library(dplyr) 
library(ggplot2)
library(gridExtra)
library(plotly)
library(shiny)

# read the space delimited file into R
data <- read.delim('SENIC.txt', header=FALSE, sep = "")

# column name vector according to SENIC.pdf
var_name = c('ID', 'X1','X2','X3','X4','X5','X6','X7','X8','X9','X10','X11') 
colnames(data) = var_name # change column names
 # changing the integer columns to numeric
data[,c(7,10,11)] <- sapply(data[,c(7,10,11)],as.numeric)


# Text to describe the variables
text<- c('Identification Number',
         'Length of Stay',
         'Age',
         'Infection Risk',
         'Routine Culturing Ratio',
         'Routine Chest X-ray Ratio',
         'Number of Beds' ,
         'Medical School Affiliation' ,
         'Region',
         'Average Daily Census',
         'Number of Nurses',
         'Available Facilities & Services')

ui <- fluidPage( # Slider input 
  sliderInput(inputId="ws", label="Choose bandwidth size", value=0.1, min=0.1, max=1),
  
  # Checkbox input for all variables except X3
  checkboxGroupInput('Variable', 'Variables to choose for graphs',
                           c('X1 - Length of Stay' = 1,
                           'X2 - Age' = 2,
                           'X4 - Routine Culturing Ratio' = 4,
                           'X5 - Routine Chest X-ray Ratio' = 5,
                           'X6 - Number of Beds' = 6,
                           'X9 - Average Daily Census' = 9,
                           'X10 - Number of Nurses' = 10,
                           'X11 - Available Facilities & Services' = 11)),
  
  plotOutput("densPlot") # output
)

server <- function(input, output,session) {

  output$densPlot <- renderPlot({
    
    p_list <- list()
    for (i in c(2,3,5,6,7,8,9,10,11,12)){
      var_name <- colnames(data[,2:12]) # Assigning the names
          # using the function to collect the outliers
      indice <- data.frame(data[quantile_func(data[,i]),i])
      colnames(indice) <- var_name[i-1]
      indice$Y <- rep(0,nrow(indice))
      # creating the plot and assigningn it to p1,p2 etc...
      p_list[[i-1]] <- ggplot(data,aes_string(x= var_name[i-1] ))  +
                   geom_density(bw=input$ws,fill=8,alpha=0.7) + 
        geom_point(data=indice,aes(y=Y),shape=5)+ ylab('Density') +
         xlab(text[i]) + theme_bw()
    }
         # Controlling that at least 1 box is marked for a graph to be printed
    if (length(input$Variable) >=1){ 
    grid.arrange(grobs = p_list[as.numeric(input$Variable)])}
  })
   
}

# Run the application 
shinyApp(ui = ui, server = server)

library(dplyr)
library(plotly)
library(rjson)
library(geojsonio)
library(sf)
library(stringr)
library(tidyr)

# changed the encoding to get the åäö in the names
data_scb <- read.csv("000006SW_20230912-130931.csv",fileEncoding = "ISO-8859-1")

data_json <- fromJSON(file="gadm41_SWE_1.json")

# splitting the age variable 
data_scb <- spread(data_scb, key=age, value=X2016)

colnames(data_scb)[2:4] <- c('Young', 'Adult', 'Senior')
data_scb$region <- str_replace(data_scb$region,' county','')

data_scb$region <- gsub("[[:digit:] ]", "",data_scb$region )

# örebro is Orebro in json file..
data_scb$region[data_scb$region=='Örebro'] <- 'Orebro' 
# there is also numbers and spaces for each region so that
# will have to be taken care of for a possible merge

g=list(fitbounds="locations", visible=FALSE)

p<-plot_geo(data_scb)%>%add_trace(type="choropleth",geojson=data_json, locations=~region,
                                  z=~Young, featureidkey="properties.NAME_1",name="")%>%
  add_trace(lat = ~58.41109, lon = ~15.62565, type='markers',marker=list(color="red"),
          name='Linköping') %>% # changed the color
  layout(geo=g, showlegend = FALSE) # and name on the marker to red and Linköping!
p