library("reader")
# SET DIRECTORY OF THIS FILE AS WORKING DIRECTORY
setwd(dirname(rstudioapi::getSourceEditorContext()$path))


# Generate signal data for subjects
## Set number of random draws for each group
sample_size = 11
## Initialize median vectors
citizen_median_immigration <- c()
mp_median_immigration <- c()
citizen_median_redistribution <- c()
mp_median_redistribution <- c()
citizen_median_climate <- c()
mp_median_climate <- c()

## For i=1
### Generate sample data for citizen and MP attitudes regarding immigration
citizen_sample_attitude_immigration_1 <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.1, 0.15, 0.2, 0.2, 0.35)), levels = 1:5)   
mp_sample_attitude_immigration_1 <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.3, 0.3, 0.15, 0.1, 0.15)), levels = 1:5) 
### Calculate and append median values for citizens and MPs as factors
citizen_sample_median_immigration_1 <- median(as.numeric(citizen_sample_attitude_immigration_1))
mp_sample_median_immigration_1 <- median(as.numeric(mp_sample_attitude_immigration_1))
citizen_median_immigration <- c(citizen_median_immigration, citizen_sample_median_immigration_1)
mp_median_immigration <- c(mp_median_immigration, mp_sample_median_immigration_1) 
### Calculate and append data table of citizen and MP attitudes as numbers
citizen_histogram_data_immigration <- as.numeric(table(citizen_sample_attitude_immigration_1))
mp_histogram_data_immigration <- as.numeric(table(mp_sample_attitude_immigration_1))

### Generate sample data for citizen and MP attitudes regarding redistribution
citizen_sample_attitude_redistribution_1 <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.35, 0.25, 0.15, 0.15, 0.1)), levels = 1:5)   
mp_sample_attitude_redistribution_1 <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.2, 0.2, 0.25, 0.2, 0.15)), levels = 1:5) 
### Calculate and append median values for citizens and MPs as factors
citizen_sample_median_redistribution_1 <- median(as.numeric(citizen_sample_attitude_redistribution_1))
mp_sample_median_redistribution_1 <- median(as.numeric(mp_sample_attitude_redistribution_1))
citizen_median_redistribution <- c(citizen_median_redistribution, citizen_sample_median_redistribution_1)
mp_median_redistribution <- c(mp_median_redistribution, mp_sample_median_redistribution_1) 
### Calculate and append data table of citizen and MP attitudes as numbers
citizen_histogram_data_redistribution <- as.numeric(table(citizen_sample_attitude_redistribution_1))
mp_histogram_data_redistribution <- as.numeric(table(mp_sample_attitude_redistribution_1))

### Generate sample data for citizen and MP attitudes regarding climate change
citizen_sample_attitude_climate_1 <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.15, 0.15, 0.25, 0.2, 0.25)), levels = 1:5)   
mp_sample_attitude_climate_1 <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.2, 0.2, 0.25, 0.2, 0.15)), levels = 1:5) 
### Calculate and append median values for citizens and MPs as factors
citizen_sample_median_climate_1 <- median(as.numeric(citizen_sample_attitude_climate_1))
mp_sample_median_climate_1 <- median(as.numeric(mp_sample_attitude_climate_1))
citizen_median_climate <- c(citizen_median_climate, citizen_sample_median_climate_1)
mp_median_climate <- c(mp_median_climate, mp_sample_median_climate_1) 
### Calculate and append data table of citizen and MP attitudes as numbers
citizen_histogram_data_climate <- as.numeric(table(citizen_sample_attitude_climate_1))
mp_histogram_data_climate <- as.numeric(table(mp_sample_attitude_climate_1))


## Loop over remaining (expected) number of subjects
for(i in 2:500){
  ### Generate sample data for citizen and MP attitudes regarding immigration
  citizen_sample_attitude_immigration_temp <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.1, 0.15, 0.2, 0.2, 0.35)), levels = 1:5)   
  mp_sample_attitude_immigration_temp <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.3, 0.3, 0.15, 0.1, 0.15)), levels = 1:5) 
  ### Calculate and append median values for citizens and MPs
  citizen_sample_median_immigration_temp <- median(as.numeric(citizen_sample_attitude_immigration_temp))
  mp_sample_median_immigration_temp <- median(as.numeric(mp_sample_attitude_immigration_temp))
  citizen_median_immigration <- c(citizen_median_immigration, citizen_sample_median_immigration_temp)
  mp_median_immigration <- c(mp_median_immigration, mp_sample_median_immigration_temp)  
  ### Calculate and append data table of citizen and MP attitudes
  citizen_histogram_data_immigration <- cbind(citizen_histogram_data_immigration, table(citizen_sample_attitude_immigration_temp))
  mp_histogram_data_immigration<-cbind(mp_histogram_data_immigration, table(mp_sample_attitude_immigration_temp))
  
  ### Generate sample data for citizen and MP attitudes regarding redistribution
  citizen_sample_attitude_redistribution_temp <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.35, 0.25, 0.15, 0.15, 0.1)), levels = 1:5)   
  mp_sample_attitude_redistribution_temp <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.2, 0.2, 0.25, 0.2, 0.15)), levels = 1:5) 
  ### Calculate and append median values for citizens and MPs
  citizen_sample_median_redistribution_temp <- median(as.numeric(citizen_sample_attitude_redistribution_temp))
  mp_sample_median_redistribution_temp <- median(as.numeric(mp_sample_attitude_redistribution_temp))
  citizen_median_redistribution <- c(citizen_median_redistribution, citizen_sample_median_redistribution_temp)
  mp_median_redistribution <- c(mp_median_redistribution, mp_sample_median_redistribution_temp)  
  ### Calculate and append data table of citizen and MP attitudes
  citizen_histogram_data_redistribution <- cbind(citizen_histogram_data_redistribution, table(citizen_sample_attitude_redistribution_temp))
  mp_histogram_data_redistribution<-cbind(mp_histogram_data_redistribution, table(mp_sample_attitude_redistribution_temp))
  
  ### Generate sample data for citizen and MP attitudes regarding climate
  citizen_sample_attitude_climate_temp <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.15, 0.15, 0.25, 0.2, 0.25)), levels = 1:5)   
  mp_sample_attitude_climate_temp <- factor(sample(x = 1:5, size = sample_size, replace = TRUE, prob = c (0.2, 0.2, 0.25, 0.2, 0.15)), levels = 1:5) 
  ### Calculate and append median values for citizens and MPs
  citizen_sample_median_climate_temp <- median(as.numeric(citizen_sample_attitude_climate_temp))
  mp_sample_median_climate_temp <- median(as.numeric(mp_sample_attitude_climate_temp))
  citizen_median_climate <- c(citizen_median_climate, citizen_sample_median_climate_temp)
  mp_median_climate <- c(mp_median_climate, mp_sample_median_climate_temp)  
  ### Calculate and append data table of citizen and MP attitudes
  citizen_histogram_data_climate <- cbind(citizen_histogram_data_climate, table(citizen_sample_attitude_climate_temp))
  mp_histogram_data_climate<-cbind(mp_histogram_data_climate, table(mp_sample_attitude_climate_temp))
  
  ### Print progress
  print(i)
}


# Convert data into dataframes
## Immigration
citizen_histogram_dataframe_immigration <- t(as.data.frame(citizen_histogram_data_immigration)*100/11) # scale in percent
mp_histogram_dataframe_immigration <- t(as.data.frame(mp_histogram_data_immigration)*100/11)
citizen_median_dataframe_immigration <- as.data.frame(citizen_median_immigration)
mp_median_dataframe_immigration <- as.data.frame(mp_median_immigration)
## Redistribution
citizen_histogram_dataframe_redistribution <- t(as.data.frame(citizen_histogram_data_redistribution)*100/11) # scale in percent
mp_histogram_dataframe_redistribution <- t(as.data.frame(mp_histogram_data_redistribution)*100/11)
citizen_median_dataframe_redistribution <- as.data.frame(citizen_median_redistribution)
mp_median_dataframe_redistribution <- as.data.frame(mp_median_redistribution)
## Climate
citizen_histogram_dataframe_climate <- t(as.data.frame(citizen_histogram_data_climate)*100/11) # scale in percent
mp_histogram_dataframe_climate <- t(as.data.frame(mp_histogram_data_climate)*100/11)
citizen_median_dataframe_climate <- as.data.frame(citizen_median_climate)
mp_median_dataframe_climate <- as.data.frame(mp_median_climate)

# Save dataframes
## Immigration
write.csv(citizen_histogram_dataframe_immigration, "signal_immi_data_citizen_hist.csv", row.names=FALSE)
write.csv(mp_histogram_dataframe_immigration, "signal_immi_data_mp_hist.csv", row.names=FALSE)
write.csv(citizen_median_dataframe_immigration, "signal_immi_data_citizen_median.csv", row.names=FALSE)
write.csv(mp_median_dataframe_immigration, "signal_immi_data_mp_median.csv", row.names=FALSE)
## Redistribution
write.csv(citizen_histogram_dataframe_redistribution, "signal_red_data_citizen_hist.csv", row.names=FALSE)
write.csv(mp_histogram_dataframe_redistribution, "signal_red_data_mp_hist.csv", row.names=FALSE)
write.csv(citizen_median_dataframe_redistribution, "signal_red_data_citizen_median.csv", row.names=FALSE)
write.csv(mp_median_dataframe_redistribution, "signal_red_data_mp_median.csv", row.names=FALSE)
## Climate
write.csv(citizen_histogram_dataframe_climate, "signal_env_data_citizen_hist.csv", row.names=FALSE)
write.csv(mp_histogram_dataframe_climate, "signal_env_data_mp_hist.csv", row.names=FALSE)
write.csv(citizen_median_dataframe_climate, "signal_env_data_citizen_median.csv", row.names=FALSE)
write.csv(mp_median_dataframe_climate, "signal_env_data_mp_median.csv", row.names=FALSE)




hist(citizen_median_immigration - mp_median_immigration)
hist(citizen_median_redistribution - mp_median_redistribution)
hist(citizen_median_climate - mp_median_climate)
#table(citizen_sample_attitude_temp)
#vllt ist table nict die richtige funktion
#kann chat gpt betsimmt
#ich will, dass er bei 1 und 4 null hinschreibt