35 lines
1.6 KiB
R
35 lines
1.6 KiB
R
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library(tidyverse)
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births <- read_csv2("/home/wn/Workspace/r-learnings/lebendgeburten-essen.csv")
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births2 <- births %>%
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mutate(StadtbezirkID = factor(Stadtbezirk)) %>%
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mutate(StadtteilID = factor(Stadtteil)) %>%
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select(-c(Stadtbezirk, Stadtteil))
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people <- read_csv2("/home/wn/Workspace/r-learnings/bevoelkerung-nach-geschlecht.csv")
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people2 <- people %>%
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mutate(StadtbezirkID = factor(Stadtbezirk)) %>%
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mutate(StadtteilID = factor(Stadtteil)) %>%
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select(-c(Stadtbezirk, Stadtteil, INSGESAMT))
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people.and.births <- births2 %>%
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full_join(people2) %>%
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mutate(birth.per.woman = LEBENDGEBORENE / BEV_W) %>%
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pivot_longer(c(LEBENDGEBORENE, BEV_M, BEV_W, birth.per.woman), names_to="key", values_to="value") %>%
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mutate(keyid = factor(key)) %>%
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select(-c(key)) %>%
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rename(key = keyid)
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ax.left.min = min(people.and.births$value[people.and.births$key %in% c("BEV_W", "BEV_M", "LEBENDGEBORENE")], na.rm=TRUE)
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ax.left.max = max(people.and.births$value[people.and.births$key %in% c("BEV_W", "BEV_M", "LEBENDGEBORENE")], na.rm=TRUE)
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ax.right.min = min(people.and.births$value[people.and.births$key == "birth.per.woman"], na.rm=TRUE)
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ax.right.max = max(people.and.births$value[people.and.births$key == "birth.per.woman"], na.rm=TRUE)
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ratio = (ax.left.max - ax.left.min) / (ax.right.max - ax.right.min)
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plot <- ggplot(data = people.and.births) +
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geom_bar(mapping = aes(x = StadtbezirkID, y = value, fill = key), stat = "identity", position = "dodge") +
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scale_y_continuous(name = "value",
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sec.axis = sec_axis(~(./ratio), name = "ratio"))
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print(plot)
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