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Aktueller Gemeinderat dazu.
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@semaphor
semaphor committed May 23, 2024
1 parent 9452d10 commit 67c6508
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196 changes: 150 additions & 46 deletions docs/index.html
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199 changes: 168 additions & 31 deletions overview.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -40,9 +40,27 @@ names(listenHelper) <- c("BerufAngabe",names(listen))
listen <- listenHelper
rm(listenHelper)
#AKTUELLER GEMEINDERAT:
aktRat <- read.csv('daten/aktueller_gemeinderat_2024-03.csv')
aktRat <- aktRat %>% mutate(Alter = 2024 - Geburtsjahr)
demografieUlmMedian = 40
# ALTER ULM, Ulmer Statisitik 2023:
demografieUlm2023 <- read.csv('daten/demografie_ulm-2023-12-31-ulmer_statistik.csv')
names(demografieUlm2023)[11] <- "Wohnbevölkerung"
# Upsampling, so irgendwie (das geht sicher besser?)
demografieUlm2023Distr <- vector()
for (i in seq_along(demografieUlm2023$Wohnbevölkerung)) {
#mittleresAlter <- demografieUlm$Altersgruppe[i] + (demografieUlm$Altersgruppe[i+1] - demografieUlm$Altersgruppe[i]) / 2
#if (is.na(mittleresAlter)) mittleresAlter <- 75
demografieUlm2023Distr <- c(demografieUlm2023Distr, rep(demografieUlm2023$Alter.von[i], round(demografieUlm2023$Wohnbevölkerung[i])))
}
demografieUlm2023Distr <- as.data.frame(demografieUlm2023Distr)
demografieUlm2023Distr <- cbind(demografieUlm2023Distr,'Ulm')
#demografieUlmDistr <- cbind(NA, NA, NA, NA, 'Ulm', demografieUlmDistr)
names(demografieUlm2023Distr) <- c('Alter', 'Liste')
# ALTER ULM, DESTATIS:
demografieUlmGenderBi <- read.csv('daten/demografie_ulm-2022-12-31-statistisches_bundesamt.csv')
Expand Down Expand Up @@ -73,7 +91,8 @@ for (i in seq_along(demografieUlm$Personen)) {
demografieUlmDistr <- as.data.frame(demografieUlmDistr)
demografieUlmDistr <- cbind(NA, NA, NA, NA, 'Ulm', demografieUlmDistr)
names(demografieUlmDistr) <- c('BerufAngabe', 'Beruf', 'Geburtsjahr', 'Stadtteil', 'Liste', 'Alter')
# Extra data.fram listen, der auch die Ulmer Bevölkerung enthält:
# Extra data.frame: listen + demografieUlmDistr:
listenMitDemografieUlm <- rbind(demografieUlmDistr, listen)
```
Expand Down Expand Up @@ -137,7 +156,9 @@ Grafische Darstellung:
* Histogram: TODO.
* Kurve: Annährung zur bessere Darstellung. Normierung auf Fläche unter der Kurve.
* Da es sich um eine Annäherung und nicht um eine exakt Reprästentazion der Alterswerte handelt, verläuft die Kurz bspw. teilw. in Bereiche < 16 Jahre, also unter das Wahlalter.
* Grauer Strich: Median als Mittelwert.
* Blauer Strich: Median als Mittelwert.
* Grüner Strich: Wahlalter 16 Jahre.


```{r echo=FALSE, message=FALSE, warning=FALSE, results='hide',fig.keep='all'}
Expand Down Expand Up @@ -166,7 +187,7 @@ listenLabels <- listen %>%
mutate(text = paste0(Liste, ' (n=', n, ')'))
plotAgeListen <- listen %>%
ggplot(aes(x = Alter, y = Liste, fill = after_stat(x))) +
ggplot(aes(x = Alter, y = Liste)) + #, fill = after_stat(x))) +
geom_density_ridges_gradient(
scale = 2.1,
rel_min_height = 0.01,
Expand All @@ -176,86 +197,202 @@ plotAgeListen <- listen %>%
vline_width = 1.5,
quantile_fun = median,
jittered_points = TRUE, #https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html
position = position_points_jitter(width = 1, height = 0),
position = position_points_jitter(width = 0.7, height = 0),
point_shape = 20, #https://ggplot2.tidyverse.org/reference/aes_linetype_size_shape.html
point_size = 2, point_alpha = .5, alpha = 0.7
) +
geom_vline(xintercept = 16, colour="#009E73", linetype = "longdash", linewidth = 0.8, show.legend = TRUE) +
geom_vline(xintercept = median(demografieUlmDistr$Alter), colour="#56B4E9", linetype = "longdash", linewidth = 0.8, show.legend = TRUE) +
geom_vline(xintercept = demografieUlmMedian, colour="#56B4E9", linetype = "longdash", linewidth = 0.8, show.legend = TRUE) +
scale_fill_viridis() +
scale_y_discrete(limits=rev, labels=rev(listenLabels$text)) +
scale_x_continuous(limits=c(0,85), breaks=seq(15, 85, by=5)) +
labs(title = 'Altersverteilung Listen') +
scale_x_continuous(limits=c(0,90), breaks=seq(0, 90, by=5)) +
labs(title = 'Altersverteilungen') +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)
)
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8),
axis.title.y=element_blank(),
axis.title.x=element_blank()
)
#plotAgeListen
```

```{r fig.width=6, fig.height=2.3}
#options(repr.plot.width = 5, repr.plot.height = 2)
plotDemografieUlmDistr <- demografieUlmDistr %>%
ggplot(aes(x = Alter, y = Liste, fill = after_stat(x))) +
plotDemografieUlmDistr <- demografieUlm2023Distr %>%
ggplot(aes(x = Alter, y = Liste)) + #, fill = after_stat(x))) +
geom_density_ridges_gradient(
scale = 45,
rel_min_height = 0.01,
bandwidth = 3,
bandwidth = 1,
quantile_lines = TRUE,
vline_color = c("#56B4E9"),
vline_width = 1.5,
quantile_fun = median
) +
geom_vline(xintercept = 16, colour="#009E73", linetype = "longdash", linewidth = 0.8, show.legend = TRUE) +
geom_vline(xintercept = median(demografieUlmDistr$Alter), colour="#56B4E9", linetype = "longdash", linewidth = 0.8, show.legend = TRUE) +
scale_fill_viridis() +
geom_vline(xintercept = 16, colour="#009E73", linetype = "longdash", linewidth = 0.8) +
geom_vline(xintercept = demografieUlmMedian, colour="#56B4E9", linetype = "longdash", linewidth = 0.8) +
#scale_fill_viridis() +
#scale_y_discrete(limits=rev, labels=rev(listenLabels$text)) +
scale_x_continuous(limits=c(0,85), breaks=seq(0, 85, by=5)) +
labs(title = 'Altersverteilung Ulmer Bevölkerung') +
scale_x_continuous(limits=c(0,91), breaks=seq(0, 90, by=5)) +
#labs(title = 'Altersverteilung Ulmer Bevölkerung') +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)
)
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8),
axis.title.y=element_blank(),
axis.title.x=element_blank()
)
#plotDemografieUlmDistr
```


```{r fig.width=6, fig.height=2.3}
#options(repr.plot.width = 5, repr.plot.height = 2)
plotAktRat <- aktRat %>%
mutate(Fraktion = "Gemeinderat\n2019-2024") %>%
ggplot(aes(x = Alter, y = Fraktion)) + #, fill = after_stat(x))) +
geom_density_ridges_gradient(
scale = 45,
rel_min_height = 0.01,
bandwidth = 1,
quantile_lines = TRUE,
vline_color = c("#56B4E9"),
vline_width = 1.5,
quantile_fun = median,
jittered_points = TRUE, #https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html
position = position_points_jitter(width = 0.7, height = 0),
point_shape = 20, #https://ggplot2.tidyverse.org/reference/aes_linetype_size_shape.html
point_size = 2, point_alpha = .5, alpha = 0.7
) +
geom_vline(xintercept = 16, colour="#009E73", linetype = "longdash", linewidth = 0.8) +
geom_vline(xintercept = demografieUlmMedian, colour="#56B4E9", linetype = "longdash", linewidth = 0.8) +
#scale_fill_viridis() +
#scale_y_discrete(limits=rev, labels=rev(listenLabels$text)) +
scale_x_continuous(limits=c(0,91), breaks=seq(0, 90, by=5)) +
#labs(title = 'Altersverteilung Ulmer Bevölkerung') +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8),
axis.title.y = element_blank(),
axis.title.x = element_blank()
)
#plotAktRat
plotAktRatMinus5 <- aktRat %>%
mutate(Fraktion = "Gemeinderat\n2019-2024\n-5 Jahre", Alter = Alter - 5) %>%
ggplot(aes(x = Alter, y = Fraktion)) + #, fill = after_stat(x))) +
geom_density_ridges_gradient(
scale = 45,
rel_min_height = 0.01,
bandwidth = 1,
quantile_lines = TRUE,
vline_color = c("#56B4E9"),
vline_width = 1.5,
quantile_fun = median,
jittered_points = TRUE, #https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html
position = position_points_jitter(width = 0.7, height = 0),
point_shape = 20, #https://ggplot2.tidyverse.org/reference/aes_linetype_size_shape.html
point_size = 2, point_alpha = .5, alpha = 0.7
) +
geom_vline(xintercept = 16, colour="#009E73", linetype = "longdash", linewidth = 0.8) +
geom_vline(xintercept = demografieUlmMedian, colour="#56B4E9", linetype = "longdash", linewidth = 0.8) +
#scale_fill_viridis() +
#scale_y_discrete(limits=rev, labels=rev(listenLabels$text)) +
scale_x_continuous(limits=c(0,91), breaks=seq(0, 90, by=5)) +
#labs(title = 'Altersverteilung Ulmer Bevölkerung') +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8),
axis.title.y=element_blank(),
#axis.title.x=element_blank()
)
```

```{r fig.width=8, fig.height=18, echo=FALSE, message=FALSE, warning=FALSE, results='hide', fig.keep='all'}
plotUnten <- ggarrange(NA, plotDemografieUlmDistr,
#heights = c(12, 1.6),
widths = c(0.27, 1),
nrow = 1, ncol = 2)
plotGanzUnten <- ggarrange(NA, plotAktRat,
#heights = c(12, 1.6),
widths = c(0.15, 1),
nrow = 1, ncol = 2)
plotGanzGanzUnten <- ggarrange(NA, plotAktRatMinus5,
#heights = c(12, 1.6),
widths = c(0.15, 1),
nrow = 1, ncol = 2)
suppressWarnings(
ggarrange(plotAgeListen, plotUnten,
heights = c(12, 1.6),
ggarrange(plotAgeListen, plotUnten, plotGanzUnten, plotGanzGanzUnten,
heights = c(12, 1.6, 1.6, 1.6),
vjust = 0,
#widths = c(1, 0.5),
nrow = 2, ncol = 1)
nrow = 4, ncol = 1)
)
# http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/
```

### Aktueller Gemeinderat (Wahlperiode 2019-2024)

TODO
```{r fig.width=6, fig.height=2.5}
ggplot(aktRat, aes(x = Alter, y = NA)) +
#geom_histogram(width = 0.5, binwidth = 1)
geom_density_ridges_gradient(
scale = 45,
rel_min_height = 0.01,
bandwidth = 1,
quantile_lines = TRUE,
vline_color = c("#56B4E9"),
vline_width = 1.5,
quantile_fun = median,
jittered_points = TRUE, #https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html
position = position_points_jitter(width = 0.7, height = 0),
point_shape = 20, #https://ggplot2.tidyverse.org/reference/aes_linetype_size_shape.html
point_size = 2, point_alpha = .5, alpha = 0.7
) +
scale_x_continuous(limits=c(25, 85), breaks=seq(30, 80, by=10)) +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
#axis.title.x=element_blank()
)
```


### Bevölkerung Ulms

* Quelle: [2], Daten per 31.12.2023
* Letzter Datenpunkt: 90 Jahre und älter.
Quelle: [2], Daten per 31.12.2023

Letzter Datenpunkt: 90 Jahre und älter.

```{r fig.width=6, fig.height=2}
plotAgeUlm2023 <- ggplot(demografieUlm2023, aes(Alter.von, Wohnbevölkerung)) +
geom_col(width = 0.5)
plotAgeUlm2023Distr <- ggplot(demografieUlm2023Distr, aes(Alter)) +
geom_histogram(binwidth = 5)
plotAgeUlm2023
plotAgeUlm2023Distr
```

Quelle: [3], Daten per 31.12.2022

```{r fig.width=5.4, fig.height=2}
plotAgeUlm2022DES <- ggplot(demografieUlmZehner, aes(AltersgruppeZehner, Personen)) +
geom_col(width = 0.8)
Expand Down

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