diff --git a/Heatmaps.R b/Heatmaps.R
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+++ b/Heatmaps.R
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+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()
+  
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