rm(list=ls())
#######get required libraries#######
library(ggplot2)
library(dplyr)
library(tidyverse)
library(RSwissMaps)
library(viridis)

#######set working direction and get the data
setwd("~/BAK_Projekt")

#base
mapCH <- mapCH2016 %>% dplyr::rename("bfs_nr"="can")

#create dataset on canton level
df_bak <- read.csv("~/BAK_Projekt/Liste_BAK2.csv", sep = ";")

df_bak_red <- df_bak %>% 
  dplyr::group_by(Kanton) %>% 
  dplyr::summarise(count=n())
df_bak_red <- df_bak_red[!(df_bak_red$Kanton ==""),]
df_bak_red$Kt <- c("AG", "AI", "AR", "BL", "BS", "BE", "FR", "GE", "GL", "GR", "JU", "LU", "NE", 
                     "NW", "OW", "SH", "SZ", "SO", "SG", "TI", "TG", "UR", "VD", "VS", "ZG", "ZH")
df_bak_red$bfs_nr <- as.integer(c("19", "16", "15", "13", "12", "2", "10", "25", "8", "18", "26", "3", "24", 
                                 "7", "6", "14", "5", "11", "17", "21", "20", "4", "22", "23", "9", "1"))

#get coordinates (required reference system CH1903/LV03)
mapCH.short <- mapCH[!duplicated(mapCH$bfs_nr),]
df.map <- full_join(df_bak_red, mapCH.short, by="bfs_nr") %>% 
  select("bfs_nr", "Kt", "name", "count", "bfs_nr", "long", "lat")



# Plotting sample data
can.plot(df.map$bfs_nr, df.map$count, 2016, 
         boundaries = "c", boundaries_size = 0.2, boundaries_color = "white",
         title = "Verteilung der Institutionen auf Kantonsebene")
  #geom_text(aes(x=df.map$long, y=df.map$lat ,label = df.map$Kt))







####Example for district map -> can be deleted
# Generating sample data:
dt.dis <- dis.template(2016)
for(i in 1:nrow(dt.dis)){dt.dis$values[i] <- sample(c(300:700), 1)/1000}
# Plotting sample data:
dis.plot(dt.dis$bfs_nr, dt.dis$values, 2016,
         boundaries = "c",
         title = "Beispiel auf Bezirksebene (random data)")
# Plotting sample data for the canton of Aargau:
dis.plot(dt.dis$bfs_nr, dt.dis$values, 2016, cantons = c("GR"),
         lakes = c("none"),
         title = "Beispiel Kanton Graubünden (Bezirksebene)") 


  


#Example Dataset
library(scatterpie)
table7 <- table7 %>% dplyr::rename("name"="Kanton")
table7 <- table7[!(table7$name ==""),]
test <- full_join(df.map, table7, by="name")
test$radius <- 6*abs(rnorm(nrow(test)))

p <- can.plot(df.map$bfs_nr, df.map$count, 2016, 
               boundaries = "c", boundaries_size = 0.2, boundaries_color = "white",
               title = "Verteilung der Institutionen auf Kantonsebene") +
  coord_quickmap()

p + geom_scatterpie(aes(x=long, y=lat, group=bfs_nr, r=radius),
                  data=test, cols=c(11:13), color=NA, alpha=.8) 

  geom_scatterpie_legend(test$radius, x=-160, y=-55)

  
  
  
  
  
  
  test %>% 
    select(c(11:13)) %>% 
    pivot_longer(cols = names(.)) %>% 
    ggplot(aes(x = value, y = 1, fill = name)) + 
    geom_col(position = "stack") + 
    coord_polar() + 
    theme_void()