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first_look.Rmd
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---
title: "R Notebook"
output:
html_document:
df_print: paged
---
## Load Libraries and functions
```{r}
library(gpclib)
library(maptools)
library(tidyverse)
source("plot_fun.R")
```
## Load data
```{r}
data_folder = "/home/alex/Obercloud/Projekt_Datenanalyse/"
data <- read.csv(paste0(data_folder, "spt_herkunft_tagessummen_view.csv"))
```
## Curate data
```{r}
#data$datum <- as.Date(data$datum, format = "%Y-%m-%d")
```
## Prepare location data
```{r}
#Get location names
unique(data$poi)
```
```{r}
#Get PLZ data
PLZ_reference <- read.csv("src/plz_ref.csv")
MB_gemeinden <- PLZ_reference$Gemeinde
#Filter data for Gemeinden in LK MB
filtered_data <- data[data$poi %in% MB_gemeinden,]
filtered_data <- filtered_data[filtered_data$personenkategorie == "tagespendler",]
recorded_days <- length(unique(filtered_data$datum))
filtered_data <- filtered_data[, c("poi", "tagessumme_kalibriert")]
summary <- aggregate(filtered_data$tagessumme_kalibriert, by=list(Gemeinde=filtered_data$poi), FUN=sum)
colnames(summary) <- c("Gemeinde", "value")
summary <- merge(summary, PLZ_reference[, c("Gemeinde", "einwohner")], by.y="Gemeinde")
summary <- mutate(summary, per_day=(summary$value/recorded_days))
```
## Plot
```{r fig.width = 12, fig.height = 12, warning=FALSE}
set_font <- "Roboto Mono"
landkreis_mb_shape <- initialize_polygons()
gemeinden_in_mb <- landkreis_mb_shape[landkreis_mb_shape$NAME_2=='Miesbach',]
location_data <- summary
oberlab_palette <- c("#00a4c4", "#3db8c1", "#fabb3a","#c7326c")
normal_palette <- c("#339933", "#339933", "#fabb3a","#ff0000")
used_palette <- oberlab_palette
gemeinden_fortification <- fortify(gemeinden_in_mb, region = "NAME_3")
faelle_labels <- aggregate(cbind(long,lat) ~ id, data = gemeinden_fortification, FUN = mean)
faelle_labels <- merge(location_data[,c("Gemeinde","per_day", "einwohner")], faelle_labels, by.x="Gemeinde", by.y="id", all=T)
faelle_labels <- mutate(faelle_labels, ratio=faelle_labels$per_day/faelle_labels$einwohner*100)
faelle_labels <- mutate(faelle_labels,label=paste(faelle_labels$Gemeinde,"\n", round(faelle_labels$ratio,digits = 0)))
# Cols: Gemeinde / value / long / lat / label
einfache_lkr_map <- fortify(gemeinden_in_mb, region = "NAME_3")
einfache_lkr_map <- merge(einfache_lkr_map,location_data[,c("Gemeinde","per_day","einwohner")],by.x="id",by.y="Gemeinde")
einfache_lkr_map <- mutate(einfache_lkr_map, ratio=einfache_lkr_map$per_day/einfache_lkr_map$einwohner*100)
# Cols: id / long / lat / order / hole / piece / group / value / ratio
einfache_lkr_ggplot_map <- ggplot(einfache_lkr_map) +
geom_polygon(aes(x = long, y = lat, group = group, fill=ratio), colour = "white") +
labs(title = "Tagespendler relativ zur Einwohnerzahl",
subtitle = "Lizenz: CC-BY-SA, FabLab Oberland e.V.") +
theme_void() +
theme(legend.position = c(0.5,0),
legend.direction = "horizontal",
legend.key.width=unit(2,"cm"),
legend.text = element_text(size = rel(1.0), color = "black", family = set_font),
legend.title = element_text(size = rel(1.0), color = "black", family = set_font),
legend.text.align = 0,
legend.title.align = 0,
plot.title = element_text(size = 12*1.2, color = "#c7326c", family = set_font, vjust = 5),
plot.margin = unit(c(2,10,4.5,0.1) , "lines"),
plot.caption = element_text(size = 12, vjust = 5, hjust = 1))
einfache_lkr_ggplot_map <- einfache_lkr_ggplot_map + geom_text(data = faelle_labels, aes(x=long, y=lat, label=label), fontface = "bold", fontfamily = "Roboto Mono")
einfache_lkr_ggplot_map <- einfache_lkr_ggplot_map + scale_fill_distiller(name = "Faktor Tagespendler \nzu Einwohnern", palette = "Blues", direction = +1)
#einfache_lkr_ggplot_map <- einfache_lkr_ggplot_map +
# scale_fill_gradientn(name="Relative Betroffenheit nach\nGemeinde (in % der Einwohner)",
# colours = used_palette, limits=c(0,maxFaelleThreshold),oob=squish)
show(einfache_lkr_ggplot_map)
```
```{r fig.width = 12, fig.height = 12, warning=FALSE}
faelle_labels <- aggregate(cbind(long,lat) ~ id, data = gemeinden_fortification, FUN = mean)
faelle_labels <- merge(location_data[,c("Gemeinde","per_day", "einwohner")], faelle_labels, by.x="Gemeinde", by.y="id", all=T)
faelle_labels <- mutate(faelle_labels,label=paste(faelle_labels$Gemeinde,"\n", round(faelle_labels$per_day,digits = 0)))
# Cols: Gemeinde / value / long / lat / label
einfache_lkr_map <- fortify(gemeinden_in_mb, region = "NAME_3")
einfache_lkr_map <- merge(einfache_lkr_map,location_data[,c("Gemeinde","per_day","einwohner")],by.x="id",by.y="Gemeinde")
einfache_lkr_map <- mutate(einfache_lkr_map, ratio=einfache_lkr_map$per_day/einfache_lkr_map$einwohner*100)
# Cols: id / long / lat / order / hole / piece / group / value / ratio
einfache_lkr_ggplot_map <- ggplot(einfache_lkr_map) +
geom_polygon(aes(x = long, y = lat, group = group, fill=per_day), colour = "white") +
labs(title = "Tagespendler pro Tag",
subtitle = "Lizenz: CC-BY-SA, FabLab Oberland e.V.") +
theme_void() +
theme(legend.position = c(0.5,0),
legend.direction = "horizontal",
legend.key.width=unit(2,"cm"),
legend.text = element_text(size = rel(1.0), color = "black", family = set_font),
legend.title = element_text(size = rel(1.0), color = "black", family = set_font),
legend.text.align = 0,
legend.title.align = 0,
plot.title = element_text(size = 12*1.2, color = "#c7326c", family = set_font, vjust = 5),
plot.margin = unit(c(2,10,4.5,0.1) , "lines"),
plot.caption = element_text(size = 12, vjust = 5, hjust = 1))
einfache_lkr_ggplot_map <- einfache_lkr_ggplot_map + geom_text(data = faelle_labels, aes(x=long, y=lat, label=label), fontface = "bold", fontfamily = "Roboto Mono")
einfache_lkr_ggplot_map <- einfache_lkr_ggplot_map + scale_fill_distiller(name = "Tagespendler pro Tag", palette = "Blues", direction = +1)
#einfache_lkr_ggplot_map <- einfache_lkr_ggplot_map +
# scale_fill_gradientn(name="Relative Betroffenheit nach\nGemeinde (in % der Einwohner)",
# colours = used_palette, limits=c(0,maxFaelleThreshold),oob=squish)
show(einfache_lkr_ggplot_map)
```