parent
ee6ad9704a
commit
55fcc0518a
87
Heatmaps.R
87
Heatmaps.R
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@ -78,24 +78,89 @@ ggsave(plot=f1_i, filename = "Figure1_i.jpg", device="jpg", width = 15, height =
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##FIGURE 2
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##Materielles mobiles Kulturerbe
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df_mm2 <- dplyr::filter(df_binKanton, !(materielles...mobiles.Kulturerbe %in% "")) %>%
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group_by(Handlungsfelder, Hauptfinanzierungsträger, bfs_nr, Kanton) %>% dplyr::summarise(materielles_mobiles_Kulturerbe=n()) %>%
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filter(Handlungsfelder == "Reaktualisieren") %>% group_by(bfs_nr) %>% mutate(percent = prop.table(materielles_mobiles_Kulturerbe)) %>%
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filter(Hauptfinanzierungsträger != "privat")
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#add additional row as Uri appears to have no data instead of 0 percentage
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new.r_mm <- data.frame("Reaktualisieren", "staatlich/öffentlich", 4, "Uri", 0, 0)
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names(new.r_mm)<- colnames(df_mm2)
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df_mm2.1 <- rbind(df_mm2, new.r_mm)
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f2_mm <- RSwissMaps::can.plot(df_mm2.1$bfs_nr, df_mm2.1$percent, 2016,
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boundaries = "c", boundaries_size = 0.2, boundaries_color = "white",
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title = "Anzahl Akteure (nach Hauptfinanzierungsträger) im Handlungsfeld\n'Reaktualisieren/Valorisieren' bezüglich materiellem und mobilem Kulturerbe",
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legend_title = "Je dunkler die Färbung, desto höher ist der\nprozentuale Anteil an staatliche/öffentliche Hauptfinanzierungsträgern")
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##Materielles immobiles Kutlurerbe
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df_mi2 <- dplyr::filter(df_binKanton, !(materielles...immobiles.Kulturerbe %in% "")) %>%
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group_by(Handlungsfelder, Hauptfinanzierungsträger, bfs_nr, Kanton) %>% dplyr::summarise(materielles_immobiles_Kulturerbe=n()) %>%
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filter(Handlungsfelder == "Reaktualisieren") %>% group_by(bfs_nr) %>% mutate(percent = prop.table(materielles_immobiles_Kulturerbe)) %>%
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filter(Hauptfinanzierungsträger != "privat")
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#add additional row as AG, GL, GR and ZH appears to have no data instead of 0 percentage
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new.r_mi <- data.frame(Handlungsfelder = c("Reaktualisieren"),
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Hauptfinanzierungsträger = "staatlich/öffentlich",
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bfs_nr = c(19, 8, 18, 1),
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Kanton = c("Aargau", "Glarus", "Graubünden", "Zürich"),
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materielles_immobiles_Kulturerbe = 0,
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percent = 0)
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df_mi2.1 <- rbind(df_mi2, new.r_mi)
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f2_mi <- RSwissMaps::can.plot(df_mi2.1$bfs_nr, df_mi2.1$percent, 2016,
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boundaries = "c", boundaries_size = 0.2, boundaries_color = "white",
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title = "Anzahl Akteure (nach Hauptfinanzierungsträger) im Handlungsfeld\n'Reaktualisieren/Valorisieren' bezüglich materiellem und immobilem Kulturerbe",
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legend_title = "Je dunkler die Färbung, desto höher ist der\nprozentuale Anteil an staatliche/öffentliche Hauptfinanzierungsträgern")
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##Immaterielles Kulturerbe
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df_i2 <- dplyr::filter(df_binKanton, !(immaterielles.Kulturerbe %in% "")) %>%
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group_by(Handlungsfelder, Hauptfinanzierungsträger, bfs_nr, Kanton) %>% dplyr::summarise(immaterielles.Kulturerbe=n()) %>%
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filter(Handlungsfelder == "Reaktualisieren") %>% group_by(bfs_nr) %>% mutate(percent = prop.table(immaterielles.Kulturerbe)) %>%
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filter(Hauptfinanzierungsträger != "privat")
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#add additional row as AI, BE, SG and VS appears to have no data instead of 0 percentage
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new.r_i <- data.frame(Handlungsfelder = c("Reaktualisieren"),
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Hauptfinanzierungsträger = "staatlich/öffentlich",
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bfs_nr = c(16, 2, 17, 23),
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Kanton = c("Appenzell Ausserrhoden", "Bern", "St. Gallen", "Wallis"),
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materielles_immobiles_Kulturerbe = 0,
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percent = 0)
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df_i2.1 <- rbind(df_i2, new.r_i)
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f2_i <- RSwissMaps::can.plot(df_i2.1$bfs_nr, df_i2.1$percent, 2016,
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boundaries = "c", boundaries_size = 0.2, boundaries_color = "white",
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title = "Anzahl Akteure (nach Hauptfinanzierungsträger) im Handlungsfeld\n'Reaktualisieren/Valorisieren' bezüglich immateriellem Kulturerbe",
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legend_title = "Je dunkler die Färbung, desto höher ist der\nprozentuale Anteil an staatliche/öffentliche Hauptfinanzierungsträgern")
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ggsave(plot=f2_mm, filename = "Figure2_mm.jpg", device="jpg", width = 15, height = 10,
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path = "~/BAK_Projekt/Figures")
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ggsave(plot=f2_mi, filename = "Figure2_mi.jpg", device="jpg", width = 15, height = 10,
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path = "~/BAK_Projekt/Figures")
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ggsave(plot=f2_i, filename = "Figure2_i.jpg", device="jpg", width = 15, height = 10,
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path = "~/BAK_Projekt/Figures")
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####Example for district map -> can be deleted####
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####Example for district map####
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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$values, 2016,
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boundaries = "c",
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title = "Beispiel auf Bezirksebene (random data)")
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#dt.dis <- dis.template(2016)
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#for(i in 1:nrow(dt.dis)){dt.dis$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 Graubünden (Bezirksebene)")
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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 Graubünden (Bezirksebene)")
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