knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
Hauptartikel ist "Aylsentscheidungen in Europa, Fokus Afghanistan". Dies gehört zu den ergänzenden Analysen.
library(knitr) library(dplyr) library(scales) migr_asydcfsta=readRDS(file="../data/migr_asydcfsta_20180812.rds") dec_palette_grey=c("#000000","#C0C0C0","#D3D3D3","#E4E4E4","#E8E8E8","#C8C8C8","#808080")
cutoff=1000 CODE="IQ" COUNTRY="Irak" major_geo_total_iq=filter(migr_asydcfsta, values > cutoff, time == "2017-01-01", decision == "TOTAL", sex == "T", age == "TOTAL", citizen == CODE, geo != "EU28") %>% select(geo,values)
Dies sind die europäischen Länder, in denen im Jahr 2017 mehr als cutoff
Entscheidungen über Asylanträge Staatsangehörigen von r COUNTRY
getroffen wurden:
library(knitr) # Die ersten paar Spalten kable(major_geo_total_iq[(-major_geo_total_iq$values),], caption = paste("Länder mit mehr als ", cutoff, " Entscheidungen zu Asylanträgen aus ", COUNTRY )) major_geo_total_iq <- filter(major_geo_total_iq, geo != "TOTAL")
Anzahl der Entscheidungen in Ländern mit weniger als r cutoff
Entscheidungen - die werden im Folgenden vernachlässigt:
other_geo_total_iq=filter(migr_asydcfsta, values <= cutoff, time == "2017-01-01", decision == "TOTAL", sex == "T", age == "TOTAL", citizen == CODE, geo != "EU28", geo != "TOTAL") %>% arrange(desc(values)) dec_others_total_iq <- sum(other_geo_total_iq$values) dec_others_total_iq
Eine Visualisierung der Gesamtanzahl von Entscheidungen zu COUNTRY
im Jahr 2017:
library(ggplot2) # some technical workarounds for adding the row with the others (XX), # and for sorting major_geo_total_iq$geo <- as.character(major_geo_total_iq$geo) all_geo_total_iq <- rbind(major_geo_total_iq, c("XX",dec_others_total_iq)) all_geo_total_iq$values <- as.numeric(all_geo_total_iq$values) all_geo_total_iq$geo <- factor(all_geo_total_iq$geo, levels = arrange(all_geo_total_iq, values)$geo) # The real thing geo_dec_pie_iq <- ggplot(all_geo_total_iq, aes(x="", y=values, fill=geo)) + geom_col(colour = "black") + coord_polar("y", start=0) + scale_fill_grey(start = 0.4, end = 0.9) + ggtitle (paste("Entscheidungen über Asyl, ",COUNTRY,", 2017"), subtitle = paste("EU-Länder; XX ist die Summe aller Länder mit < ", cutoff, " Entscheidungen")) + theme_bw() + theme(axis.title=element_blank()) geo_dec_pie_iq
major_geo_iq <-filter(migr_asydcfsta, geo %in% major_geo_total_iq$geo, time >= "2017-01-01", sex == "T", age == "TOTAL", citizen == CODE, geo != "EU28", geo != "TOTAL") %>% select(geo,decision,values) major_geo_iq$geo <- factor(major_geo_iq$geo, levels = arrange(filter(major_geo_iq, decision == "TOTAL"), desc(values))$geo) # bring decisions into correct order major_geo_iq$decision <- factor(major_geo_iq$decision, levels=c("REJECTED","TEMP_PROT","HUMSTAT","SUB_PROT","GENCONV","TOTAL_POS","TOTAL"))
dec_bar_iq <- ggplot(filter(major_geo_iq, decision != "TOTAL" & decision != "TOTAL_POS")) + geom_col(aes(x=geo, y=values, fill=decision)) dec_bar_iq + scale_fill_manual(values = dec_palette_grey) + theme_bw() + theme(legend.position="bottom", axis.title.x = element_blank(), axis.title.y = element_blank()) + ggtitle(paste("Entscheidungen über Asylanträge aus ",COUNTRY," 2017"), subtitle = "in absoluten Zahlen")
dec_bar_iq_fill <- ggplot(filter(major_geo_iq, decision != "TOTAL" & decision != "TOTAL_POS")) + geom_col(aes(x=geo, y=values, fill=decision), position="fill") + scale_fill_grey() + theme_bw() + theme(legend.position="bottom", axis.title.x = element_blank(), axis.title.y = element_blank()) + scale_y_continuous(label=percent) + ggtitle(paste("Entscheidungen über Asylanträge aus ",COUNTRY," 2017"), subtitle = "in Prozent") dec_bar_iq_fill
COUNTRY="Iran" CODE="IR" cutoff=1000 major_geo_total_ir=filter(migr_asydcfsta, values > cutoff, time == "2017-01-01", decision == "TOTAL", sex == "T", age == "TOTAL", citizen == CODE, geo != "EU28") %>% select(geo,values)
Dies sind die europäischen Länder, in denen im Jahr 2017 mehr als cut1off
Entscheidungen über Asylanträge aus r COUNTRY
getroffen wurden:
library(knitr) # Die ersten paar Spalten kable(major_geo_total_ir[order(-major_geo_total_ir$values),], caption = paste("Länder mit mehr als ",cutoff, " Entscheidungen zu Asylanträgen aus " , COUNTRY)) major_geo_total_ir <- filter(major_geo_total_ir, geo != "TOTAL")
Anzahl der Entscheidungen in Ländern mit weniger als r cutoff
Entscheidungen - die werden im Folgenden vernachlässigt:
other_geo_total_ir=filter(migr_asydcfsta, values <= cutoff, time == "2017-01-01", decision == "TOTAL", sex == "T", age == "TOTAL", citizen == CODE, geo != "EU28", geo != "TOTAL") %>% arrange(desc(values)) dec_others_total_ir <- sum(other_geo_total_ir$values) dec_others_total_ir
Eine Visualisierung der Gesamtanzahl von Entscheidungen zu COUNTRY
im Jahr 2017:
library(ggplot2) # some technical workarounds for adding the row with the others (XX), # and for sorting major_geo_total_ir$geo <- as.character(major_geo_total_ir$geo) all_geo_total_ir <- rbind(major_geo_total_ir, c("XX",dec_others_total_ir)) all_geo_total_ir$values <- as.numeric(all_geo_total_ir$values) all_geo_total_ir$geo <- factor(all_geo_total_ir$geo, levels = arrange(all_geo_total_ir, values)$geo) # The real thing geo_dec_pie_ir <- ggplot(all_geo_total_ir, aes(x="", y=values, fill=geo)) + geom_col(colour = "black") + coord_polar("y", start=0) + scale_fill_grey(start = 0.4, end = 0.9) + ggtitle(paste("Entscheidungen über Asyl, ",COUNTRY, " , 2017"), subtitle = paste("EU-Länder; XX ist die Summe aller Länder mit < ", cutoff, " Entscheidungen")) + theme_bw() + theme(axis.title=element_blank()) geo_dec_pie_ir
major_geo_ir <-filter(migr_asydcfsta, geo %in% major_geo_total_ir$geo, time >= "2017-01-01", sex == "T", age == "TOTAL", citizen == CODE, geo != "EU28", geo != "TOTAL") %>% select(geo,decision,values) major_geo_ir$geo <- factor(major_geo_ir$geo, levels = arrange(filter(major_geo_ir, decision == "TOTAL"), desc(values))$geo) # bring decisions into correct order major_geo_ir$decision <- factor(major_geo_ir$decision, levels=c("REJECTED","TEMP_PROT","HUMSTAT","SUB_PROT","GENCONV","TOTAL_POS","TOTAL"))
dec_bar_ir <- ggplot(filter(major_geo_ir, decision != "TOTAL" & decision != "TOTAL_POS")) + geom_col(aes(x=geo, y=values, fill=decision)) dec_bar_ir + scale_fill_manual(values = dec_palette_grey) + theme_bw() + theme(legend.position="bottom") + ggtitle(paste("Entscheidungen über Asylanträge aus ",COUNTRY,", 2017"), subtitle = "in absoluten Zahlen")
dec_bar_ir_fill <- ggplot(filter(major_geo_ir, decision != "TOTAL" & decision != "TOTAL_POS")) + geom_col(aes(x=geo, y=values, fill=decision), position="fill") + scale_fill_grey() + theme_bw() + theme(legend.position="bottom", axis.title.x = element_blank(), axis.title.y = element_blank()) + scale_y_continuous(label=percent) + ggtitle(paste("Entscheidungen über Asylanträge aus ",COUNTRY," 2017"), subtitle = "in Prozent") dec_bar_ir_fill
Schweden monthly
years 2015, 2016 (simple), 2018 (quarterly data)
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