omega,
  dim = c(4,4,1000,4),
  dimnames = list(Q1 = 1:4, Q2 = 1:4, Iter = 1:1000, Seed = 1:4)
)
scatterplotMatrix(t(j.Omega[1,,,1]))

j.Omega.melt <- melt(j.Omega)
ggplot(j.Omega.melt, aes(x = Iter, y = value, colour = as.character(Seed)))+
  geom_line()+facet_grid(Q1 ~ Q2)

samps.c <- coda.samples(jags, c('Omega','p'), 1000)
plot(samps.c)

############################################################
cat("Version with multivariate normal.\n")

mod <- textConnection("
  model{
  for(i in 1:S) {
    surv[i,1:4] ~ dmnorm(mu[], Omega[,])
  }
  mu[1:4] ~ dmnorm(mu0[1:4], S2[1:4,1:4])
  Omega[1:4,1:4] ~ dwish(S3[1:4,1:4],4)
  }
")

jags <- jags.model(
  mod,
  data = list(
    surv = surv[eatherr,13:16],
    S = length(eatherr),
    mu0 = rep(0.5,4),
    S2 = diag(4)/100000,
    S3 = diag(4)/10000
  ),
  n.chains = 4,
  n.adapt = 100
)

update(jags, 100)
samps.j <- jags.samples(jags, c('mu','Omega', 'z'), 1000)
j <- array(
  samps.j$mu,
  dim = c(4,1000,4),
  dimnames = list(Question = 1:4, Iter = 1:1000, Seed = 1:4)
)
scatterplotMatrix(t(j[,,1]))
samps.c <- coda.samples(jags, c('mu','Omega','z'), 1000)
plot(samps.c)

###########################################################
cat("Version with multivariate normal and fractile conversion.\n")

# Assume linearity in the responses, but it is artificially binned into survey answers.
# Assume that answers are uncorrelated with a subgroup of respondents that have
# answered in the same way to two questions. So, for each answer column d, you can
#1 shuffle the vector d (keeping in mind the original order)
#2 sort it based on the answer
#3 renumber it by (1:length(d) - 0.5)/length(d) to get a fractile from 0..1.
#4 transform it to normal distribution by qnorm(d)
#5 reorder d to the original order
# Then, use multivariate normal distribution to estimate parameters.


fractalise <- function(d) {
  f <- sample(seq_along(d)) # Create a random order for data
  g <- order(d[f]) # First randomorder and then put in order by size
  h <- qnorm((seq_along(d)-0.5)/length(d)) # 'z scores' of data points
  return(h[order(g)][order(f)]) # Reorder fractiles to original order
}

surv.f <- surv[1:nrow(surv) %in% eatherr & !is.na(rowSums(surv[13:16])) , ]
surv.f[13:16] <- lapply(surv.f[13:16], fractalise)


mod <- textConnection("
  model{
    for(i in 1:S) {
      surv.f[i,1:4] ~ dmnorm(mu[], Omega[,])
    }
    for(j in 1:4) {
      mu[j] ~ dnorm(0, 3)
    }
    Omega[1:4,1:4] ~ dwish(S3[1:4,1:4],5)
    surv.pred[1:4] ~ dmnorm(mu[1:4], Omega[1:4,1:4])
    Opri[1:4,1:4] ~ dwish(S3[1:4,1:4],5)
  }
")

jags <- jags.model(
  mod,
  data = list(
    surv.f = surv.f,
    S = nrow(surv.f),
#    mu0 = rep(0.5,4), # hyperprior for mean
#    S2 = diag(4)/100000, # hyperprior for mean
    S3 = diag(4)*5#/10000 # hyperprior for covariance table
  ),
  n.chains = 4,
  n.adapt = 100
)

update(jags, 100)
samps.j <- jags.samples(jags, c('mu','Omega', 'surv.pred', 'Opri'), 1000)
j <- array(
  samps.j$surv.pred,
  dim = c(4,1000,4),
  dimnames = list(Question = 1:4, Iter = 1:1000, Seed = 1:4)
)

oprint(cor(t(samps.j$surv.pred[,,1])))
oprint(cor(surv.f[13:16]))
scatterplotMatrix(t(j[,,1]))
scatterplotMatrix(surv.f[13:16])

j2 <- array(
  samps.j$Opri,
  dim = c(4,4,1000,4),
  dimnames = list(Q1 = 1:4, Q2 = 1:4, Iter = 1:1000, Seed = 1:4)
)
j2.cov <- apply(j2[,,,1], MARGIN = 1:2, FUN = mean)
oprint(j2.cov)
scatterplotMatrix(t(j2[1,,,1]))

samps.c <- coda.samples(jags, c('mu','Omega', 'surv.pred'), 1000)
plot(samps.c)

################################################################3
cat("MODEL WITH BINOMIAL DISTRIBUTION ASSUMPTION.\n")

# Interesting fish eating questions
surv <- survey[c(1,3,4,16,29,30,31,46:49,86,95:98,158)]
colnames(surv)

consume <- data.matrix(survey[c(
  "Baltic salmon",
  "Eat Baltic herring"
)])
consume <- 1 - (consume!=2 | is.na(consume)) # Yes=1, other=0

agel <- as.character(unique(survey$Ages))
countryl <- sort(as.character(unique(survey$Country)))
genderl <- sort(as.character(unique(survey$Gender)))
fisl <- c("Salmon", "Herring")

ans.sal <- survey[
    grep("Yes", survey[["Baltic salmon"]]) ,
    c(
      "How often Baltic salmon",
      "How much Baltic salmon",
      "How often side Baltic salmon",
      "How much side Baltic salmon",
      "Ages",
      "Country",
      "Gender"
    )]

ans.her <- survey[
    grep("Yes", survey[["Eat Baltic herring"]]) ,
    c(
      "How often Baltic herring",
      "How much Baltic herring",
      "How often side Baltic herring",
      "How much side Baltic herring",
      "Ages",
      "Country",
      "Gender"
    )]
quesl <-     c(
  "How often",
  "How much",
  "How often side",
  "How much side",
  "Ages",
  "Country",
  "Gender"
)
colnames(ans.sal) <- quesl
colnames(ans.her) <- quesl
ans <- rbind(ans.sal, ans.her)

head(ans)
qlen <-  c(7,7,7,5) # Number of options in each question

questionl <- colnames(ans.sal)
questionl.her <- colnames(ans.her)

agel
countryl
genderl
fisl

mod <- textConnection("
  model{
# Data1: Full questionnaire
    for(f in 1:2) { # Salmon or herring
      for(i in 1:U) { # Full questionnaire
        consume[i,f] ~ dbern(p[f,ageU[i],countryU[i],genderU[i]])
      }
    }
# Data2: Those responses that reported either Baltic herring or salmon
    for(q in 1:Q) { # Q = # of fish-specific questions
      for(i in 1:S) { # S = # respondents who eat Baltic salmon or herring
        ans[i,q] ~ dbin(p2[fis[i],age[i],country[i],gender[i],q], qlen[q])
      }
    }
# Priors (multidimensional)
    for(i in 1:A) { # Age groups
      for(j in 1:C) { # Countries
        for(k in 1:G) { #Genders
          for(f in 1:2) { # Fish species
            p[f,i,j,k] ~ dbeta(1,1)
            for(q in 1:Q) { # Q = # of fish-specific questions
              p2[f,i,j,k,q] ~ dbeta(1,1) 
              ans.pred[f,i,j,k,q] ~ dbin(p2[f,i,j,k,q], qlen[q])
            }
          }
        }
      }
    }
  }
")

jags <- jags.model(
  mod,
  data = list(
    U = nrow(surv),
    Q = 4,
    S = nrow(ans),
    consume = consume,
    ans = data.matrix(ans[1:4]) - 1, # Zero is the first option
    qlen = qlen-1, # Subtract 1 because starts from 0.
    age = match(ans$Ages, agel),
    country = match(ans$Country, countryl),
    gender = match(ans$Gender, genderl),
    ageU = match(survey$Ages, agel),
    countryU = match(survey$Country, countryl),
    genderU = match(survey$Gender, genderl),
    fis = c(rep(1, nrow(ans.sal)), rep(2, nrow(ans.her))),
    A = length(agel),
    C = length(countryl),
    G = length(genderl)
  ),
  n.chains = 4,
  n.adapt = 100
)

update(jags, 100)
samps <- jags.samples(jags, c('ans.pred','p','p2'), 1000)
#samps.coda <- coda.samples(jags, c('mu', 'pcd.pred', 'ans.pred'), 1000)

ans.pred <- array(
  samps$ans.pred, 
  dim = c(length(fisl), length(agel), length(countryl), length(genderl), length(quesl), 1000, 4), 
  dimnames = list(
    Fish = fisl,
    Age = agel,
    Country = countryl,
    Gender = genderl,
    Question = quesl,
    Iter = 1:1000,
    Seed = c("S1","S2","S3","S4")
  )
)

p.pred <- array(
  samps$p, 
  dim = c(length(fisl), length(agel), length(countryl), length(genderl), 1000, 4), 
  dimnames = list(
    Fish = fisl,
    Age = agel,
    Country = countryl,
    Gender = genderl,
    Iter = 1:1000,
    Seed = c("S1","S2","S3","S4")
  )
)


p2.pred <- array(
  samps$p2, 
  dim = c(length(fisl), length(agel), length(countryl), length(genderl), length(quesl), 1000, 4), 
  dimnames = list(
    Fish = fisl,
    Age = agel,
    Country = countryl,
    Gender = genderl,
    Question = quesl,
    Iter = 1:1000,
    Seed = c("S1","S2","S3","S4")
  )
)

ansm <- melt(ans.pred)
pm <- melt(p.pred)
p2m <- melt(p2.pred)

scatterplotMatrix(t(ans.pred[1,1,,1,1,,1]))

scatterplotMatrix(t(p2.pred[1,1,,1,1,,1]))
scatterplotMatrix(t(p2.pred[,1,3,1,1,,1]))
scatterplotMatrix(t(p2.pred[1,,3,1,1,,1]))
scatterplotMatrix(t(p2.pred[1,1,3,,1,,1]))
scatterplotMatrix(t(p2.pred[1,1,3,1,,,1]))

scatterplotMatrix(t(p.pred[1,1,,1,,1]))
scatterplotMatrix(t(p.pred[,1,3,1,,1]))
scatterplotMatrix(t(p.pred[1,,3,1,,1]))
scatterplotMatrix(t(p.pred[1,1,3,,,1]))

#objects.store(ans.pred, p.pred, p2.pred)
#cat("Arrays ans.pred (answer), p.pred (probability to eat), p2.pred (p of answer) stored.\n")

# This is code Op_en7749/calculate_amount on page [[Goherr: Fish consumption study]]

library(OpasnetUtils)
library(ggplot2)
library(reshape2)
library(rjags)

objects.latest("Op_en7749", code_name = "bayes") #: ans.pred, p.pred, p2.pred

ql <- melt(ans.pred)

##Assumptions for amount and frequencies

assumptions <- opbase.data("Op_en7749.assumptions_for_calculations")

salmon_dishtimesassump <- Ovariable("salmon_dishtimesassump", data = assumptions[assumptions$Variable == "freq" , c("value", "Result")])

salmon_dishamountassump <- Ovariable("salmon_dishamountassump", data = assumptions[assumptions$Variable == "amdish" , c("value", "Result")])

salmon_ingridient <- Ovariable("salmon_ingridient", data = assumptions[assumptions$Variable == "ingridient", ]["Result"])

salmon_sidetimesassump <- Ovariable("salmon_wholetimesassump", data = assumptions[assumptions$Variable == "freq" , c("value", "Result")])

salmon_sideamountassump <- Ovariable("salmon_wholeamountassump", data = assumptions[assumptions$Variable == "amside" , c("value", "Result")])

##Build up the variables to calculate amount of Baltic salmon eating

salmon_dishtimes <-  Ovariable("salmon_dishtimes", data = data.frame(
  subset(ql, ql$Fish == "Salmon" & ql$Question == "How often" & ql$Seed == "S1"),
  Result = 1
))

salmon_dishtimes@data$Question <- NULL

salmon_dishamount <- Ovariable("salmon_dishamount", data = data.frame(
  subset(ql, ql$Fish == "Salmon" & ql$Question == "How much" & ql$Seed == "S1"),
  Result = 1
)) #salmon with dish amount per one serving

salmon_dishamount@data$Question <- NULL  

salmon_sidetimes <- Ovariable("salmon_sidetimes", data = data.frame(
  subset(ql, ql$Fish == "Salmon" & ql$Question == "How often side" & ql$Seed == "S1"),
  Result = 1
)) #salmon as such times / year

salmon_sidetimes@data$Question <- NULL

salmon_sideamount <- Ovariable("salmon_sideamount", data = data.frame(
  subset(ql, ql$Fish == "Salmon" & ql$Question == "How much side" & ql$Seed == "S1"),
  Result = 1
)) #salmon as whole amount per one serving

salmon_sideamount@data$Question <- NULL

amount <- Ovariable("amount", 
                    dependencies = data.frame(Name = c(
                      "salmon_dishamount",
                      "salmon_dishamountassump",
                      "salmon_ingridient",
                      "salmon_dishtimes",
                      "salmon_dishtimesassump",
                      "salmon_sideamount",
                      "salmon_sideamountassump",
                      "salmon_sidetimes",
                      "salmon_sidetimesassump"
                    )),
                    formula = function(...) {
                      
                      out1 <- (salmon_dishamount * salmon_dishamountassump * salmon_ingridient) 
                      out1@output$value <- NULL
                      out1 <- ((salmon_dishtimes * salmon_dishtimesassump) * out1) / 365
                      out1@output$value <- NULL
                      out2 <- (salmon_sideamount * salmon_sideamountassump)
                      out2@output$value <- NULL
                      out2 <- ((salmon_sidetimes * salmon_sidetimesassump) * out2) / 365
                      out2@output$value <- NULL
                      out <- out1 + out2
                      
                      # Per vuosi -> per d
                      
                      #result(out)[result(out) == 0] <- 0.01 # Ei jätetä nollia saantiin
                      return(out)
                    }
)

amountSalmon <- EvalOutput(amount, forceEval = TRUE, N = 1000)

temp <- amountSalmon@output 
temp <- aggregate(temp$amountResult, by=list(temp$Country, temp$Age, temp$Gender), FUN = mean)
colnames(temp) <- c("Country","Age","Gender","Salmon amount")
oprint(temp)

##Build up the variables to calculate amount of Baltic herring eating

herring_dishtimes <-  Ovariable("herring_dishtimes", data = data.frame(
  subset(ql, ql$Fish == "Herring" & ql$Question == "How often" & ql$Seed == "S1"),
  Result = 1
))

herring_dishtimes@data$Question <- NULL

herring_dishamount <- Ovariable("herring_dishamount", data = data.frame(
  subset(ql, ql$Fish == "Herring" & ql$Question == "How much" & ql$Seed == "S1"),
  Result = 1
)) #herring with dish amount per one serving

herring_dishamount@data$Question <- NULL  

herring_sidetimes <- Ovariable("herring_sidetimes", data = data.frame(
  subset(ql, ql$Fish == "Herring" & ql$Question == "How often side" & ql$Seed == "S1"),
  Result = 1
)) #herring as such times / year

herring_sidetimes@data$Question <- NULL

herring_sideamount <- Ovariable("herring_sideamount", data = data.frame(
  subset(ql, ql$Fish == "Herring" & ql$Question == "How much side" & ql$Seed == "S1"),
  Result = 1
)) #herring as whole amount per one serving

herring_sideamount@data$Question <- NULL

herring_dishtimesassump <- Ovariable("herring_dishtimesassump", data = assumptions[assumptions$Variable == "freq" , c("value", "Result")])

herring_dishamountassump <- Ovariable("herring_dishamountassump", data = assumptions[assumptions$Variable == "amdish" , c("value", "Result")])

herring_ingridient <- Ovariable("herring_ingridient", data = assumptions[assumptions$Variable == "ingridient", ]["Result"])

herring_sidetimesassump <- Ovariable("herring_wholetimesassump", data = assumptions[assumptions$Variable == "freq" , c("value", "Result")])

herring_sideamountassump <- Ovariable("herring_wholeamountassump", data = assumptions[assumptions$Variable == "amside" , c("value", "Result")])

amount <- Ovariable("amount", 
                    dependencies = data.frame(Name = c(
                      "herring_dishamount",
                      "herring_dishamountassump",
                      "herring_ingridient",
                      "herring_dishtimes",
                      "herring_dishtimesassump",
                      "herring_sideamount",
                      "herring_sideamountassump",
                      "herring_sidetimes",
                      "herring_sidetimesassump"
                    )),
                    formula = function(...) {
                      
                      out1 <- (herring_dishamount * herring_dishamountassump * herring_ingridient) 
                      out1@output$value <- NULL
                      out1 <- ((herring_dishtimes * herring_dishtimesassump) * out1) / 365
                      out1@output$value <- NULL
                      out2 <- (herring_sideamount * herring_sideamountassump)
                      out2@output$value <- NULL
                      out2 <- ((herring_sidetimes * herring_sidetimesassump) * out2) / 365
                      out2@output$value <- NULL
                      out <- out1 + out2
                      
                      # Per vuosi -> per d
                      
                      #result(out)[result(out) == 0] <- 0.01 # Ei jätetä nollia saantiin
                      return(out)
                    }
)

amountHerring <- EvalOutput(amount, forceEval = TRUE, N = 1000)

temp <- amountHerring@output 
temp <- aggregate(temp$amountResult, by=list(temp$Country, temp$Age, temp$Gender), FUN = mean)
colnames(temp) <- c("Country","Age","Gender","Herring amount")
oprint(temp)

objects.store(amountHerring, amountSalmon)
cat("objects amountHerring, amountSalmon were stored.\n")