93 lines
2.8 KiB
R
93 lines
2.8 KiB
R
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rm(list=ls())
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#######get required libraries#######
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library(ggplot2)
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library(dplyr)
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library(tidyverse)
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library(RSwissMaps)
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library(viridis)
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#######set working direction and get the data
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setwd("~/BAK_Projekt")
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#base
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mapCH <- mapCH2016 %>% dplyr::rename("bfs_nr"="can")
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#create dataset on canton level
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df_bak <- read.csv("~/BAK_Projekt/Liste_BAK2.csv", sep = ";")
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df_bak_red <- df_bak %>%
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dplyr::group_by(Kanton) %>%
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dplyr::summarise(count=n())
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df_bak_red <- df_bak_red[!(df_bak_red$Kanton ==""),]
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df_bak_red$Kt <- c("AG", "AI", "AR", "BL", "BS", "BE", "FR", "GE", "GL", "GR", "JU", "LU", "NE",
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"NW", "OW", "SH", "SZ", "SO", "SG", "TI", "TG", "UR", "VD", "VS", "ZG", "ZH")
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df_bak_red$bfs_nr <- as.integer(c("19", "16", "15", "13", "12", "2", "10", "25", "8", "18", "26", "3", "24",
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"7", "6", "14", "5", "11", "17", "21", "20", "4", "22", "23", "9", "1"))
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#get coordinates (required reference system CH1903/LV03)
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mapCH.short <- mapCH[!duplicated(mapCH$bfs_nr),]
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df.map <- full_join(df_bak_red, mapCH.short, by="bfs_nr") %>%
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select("bfs_nr", "Kt", "name", "count", "bfs_nr", "long", "lat")
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# Plotting sample data
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can.plot(df.map$bfs_nr, df.map$count, 2016,
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boundaries = "c", boundaries_size = 0.2, boundaries_color = "white",
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title = "Verteilung der Institutionen auf Kantonsebene")
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#geom_text(aes(x=df.map$long, y=df.map$lat ,label = df.map$Kt))
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####Example for district map -> can be deleted
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# Generating sample data:
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dt.dis <- dis.template(2016)
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for(i in 1:nrow(dt.dis)){dt.dis$values[i] <- sample(c(300:700), 1)/1000}
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# Plotting sample data:
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dis.plot(dt.dis$bfs_nr, dt.dis$values, 2016,
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boundaries = "c",
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title = "Beispiel auf Bezirksebene (random data)")
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# Plotting sample data for the canton of Aargau:
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dis.plot(dt.dis$bfs_nr, dt.dis$values, 2016, cantons = c("GR"),
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lakes = c("none"),
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title = "Beispiel Kanton Graub<75>nden (Bezirksebene)")
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#Example Dataset
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library(scatterpie)
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table7 <- table7 %>% dplyr::rename("name"="Kanton")
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table7 <- table7[!(table7$name ==""),]
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test <- full_join(df.map, table7, by="name")
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test$radius <- 6*abs(rnorm(nrow(test)))
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p <- can.plot(df.map$bfs_nr, df.map$count, 2016,
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boundaries = "c", boundaries_size = 0.2, boundaries_color = "white",
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title = "Verteilung der Institutionen auf Kantonsebene") +
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coord_quickmap()
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p + geom_scatterpie(aes(x=long, y=lat, group=bfs_nr, r=radius),
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data=test, cols=c(11:13), color=NA, alpha=.8)
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geom_scatterpie_legend(test$radius, x=-160, y=-55)
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test %>%
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select(c(11:13)) %>%
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pivot_longer(cols = names(.)) %>%
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ggplot(aes(x = value, y = 1, fill = name)) +
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geom_col(position = "stack") +
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coord_polar() +
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theme_void()
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